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file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/boolq/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/boolq/tokenizer_config.json b/finetune/boolq/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/boolq/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + 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+ }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/cola/checkpoint-400/structformer_as_hf.py b/finetune/cola/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/cola/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/cola/checkpoint-400/tokenizer_config.json b/finetune/cola/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/cola/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/cola/checkpoint-400/trainer_state.json b/finetune/cola/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..15cb46ccb43796e1591baaeed47fe7aa80ccc20c --- /dev/null +++ b/finetune/cola/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.7931937172774869, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/cola/checkpoint-400", + "epoch": 5.797101449275362, + "global_step": 400, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 5.8, + 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--- /dev/null +++ b/finetune/cola/config.json @@ -0,0 +1,57 @@ +{ + "_name_or_path": "omarmomen/structformer_s1_final_with_pos", + "architectures": [ + "StructformerModelForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "structformer_as_hf.StructformerConfig", + "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel", + "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "dropatt": 0.1, + "dropout": 0.1, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "structformer", + "n_context_layers": 0, + "n_parser_layers": 4, + "nhead": 12, + "nlayers": 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+1012 1 +1013 1 +1014 1 +1015 1 +1016 1 +1017 0 +1018 1 diff --git a/finetune/cola/pytorch_model.bin b/finetune/cola/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..79d37731d52bd2afc95be8acbb08b9a1c1f21971 --- /dev/null +++ b/finetune/cola/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8bc16b9a152d3c83b6da1a5a69be258541741700af78a1cb388eaf13d5424ed4 +size 534669003 diff --git a/finetune/cola/special_tokens_map.json b/finetune/cola/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/cola/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/cola/structformer_as_hf.py b/finetune/cola/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/cola/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/cola/tokenizer_config.json b/finetune/cola/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/cola/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + 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"normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/cola/train_results.json b/finetune/cola/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..d3cdf9de63c44a7ece0ac4dd4161865883697b06 --- /dev/null +++ b/finetune/cola/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.3857684093972911, + "train_runtime": 391.5771, + "train_samples": 8164, + "train_samples_per_second": 208.49, + "train_steps_per_second": 1.762 +} \ No newline at end of file diff --git a/finetune/cola/trainer_state.json b/finetune/cola/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..d1a57ec9d23f8d8b4a77f197c13c311a981bfc82 --- /dev/null +++ b/finetune/cola/trainer_state.json @@ -0,0 +1,42 @@ +{ + "best_metric": 0.7931937172774869, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/cola/checkpoint-400", + "epoch": 10.0, + "global_step": 690, + 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b/finetune/control_raising_control/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/control_raising_control/checkpoint-400/structformer_as_hf.py b/finetune/control_raising_control/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/control_raising_control/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/control_raising_control/checkpoint-400/tokenizer_config.json b/finetune/control_raising_control/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/control_raising_control/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/control_raising_control/checkpoint-400/trainer_state.json b/finetune/control_raising_control/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..5867ed07ff7f2fac367ec962334de936fcee8dee --- /dev/null +++ b/finetune/control_raising_control/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.8853014990164824, + 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b/finetune/control_raising_control/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/control_raising_control/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/control_raising_control/structformer_as_hf.py b/finetune/control_raising_control/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/control_raising_control/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/control_raising_control/tokenizer_config.json b/finetune/control_raising_control/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/control_raising_control/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/control_raising_control/train_results.json b/finetune/control_raising_control/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..06083e0e2ffa60f3ecbc233741944b3d163e2f01 --- /dev/null +++ b/finetune/control_raising_control/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.08940400845815681, + "train_runtime": 342.2215, + "train_samples": 6570, + 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a/finetune/control_raising_lexical_content_the/checkpoint-400/special_tokens_map.json b/finetune/control_raising_lexical_content_the/checkpoint-400/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/control_raising_lexical_content_the/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/control_raising_lexical_content_the/checkpoint-400/structformer_as_hf.py b/finetune/control_raising_lexical_content_the/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/control_raising_lexical_content_the/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/control_raising_lexical_content_the/checkpoint-400/tokenizer_config.json b/finetune/control_raising_lexical_content_the/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/control_raising_lexical_content_the/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/control_raising_lexical_content_the/checkpoint-400/trainer_state.json b/finetune/control_raising_lexical_content_the/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..b6a1db1aa76ba1bd3f2c9cbe931d54d14020acd5 --- /dev/null +++ b/finetune/control_raising_lexical_content_the/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.49324522760646106, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/control_raising_lexical_content_the/checkpoint-400", + "epoch": 7.017543859649122, + "global_step": 400, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 7.02, + "eval_accuracy": 0.6650002598762512, + "eval_f1": 0.49324522760646106, + "eval_loss": 3.352391481399536, + "eval_mcc": 0.43710057077002573, + "eval_runtime": 40.0822, + "eval_samples_per_second": 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534669003 diff --git a/finetune/control_raising_lexical_content_the/special_tokens_map.json b/finetune/control_raising_lexical_content_the/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/control_raising_lexical_content_the/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/control_raising_lexical_content_the/structformer_as_hf.py b/finetune/control_raising_lexical_content_the/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/control_raising_lexical_content_the/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/control_raising_lexical_content_the/tokenizer_config.json b/finetune/control_raising_lexical_content_the/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/control_raising_lexical_content_the/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/control_raising_lexical_content_the/train_results.json b/finetune/control_raising_lexical_content_the/train_results.json new file mode 100644 index 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+gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri 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a/finetune/control_raising_relative_token_position/checkpoint-400/special_tokens_map.json b/finetune/control_raising_relative_token_position/checkpoint-400/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/control_raising_relative_token_position/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/control_raising_relative_token_position/checkpoint-400/structformer_as_hf.py b/finetune/control_raising_relative_token_position/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/control_raising_relative_token_position/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/control_raising_relative_token_position/checkpoint-400/tokenizer_config.json b/finetune/control_raising_relative_token_position/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/control_raising_relative_token_position/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + 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"single_word": false + } +} diff --git a/finetune/control_raising_relative_token_position/checkpoint-400/trainer_state.json b/finetune/control_raising_relative_token_position/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..4ba835c4ba1e8baa15099d3d692aef86e9794238 --- /dev/null +++ b/finetune/control_raising_relative_token_position/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.6677243511512488, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/control_raising_relative_token_position/checkpoint-400", + "epoch": 5.797101449275362, + "global_step": 400, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 5.8, + "eval_accuracy": 0.6670335531234741, + "eval_f1": 0.6677243511512488, + "eval_loss": 3.7142767906188965, + "eval_mcc": 0.3343358478191253, + "eval_runtime": 47.6877, + 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b/finetune/control_raising_relative_token_position/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..0bd1a684d3d956f439ff172ec647260b93d16b42 --- /dev/null +++ b/finetune/control_raising_relative_token_position/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24a58187982202020e49ac45ca1711c4dd727651a9c0c64857fb974c94aa87cd +size 534669003 diff --git a/finetune/control_raising_relative_token_position/special_tokens_map.json b/finetune/control_raising_relative_token_position/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/control_raising_relative_token_position/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/control_raising_relative_token_position/structformer_as_hf.py b/finetune/control_raising_relative_token_position/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/control_raising_relative_token_position/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/control_raising_relative_token_position/tokenizer_config.json b/finetune/control_raising_relative_token_position/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/control_raising_relative_token_position/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/control_raising_relative_token_position/train_results.json b/finetune/control_raising_relative_token_position/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..5eac1834612ba18d64df1665386b2162f8cc4f97 --- /dev/null +++ b/finetune/control_raising_relative_token_position/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.043953382839327274, + "train_runtime": 441.497, + "train_samples": 8166, + "train_samples_per_second": 184.962, + 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30970, + "Crazy": 28512, + "Cre": 20992, + "Creat": 30968, + "Crit": 20723, + "Cro": 22997, + "Cross": 18778, + "Crow": 21796, + "Crying": 31682, + "Cu": 24735, + "Cube": 22998, + "Culture": 31681, + "Cur": 29683, + "Currently": 26428, + "Cut": 10540, + "Cy": 19784, + "Cæsar": 27926, + "D": 40, + "DA": 13416, + "DAN": 28513, + "DAY": 30974, + "DC": 25981, + "DE": 13017, + "DEF": 22999, + "DEFGHI": 25522, + "DEFGHIJK": 25974, + "DEFGHIJKLM": 26418, + "DER": 17372, + "DERIV": 31618, + "DG": 25535, + "DH": 30286, + "DI": 20724, + "DIC": 11679, + "DICAPRIO": 12190, + "DING": 29686, + "DO": 9052, + "DON": 14970, + "DOOR": 16785, + "DOS": 28515, + "DOSE": 26816, + "DP": 11446, + "DR": 12797, + "DS": 15426, + "DY": 16509, + "Da": 11166, + "Dad": 3668, + "Daddy": 8508, + "Daisy": 26430, + "Dal": 27927, + "Dale": 19559, + "Dam": 16932, + "Damn": 5814, + "Dan": 6788, + "Dance": 21797, + "Dang": 31684, + "Daniel": 18746, + "Danny": 17512, + "Dar": 16510, + "Dark": 24736, + "Darling": 17548, + 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--- /dev/null +++ b/finetune/lexical_content_the_control/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/lexical_content_the_control/checkpoint-400/structformer_as_hf.py b/finetune/lexical_content_the_control/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/lexical_content_the_control/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/lexical_content_the_control/checkpoint-400/tokenizer_config.json b/finetune/lexical_content_the_control/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/lexical_content_the_control/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/lexical_content_the_control/checkpoint-400/trainer_state.json b/finetune/lexical_content_the_control/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..a543714923293e2de49d0b70f5f33f8309109ada --- /dev/null +++ b/finetune/lexical_content_the_control/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 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index 0000000000000000000000000000000000000000..5fb86c34725d033c0f75490e50fc103fbf4dfa6a --- /dev/null +++ b/finetune/lexical_content_the_control/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1936d4afe13fbff3ffc791d4500f2996508117dc53c032a6590e9ab81c08e7a0 +size 534669003 diff --git a/finetune/lexical_content_the_control/special_tokens_map.json b/finetune/lexical_content_the_control/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/lexical_content_the_control/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/lexical_content_the_control/structformer_as_hf.py b/finetune/lexical_content_the_control/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/lexical_content_the_control/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/lexical_content_the_control/tokenizer_config.json b/finetune/lexical_content_the_control/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/lexical_content_the_control/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + 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"Princess": 27238, + "Prior": 23656, + "Prison": 31443, + "Private": 25494, + "Pro": 3438, + "Probably": 8307, + "Produ": 15653, + "Production": 25500, + "Prof": 24229, + "Professor": 12854, + "Project": 31390, + "Prom": 27409, + "Promise": 16658, + "Proper": 25807, + "Prot": 15065, + "Protest": 26852, + "Protestant": 31660, + "Prov": 24613, + "Ps": 30997, + "Psych": 22102, + "Public": 19570, + "Pull": 13178, + "Pun": 27408, + "Pur": 25998, + "Pure": 30998, + "Push": 26440, + "Put": 4335, + "Q": 53, + "QU": 13249, + "Qu": 3631, + "Que": 10249, + "Queen": 11227, + "Question": 26266, + "Quick": 9475, + "Quickly": 22337, + "Quiet": 14314, + "Quit": 24976, + "Quite": 11374, + "R": 54, + "RA": 6177, + "RAB": 21196, + "RAY": 30310, + "RB": 5290, + "RE": 2300, + "READ": 24940, + "REAM": 10932, + "REAMING": 16920, + "REAMS": 19988, + "REAT": 19266, + "RED": 27685, + "REE": 11666, + "REET": 30011, + "RET": 23174, + "REY": 26680, + "RH": 29704, + "RI": 4214, + "RIGHT": 29447, + "RING": 24759, + 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"SEL": 19726, + "SELF": 22678, + "SF": 29072, + "SG": 17871, + "SH": 8582, + "SHE": 18597, + "SHOT": 16782, + "SI": 12935, + "SIGHS": 14558, + "SINGING": 27412, + "SIR": 21261, + "SM": 26000, + "SN": 13951, + "SO": 10344, + "SOLD": 27303, + "SON": 11921, + "SP": 5396, + "SPEAK": 8480, + "SPEAKER": 18216, + "SPEAKING": 17057, + "SPEAKS": 18563, + "SR": 11683, + "SS": 10895, + "ST": 4072, + "STE": 24243, + "STUD": 31417, + "SU": 21812, + "SUB": 26938, + "SW": 15192, + "Sa": 10214, + "Sab": 23320, + "Sad": 18596, + "Saf": 27951, + "Said": 14977, + "Saint": 7822, + "Sal": 11066, + "Salem": 31006, + "Sally": 21260, + "Salt": 31719, + "Sam": 9689, + "Same": 10099, + "San": 11564, + "Sand": 14761, + "Sandy": 26936, + "Sant": 21259, + "Santa": 26937, + "Sar": 10250, + "Sara": 24385, + "Sarah": 16915, + "Sasha": 28537, + "Sat": 11920, + "Saturday": 23274, + "Sav": 23659, + "Save": 13099, + "Saw": 19137, + "Sax": 19138, + "Saxon": 23294, + "Say": 5239, + "Says": 18962, + "Sc": 4060, + "Sch": 11629, + "School": 15881, + "Science": 29707, + "Scot": 15847, + "Scotland": 21489, + "Scott": 13158, + "Scottish": 30859, + "Screaming": 23548, + "Screw": 28312, + "Script": 23322, + "Se": 4027, + "Sea": 23010, + "Sean": 27221, + "Search": 25551, + "Sebastian": 27953, + "Sec": 6755, + "Second": 9846, + "Secret": 18922, + "Section": 21520, + "Security": 27773, + "See": 2525, + "Seeing": 16194, + "Seems": 16478, + "Sel": 16659, + "Selena": 23289, + "Self": 25552, + "Sem": 28534, + "Sen": 13686, + "Senator": 21773, + "Send": 13784, + "Sent": 31001, + "Sep": 31002, + "September": 13022, + "Ser": 5531, + "Sergeant": 17043, + "Series": 30739, + "Seriously": 10627, + "Set": 15654, + "Seth": 31003, + "Seven": 12519, + "Sevent": 22282, + "Seventh": 27733, + "Several": 17872, + "Sex": 18412, + "Sh": 2044, + "Sha": 5271, + "Shad": 31005, + "Shake": 28335, + "Shakespeare": 23563, + "Shaku": 30091, + "Shakugan": 30958, + "Shall": 7004, + "Shame": 21005, + "Shar": 15193, + "Sharif": 25841, + 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b/finetune/main_verb_control/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/main_verb_control/checkpoint-400/structformer_as_hf.py b/finetune/main_verb_control/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/main_verb_control/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/main_verb_control/checkpoint-400/tokenizer_config.json b/finetune/main_verb_control/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/main_verb_control/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/main_verb_control/checkpoint-400/trainer_state.json b/finetune/main_verb_control/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..d40016cbf55fa4d078a5d091a668f84c0cde589a --- /dev/null +++ b/finetune/main_verb_control/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.9892393777285864, + 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0000000000000000000000000000000000000000..3e15b7d4c1c196c204463bad013ae4a5fe65d9e6 --- /dev/null +++ b/finetune/main_verb_control/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.9801188111305237, + "eval_f1": 0.9799461971142089, + "eval_loss": 0.12109000980854034, + "eval_mcc": 0.9604284077945682, + "eval_runtime": 15.9756, + "eval_samples": 8249, + "eval_samples_per_second": 516.351, + "eval_steps_per_second": 64.599 +} \ No newline at end of file diff --git a/finetune/main_verb_control/merges.txt b/finetune/main_verb_control/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/main_verb_control/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n 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"mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/main_verb_control/structformer_as_hf.py b/finetune/main_verb_control/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/main_verb_control/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/main_verb_control/tokenizer_config.json b/finetune/main_verb_control/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/main_verb_control/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/main_verb_control/train_results.json b/finetune/main_verb_control/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..2971d999fd24ec3987a148d3f8341574f3200630 --- /dev/null +++ b/finetune/main_verb_control/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.0988679925708667, + "train_runtime": 423.3879, + "train_samples": 8166, + "train_samples_per_second": 192.873, + "train_steps_per_second": 1.63 +} \ No newline at end of file diff --git a/finetune/main_verb_control/trainer_state.json b/finetune/main_verb_control/trainer_state.json new file mode 100644 index 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100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/main_verb_lexical_content_the/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/main_verb_lexical_content_the/checkpoint-400/structformer_as_hf.py b/finetune/main_verb_lexical_content_the/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/main_verb_lexical_content_the/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/main_verb_lexical_content_the/checkpoint-400/tokenizer_config.json b/finetune/main_verb_lexical_content_the/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/main_verb_lexical_content_the/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/main_verb_lexical_content_the/checkpoint-400/trainer_state.json b/finetune/main_verb_lexical_content_the/checkpoint-400/trainer_state.json new file mode 100644 index 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a/finetune/main_verb_lexical_content_the/pytorch_model.bin b/finetune/main_verb_lexical_content_the/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..578a2951abc5b80c46ede65a9368271429cca86f --- /dev/null +++ b/finetune/main_verb_lexical_content_the/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:752b8d28b051c598d09e73835284df1346856d12e973f5aeed6ac971e88736c0 +size 534669003 diff --git a/finetune/main_verb_lexical_content_the/special_tokens_map.json b/finetune/main_verb_lexical_content_the/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/main_verb_lexical_content_the/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/main_verb_lexical_content_the/structformer_as_hf.py b/finetune/main_verb_lexical_content_the/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/main_verb_lexical_content_the/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/main_verb_lexical_content_the/tokenizer_config.json b/finetune/main_verb_lexical_content_the/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/main_verb_lexical_content_the/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/main_verb_lexical_content_the/train_results.json b/finetune/main_verb_lexical_content_the/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..4f157d15243bb87a005c403c73e82119bd9ff0e9 --- /dev/null +++ b/finetune/main_verb_lexical_content_the/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.12083642835591415, + "train_runtime": 395.0245, + "train_samples": 7306, + "train_samples_per_second": 184.951, + "train_steps_per_second": 1.544 +} \ No newline at end of file diff --git a/finetune/main_verb_lexical_content_the/trainer_state.json 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27938, + "Most": 5124, + "Mostly": 26752, + "Mot": 24745, + "Mother": 6568, + "Mouna": 27941, + "Mount": 18960, + "Move": 7073, + "Moving": 27406, + "Mr": 1929, + "Mrs": 5165, + "Ms": 13600, + "Mu": 17375, + "Much": 14238, + "Mull": 30303, + "Mult": 18249, + "Mum": 8864, + "Mumbai": 24005, + "Mummy": 25132, + "Municip": 30994, + "Mur": 9444, + "Murder": 23589, + "Murphy": 31526, + "Murray": 28941, + "Mus": 7976, + "Mush": 27939, + "Music": 10633, + "Must": 8921, + "Mut": 30991, + "My": 1107, + "N": 50, + "NA": 4523, + "NAMES": 24749, + "NARR": 8485, + "NARRATOR": 8533, + "NBA": 26931, + "NC": 16936, + "ND": 3467, + "NDER": 22266, + "NE": 7957, + "NER": 9159, + "NEWS": 30144, + "NEY": 23258, + "NFL": 26930, + "NG": 27942, + "NN": 16937, + "NO": 7889, + "NOCK": 21450, + "NOT": 18251, + "NOW": 7234, + "NS": 31707, + "NT": 17228, + "NTER": 24379, + "NY": 24378, + "Na": 18250, + "Nad": 22101, + "Nadim": 30251, + "Nah": 15191, + "Name": 16240, + "Nan": 23654, + "Nancy": 29699, + "Nap": 24748, + "Nar": 26926, + "Narr": 25545, + "Narrator": 27913, + "Nat": 10745, + "Natalie": 29566, + "Nathan": 28529, + "National": 12735, + "Naturally": 27308, + "Nature": 25544, + "Nay": 15768, + "Ne": 2939, + "Near": 21516, + "Nearly": 25546, + "Need": 17491, + "Neil": 15279, + "Neill": 20384, + "Neither": 11486, + "Net": 26927, + "Never": 4683, + "Nevertheless": 25404, + "New": 4096, + "News": 17828, + "Next": 5322, + "Nic": 19568, + "Nice": 5520, + "Nich": 23655, + "Nick": 12462, + "Night": 17376, + "Nik": 25133, + "Nin": 25995, + "Nina": 26929, + "Nine": 15081, + "Nit": 23653, + "Nixon": 25996, + "Nl": 22695, + "No": 573, + "Noah": 30499, + "Nobody": 5896, + "Noll": 31940, + "Nominated": 29062, + "Non": 23006, + "None": 8484, + "Nong": 26928, + "Nonsense": 22696, + "Nope": 16005, + "Nor": 10248, + "Norm": 5924, + "Normal": 30102, + "Normally": 29500, + "Norman": 22935, + "Normandie": 7956, + "North": 10586, + "Northern": 29698, + "Not": 1197, + "Note": 17004, + "Notes": 28192, + "Nothin": 31709, + "Nothing": 3277, + "Notice": 26635, + "November": 14758, + "Now": 938, + "Number": 10391, + "Nurse": 31708, + "O": 51, + "OB": 7050, + "OD": 5815, + "ODY": 15739, + "OE": 30304, + "OF": 27407, + "OFF": 20485, + "OH": 8203, + "OHN": 19969, + "OK": 3076, + "OKAY": 28827, + "OL": 6569, + "OM": 2994, + "OMAN": 12240, + "OME": 6519, + "OMEN": 28282, + "ON": 1608, + "OND": 13299, + "ONE": 7750, + "ONG": 12298, + "OO": 15430, + "OP": 10213, + "OR": 1755, + "ORD": 24924, + "ORE": 11259, + "ORGE": 14604, + "ORK": 31310, + "ORN": 19054, + "ORS": 28225, + "ORT": 17963, + "ORY": 17635, + "OS": 5985, + "OSE": 19135, + "OT": 3423, + "OTHER": 27943, + "OTS": 28837, + "OTT": 30038, + "OU": 1503, + "OUGH": 11151, + "OUGHT": 24312, + "OULD": 8501, + "OUND": 13737, + "OUR": 10420, + "OUS": 18867, + "OUSE": 22863, + "OUT": 10520, + "OW": 3137, + "OWER": 21406, + "OWN": 12783, + "OWS": 28829, + "OY": 6920, + "OYD": 27777, + "Oak": 26933, + "Ob": 9335, + "Ober": 21808, + "Obs": 28530, + 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b/finetune/main_verb_relative_token_position/checkpoint-400/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/main_verb_relative_token_position/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/main_verb_relative_token_position/checkpoint-400/structformer_as_hf.py b/finetune/main_verb_relative_token_position/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/main_verb_relative_token_position/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/main_verb_relative_token_position/checkpoint-400/tokenizer_config.json b/finetune/main_verb_relative_token_position/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/main_verb_relative_token_position/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/main_verb_relative_token_position/checkpoint-400/trainer_state.json 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sha256:1dfc4f98ad92b1b230b820b0f75cde4ff698e7becce1a99b6c792018fe9526c1 +size 534669003 diff --git a/finetune/main_verb_relative_token_position/special_tokens_map.json b/finetune/main_verb_relative_token_position/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/main_verb_relative_token_position/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/main_verb_relative_token_position/structformer_as_hf.py b/finetune/main_verb_relative_token_position/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/main_verb_relative_token_position/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/main_verb_relative_token_position/tokenizer_config.json b/finetune/main_verb_relative_token_position/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/main_verb_relative_token_position/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + 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"Fi": 17373, + "Fifteen": 23644, + "Fifty": 20010, + "Fig": 18589, + "Fight": 14040, + "Fighting": 31585, + "Fil": 14556, + "Film": 31591, + "Fin": 7495, + "Final": 21511, + "Finally": 11439, + "Find": 8513, + "Finding": 20443, + "Fine": 5197, + "Finish": 31491, + "Fire": 10247, + "First": 3946, + "Fish": 27930, + "Five": 7756, + "Fl": 6412, + "Flash": 29507, + "Fle": 31685, + "Flor": 21799, + "Flowers": 27770, + "Flying": 29688, + "Follow": 8333, + "Following": 14326, + "Food": 20726, + "Foot": 7641, + "Football": 17549, + "Footnote": 8577, + "For": 1451, + "Fore": 14750, + "Forget": 9176, + "Forgive": 13389, + "Form": 29048, + "Former": 31301, + "Fort": 11561, + "Fortunately": 20260, + "Forty": 18943, + "Found": 18590, + "Four": 6939, + "Fox": 26913, + "Fr": 14038, + "Fran": 6626, + "France": 14648, + "Frances": 15167, + "Francesco": 22665, + "Franche": 28357, + "Francis": 19953, + "Frank": 6880, + "Frankie": 25434, + "Franklin": 28909, + "Franz": 19095, + "Fre": 13598, + "Fred": 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l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay +ad e +Ġt han +i re +Ġal so +o y +f f +itt le +Ġc all +i z +m er +Ġa m +ou ght +Ġy ear +h y +at er +Ġp r +Ġcom p +o v +Ġc ome +W ell +Ġb l +Ġ our +an ce +Ġwhe re +nd er +ac h +if e +Ġag ain +Ġ im +n g +Ġon ly +Ġ V +Ġe very +ĠI n +Ġl ittle +Ġa d +c c 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false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/mnli-mm/checkpoint-6000/structformer_as_hf.py b/finetune/mnli-mm/checkpoint-6000/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/mnli-mm/checkpoint-6000/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/mnli-mm/checkpoint-6000/tokenizer_config.json b/finetune/mnli-mm/checkpoint-6000/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ 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30969, + "Coffee": 23308, + "Col": 6565, + "Cold": 26427, + "Cole": 21248, + "Coll": 10052, + "College": 24683, + "Colonel": 17837, + "Columb": 20674, + "Columbia": 29022, + "Com": 5331, + "Come": 1049, + "Coming": 12123, + "Comm": 6879, + "Commander": 21739, + "Common": 28908, + "Communes": 22315, + "Comp": 8948, + "Complete": 21185, + "Completely": 29024, + "Computer": 26813, + "Comrade": 26756, + "Comté": 27245, + "Con": 3805, + "Cond": 22685, + "Conf": 19287, + "Cong": 7888, + "Congratulations": 9729, + "Congress": 27793, + "Conne": 26270, + "Connor": 11376, + "Cons": 11678, + "Consider": 25480, + "Cont": 11228, + "Contin": 17545, + "Cook": 21509, + "Cool": 14133, + "Cop": 23990, + "Copy": 23993, + "Cor": 6641, + "Corn": 30284, + "Coronation": 30124, + "Correct": 20431, + "Cos": 4707, + "Cost": 20722, + "Cotton": 21249, + "Cou": 16236, + "Could": 4178, + "Couldn": 14842, + "Coun": 20007, + "Count": 11500, + "Country": 29570, + "Couple": 25937, + "Cour": 20476, + "Course": 14968, + 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"initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1, + "2": 2 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "structformer", + "n_context_layers": 0, + "n_parser_layers": 4, + "nhead": 12, + "nlayers": 12, + "ntokens": 32000, + "num_attention_heads": 8, + "num_hidden_layers": 8, + "pad": 0, + "pad_token_id": 1, + "pos_emb": true, + "position_embedding_type": "absolute", + "problem_type": "single_label_classification", + "relations": [ + "head", + "child" + ], + "relative_bias": false, + "torch_dtype": "float32", + "transformers_version": "4.26.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 32000, + "weight_act": "softmax" +} diff --git a/finetune/mnli-mm/eval_results.json b/finetune/mnli-mm/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..d31d629b1b8d43eb425ad669f9042be04818b38b --- /dev/null +++ b/finetune/mnli-mm/eval_results.json @@ -0,0 +1,9 @@ +{ + 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"normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/mnli-mm/structformer_as_hf.py b/finetune/mnli-mm/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/mnli-mm/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/mnli-mm/tokenizer_config.json b/finetune/mnli-mm/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/mnli-mm/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": 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"Chapter": 31484, + "Char": 4847, + "Charl": 22012, + "Charles": 9843, + "Charlie": 15618, + "Charlotte": 29026, + "Chat": 19557, + "Che": 4490, + "Check": 9112, + "Cheers": 13079, + "Chel": 29684, + "Ches": 29044, + "Chess": 26907, + "Chi": 18777, + "Chicago": 26226, + "Chicken": 28241, + "Chief": 12919, + "Child": 18587, + "Children": 15321, + "China": 23519, + "Chinese": 22882, + "Cho": 15188, + "Choose": 31336, + "Chp": 16315, + "Chris": 14514, + "Christ": 5128, + "Christian": 11908, + "Christine": 24997, + "Christmas": 24260, + "Christopher": 21184, + "Chuck": 17317, + "Chuckles": 13220, + "Chuckling": 31617, + "Chun": 26672, + "Church": 19483, + "Cir": 27390, + "City": 20257, + "Civil": 30285, + "Cl": 2870, + "Cla": 13016, + "Claire": 22653, + "Clara": 25793, + "Clare": 25792, + "Clark": 26696, + "Class": 14413, + "Clause": 17079, + "Clay": 21692, + "Cle": 11677, + "Clean": 23599, + "Clear": 11445, + "Clearly": 25477, + "Climate": 21402, + "Close": 14832, + "Club": 11262, + "Co": 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false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/mnli/checkpoint-6000/structformer_as_hf.py b/finetune/mnli/checkpoint-6000/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/mnli/checkpoint-6000/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/mnli/checkpoint-6000/tokenizer_config.json b/finetune/mnli/checkpoint-6000/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ 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/dev/null +++ b/finetune/mnli/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/mnli/structformer_as_hf.py b/finetune/mnli/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/mnli/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/mnli/tokenizer_config.json b/finetune/mnli/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/mnli/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/mnli/train_results.json b/finetune/mnli/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..85386d528c6077b510ecf00d07ad85d68afa8379 --- /dev/null +++ b/finetune/mnli/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 3.7, + "train_loss": 0.6384720687866211, + "train_runtime": 4882.2407, + "train_samples": 259780, + "train_samples_per_second": 532.092, + "train_steps_per_second": 4.434 +} \ No newline at end of file diff --git a/finetune/mnli/trainer_state.json b/finetune/mnli/trainer_state.json new file mode 100644 index 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"Char": 4847, + "Charl": 22012, + "Charles": 9843, + "Charlie": 15618, + "Charlotte": 29026, + "Chat": 19557, + "Che": 4490, + "Check": 9112, + "Cheers": 13079, + "Chel": 29684, + "Ches": 29044, + "Chess": 26907, + "Chi": 18777, + "Chicago": 26226, + "Chicken": 28241, + "Chief": 12919, + "Child": 18587, + "Children": 15321, + "China": 23519, + "Chinese": 22882, + "Cho": 15188, + "Choose": 31336, + "Chp": 16315, + "Chris": 14514, + "Christ": 5128, + "Christian": 11908, + "Christine": 24997, + "Christmas": 24260, + "Christopher": 21184, + "Chuck": 17317, + "Chuckles": 13220, + "Chuckling": 31617, + "Chun": 26672, + "Church": 19483, + "Cir": 27390, + "City": 20257, + "Civil": 30285, + "Cl": 2870, + "Cla": 13016, + "Claire": 22653, + "Clara": 25793, + "Clare": 25792, + "Clark": 26696, + "Class": 14413, + "Clause": 17079, + "Clay": 21692, + "Cle": 11677, + "Clean": 23599, + "Clear": 11445, + "Clearly": 25477, + "Climate": 21402, + "Close": 14832, + "Club": 11262, + "Co": 8070, + "CoV": 20951, + "Coach": 23933, + "Code": 30969, + "Coffee": 23308, + "Col": 6565, + "Cold": 26427, + "Cole": 21248, + "Coll": 10052, + "College": 24683, + "Colonel": 17837, + "Columb": 20674, + "Columbia": 29022, + "Com": 5331, + "Come": 1049, + "Coming": 12123, + "Comm": 6879, + "Commander": 21739, + "Common": 28908, + "Communes": 22315, + "Comp": 8948, + "Complete": 21185, + "Completely": 29024, + "Computer": 26813, + "Comrade": 26756, + "Comté": 27245, + "Con": 3805, + "Cond": 22685, + "Conf": 19287, + "Cong": 7888, + "Congratulations": 9729, + "Congress": 27793, + "Conne": 26270, + "Connor": 11376, + "Cons": 11678, + "Consider": 25480, + "Cont": 11228, + "Contin": 17545, + "Cook": 21509, + "Cool": 14133, + "Cop": 23990, + "Copy": 23993, + "Cor": 6641, + "Corn": 30284, + "Coronation": 30124, + "Correct": 20431, + "Cos": 4707, + "Cost": 20722, + "Cotton": 21249, + "Cou": 16236, + "Could": 4178, + "Couldn": 14842, + "Coun": 20007, + "Count": 11500, + "Country": 29570, + "Couple": 25937, + 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file diff --git a/finetune/mrpc/merges.txt b/finetune/mrpc/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/mrpc/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e 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+ "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/mrpc/structformer_as_hf.py b/finetune/mrpc/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/mrpc/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/mrpc/tokenizer_config.json b/finetune/mrpc/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/mrpc/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + 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0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/multirc/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay +ad e +Ġt han +i re +Ġal so +o y +f f +itt le +Ġc all +i z +m er +Ġa m +ou ght +Ġy ear +h y +at er +Ġp r +Ġcom p +o v +Ġc ome +W ell +Ġb l +Ġ our +an ce +Ġwhe re +nd er +ac h +if e +Ġag ain +Ġ im +n g +Ġon ly +Ġ V +Ġe very +ĠI n +Ġl ittle +Ġa d +c c +ur n +ha r +o h +Ġs 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"bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/multirc/structformer_as_hf.py b/finetune/multirc/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/multirc/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/multirc/tokenizer_config.json b/finetune/multirc/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/multirc/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", 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o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo 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+ity a +ĠO D +ies selin +Ġman nik +ud ers +ud eau +ap o +ĠK iri +ven lock +Ġtr acing +ure en +ĠHe w +ĠHe gel +ove l +ick i +ci us +ĠU CLA +reat ic +ĠSt ras +ĠSt empniak +No ll +other wise +ne ither +Ġback grounds +Ġover d +Ġgr inding +That s +Ġen act +Ġen cl +Ġen vious +og g +og un +pl anet +ord ered +Ġdown stream +Ġun real +Ġun wanted +Ġun ravel +iz oph +Ġam iable +Ġcomp osing +Ġbl ush +ĠV on +ĠV ig +ĠIn v +urn al +Ġplay doh +ress ions +av or +av ius +Ġdis ks +Ġgu iding +ĠY ek +be es +ip ani +ip hon +Ġfl aws +ect ing +vers ation +ks het +Ġrem nants +ix by +Ġsha ving +Ġsec urities +Ġspe ars +Ġlast ly +li k +Ġend ocr +Ġlea ked +Ġfam ine +Ġnight gown +ny a +de gree +Ġ18 10 +Ġ18 28 +Ġ18 32 +gg les +ĠShe ldon +ĠAr um +Ġmar ries diff --git a/finetune/qnli/checkpoint-2000/optimizer.pt b/finetune/qnli/checkpoint-2000/optimizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..3799b4281d0d4f270bab52a0f55562fd83c26a16 --- /dev/null +++ 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false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/qnli/checkpoint-2000/structformer_as_hf.py b/finetune/qnli/checkpoint-2000/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/qnli/checkpoint-2000/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/qnli/checkpoint-2000/tokenizer_config.json b/finetune/qnli/checkpoint-2000/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/qnli/checkpoint-2000/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/qnli/checkpoint-2000/trainer_state.json b/finetune/qnli/checkpoint-2000/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..14e1d18c1ea242b0d70259c261894ff5c58d617b --- /dev/null +++ b/finetune/qnli/checkpoint-2000/trainer_state.json @@ -0,0 +1,95 @@ +{ + "best_metric": 0.7862407862407862, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/qnli/checkpoint-2000", + "epoch": 5.46448087431694, + "global_step": 2000, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 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"Fi": 17373, + "Fifteen": 23644, + "Fifty": 20010, + "Fig": 18589, + "Fight": 14040, + "Fighting": 31585, + "Fil": 14556, + "Film": 31591, + "Fin": 7495, + "Final": 21511, + "Finally": 11439, + "Find": 8513, + "Finding": 20443, + "Fine": 5197, + "Finish": 31491, + "Fire": 10247, + "First": 3946, + "Fish": 27930, + "Five": 7756, + "Fl": 6412, + "Flash": 29507, + "Fle": 31685, + "Flor": 21799, + "Flowers": 27770, + "Flying": 29688, + "Follow": 8333, + "Following": 14326, + "Food": 20726, + "Foot": 7641, + "Football": 17549, + "Footnote": 8577, + "For": 1451, + "Fore": 14750, + "Forget": 9176, + "Forgive": 13389, + "Form": 29048, + "Former": 31301, + "Fort": 11561, + "Fortunately": 20260, + "Forty": 18943, + "Found": 18590, + "Four": 6939, + "Fox": 26913, + "Fr": 14038, + "Fran": 6626, + "France": 14648, + "Frances": 15167, + "Francesco": 22665, + "Franche": 28357, + "Francis": 19953, + "Frank": 6880, + "Frankie": 25434, + "Franklin": 28909, + "Franz": 19095, + "Fre": 13598, + "Fred": 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"4.26.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 32000, + "weight_act": "softmax" +} diff --git a/finetune/qnli/eval_results.json b/finetune/qnli/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..72726363dff93a95e5ae50b2f405a14e6bbffbf9 --- /dev/null +++ b/finetune/qnli/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.7716535329818726, + "eval_f1": 0.7862407862407862, + "eval_loss": 0.8745389580726624, + "eval_mcc": 0.5412131716827608, + "eval_runtime": 4.4166, + "eval_samples": 2286, + "eval_samples_per_second": 517.589, + "eval_steps_per_second": 64.755 +} \ No newline at end of file diff --git a/finetune/qnli/merges.txt b/finetune/qnli/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/qnli/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is 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"rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/qnli/structformer_as_hf.py b/finetune/qnli/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/qnli/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/qnli/tokenizer_config.json b/finetune/qnli/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/qnli/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, 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+Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e 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+Ġh uts +Ġhe ur +al ming +Ġit ching +Ġit chy +Ġe up +ut sc +Ġwe ights +Ġfor fe +Ġfor ci +ĠS ter +ĠS ino +ĠS ales +ĠS hib +ĠS iri +st ed +st ack +st range +st icks +ri y +ad itionally +Ġst if +Ġst rew +Ġst ains +Ġst ables +Ġst unning +oo ges +ĠA part +ĠA men +ĠA cha +ĠC ertain +ĠC ey +ĠC LA +ĠC ARR +Ġu gh +ĠM ist +ĠM OM +ĠM IGHT +Ġas semble +ill ors +Ġse ag +ĠH ollow +ĠH ades +ĠH ym +il ization +ĠB old +Ġli ability +op ping +ĠP K +ĠP ill +ĠP ew +ĠP ied +ĠP oe +ĠP OWER +ĠP iazza +ĠT il +ĠT ia +ĠT ick +un er +ĠW ap +Ġr ushes +Ġj est +Ġj ets +ĠD M +ĠD ort +ĠD OU +ĠD IS +ĠD uart +ĠD izzy +ĠG ina +um en +Ġso ils +Ġso aring +est us +Ġal igned +Ġall erg +ist ered +ĠR T +ĠR im +ĠR ac +ĠR iding +ĠR umble +ie ving +art y +ab lan +ĠL ac +ĠL ust +ĠL um +ĠL em +ĠL ions +ĠL ars +Ġcon gest +ĠN ights +ig l +han na +han sa +Ġup hold +Ġcom ical +Ġher ring +ĠJ ude +Ġout look +Ġout lined +ra ult +Ġpl umber +Ġpl aus +od i +ink meaner +Ġpro state +Ġpro stitutes +Ġex clude +Ġv ener +Ġv ines +Ġv ipers +ard y +ity a +ĠO D +ies selin +Ġman nik +ud ers +ud eau +ap o +ĠK iri +ven lock +Ġtr acing +ure en +ĠHe w +ĠHe gel +ove l +ick i +ci us +ĠU CLA +reat ic +ĠSt ras +ĠSt empniak +No ll +other wise +ne ither +Ġback grounds +Ġover d +Ġgr inding +That s +Ġen act +Ġen cl +Ġen vious +og g +og un +pl anet +ord ered +Ġdown stream +Ġun real +Ġun wanted +Ġun ravel +iz oph +Ġam iable +Ġcomp osing +Ġbl ush +ĠV on +ĠV ig +ĠIn v +urn al +Ġplay doh +ress ions +av or +av ius +Ġdis ks +Ġgu iding +ĠY ek +be es +ip ani +ip hon +Ġfl aws +ect ing +vers ation +ks het +Ġrem nants +ix by +Ġsha ving +Ġsec urities +Ġspe ars +Ġlast ly +li k +Ġend ocr +Ġlea ked +Ġfam ine +Ġnight gown +ny a +de gree +Ġ18 10 +Ġ18 28 +Ġ18 32 +gg les +ĠShe ldon +ĠAr um +Ġmar ries diff --git a/finetune/qqp/checkpoint-7200/optimizer.pt b/finetune/qqp/checkpoint-7200/optimizer.pt new file mode 100644 index 0000000000000000000000000000000000000000..62bf42ff4478d30ce4217de22329332a33394eb6 --- /dev/null +++ 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+ }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/qqp/checkpoint-7200/structformer_as_hf.py b/finetune/qqp/checkpoint-7200/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/qqp/checkpoint-7200/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/qqp/checkpoint-7200/tokenizer_config.json b/finetune/qqp/checkpoint-7200/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/qqp/checkpoint-7200/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/qqp/checkpoint-7200/trainer_state.json b/finetune/qqp/checkpoint-7200/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..91ed9683a2fed5d6356751b178643053a162927b --- /dev/null +++ b/finetune/qqp/checkpoint-7200/trainer_state.json @@ -0,0 +1,298 @@ +{ + "best_metric": 0.8167628096369188, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/qqp/checkpoint-7200", + "epoch": 3.5467980295566504, + "global_step": 7200, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 0.2, 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"ľ": 201, + "Ŀ": 202, + "ŀ": 203, + "Ł": 204, + "ł": 205, + "Ń": 206 +} diff --git a/finetune/qqp/config.json b/finetune/qqp/config.json new file mode 100644 index 0000000000000000000000000000000000000000..997c7c9e67e0d044c237d012f6d1fe237a3894a5 --- /dev/null +++ b/finetune/qqp/config.json @@ -0,0 +1,57 @@ +{ + "_name_or_path": "omarmomen/structformer_s1_final_with_pos", + "architectures": [ + "StructformerModelForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "structformer_as_hf.StructformerConfig", + "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel", + "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "dropatt": 0.1, + "dropout": 0.1, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "structformer", + "n_context_layers": 0, + "n_parser_layers": 4, + "nhead": 12, + "nlayers": 12, + "ntokens": 32000, + "num_attention_heads": 8, + "num_hidden_layers": 8, + "pad": 0, + "pad_token_id": 1, + "pos_emb": true, + "position_embedding_type": "absolute", + "problem_type": "single_label_classification", + "relations": [ + "head", + "child" + ], + "relative_bias": false, + "torch_dtype": "float32", + "transformers_version": "4.26.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 32000, + "weight_act": "softmax" +} diff --git a/finetune/qqp/eval_results.json b/finetune/qqp/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..bf72c2df109ccbdb32830bac3294432409478104 --- /dev/null +++ b/finetune/qqp/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 4.53, + "eval_accuracy": 0.8393394947052002, + "eval_f1": 0.8167628096369188, + "eval_loss": 0.43506747484207153, + "eval_mcc": 0.6759774095179583, + "eval_runtime": 51.9461, + "eval_samples": 26889, + "eval_samples_per_second": 517.633, + "eval_steps_per_second": 64.721 +} \ No newline at end of file diff --git a/finetune/qqp/merges.txt b/finetune/qqp/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/qqp/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on 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+26883 1 +26884 1 +26885 1 +26886 1 +26887 1 +26888 1 diff --git a/finetune/qqp/pytorch_model.bin b/finetune/qqp/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..9bcde87bc1a760fe056d4e3f92269175e6fd2d15 --- /dev/null +++ b/finetune/qqp/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f161856fc6b233d58337ab9cea1adab2cb1bfdd2193421b6b8e66bee907d72e5 +size 534669003 diff --git a/finetune/qqp/special_tokens_map.json b/finetune/qqp/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/qqp/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/qqp/structformer_as_hf.py b/finetune/qqp/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/qqp/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/qqp/tokenizer_config.json b/finetune/qqp/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/qqp/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + 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b/finetune/relative_position_control/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/relative_position_control/checkpoint-400/structformer_as_hf.py b/finetune/relative_position_control/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/relative_position_control/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/relative_position_control/checkpoint-400/tokenizer_config.json b/finetune/relative_position_control/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/relative_position_control/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/relative_position_control/checkpoint-400/trainer_state.json b/finetune/relative_position_control/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..cd0fb6c7b41be96249788ac55e75fbb31b887d8b --- /dev/null +++ b/finetune/relative_position_control/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.9534384722669241, + 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b/finetune/relative_position_control/pytorch_model.bin new file mode 100644 index 0000000000000000000000000000000000000000..16bd4c82a58645df99044ee54d0c57f3d4d90611 --- /dev/null +++ b/finetune/relative_position_control/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c96fcf94f5436eecdcf887404b342087a05b785dccdd97dd0af7056793639ede +size 534669003 diff --git a/finetune/relative_position_control/special_tokens_map.json b/finetune/relative_position_control/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/relative_position_control/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/relative_position_control/structformer_as_hf.py b/finetune/relative_position_control/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/relative_position_control/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/relative_position_control/tokenizer_config.json b/finetune/relative_position_control/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/relative_position_control/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/relative_position_control/train_results.json b/finetune/relative_position_control/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..429259075ef17188b8d10d5056f7facbd91acc3f --- /dev/null +++ b/finetune/relative_position_control/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.030746202896300114, + "train_runtime": 471.9969, + "train_samples": 9068, + "train_samples_per_second": 192.12, + "train_steps_per_second": 1.61 +} \ No newline at end of file diff --git a/finetune/relative_position_control/trainer_state.json b/finetune/relative_position_control/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..6dd2e2bf0b782059fba31dec2410db0d782129ce --- /dev/null +++ b/finetune/relative_position_control/trainer_state.json @@ -0,0 +1,42 @@ +{ + "best_metric": 0.9534384722669241, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/relative_position_control/checkpoint-400", + "epoch": 10.0, + "global_step": 760, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 5.26, + "eval_accuracy": 0.9520229697227478, + "eval_f1": 0.9534384722669241, + "eval_loss": 0.312172532081604, + "eval_mcc": 0.9057216200462846, + "eval_runtime": 35.1924, + "eval_samples_per_second": 514.088, + "eval_steps_per_second": 64.275, + "step": 400 + }, + { + "epoch": 6.58, + "learning_rate": 1.7105263157894737e-05, + "loss": 0.0464, + "step": 500 + }, + { + "epoch": 10.0, + "step": 760, + "total_flos": 7569394312212480.0, + "train_loss": 0.030746202896300114, + "train_runtime": 471.9969, + "train_samples_per_second": 192.12, + "train_steps_per_second": 1.61 + } + ], + "max_steps": 760, + "num_train_epochs": 10, + "total_flos": 7569394312212480.0, + "trial_name": null, + "trial_params": null +} diff --git a/finetune/relative_position_control/training_args.bin b/finetune/relative_position_control/training_args.bin new file mode 100644 index 0000000000000000000000000000000000000000..322a5dcfc7c7eb52580e304d0effb2edcb05ec11 --- /dev/null +++ b/finetune/relative_position_control/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3a41cc9246c9db778895c98f7502313158bc9e8952325e7addb2bb7e9b8ee04 +size 3567 diff --git a/finetune/relative_position_control/vocab.json b/finetune/relative_position_control/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..952189775933fbe4b034a3f5eb4343fd4091093f --- /dev/null +++ b/finetune/relative_position_control/vocab.json @@ -0,0 +1,32002 @@ +{ + "!": 5, + "!!": 10295, + "!!!": 25895, + "!\"": 1718, + "!\",": 26420, + "!\"--": 28223, + "!'": 11115, + 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6569, + "OM": 2994, + "OMAN": 12240, + "OME": 6519, + "OMEN": 28282, + "ON": 1608, + "OND": 13299, + "ONE": 7750, + "ONG": 12298, + "OO": 15430, + "OP": 10213, + "OR": 1755, + "ORD": 24924, + "ORE": 11259, + "ORGE": 14604, + "ORK": 31310, + "ORN": 19054, + "ORS": 28225, + "ORT": 17963, + "ORY": 17635, + "OS": 5985, + "OSE": 19135, + "OT": 3423, + "OTHER": 27943, + "OTS": 28837, + "OTT": 30038, + "OU": 1503, + "OUGH": 11151, + "OUGHT": 24312, + "OULD": 8501, + "OUND": 13737, + "OUR": 10420, + "OUS": 18867, + "OUSE": 22863, + "OUT": 10520, + "OW": 3137, + "OWER": 21406, + "OWN": 12783, + "OWS": 28829, + "OY": 6920, + "OYD": 27777, + "Oak": 26933, + "Ob": 9335, + "Ober": 21808, + "Obs": 28530, + "Obviously": 13410, + "Occ": 24380, + "Oct": 29700, + "October": 13949, + "Of": 2362, + "Off": 10297, + "Offic": 14759, + "Officer": 22360, + "Often": 29064, + "Oh": 584, + "Ohh": 14495, + "Oi": 17553, + "Ok": 4976, + "Okay": 1103, + "Ol": 12327, + "Old": 9561, + "Ole": 15769, + "Oliver": 29701, + 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+ "RNA": 30311, + "RO": 4192, + "ROM": 21811, + "RON": 28533, + "ROOK": 30708, + "RRB": 8755, + "RSB": 12464, + "RUN": 7202, + "RY": 8072, + "Ra": 20730, + "Rachael": 27411, + "Rachel": 23319, + "Rad": 11502, + "Radha": 27313, + "Radio": 22064, + "Rain": 22392, + "Raj": 29070, + "Ralph": 24384, + "Ram": 16807, + "Rap": 31000, + "Rat": 24757, + "Rather": 19361, + "Rats": 26934, + "Raw": 13601, + "Ray": 11278, + "Re": 1815, + "Read": 14651, + "Ready": 8979, + "Real": 15720, + "Really": 3344, + "Rebecca": 24575, + "Rec": 10134, + "Recently": 31540, + "Reception": 22540, + "Red": 9196, + "References": 21960, + "Reg": 12572, + "Rein": 30617, + "Rel": 8663, + "Relax": 13766, + "Release": 23508, + "Rem": 4538, + "Remember": 5472, + "Remind": 31432, + "Ren": 24756, + "Rep": 10638, + "Republic": 25473, + "Res": 9269, + "Rest": 15532, + "Ret": 13340, + "Return": 28974, + "Rev": 24574, + "Revenge": 29988, + "Ri": 31715, + "Ric": 10098, + "Rich": 18961, + "Richard": 10997, + "Richie": 31628, + "Rick": 17554, + "Ricky": 27946, + "Right": 1506, + "Riley": 23657, + "Rilla": 16369, + "Rio": 25999, + "Rip": 30309, + "Rise": 26441, + "Ro": 24382, + "Road": 20263, + "Rob": 7512, + "Rober": 21004, + "Robert": 7958, + "Robin": 16907, + "Robinson": 20435, + "Rock": 10639, + "Rocky": 29563, + "Rod": 20022, + "Roger": 16123, + "Roll": 12529, + "Rolling": 26360, + "Rollo": 26359, + "Rom": 24758, + "Roman": 12463, + "Rome": 21518, + "Romeo": 27901, + "Ron": 13419, + "Ronald": 31577, + "Room": 29071, + "Rose": 14042, + "Ross": 24383, + "Round": 21519, + "Roy": 19791, + "Royal": 17378, + "Ru": 21258, + "Rub": 20023, + "Rum": 29069, + "Run": 7418, + "Running": 25864, + "Rural": 14450, + "Russ": 31716, + "Russian": 24760, + "Ry": 25135, + "Ryan": 18039, + "S": 55, + "SA": 11279, + "SAM": 26443, + "SB": 6478, + "SBG": 21439, + "SC": 6462, + "SCC": 24650, + "SCOFF": 26315, + "SCOFFS": 28504, + "SCREAMING": 24651, + "SCREAMS": 30762, + "SD": 25136, + "SE": 4566, + "SEL": 19726, + "SELF": 22678, 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"Science": 29707, + "Scot": 15847, + "Scotland": 21489, + "Scott": 13158, + "Scottish": 30859, + "Screaming": 23548, + "Screw": 28312, + "Script": 23322, + "Se": 4027, + "Sea": 23010, + "Sean": 27221, + "Search": 25551, + "Sebastian": 27953, + "Sec": 6755, + "Second": 9846, + "Secret": 18922, + "Section": 21520, + "Security": 27773, + "See": 2525, + "Seeing": 16194, + "Seems": 16478, + "Sel": 16659, + "Selena": 23289, + "Self": 25552, + "Sem": 28534, + "Sen": 13686, + "Senator": 21773, + "Send": 13784, + "Sent": 31001, + "Sep": 31002, + "September": 13022, + "Ser": 5531, + "Sergeant": 17043, + "Series": 30739, + "Seriously": 10627, + "Set": 15654, + "Seth": 31003, + "Seven": 12519, + "Sevent": 22282, + "Seventh": 27733, + "Several": 17872, + "Sex": 18412, + "Sh": 2044, + "Sha": 5271, + "Shad": 31005, + "Shake": 28335, + "Shakespeare": 23563, + "Shaku": 30091, + "Shakugan": 30958, + "Shall": 7004, + "Shame": 21005, + "Shar": 15193, + "Sharif": 25841, + "Sharifa": 28502, + "Sharon": 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"Smith": 16763, + "Sn": 13250, + "Snow": 19792, + "So": 564, + "Social": 18115, + "Soft": 23445, + "Sol": 18411, + "Sold": 24870, + "Some": 2038, + "Somebody": 8083, + "Somehow": 26663, + "Someone": 5979, + "Somet": 3097, + "Something": 4706, + "Sometimes": 5357, + "Somewhere": 18160, + "Son": 9411, + "Song": 27947, + "Songs": 31007, + "Soon": 9783, + "Sophie": 23012, + "Sorry": 2747, + "Sort": 19136, + "Sou": 31717, + "Sound": 22697, + "Sounds": 10298, + "Sources": 21813, + "South": 9336, + "Southern": 26935, + "Sp": 3433, + "Space": 23201, + "Spain": 29430, + "Spanish": 23871, + "Spart": 29706, + "Spe": 7168, + "Speak": 14618, + "Speaking": 11965, + "Special": 19961, + "Species": 11981, + "Speed": 29528, + "Spl": 27949, + "Sport": 30312, + "Sports": 14557, + "Spot": 26264, + "Spring": 20264, + "Squ": 16370, + "St": 1018, + "Stan": 15371, + "Stand": 8193, + "Standing": 30149, + "Star": 8975, + "Stargate": 26814, + "Start": 9468, + "Starting": 19335, + "Stat": 20842, + "State": 19036, 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2876, + "TH": 2385, + "THAT": 11542, + "THE": 4393, + "THER": 10370, + "THERE": 23843, + "THEY": 16332, + "THING": 13395, + "THIS": 26234, + "THlNG": 30014, + "TIES": 26448, + "TIN": 16372, + "TING": 6893, + "TINUES": 21492, + "TO": 11387, + "TON": 16373, + "TR": 13023, + "TS": 5065, + "TV": 6581, + "TW": 27955, + "TY": 18784, + "Ta": 18413, + "Tai": 26446, + "Tak": 26001, + "Take": 2452, + "Taking": 17085, + "Talk": 8249, + "Talking": 24665, + "Tan": 23323, + "Tar": 22699, + "Tax": 28538, + "Tay": 21262, + "Taylor": 25961, + "Te": 5263, + "Teach": 29476, + "Teacher": 29477, + "Team": 22596, + "Techn": 21725, + "Ted": 15194, + "Teen": 24387, + "Tele": 30090, + "Television": 26295, + "Tell": 2766, + "Tem": 17556, + "Ten": 7866, + "Tenant": 30142, + "Tend": 31008, + "Ter": 12530, + "Terrible": 23602, + "Terry": 26939, + "Test": 30314, + "Tex": 21006, + "Texas": 29637, + "Text": 28880, + "Th": 1432, + "Tha": 22105, + "Than": 1160, + "Thank": 1488, + "Thanks": 2679, + "That": 603, + 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--git a/finetune/rte/special_tokens_map.json b/finetune/rte/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/rte/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/rte/structformer_as_hf.py b/finetune/rte/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/rte/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/rte/tokenizer_config.json b/finetune/rte/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/rte/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/rte/train_results.json b/finetune/rte/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..a72098977ed8336f7766939c9ab42176cd7665ec --- /dev/null +++ b/finetune/rte/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.68456130027771, + "train_runtime": 41.3585, + "train_samples": 858, + "train_samples_per_second": 207.455, + "train_steps_per_second": 1.934 +} \ No newline at end of file diff --git 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"Chapter": 31484, + "Char": 4847, + "Charl": 22012, + "Charles": 9843, + "Charlie": 15618, + "Charlotte": 29026, + "Chat": 19557, + "Che": 4490, + "Check": 9112, + "Cheers": 13079, + "Chel": 29684, + "Ches": 29044, + "Chess": 26907, + "Chi": 18777, + "Chicago": 26226, + "Chicken": 28241, + "Chief": 12919, + "Child": 18587, + "Children": 15321, + "China": 23519, + "Chinese": 22882, + "Cho": 15188, + "Choose": 31336, + "Chp": 16315, + "Chris": 14514, + "Christ": 5128, + "Christian": 11908, + "Christine": 24997, + "Christmas": 24260, + "Christopher": 21184, + "Chuck": 17317, + "Chuckles": 13220, + "Chuckling": 31617, + "Chun": 26672, + "Church": 19483, + "Cir": 27390, + "City": 20257, + "Civil": 30285, + "Cl": 2870, + "Cla": 13016, + "Claire": 22653, + "Clara": 25793, + "Clare": 25792, + "Clark": 26696, + "Class": 14413, + "Clause": 17079, + "Clay": 21692, + "Cle": 11677, + "Clean": 23599, + "Clear": 11445, + "Clearly": 25477, + "Climate": 21402, + "Close": 14832, + "Club": 11262, + "Co": 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false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/sst2/checkpoint-4000/structformer_as_hf.py b/finetune/sst2/checkpoint-4000/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/sst2/checkpoint-4000/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/sst2/checkpoint-4000/tokenizer_config.json b/finetune/sst2/checkpoint-4000/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ 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"ľ": 201, + "Ŀ": 202, + "ŀ": 203, + "Ł": 204, + "ł": 205, + "Ń": 206 +} diff --git a/finetune/sst2/config.json b/finetune/sst2/config.json new file mode 100644 index 0000000000000000000000000000000000000000..997c7c9e67e0d044c237d012f6d1fe237a3894a5 --- /dev/null +++ b/finetune/sst2/config.json @@ -0,0 +1,57 @@ +{ + "_name_or_path": "omarmomen/structformer_s1_final_with_pos", + "architectures": [ + "StructformerModelForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "structformer_as_hf.StructformerConfig", + "AutoModelForMaskedLM": "structformer_as_hf.StructformerModel", + "AutoModelForSequenceClassification": "structformer_as_hf.StructformerModelForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "dropatt": 0.1, + "dropout": 0.1, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + 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0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/sst2/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/sst2/structformer_as_hf.py b/finetune/sst2/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/sst2/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/sst2/tokenizer_config.json b/finetune/sst2/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/sst2/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": 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b/finetune/syntactic_category_control/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/syntactic_category_control/checkpoint-400/structformer_as_hf.py b/finetune/syntactic_category_control/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/syntactic_category_control/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/syntactic_category_control/checkpoint-400/tokenizer_config.json b/finetune/syntactic_category_control/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/syntactic_category_control/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/syntactic_category_control/checkpoint-400/trainer_state.json b/finetune/syntactic_category_control/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..228eb2a12f72f1f4f0b4400cff26904f72c4b9c4 --- /dev/null +++ b/finetune/syntactic_category_control/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 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index 0000000000000000000000000000000000000000..6dc242306f833569a4083483893969337454b1fc --- /dev/null +++ b/finetune/syntactic_category_control/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.5808023810386658, + "eval_f1": 0.6853789231946925, + "eval_loss": 5.129516124725342, + "eval_mcc": 0.2261764035082515, + "eval_runtime": 17.1637, + "eval_samples": 8824, + "eval_samples_per_second": 514.109, + "eval_steps_per_second": 64.264 +} \ No newline at end of file diff --git a/finetune/syntactic_category_control/merges.txt b/finetune/syntactic_category_control/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/syntactic_category_control/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa 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b/finetune/syntactic_category_control/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94744127e9397f00219b480c7c82dcd17731644852e340b2b6f62b835d15a0f8 +size 534669003 diff --git a/finetune/syntactic_category_control/special_tokens_map.json b/finetune/syntactic_category_control/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/syntactic_category_control/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/syntactic_category_control/structformer_as_hf.py b/finetune/syntactic_category_control/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/syntactic_category_control/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/syntactic_category_control/tokenizer_config.json b/finetune/syntactic_category_control/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/syntactic_category_control/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/syntactic_category_control/train_results.json b/finetune/syntactic_category_control/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..69edc010b65b1dfb446b2ba06f87f6230d0e44cc --- /dev/null +++ b/finetune/syntactic_category_control/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.03989357728712881, + "train_runtime": 463.7786, + "train_samples": 8930, + "train_samples_per_second": 192.549, + "train_steps_per_second": 1.617 +} \ No newline at end of file diff --git a/finetune/syntactic_category_control/trainer_state.json b/finetune/syntactic_category_control/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..fc0cdc7006cd9436e1e96165100be192360e84e3 --- /dev/null +++ b/finetune/syntactic_category_control/trainer_state.json @@ -0,0 +1,42 @@ +{ + "best_metric": 0.8177277796634825, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/syntactic_category_control/checkpoint-400", + "epoch": 10.0, + "global_step": 750, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 5.33, + "eval_accuracy": 0.7881179451942444, + "eval_f1": 0.8177277796634825, + "eval_loss": 2.5797159671783447, + "eval_mcc": 0.6097691974403308, + "eval_runtime": 34.4859, + "eval_samples_per_second": 514.442, + "eval_steps_per_second": 64.316, + "step": 400 + }, + { + "epoch": 6.67, + "learning_rate": 1.6666666666666667e-05, + "loss": 0.0598, + "step": 500 + }, + { + "epoch": 10.0, + "step": 750, + "total_flos": 7454200618444800.0, + "train_loss": 0.03989357728712881, + "train_runtime": 463.7786, + "train_samples_per_second": 192.549, + "train_steps_per_second": 1.617 + } + ], + "max_steps": 750, + "num_train_epochs": 10, + "total_flos": 7454200618444800.0, + "trial_name": null, + "trial_params": null +} diff 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10486, + "BF": 25530, + "BI": 8282, + "BL": 25112, + "BLE": 13597, + "BM": 14235, + "BO": 8042, + "BODY": 22384, + "BOTH": 22326, + "BOY": 31679, + "BR": 13682, + "BS": 7251, + "BUT": 12323, + "BY": 8534, + "Ba": 17371, + "Bab": 26901, + "Baby": 10296, + "Back": 5889, + "Background": 17046, + "Bad": 9732, + "Bah": 31678, + "Bain": 29680, + "Baker": 27922, + "Bal": 21246, + "Ball": 20006, + "Ban": 18585, + "Bang": 18243, + "Bank": 29682, + "Bar": 7298, + "Barb": 21746, + "Barbara": 26405, + "Bark": 30282, + "Barry": 30965, + "Bas": 12873, + "Based": 22089, + "Basically": 18765, + "Bass": 27920, + "Bastard": 20256, + "Bat": 16508, + "Batman": 27346, + "Battle": 19132, + "Bauer": 29043, + "Bay": 17688, + "Be": 1163, + "Bea": 18774, + "Beat": 16720, + "Beautiful": 14132, + "Beauty": 30909, + "Because": 1814, + "Bed": 25113, + "Bee": 27388, + "Been": 17953, + "Before": 5318, + "Behind": 20136, + "Being": 12368, + "Bel": 8172, + "Believe": 14636, + "Bell": 15762, + "Ben": 8201, + "Bend": 29681, + "Benigno": 31507, + "Bent": 30964, + "Ber": 13596, + "Berm": 31677, + "Bern": 17689, + "Bernard": 29613, + "Bert": 24734, + "Besides": 8283, + "Best": 10894, + "Bet": 5092, + "Beth": 13683, + "Better": 9049, + "Betty": 20411, + "Between": 12184, + "Bew": 25532, + "Beyond": 26633, + "Bi": 11986, + "Big": 6653, + "Bill": 8429, + "Billboard": 13860, + "Billy": 15320, + "Biography": 19341, + "Bir": 25531, + "Birds": 26904, + "Bishop": 26902, + "Bit": 22683, + "Bitch": 21794, + "Bl": 3084, + "Black": 8552, + "Blair": 18718, + "Ble": 29678, + "Bless": 18775, + "Blood": 17981, + "Bloody": 13569, + "Blow": 24959, + "Blue": 17030, + "Bo": 13095, + "Bob": 7049, + "Bobby": 19526, + "Body": 25533, + "Bon": 13096, + "Boo": 29679, + "Book": 17863, + "Boom": 26426, + "Bor": 23307, + "Born": 12130, + "Bos": 27919, + "Boss": 9960, + "Boston": 21247, + "Both": 7556, + "Bou": 29677, + "Bour": 26425, + "Bow": 30280, + "Box": 23988, + "Boy": 7180, + "Boys": 18376, + "Br": 3284, + "Brad": 17187, + 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5300, + "CL": 9025, + "CLA": 20477, + "CLARE": 15861, + "CLE": 19785, + "CO": 12528, + "COME": 25116, + "CON": 16802, + "CP": 23989, + "CR": 13946, + "CROW": 29592, + "CROWD": 30956, + "CT": 8173, + "CU": 25978, + "CW": 27389, + "CY": 16801, + "Ca": 16118, + "Cab": 22090, + "Cal": 9919, + "California": 28400, + "Call": 6189, + "Calm": 13248, + "Cam": 12732, + "Camb": 18032, + "Cambrian": 20715, + "Came": 25534, + "Camer": 20478, + "Camp": 15874, + "Can": 1597, + "Canada": 23499, + "Cand": 25979, + "Cap": 5405, + "Capt": 25980, + "Captain": 6477, + "Car": 4106, + "Card": 21795, + "Care": 6737, + "Career": 9650, + "Careful": 16905, + "Carl": 12041, + "Carlos": 19538, + "Carol": 19289, + "Caroline": 31420, + "Carr": 31680, + "Carry": 27737, + "Cart": 17864, + "Carter": 14942, + "Cas": 20721, + "Case": 28510, + "Cash": 28511, + "Cass": 27924, + "Cast": 11738, + "Castle": 30190, + "Cat": 15528, + "Catch": 16119, + "Catherine": 26906, + "Catholic": 31683, + "Cats": 27925, + "Cause": 5406, + 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t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght 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a/finetune/syntactic_category_lexical_content_the/checkpoint-400/special_tokens_map.json b/finetune/syntactic_category_lexical_content_the/checkpoint-400/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/syntactic_category_lexical_content_the/checkpoint-400/structformer_as_hf.py b/finetune/syntactic_category_lexical_content_the/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/syntactic_category_lexical_content_the/checkpoint-400/tokenizer_config.json b/finetune/syntactic_category_lexical_content_the/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/syntactic_category_lexical_content_the/checkpoint-400/trainer_state.json b/finetune/syntactic_category_lexical_content_the/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..0d572b01ba2c451195085ea3f6f9e60e342802bb --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.7606557377049179, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/syntactic_category_lexical_content_the/checkpoint-400", + "epoch": 5.797101449275362, + "global_step": 400, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 5.8, + "eval_accuracy": 0.7685617804527283, + "eval_f1": 0.7606557377049179, + "eval_loss": 2.2707250118255615, + "eval_mcc": 0.5384741271177926, + "eval_runtime": 45.3911, + 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27938, + "Most": 5124, + "Mostly": 26752, + "Mot": 24745, + "Mother": 6568, + "Mouna": 27941, + "Mount": 18960, + "Move": 7073, + "Moving": 27406, + "Mr": 1929, + "Mrs": 5165, + "Ms": 13600, + "Mu": 17375, + "Much": 14238, + "Mull": 30303, + "Mult": 18249, + "Mum": 8864, + "Mumbai": 24005, + "Mummy": 25132, + "Municip": 30994, + "Mur": 9444, + "Murder": 23589, + "Murphy": 31526, + "Murray": 28941, + "Mus": 7976, + "Mush": 27939, + "Music": 10633, + "Must": 8921, + "Mut": 30991, + "My": 1107, + "N": 50, + "NA": 4523, + "NAMES": 24749, + "NARR": 8485, + "NARRATOR": 8533, + "NBA": 26931, + "NC": 16936, + "ND": 3467, + "NDER": 22266, + "NE": 7957, + "NER": 9159, + "NEWS": 30144, + "NEY": 23258, + "NFL": 26930, + "NG": 27942, + "NN": 16937, + "NO": 7889, + "NOCK": 21450, + "NOT": 18251, + "NOW": 7234, + "NS": 31707, + "NT": 17228, + "NTER": 24379, + "NY": 24378, + "Na": 18250, + "Nad": 22101, + "Nadim": 30251, + "Nah": 15191, + "Name": 16240, + "Nan": 23654, + "Nancy": 29699, + "Nap": 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b/finetune/syntactic_category_lexical_content_the/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/syntactic_category_lexical_content_the/structformer_as_hf.py b/finetune/syntactic_category_lexical_content_the/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/syntactic_category_lexical_content_the/tokenizer_config.json b/finetune/syntactic_category_lexical_content_the/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/syntactic_category_lexical_content_the/train_results.json b/finetune/syntactic_category_lexical_content_the/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..f6ea717cef47f1d23b5a0ae1c85ffab98b8738f5 --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 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a/finetune/syntactic_category_relative_position/checkpoint-400/special_tokens_map.json b/finetune/syntactic_category_relative_position/checkpoint-400/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/syntactic_category_relative_position/checkpoint-400/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/syntactic_category_relative_position/checkpoint-400/structformer_as_hf.py b/finetune/syntactic_category_relative_position/checkpoint-400/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/syntactic_category_relative_position/checkpoint-400/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/syntactic_category_relative_position/checkpoint-400/tokenizer_config.json b/finetune/syntactic_category_relative_position/checkpoint-400/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/syntactic_category_relative_position/checkpoint-400/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + 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"single_word": false + } +} diff --git a/finetune/syntactic_category_relative_position/checkpoint-400/trainer_state.json b/finetune/syntactic_category_relative_position/checkpoint-400/trainer_state.json new file mode 100644 index 0000000000000000000000000000000000000000..43e76f6452453b85d3e1ed87130ca998c48d59a4 --- /dev/null +++ b/finetune/syntactic_category_relative_position/checkpoint-400/trainer_state.json @@ -0,0 +1,27 @@ +{ + "best_metric": 0.6829371666725533, + "best_model_checkpoint": "finetune_results/omarmomen/structformer_s1_final_with_pos/syntactic_category_relative_position/checkpoint-400", + "epoch": 5.194805194805195, + "global_step": 400, + "is_hyper_param_search": false, + "is_local_process_zero": true, + "is_world_process_zero": true, + "log_history": [ + { + "epoch": 5.19, + "eval_accuracy": 0.6527138352394104, + "eval_f1": 0.6829371666725533, + "eval_loss": 4.251095771789551, + "eval_mcc": 0.31463876583272515, + "eval_runtime": 50.0389, + "eval_samples_per_second": 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0000000000000000000000000000000000000000..1d3f95f1e2257f39195d625880f29494b72a9351 --- /dev/null +++ b/finetune/syntactic_category_relative_position/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d1a5837e89f7e571795594ca68f609263355c0ba0b6ff02ebc6aa14de4b3f70 +size 534669003 diff --git a/finetune/syntactic_category_relative_position/special_tokens_map.json b/finetune/syntactic_category_relative_position/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/syntactic_category_relative_position/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/syntactic_category_relative_position/structformer_as_hf.py b/finetune/syntactic_category_relative_position/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/syntactic_category_relative_position/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/syntactic_category_relative_position/tokenizer_config.json b/finetune/syntactic_category_relative_position/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/syntactic_category_relative_position/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/syntactic_category_relative_position/train_results.json b/finetune/syntactic_category_relative_position/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..44d9f41972b47ad702817d6805b51f9474102ef7 --- /dev/null +++ b/finetune/syntactic_category_relative_position/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.024354383155897066, + "train_runtime": 487.3319, + "train_samples": 9159, + "train_samples_per_second": 187.942, + "train_steps_per_second": 1.58 +} \ No newline at end of file diff --git a/finetune/syntactic_category_relative_position/trainer_state.json 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27938, + "Most": 5124, + "Mostly": 26752, + "Mot": 24745, + "Mother": 6568, + "Mouna": 27941, + "Mount": 18960, + "Move": 7073, + "Moving": 27406, + "Mr": 1929, + "Mrs": 5165, + "Ms": 13600, + "Mu": 17375, + "Much": 14238, + "Mull": 30303, + "Mult": 18249, + "Mum": 8864, + "Mumbai": 24005, + "Mummy": 25132, + "Municip": 30994, + "Mur": 9444, + "Murder": 23589, + "Murphy": 31526, + "Murray": 28941, + "Mus": 7976, + "Mush": 27939, + "Music": 10633, + "Must": 8921, + "Mut": 30991, + "My": 1107, + "N": 50, + "NA": 4523, + "NAMES": 24749, + "NARR": 8485, + "NARRATOR": 8533, + "NBA": 26931, + "NC": 16936, + "ND": 3467, + "NDER": 22266, + "NE": 7957, + "NER": 9159, + "NEWS": 30144, + "NEY": 23258, + "NFL": 26930, + "NG": 27942, + "NN": 16937, + "NO": 7889, + "NOCK": 21450, + "NOT": 18251, + "NOW": 7234, + "NS": 31707, + "NT": 17228, + "NTER": 24379, + "NY": 24378, + "Na": 18250, + "Nad": 22101, + "Nadim": 30251, + "Nah": 15191, + "Name": 16240, + "Nan": 23654, + "Nancy": 29699, + "Nap": 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100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/wsc/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/wsc/structformer_as_hf.py b/finetune/wsc/structformer_as_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf85ed647450064b23899d1cedb259f4c707774 --- /dev/null +++ b/finetune/wsc/structformer_as_hf.py @@ -0,0 +1,1123 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from transformers import PreTrainedModel +from transformers import PretrainedConfig +from transformers.modeling_outputs import MaskedLMOutput +from typing import List +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) + +########################################## +# HuggingFace Config +########################################## +class StructformerConfig(PretrainedConfig): + model_type = "structformer" + + def __init__( + self, + hidden_size=768, + n_context_layers=2, + nlayers=6, + ntokens=32000, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + **kwargs, + ): + self.hidden_size = hidden_size + self.n_context_layers = n_context_layers + self.nlayers = nlayers + self.ntokens = ntokens + self.nhead = nhead + self.dropout = dropout + self.dropatt = dropatt + self.relative_bias = relative_bias + self.pos_emb = pos_emb + self.pad = pad + self.n_parser_layers = n_parser_layers + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + super().__init__(**kwargs) + +########################################## +# Custom Layers +########################################## +def _get_activation_fn(activation): + """Get specified activation function.""" + if activation == "relu": + return nn.ReLU() + elif activation == "gelu": + return nn.GELU() + elif activation == "leakyrelu": + return nn.LeakyReLU() + + raise RuntimeError( + "activation should be relu/gelu, not {}".format(activation)) + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, + hidden_size, + kernel_size, + padding=padding, + dilation=dilation) + + def forward(self, x): + """Compute convolution. + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + +class MultiheadAttention(nn.Module): + """Multi-head self-attention layer.""" + + def __init__(self, + embed_dim, + num_heads, + dropout=0., + bias=True, + v_proj=True, + out_proj=True, + relative_bias=True): + """Initialization. + Args: + embed_dim: dimension of input embeddings + num_heads: number of self-attention heads + dropout: dropout rate + bias: bool, indicate whether include bias for linear transformations + v_proj: bool, indicate whether project inputs to new values + out_proj: bool, indicate whether project outputs to new values + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + + self.num_heads = num_heads + self.drop = nn.Dropout(dropout) + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, ("embed_dim must be " + "divisible by " + "num_heads") + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + if v_proj: + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.v_proj = nn.Identity() + + if out_proj: + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + else: + self.out_proj = nn.Identity() + + if relative_bias: + self.relative_bias = nn.Parameter(torch.zeros((self.num_heads, 512))) + else: + self.relative_bias = None + + self._reset_parameters() + + def _reset_parameters(self): + """Initialize attention parameters.""" + + init.xavier_uniform_(self.q_proj.weight) + init.constant_(self.q_proj.bias, 0.) + + init.xavier_uniform_(self.k_proj.weight) + init.constant_(self.k_proj.bias, 0.) + + if isinstance(self.v_proj, nn.Linear): + init.xavier_uniform_(self.v_proj.weight) + init.constant_(self.v_proj.bias, 0.) + + if isinstance(self.out_proj, nn.Linear): + init.xavier_uniform_(self.out_proj.weight) + init.constant_(self.out_proj.bias, 0.) + + def forward(self, query, key_padding_mask=None, attn_mask=None): + """Compute multi-head self-attention. + Args: + query: input embeddings + key_padding_mask: 3D mask that prevents attention to certain positions + attn_mask: 3D mask that rescale the attention weight at each position + Returns: + attn_output: self-attention output + """ + + length, bsz, embed_dim = query.size() + assert embed_dim == self.embed_dim + + head_dim = embed_dim // self.num_heads + assert head_dim * self.num_heads == embed_dim, ("embed_dim must be " + "divisible by num_heads") + scaling = float(head_dim)**-0.5 + + q = self.q_proj(query) + k = self.k_proj(query) + v = self.v_proj(query) + + q = q * scaling + + if attn_mask is not None: + assert list(attn_mask.size()) == [bsz * self.num_heads, + query.size(0), query.size(0)] + + q = q.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + k = k.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + v = v.contiguous().view(length, bsz * self.num_heads, + head_dim).transpose(0, 1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list( + attn_output_weights.size()) == [bsz * self.num_heads, length, length] + + if self.relative_bias is not None: + pos = torch.arange(length, device=query.device) + relative_pos = torch.abs(pos[:, None] - pos[None, :]) + 256 + relative_pos = relative_pos[None, :, :].expand(bsz * self.num_heads, -1, + -1) + + relative_bias = self.relative_bias.repeat_interleave(bsz, dim=0) + relative_bias = relative_bias[:, None, :].expand(-1, length, -1) + relative_bias = torch.gather(relative_bias, 2, relative_pos) + attn_output_weights = attn_output_weights + relative_bias + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights + key_padding_mask + + if attn_mask is None: + attn_output_weights = torch.softmax(attn_output_weights, dim=-1) + else: + attn_output_weights = torch.sigmoid(attn_output_weights) * attn_mask + + attn_output_weights = self.drop(attn_output_weights) + + attn_output = torch.bmm(attn_output_weights, v) + + assert list(attn_output.size()) == [bsz * self.num_heads, length, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view( + length, bsz, embed_dim) + attn_output = self.out_proj(attn_output) + + return attn_output + +class TransformerLayer(nn.Module): + """TransformerEncoderLayer is made up of self-attn and feedforward network.""" + + def __init__(self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + dropatt=0.1, + activation="leakyrelu", + relative_bias=True): + """Initialization. + Args: + d_model: dimension of inputs + nhead: number of self-attention heads + dim_feedforward: dimension of hidden layer in feedforward layer + dropout: dropout rate + dropatt: drop attention rate + activation: activation function + relative_bias: bool, indicate whether use a relative position based + attention bias + """ + + super(TransformerLayer, self).__init__() + + self.self_attn = MultiheadAttention( + d_model, nhead, dropout=dropatt, relative_bias=relative_bias) + + # Implementation of Feedforward model + self.feedforward = nn.Sequential( + nn.LayerNorm(d_model), nn.Linear(d_model, dim_feedforward), + _get_activation_fn(activation), nn.Dropout(dropout), + nn.Linear(dim_feedforward, d_model)) + + self.norm = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.nhead = nhead + + def forward(self, src, attn_mask=None, key_padding_mask=None): + """Pass the input through the encoder layer. + Args: + src: the sequence to the encoder layer (required). + attn_mask: the mask for the src sequence (optional). + key_padding_mask: the mask for the src keys per batch (optional). + Returns: + src3: the output of transformer layer, share the same shape as src. + """ + src2 = self.self_attn( + self.norm(src), attn_mask=attn_mask, key_padding_mask=key_padding_mask) + src2 = src + self.dropout1(src2) + src3 = self.feedforward(src2) + src3 = src2 + self.dropout2(src3) + + return src3 + + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +########################################## +# Custom Models +########################################## +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x + +class Transformer(nn.Module): + """Transformer model.""" + + def __init__(self, + hidden_size, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=True, + pos_emb=False, + pad=0): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + """ + + super(Transformer, self).__init__() + + self.drop = nn.Dropout(dropout) + + self.emb = nn.Embedding(ntokens, hidden_size) + if pos_emb: + self.pos_emb = nn.Embedding(500, hidden_size) + + self.layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(nlayers)]) + + self.norm = nn.LayerNorm(hidden_size) + + self.output_layer = nn.Linear(hidden_size, ntokens) + self.output_layer.weight = self.emb.weight + + self.init_weights() + + self.nlayers = nlayers + self.nhead = nhead + self.ntokens = ntokens + self.hidden_size = hidden_size + self.pad = pad + + def init_weights(self): + """Initialize token embedding and output bias.""" + initrange = 0.1 + self.emb.weight.data.uniform_(-initrange, initrange) + if hasattr(self, 'pos_emb'): + self.pos_emb.weight.data.uniform_(-initrange, initrange) + self.output_layer.bias.data.fill_(0) + + def visibility(self, x, device): + """Mask pad tokens.""" + visibility = (x != self.pad).float() + visibility = visibility[:, None, :].expand(-1, x.size(1), -1) + visibility = torch.repeat_interleave(visibility, self.nhead, dim=0) + return visibility.log() + + def encode(self, x, pos): + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + + for i in range(self.nlayers): + h_list.append(h) + h = self.layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + + return output, h_array + + def forward(self, x, pos): + """Pass the input through the encoder layer. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + output: probability distributions for missing tokens. + state_dict: parsing results and raw output + """ + + batch_size, length = x.size() + + raw_output, _ = self.encode(x, pos) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + return output.view(batch_size * length, -1), {'raw_output': raw_output,} + +class StructFormer(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax'): + """Initialization. + Args: + hidden_size: dimension of inputs and hidden states + nlayers: number of layers + ntokens: number of output categories + nhead: number of self-attention heads + dropout: dropout rate + dropatt: drop attention rate + relative_bias: bool, indicate whether use a relative position based + attention bias + pos_emb: bool, indicate whether use a learnable positional embedding + pad: pad token index + n_parser_layers: number of parsing layers + conv_size: convolution kernel size for parser + relations: relations that are used to compute self attention + weight_act: relations distribution activation function + """ + + super(StructFormer, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + output = self.output_layer(raw_output) + + loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + loss = loss_fct(output.view(batch_size * length, -1), labels.reshape(-1)) + + return MaskedLMOutput( + loss=loss, # shape: 1 + logits=output, # shape: (batch_size * length, ntokens) + hidden_states=None, + attentions=None, + ) + + + + +class StructFormerClassification(Transformer): + """StructFormer model.""" + + def __init__(self, + hidden_size, + n_context_layers, + nlayers, + ntokens, + nhead=8, + dropout=0.1, + dropatt=0.1, + relative_bias=False, + pos_emb=False, + pad=0, + n_parser_layers=4, + conv_size=9, + relations=('head', 'child'), + weight_act='softmax', + config=None, + ): + + + super(StructFormerClassification, self).__init__( + hidden_size, + nlayers, + ntokens, + nhead=nhead, + dropout=dropout, + dropatt=dropatt, + relative_bias=relative_bias, + pos_emb=pos_emb, + pad=pad) + + self.num_labels = config.num_labels + self.config = config + + if n_context_layers > 0: + self.context_layers = nn.ModuleList([ + TransformerLayer(hidden_size, nhead, hidden_size * 4, dropout, + dropatt=dropatt, relative_bias=relative_bias) + for _ in range(n_context_layers)]) + + self.parser_layers = nn.ModuleList([ + nn.Sequential(Conv1d(hidden_size, conv_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), + nn.Tanh()) for i in range(n_parser_layers)]) + + self.distance_ff = nn.Sequential( + Conv1d(hidden_size, 2), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + self.height_ff = nn.Sequential( + nn.Linear(hidden_size, hidden_size), + nn.LayerNorm(hidden_size, elementwise_affine=False), nn.Tanh(), + nn.Linear(hidden_size, 1)) + + n_rel = len(relations) + self._rel_weight = nn.Parameter(torch.zeros((nlayers, nhead, n_rel))) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.n_parse_layers = n_parser_layers + self.n_context_layers = n_context_layers + self.weight_act = weight_act + self.relations = relations + + self.classifier = RobertaClassificationHead(config) + + @property + def scaler(self): + return self._scaler.exp() + + @property + def rel_weight(self): + if self.weight_act == 'sigmoid': + return torch.sigmoid(self._rel_weight) + elif self.weight_act == 'softmax': + return torch.softmax(self._rel_weight, dim=-1) + + def parse(self, x, pos, embeds=None): + """Parse input sentence. + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = (x != self.pad) + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + + if embeds is not None: + h = embeds + else: + h = self.emb(x) + + for i in range(self.n_parse_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, + dim=-1)[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where(margin_left > margin_right, margin_right, + margin_left).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def compute_block(self, distance, height): + """Compute constituents from distance and height.""" + + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True).triu(1) + + return block_p, block + + def compute_head(self, height): + """Estimate head for each constituent.""" + + _, length = height.size() + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None]) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def generate_mask(self, x, distance, height): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height) + head_p = self.compute_head(height) + head = torch.einsum('blij,bijh->blh', block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if 'head' in self.relations: + rel_list.append(head) + if 'child' in self.relations: + rel_list.append(child) + if 'cibling' in self.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + rel_weight = self.rel_weight + + dep = torch.einsum('lhr,brij->lbhij', rel_weight, rel) + att_mask = dep.reshape(self.nlayers, bsz * self.nhead, length, length) + + return att_mask, cibling, head, block + + def encode(self, x, pos, att_mask=None, context_layers=False): + """Structformer encoding process.""" + + if context_layers: + """Standard transformer encode process.""" + h = self.emb(x) + if hasattr(self, 'pos_emb'): + h = h + self.pos_emb(pos) + h_list = [] + visibility = self.visibility(x, x.device) + for i in range(self.n_context_layers): + h_list.append(h) + h = self.context_layers[i]( + h.transpose(0, 1), key_padding_mask=visibility).transpose(0, 1) + + output = h + h_array = torch.stack(h_list, dim=2) + return output + + else: + visibility = self.visibility(x, x.device) + h = self.emb(x) + if hasattr(self, 'pos_emb'): + assert pos.max() < 500 + h = h + self.pos_emb(pos) + for i in range(self.nlayers): + h = self.layers[i]( + h.transpose(0, 1), attn_mask=att_mask[i], + key_padding_mask=visibility).transpose(0, 1) + return h + + def forward(self, input_ids, labels=None, position_ids=None, **kwargs): + + x = input_ids + batch_size, length = x.size() + + if position_ids is None: + pos = torch.arange(length, device=x.device).expand(batch_size, length) + + context_layers_output = None + if self.n_context_layers > 0: + context_layers_output = self.encode(x, pos, context_layers=True) + + distance, height = self.parse(x, pos, embeds=context_layers_output) + att_mask, cibling, head, block = self.generate_mask(x, distance, height) + + raw_output = self.encode(x, pos, att_mask) + raw_output = self.norm(raw_output) + raw_output = self.drop(raw_output) + + #output = self.output_layer(raw_output) + logits = self.classifier(raw_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=None, + attentions=None, + ) + + + +########################################## +# HuggingFace Model +########################################## +class StructformerModel(PreTrainedModel): + config_class = StructformerConfig + + def __init__(self, config): + super().__init__(config) + self.model = StructFormer( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act + ) + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) + + + +class StructformerModelForSequenceClassification(PreTrainedModel): + config_class = StructformerConfig + def __init__(self, config): + super().__init__(config) + self.model = StructFormerClassification( + hidden_size=config.hidden_size, + n_context_layers=config.n_context_layers, + nlayers=config.nlayers, + ntokens=config.ntokens, + nhead=config.nhead, + dropout=config.dropout, + dropatt=config.dropatt, + relative_bias=config.relative_bias, + pos_emb=config.pos_emb, + pad=config.pad, + n_parser_layers=config.n_parser_layers, + conv_size=config.conv_size, + relations=config.relations, + weight_act=config.weight_act, + config=config) + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + + def forward(self, input_ids, labels=None, **kwargs): + return self.model(input_ids, labels=labels, **kwargs) \ No newline at end of file diff --git a/finetune/wsc/tokenizer_config.json b/finetune/wsc/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..41d49eb18973732e5278b64eba573fb8f80a7b8c --- /dev/null +++ b/finetune/wsc/tokenizer_config.json @@ -0,0 +1,65 @@ +{ + "add_prefix_space": false, + "bos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "errors": "replace", + "mask_token": { + "__type": "AddedToken", + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "model_max_length": 512, + "name_or_path": "omarmomen/structformer_s1_final_with_pos", + "pad_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "sep_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + }, + "special_tokens_map_file": null, + "tokenizer_class": "RobertaTokenizer", + "trim_offsets": true, + "unk_token": { + "__type": "AddedToken", + "content": "", + "lstrip": false, + "normalized": true, + "rstrip": false, + "single_word": false + } +} diff --git a/finetune/wsc/train_results.json b/finetune/wsc/train_results.json new file mode 100644 index 0000000000000000000000000000000000000000..3acf8bef06c49d61378dbde9526bf0260b947098 --- /dev/null +++ b/finetune/wsc/train_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 10.0, + "train_loss": 0.703366470336914, + "train_runtime": 23.6016, + "train_samples": 487, + "train_samples_per_second": 206.342, + "train_steps_per_second": 2.119 +} \ No newline at end of file diff --git a/finetune/wsc/trainer_state.json b/finetune/wsc/trainer_state.json 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"ľ": 201, + "Ŀ": 202, + "ŀ": 203, + "Ł": 204, + "ł": 205, + "Ń": 206 +} diff --git a/zeroshot/anaphor_agreement/eval_results.json b/zeroshot/anaphor_agreement/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..cd757e168002a71d0ac1bd51a15dc29734ead765 --- /dev/null +++ b/zeroshot/anaphor_agreement/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.878323108384458} \ No newline at end of file diff --git a/zeroshot/anaphor_agreement/predictions.txt b/zeroshot/anaphor_agreement/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..4c40f6ef4353629e38f02130928387d124c3035a --- /dev/null +++ b/zeroshot/anaphor_agreement/predictions.txt @@ -0,0 +1,1957 @@ +index prediction +0 Katherine can't help herself. +1 Karla could listen to himself. +2 Marie won't think about herself. +3 Mark hasn't discussed himself. +4 Stephen impressed himself. +5 Christina boasts about himself. +6 Suzanne would irritate herself. +7 Rose couldn't 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Aaron escaped from himself. +1819 Jessica will fire herself. +1820 Amelia will flee from herself. +1821 Joseph isn't shocking himself. +1822 The Impressionists don't question themselves. +1823 Many students aren't aggravating themselves. +1824 All drivers are attacking themselves. +1825 Students had approached themselves. +1826 Mitchell has insulted himself. +1827 Sheila is boasting about herself. +1828 The doctors are observing themselves. +1829 Suzanne has alarmed herself. +1830 Stephen has helped himself. +1831 Most doctors weren't observing themselves. +1832 David couldn't scare himself. +1833 Many children escape from themselves. +1834 Many customers wouldn't bother themselves. +1835 That hospital upsets itself. +1836 Guy described himself. +1837 Carol scared themselves. +1838 Susan wasn't watching herself. +1839 These children wouldn't admire themselves. +1840 Many banks confused themselves. +1841 Patricia disliked herself. +1842 Those students complained about themselves. +1843 The Lutherans can't admire themselves. +1844 Children returned to themselves. +1845 A lot of teachers had talked about themselves. +1846 Janet had hired herself. +1847 Many senators investigate themselves. +1848 Some guys might annoy themselves. +1849 These guests praise themselves. +1850 All actresses aren't firing themselves. +1851 A lot of pedestrians won't forget themselves. +1852 Sarah wasn't approaching herself. +1853 Guy talked to himself. +1854 William hasn't hurt himself. +1855 Actors hadn't concealed themselves. +1856 Customers don't respect themselves. +1857 Some children can visit themselves. +1858 Many cashiers have questioned themselves. +1859 All senators haven't impressed themselves. +1860 Dana investigated herself. +1861 Lisa couldn't flee from herself. +1862 April isn't noticing herself. +1863 Many schools irritated themselves. +1864 Emily had distracted herself. +1865 Deborah criticized herself. +1866 These adults have disgusted themselves. +1867 Deborah wasn't helping herself. +1868 Tonya thought about herself. +1869 Those cashiers hadn't kissed themselves. +1870 Ann could think about herself. +1871 Children can't escape from themselves. +1872 Katherine shocked herself. +1873 Those guys approach themselves. +1874 A hospital aggravates itself. +1875 Some women aren't helping herself. +1876 Susan hugged herself. +1877 Many adults cured themselves. +1878 The ladies bore themselves. +1879 Gerald had concealed himself. +1880 Amy had watched herself. +1881 Bill should shock himself. +1882 Sandra respected herself. +1883 Melanie isn't talking about herself. +1884 The Impressionists aren't criticizing themselves. +1885 Waiters were escaping from themselves. +1886 Some guests astound themselves. +1887 Marcus saw himself. +1888 Robert talked about himself. +1889 All girls forgot themselves. +1890 These ladies aren't shocking themselves. +1891 April has cared for herself. +1892 Carolyn shouldn't hire herself. +1893 Ellen knew herself. +1894 Some girls help themselves. +1895 The boys weren't referencing themselves. +1896 Donald does cure himself. +1897 Robert has worried himself. +1898 Many waitresses attacked themselves. +1899 Children appreciate themselves. +1900 Benjamin had argued about himself. +1901 Most guests won't flee from themselves. +1902 Most dancers distract themselves. +1903 The Borgias haven't approached themselves. +1904 Those pedestrians aren't healing themselves. +1905 Boys hurt themselves. +1906 Douglas hasn't bothered himself. +1907 These women observed herself. +1908 Cheryl hasn't criticized themselves. +1909 Sara shocked herself. +1910 Maria thought about herself. +1911 Donald respected himself. +1912 All actors aren't noticing themselves. +1913 Most pedestrians had irritated themselves. +1914 Jessica watched herself. +1915 All actresses couldn't reveal themselves. +1916 Every organization aggravates itself. +1917 The Borgias are admiring themselves. +1918 John stunned himself. +1919 Irene wasn't concealing herself. +1920 The governments hadn't alarmed themselves. +1921 Many waitresses were scaring themselves. +1922 A story disagreed with themselves. +1923 Craig will annoy himself. +1924 Martha shocked herself. +1925 Tara wasn't describing themselves. +1926 The teachers disturb themselves. +1927 Some ladies disagree with themselves. +1928 Most adults didn't help themselves. +1929 Some people are talking about themselves. +1930 Alicia can heal herself. +1931 Alan escaped from himself. +1932 Raymond fled from themselves. +1933 Benjamin doesn't distract himself. +1934 Most pedestrians didn't visit themselves. +1935 Melanie hasn't visited herself. +1936 Ella shouldn't listen to herself. +1937 Most pedestrians hadn't disgusted themselves. +1938 The dancers were attacking themselves. +1939 Ronald hurt himself. +1940 The waiters couldn't reveal themselves. +1941 Lisa did find herself. +1942 Christina can't respect herself. +1943 Donald hadn't kissed himself. +1944 Heidi isn't questioning herself. +1945 Those boys wouldn't heal themselves. +1946 Paul isn't stunning himself. +1947 George had bothered himself. +1948 Those patients did disgust themselves. +1949 The Lutherans couldn't visit themselves. +1950 Some customers shouldn't hire himself. +1951 Those guys cured themselves. +1952 Tara wasn't referencing herself. +1953 Most students attacked themselves. +1954 A lot of actors upset themselves. +1955 Keith could respect himself. diff --git a/zeroshot/argument_structure/eval_results.json b/zeroshot/argument_structure/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..6cb9522a154be0e819f08d279e1f6ef4460de9c4 --- /dev/null +++ b/zeroshot/argument_structure/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.6932589718719689} \ No newline at end of file diff --git a/zeroshot/argument_structure/predictions.txt b/zeroshot/argument_structure/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c0f4f659fbfd3c1b152ba030a841a40a94be528 --- /dev/null +++ b/zeroshot/argument_structure/predictions.txt @@ -0,0 +1,8249 @@ +index prediction +0 Amanda was respected by some waitresses. +1 Some lake was passed by some cashiers. +2 Lisa was kissed by the blouses. +3 Amanda isn't respected by the children. +4 The glove was noticed by some woman. +5 Gerald is hated by the pie. +6 Most actresses weren't discussed by some offspring. +7 These students were talked about by the teacher. +8 Cafes were cleaned by some lady. +9 Patricia is referenced by some offspring. +10 Galileo is concealed by the horse. +11 A lot of high schools aren't visited by some teachers. +12 Andrew was questioned by some pedestrian. +13 A lot of computers aren't bought by the senators. +14 Those schools are cleaned by the customers. +15 Chad wasn't forgotten by the brochure. +16 Deanna was hired by the offspring. +17 Lisa wasn't cured by the ladies. +18 Valerie was hugged by the diagnoses. +19 Nina isn't escaped from by some customer. +20 A lot of mountains were ascended by the dog. +21 Tamara was praised by some waitress. +22 William is discovered by some students. +23 Mitchell was remembered by some men. +24 Rachel isn't investigated by the books. +25 Grace was returned to by some cashiers. +26 Michael is loved by some offspring. +27 Nancy wasn't attacked by some actor. +28 That company wasn't questioned by the customer. +29 Jill isn't returned to by the doctors. +30 Ruth was listened to by the phenomena. +31 Sabrina was listened to by some adult. +32 That library was toured by the dancers. +33 All closets were bought by the peppers. +34 Janet was fired by some offspring. +35 Russell wasn't referenced by some girl. +36 Carmen is talked about by the waiters. +37 A story wasn't scanned by the newspaper article. +38 Sherry wasn't criticized by some customers. +39 Joseph isn't praised by the pedestrians. +40 Gregory wasn't helped by some legislature. +41 Every student was listened to by some guys. +42 Mitchell isn't hated by some man. +43 Those stairs aren't climbed down by the cashiers. +44 Some mushrooms were brought by the actors. +45 Most children aren't listened to by some fungus. +46 A hospital is exited by some adults. +47 These actresses were referenced by some lady. +48 Stacey wasn't fired by some guest. +49 A mall is explored by the people. +50 The women were kissed by some teenagers. +51 The commentaries weren't examined by some lady. +52 Homer isn't described by the snake. +53 Spain is toured by the turtle. +54 Some hills aren't climbed down by some government. +55 Aaron isn't referenced by the teacher. +56 Carolyn is referenced by some adults. +57 Donna wasn't disliked by the patients. +58 The commentaries weren't scanned by the patient. +59 Many grocery stores are passed by the children. +60 Some book isn't scanned by some reports. +61 Julia was talked to by the lady. +62 A lot of malls are exited by some children. +63 Mitchell was discussed by some boy. +64 Kevin was loved by the teenagers. +65 The cafes aren't appreciated by some ellipsis. +66 Many glasses aren't cleaned by the men. +67 Cilantro wasn't hidden by some fish. +68 That hill is seen by the children. +69 Debra wasn't admired by the drivers. +70 These sketches were researched by some girls. +71 Bethany isn't thought about by the ladies. +72 A grocery store is researched by the actor. +73 That cafe isn't driven to by some teachers. +74 Spain wasn't toured by some company. +75 Thomas wasn't hugged by some projectors. +76 That fish is taken by the series. +77 This art gallery wasn't passed by some woman. +78 Timothy was praised by the waitresses. +79 Curtis is concealed by some paralysis. +80 These universities were boycotted by some lady. +81 Many malls aren't messed up by some actresses. +82 Deanna isn't boasted about by some wheelbarrows. +83 Jacqueline was talked to by the cashiers. +84 That patient wasn't helped by the boy. +85 Deborah isn't watched by some children. +86 Some hill was escaped from by the fungus. +87 Theodore is referenced by some muffins. +88 Every skateboard isn't lifted by the man. +89 All public parks were explored by some teenager. +90 This cake is seen by the pedestrians. +91 Every person wasn't cared for by the fungus. +92 Edward wasn't insulted by some oxen. +93 Brad was investigated by the actresses. +94 Many glaciers weren't researched by the muffin. +95 This pedestrian isn't thought about by the senators. +96 This art gallery was driven to by some alumni. +97 The banks weren't arrived at by the gloves. +98 Kevin isn't hidden by the cashiers. +99 These grocery stores were concealed by some teenagers. +100 Holly is seen by some girls. +101 Some drawing was discovered by the paralysis. +102 Most gates are bought by the children. +103 A lot of libraries are sold by the guys. +104 Rebecca wasn't cared for by some diagnosis. +105 The hills were dropped by by some ice. +106 Debra was described by some documentaries. +107 Michelle was praised by some jackets. +108 Monica was hated by the guy. +109 The school was run around by some cashier. +110 Waitresses are argued about by the alumni. +111 The high schools are toured by some men. +112 That book isn't argued about by the alumnus. +113 A convertible wasn't broken by some man. +114 That child wasn't complained about by the wheelbarrows. +115 George was helped by the pedestrians. +116 Chairs are messed up by some pamphlet. +117 Becky wasn't described by the driver. +118 Every unicycle was brought by some cucumber. +119 Some mountain was left by some man. +120 The Impressionists aren't admired by the waiters. +121 The eyes are discovered by the sweaters. +122 The screens are broken by the cashiers. +123 Allison wasn't questioned by the man. +124 These skateboards weren't broken by the children. +125 Omar wasn't questioned by some ladies. +126 The pedestrian is revealed by the pedestrians. +127 Holly isn't hired by some dish. +128 Larry was approached by the men. +129 That adult isn't visited by the ladies. +130 Brett isn't helped by the child. +131 Galileo isn't watched by some customer. +132 Mary wasn't hired by some students. +133 Melinda was left by some actresses. +134 A lot of high schools aren't driven to by some boy. +135 The bank isn't gone to by some shawl. +136 A library wasn't toured by some senator. +137 Actors are seen by the book. +138 Winston Churchill isn't seen by the person. +139 George wasn't hired by some teacher. +140 Some chair was broken by the dishes. +141 Veronica was forgotten by the guys. +142 That bike was broken by the waiters. +143 All cars aren't discovered by some alumnus. +144 This restaurant is arrived at by some senators. +145 Claire wasn't concealed by some actor. +146 Tamara wasn't noticed by some dancer. +147 Those windows were broken by some person. +148 Daniel isn't praised by the driver. +149 Those cafes are seen by some bird. +150 Those public parks weren't found by some cashiers. +151 Some mountain isn't climbed down by the cashiers. +152 Every unicycle isn't fixed by some bananas. +153 Colleen wasn't sounded like by some boy. +154 Florence is forgotten by some boy. +155 Some shoe wasn't cleaned by the teachers. +156 Samantha is insulted by some children. +157 Gerald was described by some men. +158 Connie isn't left by some teachers. +159 Allison isn't attacked by the cats. +160 Tina is fired by the guests. +161 Some door wasn't revealed by the chairs. +162 These women are investigated by some customer. +163 Stephanie isn't forgotten by some pamphlets. +164 Kathleen was worked with by the child. +165 The adults aren't cured by some doctors. +166 Kathleen wasn't complained about by some lady. +167 Liam isn't complained about by the muffin. +168 Keith was known by the alumnus. +169 Many guests were insulted by some senators. +170 Melinda isn't criticized by the adult. +171 Some coat is worn by some waitress. +172 Keith isn't talked about by the cashier. +173 All public parks were passed by some dancers. +174 Carrie wasn't insulted by some person. +175 Some public parks are toured by some offspring. +176 Every actress was loved by some waitress. +177 The art galleries are walked through by the light. +178 All planes were broken by some government. +179 Gerald isn't discussed by the paralyses. +180 Every mountain was climbed down by some legislatures. +181 Melanie is worked with by some woman. +182 Most pictures aren't cleaned by the customer. +183 That boy is investigated by the women. +184 Andrea is escaped from by the lights. +185 Teresa was talked about by the offspring. +186 Elaine isn't escaped from by the pedestrians. +187 Christina is boasted about by the waitresses. +188 Sharon isn't known by the eye. +189 Some ladder is descended by the computer. +190 Rivers aren't run around by some trucks. +191 Melinda isn't criticized by some guys. +192 Rebecca was helped by the driver. +193 All patients were visited by the paralysis. +194 Phillip wasn't listened to by the cashier. +195 Some banks weren't passed by some waitresses. +196 Bethany wasn't seen by some guy. +197 Some slope was climbed down by the senators. +198 Every restaurant is driven to by some teenagers. +199 Alan was worked with by some teenagers. +200 The glass isn't broken by the couches. +201 Leslie wasn't healed by the glass. +202 A window is seen by the stairs. +203 Many glaciers aren't researched by the radius. +204 Karla is hated by some adult. +205 A mountain wasn't descended by some light. +206 Richard is hated by some bicycles. +207 Joel isn't criticized by the pedestrians. +208 Aaron was loved by the book. +209 A vase wasn't broken by some cashier. +210 The boy isn't investigated by some senator. +211 Becky was attacked by the men. +212 Diane isn't investigated by the association. +213 Plays weren't scanned by the pedestrians. +214 All screens weren't lifted by some bananas. +215 Some drawings weren't admired by some man. +216 Helen was referenced by the pasta. +217 The report wasn't scanned by the cashiers. +218 All cups were cleaned by the jacket. +219 Karen was approached by some brochures. +220 Julia was concealed by some offspring. +221 Those doctors were discussed by the dishes. +222 This school was left by some ladies. +223 Tanya is boasted about by some drivers. +224 Most actors aren't loved by some teacher. +225 The library was run around by some cashier. +226 Mary wasn't referenced by the governments. +227 Bradley is complained about by some shawls. +228 These grocery stores aren't walked through by the pedestrians. +229 Dawn wasn't talked about by some paralyses. +230 Aaron was argued about by some dancers. +231 Brenda is helped by some child. +232 Tonya wasn't cared for by some shawls. +233 That vase is broken by some boys. +234 Douglas wasn't forgotten by the cashiers. +235 Renee wasn't worked with by some adult. +236 Some photograph isn't researched by the candle. +237 Some mountains weren't ascended by some ladder. +238 Pamela was revealed by some teenagers. +239 Gary is worked with by the woman. +240 Those schools are passed by the adults. +241 Many movies aren't hated by the children. +242 Alice wasn't admired by the boys. +243 Grace wasn't hugged by the actor. +244 Every newspaper article isn't scanned by the people. +245 Every cashier isn't hated by the man. +246 These teachers aren't admired by the alumnus. +247 Every dancer is described by the projector. +248 That fish is taken by some shoes. +249 Teresa wasn't seen by some guest. +250 Many college campuses were walked through by some children. +251 This mall isn't walked through by some pictures. +252 Some closets weren't fled from by some cashier. +253 Some boy isn't revealed by some senators. +254 The cafes weren't gone to by some student. +255 Dana is observed by some sketch. +256 Rhonda wasn't insulted by the guy. +257 Tiffany is attacked by the fungus. +258 Many libraries are driven to by some adult. +259 That college campus was explored by the media. +260 These casseroles were noticed by some dish. +261 Every child wasn't remembered by some guy. +262 That eye was found by the teachers. +263 Paula was described by the patient. +264 All reports weren't scanned by the guys. +265 Ann wasn't worked with by some hypotheses. +266 A doctor wasn't boasted about by some dancers. +267 Stories aren't scanned by some students. +268 Rice isn't bought by some children. +269 Some guys weren't criticized by some reports. +270 Those high schools aren't messed up by some doctor. +271 The documentary wasn't examined by the association. +272 Naomi is loved by the dancers. +273 Jodi isn't loved by the senators. +274 Jason isn't hired by some projectors. +275 Every library wasn't explored by the students. +276 These rabbits are taken by some birds. +277 James wasn't attacked by some man. +278 Sheila is remembered by some actor. +279 These documentaries were argued about by some paralysis. +280 Brenda was listened to by the doctor. +281 Rivers weren't gone to by the teenagers. +282 Peter isn't escaped from by the waitress. +283 Bread is cleaned by the dogs. +284 Brad is forgotten by some actress. +285 All museums aren't driven to by the driver. +286 Some cafe isn't messed up by some pedestrians. +287 Companies were researched by the casseroles. +288 Amelia wasn't watched by some people. +289 Denise was argued about by the senator. +290 Most hills aren't ascended by some waitresses. +291 Joseph is described by some adults. +292 The stairs are hidden by some deer. +293 Jennifer isn't hated by the children. +294 The stairs were ascended by the alumnus. +295 Every truck wasn't bought by the jackets. +296 The screen isn't fixed by some windows. +297 Most children are praised by some boys. +298 Some newspaper article is scanned by the doctors. +299 Many grocery stores are arrived at by some projectors. +300 Every hospital isn't admired by the boys. +301 These boxes aren't brought by some man. +302 Rodney isn't hired by some hypotheses. +303 Most computers weren't broken by some men. +304 This play wasn't boasted about by some species. +305 Sally is observed by some people. +306 Monica isn't fled from by the offspring. +307 Anne isn't insulted by the cucumber. +308 Curtis is returned to by the vase. +309 Some people aren't argued about by some lady. +310 Carmen is healed by some lady. +311 Those museums are messed up by the lady. +312 Many plates weren't broken by some ice cream. +313 These forks aren't taken by some senators. +314 A lot of organizations were boycotted by the university. +315 Brad wasn't forgotten by some shawls. +316 Meredith was hated by some casserole. +317 Martin wasn't escaped from by some offspring. +318 This slope is climbed up by the pedestrian. +319 Jacqueline isn't loved by the phenomena. +320 Gary is discussed by the alumnus. +321 The glacier isn't exited by the fish. +322 An association is researched by some paintings. +323 Leslie wasn't fired by the senator. +324 Those teachers were hidden by the child. +325 The cashier was disliked by the paralyses. +326 A museum isn't passed by the casserole. +327 Charles is respected by the women. +328 Charles isn't kissed by some men. +329 Janice wasn't referenced by some alumnus. +330 Vincent is talked about by some patients. +331 Winston Churchill is loved by some lady. +332 Guests were thought about by some guests. +333 These doctors weren't escaped from by some lamps. +334 William is worked with by the girl. +335 The steak is taken by some customers. +336 Heather isn't questioned by the girls. +337 A lot of mountains are ascended by some chair. +338 The glacier wasn't exited by the association. +339 The mountain was boycotted by the man. +340 A lot of actors aren't hired by some child. +341 Christina isn't remembered by some girls. +342 Kathleen was healed by the patient. +343 Candice is healed by the carriage. +344 Tonya isn't complained about by the offspring. +345 A grocery store isn't skated around by some light. +346 Marie was admired by some spinach. +347 The glaciers aren't explored by some horses. +348 Some high school isn't passed by some men. +349 The Lutherans are sounded like by the alumnus. +350 Carla isn't found by some shawls. +351 A high school is walked through by the truck. +352 Patrick is referenced by some senator. +353 That closet was exited by the person. +354 A coat is worn by the boys. +355 That chicken was lifted by the fungus. +356 Theresa wasn't referenced by some boys. +357 Most stairs aren't climbed up by the children. +358 Every picture isn't brought by the drivers. +359 Some malls are walked through by some means. +360 A lot of rivers are boycotted by some waitress. +361 These grocery stores were driven to by some waiters. +362 Most people were remembered by some man. +363 Sherry was thought about by some girls. +364 Rivers are insulted by some guy. +365 Tiffany wasn't cared for by the children. +366 Rose isn't attacked by the alumnus. +367 Heidi isn't cared for by some actresses. +368 Gerald isn't cured by the person. +369 A lot of wheelbarrows aren't broken by some alumnus. +370 Most grocery stores weren't cleaned by some girls. +371 Most shoes weren't messed up by the pictures. +372 A waiter wasn't listened to by some guys. +373 Kristen wasn't loved by the women. +374 Rodney isn't described by some movies. +375 The cilantro wasn't noticed by some stimulus. +376 A mountain was ascended by the candle. +377 Deborah isn't visited by some offspring. +378 The Great Lakes were walked through by the ice cream. +379 Carlos wasn't fled from by some actors. +380 Sonia wasn't hidden by the waitresses. +381 The hospital wasn't walked through by some waiter. +382 Some adult was argued about by the pedestrians. +383 Christine wasn't investigated by some driver. +384 Timothy wasn't referenced by the offspring. +385 Many closets are walked through by the teenagers. +386 James was questioned by the woman. +387 That play wasn't argued about by some dogs. +388 Stephanie isn't watched by the waiters. +389 Samantha is complained about by some actor. +390 Connie is appreciated by the actors. +391 That report isn't sold by the waiter. +392 Victoria wasn't cured by some dancers. +393 This essay was examined by some commentaries. +394 Most shoes aren't cleaned by some pie. +395 Current events were boasted about by the movie. +396 That drawing was brought by the students. +397 Victoria wasn't concealed by some customers. +398 James wasn't forgotten by some doctor. +399 Some screens weren't bought by some species. +400 Those public parks aren't passed by the brochures. +401 That mirror wasn't broken by some girls. +402 The plays are scanned by the eye. +403 This child was escaped from by the teenager. +404 This glove was admired by the men. +405 Hospitals are discovered by some man. +406 A lot of girls are revealed by some children. +407 All vases weren't fixed by some guys. +408 Larry wasn't praised by some men. +409 A sock wasn't worn by some forks. +410 Paul is praised by the women. +411 Anna is healed by some casserole. +412 Ann was talked about by the patient. +413 That glacier was arrived at by some cars. +414 Jesus is found by the jackets. +415 Victoria is attacked by some person. +416 Sherry was hidden by some guys. +417 A lot of stories weren't examined by some men. +418 This grocery store isn't skated around by the movies. +419 That actor isn't questioned by the guest. +420 Some boy isn't questioned by the children. +421 The public park was arrived at by some actors. +422 Anna isn't concealed by some waitresses. +423 A mall was explored by some girls. +424 Those women are described by the children. +425 That hill is descended by the cashiers. +426 The newspaper article is discovered by the waiter. +427 Cheryl was healed by the dancer. +428 Some newspaper articles weren't examined by the nuclei. +429 This doctor isn't respected by some person. +430 Some lady isn't seen by the picture. +431 Tina isn't kissed by some person. +432 Rebecca wasn't cured by some tomatoes. +433 Jacqueline is loved by the senator. +434 Tamara was concealed by some woman. +435 A mountain was explored by the boys. +436 A lot of jackets are worn by some pedestrians. +437 That glove was worn by the senators. +438 That senator isn't boasted about by the broccoli. +439 Harvard University wasn't dropped by by the waitress. +440 Phillip isn't visited by some pedestrian. +441 The photograph is seen by some mushrooms. +442 Heidi wasn't observed by the fish. +443 Some mountains weren't arrived at by the forks. +444 Noah was forgotten by some photograph. +445 All steps aren't ascended by the planes. +446 Every hill isn't brought by the candle. +447 Meredith isn't observed by some waiter. +448 All dresses are brought by some women. +449 Marie is admired by some offspring. +450 Helen is observed by the doctor. +451 Russell wasn't praised by some pamphlet. +452 That slope isn't descended by the birds. +453 Lawrence isn't cured by some woman. +454 Omar is returned to by some movies. +455 Tracy wasn't listened to by some lady. +456 Some high school wasn't gone to by the drivers. +457 Every restaurant was explored by the gate. +458 That cup wasn't fixed by the socks. +459 All restaurants weren't discussed by the cucumbers. +460 Elizabeth isn't disliked by some woman. +461 Travis wasn't helped by the actresses. +462 Every window isn't fixed by some men. +463 A museum is exited by the government. +464 These planes weren't thought about by some child. +465 A man was healed by some octopus. +466 Mitchell isn't fled from by the paralyses. +467 Malls aren't talked about by some dog. +468 Michael is remembered by some ladies. +469 Chad is remembered by the waiters. +470 Rhonda isn't hated by some adults. +471 That school was passed by some girls. +472 Melinda was talked about by the patients. +473 Some books aren't examined by some brochure. +474 Some slope was climbed up by the means. +475 Julie wasn't admired by some children. +476 Spain wasn't walked through by some ladders. +477 Angela was left by the guys. +478 Emily was hired by some students. +479 Every art gallery isn't explored by the actresses. +480 Alicia was known by the waiters. +481 Many hills weren't climbed up by the crisis. +482 Those stairs were climbed up by some student. +483 A lot of waiters aren't praised by the offspring. +484 Steven is cared for by some story. +485 Some mall wasn't toured by the actresses. +486 Cheryl wasn't cured by the teachers. +487 This bicycle is fixed by the man. +488 Margaret isn't loved by some children. +489 A lot of mirrors weren't fixed by some guys. +490 Every guest was healed by some species. +491 Some museum was run around by the child. +492 Every cafe isn't passed by the waiters. +493 A sketch is sold by the adults. +494 Laura is returned to by some offspring. +495 Sandra was talked to by some actress. +496 Every high school isn't explored by the actress. +497 Every plane isn't broken by some mouse. +498 Mark is talked to by some driver. +499 A lot of college campuses were skated around by the man. +500 Judy is boasted about by the person. +501 Every movie was scanned by some guys. +502 All planes are thought about by the computer. +503 All ladders aren't descended by the women. +504 That truck wasn't thought about by some adults. +505 Most ladders aren't descended by the men. +506 The bikes were seen by the ice. +507 Ella is appreciated by some alumni. +508 A skateboard isn't brought by some fungus. +509 A lot of skateboards aren't broken by some actress. +510 An art gallery wasn't exited by the movie. +511 All mountains were descended by some children. +512 Richard is worked with by some actresses. +513 Those high schools aren't walked through by some children. +514 Holly wasn't visited by some doctors. +515 These socks were worn by the brochure. +516 Rodney is kissed by some cats. +517 Angela is watched by some woman. +518 Mark is revealed by the guy. +519 A report was scanned by the light. +520 Nina wasn't known by the ladies. +521 Tina was observed by the synopsis. +522 Some stories are discussed by some guys. +523 A mall was skated around by some teenager. +524 A hill is passed by some customers. +525 Every documentary isn't thought about by the pedestrians. +526 That jacket was hidden by the child. +527 Jennifer is healed by some waiter. +528 All lakes aren't talked about by the nucleus. +529 Some computer wasn't broken by some woman. +530 This book wasn't taken by some pedestrians. +531 Jesus isn't researched by some adults. +532 The guest wasn't attacked by some people. +533 Maria was argued about by some books. +534 All cafes aren't researched by the nuclei. +535 Laurie was disliked by the actresses. +536 Denise is concealed by the couch. +537 Most grocery stores weren't cleaned by some drivers. +538 Ellen isn't argued about by some carts. +539 Tracy was hugged by some children. +540 Catherine is disliked by the girls. 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Becky didn't reveal Marla. +1375 Carriages had sold all photographs. +1376 People cleaned the pork. +1377 Linda wasn't breaking some car. +1378 Eric might heal Winston Churchill. +1379 Naomi hasn't sounded like Jennifer. +1380 A cashier fixed those windows. +1381 Derek criticizes Julie. +1382 Elaine examines documentaries. +1383 Stacy criticizes Carmen. +1384 Heather has criticized Dana. +1385 Curtis can talk about Donna. +1386 Travis hadn't listened to Gregory. +1387 Most photographs are selling a plate. +1388 Theresa wouldn't praise Carlos. +1389 All projectors hadn't fled from Jesus. +1390 Tina heals Russell. +1391 Kayla arrived at these cafes. +1392 Sara can't examine most newspaper articles. +1393 A couch helped Vanessa. +1394 Sara can't scan that story. +1395 That movie hates Cynthia. +1396 Helen climbs down these hills. +1397 Derek should dislike Amelia. +1398 April is going to that museum. +1399 A restaurant flees from Deborah. +1400 Robert visits the teachers. +1401 Candice 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Brenda has brought this window. +1455 Caroline isn't exploring closets. +1456 Carol isn't observing Jessica. +1457 That doctor hid Victoria. +1458 That nose disturbs Ellen. +1459 Florence won't exit this hospital. +1460 Diane doesn't hate Sabrina. +1461 This company hasn't helped Maria. +1462 The pasta dislikes some restaurant. +1463 Dan messed up this couch. +1464 Tanya couldn't take these bikes. +1465 Gina brought some mouth. +1466 Amelia references that book. +1467 Connie appreciated Steve. +1468 Every dancer cleaned the lights. +1469 Eva fixed the carts. +1470 Irene hadn't fled from Laurie. +1471 Every library respected Travis. +1472 All doctors had respected Donna. +1473 The river was attacking all waitresses. +1474 Rebecca wasn't bringing some shirts. +1475 Karla discussed Caroline. +1476 Marcus researched all college campuses. +1477 Kristin went to these malls. +1478 Tammy isn't biking to that closet. +1479 Edward talked to a girl. +1480 Katherine won't watch Dan. +1481 Emily 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Christopher didn't talk about Vanessa. +1564 Edward is going to this high school. +1565 Ann didn't care for David. +1566 This sketch isn't curing Barbara. +1567 Every wheelbarrow had taken the cup. +1568 Vincent hasn't researched a lot of books. +1569 The face noticed Omar. +1570 Brenda is escaping from all lakes. +1571 Benjamin was going to the Great Lakes. +1572 Bruce was talking to Samantha. +1573 Michelle observes Gregory. +1574 Stacy had appreciated Colleen. +1575 Holly isn't cleaning a lot of restaurants. +1576 Caroline isn't thinking about Cindy. +1577 This patient hasn't boycotted the organization. +1578 Katherine disliked some libraries. +1579 Most doctors had some water. +1580 Harvard University hasn't bought the muffins. +1581 That broccoli is healing Carmen. +1582 Mitchell hasn't investigated many dancers. +1583 A lot of bicycles haven't hired Jessica. +1584 Dana can attack Aaron. +1585 Amelia hadn't escaped from Daniel. +1586 Tara discussed Christopher. +1587 Carmen skates 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Nina referenced Marcus. +1615 Ice cream arrives at this cafe. +1616 Many art galleries sold a shoe. +1617 The drawings don't skate around Spain. +1618 Samantha would take the sock. +1619 Jodi shouldn't arrive at every library. +1620 David discovered this ice cream. +1621 Brad hadn't worn the coat. +1622 This wheelbarrow was wearing every shawl. +1623 Some computer skates around that high school. +1624 Patricia sold some apples. +1625 The Lutherans weren't kissing Marie. +1626 The screen hasn't revealed some computers. +1627 Every wheelbarrow sounds like Cheryl. +1628 Politics researched these newspaper articles. +1629 John wouldn't approach Carrie. +1630 Amelia was walking through some art gallery. +1631 A turtle brought those art galleries. +1632 A lot of senators weren't fleeing from Leslie. +1633 This cashier does clean every cup. +1634 Carolyn hadn't respected Heidi. +1635 Some cakes do talk about Ellen. +1636 The door cleans most movie theaters. +1637 Nina couldn't escape from 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Benjamin wasn't vanishing all cars. +1979 Kristin crashed a wheelbarrow. +1980 Janice is melting a popsicle. +1981 Carl has turned that carriage. +1982 Samuel will appear all bikes. +1983 Rachel had turned this carriage. +1984 Tonya tightened the hat. +1985 Linda shrank a lot of pants. +1986 Eva wasn't evaporating some icicle. +1987 Nina isn't vanishing some casserole. +1988 Stacey couldn't flatten a lot of dresses. +1989 Carolyn loosened the skirts. +1990 Vanessa loosened that sweater. +1991 Mary folded every skirt. +1992 David steers some truck. +1993 Peter has broken the convertible. +1994 Dennis wasn't screaming a lot of cashiers. +1995 Stephen was shattering a lot of trucks. +1996 The actress learned Katherine. +1997 That doctor faded those projectors. +1998 The cashiers can crack that plate. +1999 Eva soaked all socks. +2000 Christine sits down Tara. +2001 Adam will melt these rivers. +2002 Alice hadn't brightened the lights. +2003 Some teachers haven't played Carl. +2004 Brett 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Ella might return to. +2898 Catherine hadn't lifted. +2899 Travis had worked with. +2900 That cashier isn't wearing. +2901 Diane hasn't approached. +2902 Gerald's brothers went to. +2903 A lot of waiters tour. +2904 Stacy observes. +2905 That organization referenced. +2906 Elaine had approached. +2907 Jason's spouse isn't listening to. +2908 Some university hasn't forgotten. +2909 Some guest talks about. +2910 A guy's plumbers had cleaned. +2911 Theodore was boasting about. +2912 A son of Homer shouldn't leave. +2913 Jerry helps. +2914 Connie's contractor hasn't toured. +2915 Debra couldn't buy. +2916 Amanda has sold. +2917 Bradley hadn't passed. +2918 Ruth's daughters reveal. +2919 These boys' guests pass. +2920 Rachel's girlfriend dropped by. +2921 Suzanne had cleaned. +2922 Rose buys. +2923 Alan's doctor can't attack. +2924 Benjamin escaped from. +2925 A lot of boys explored. +2926 Lucille's lawyer returns to. +2927 Those hospitals buy. +2928 Nancy descended. +2929 Valerie complains 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Jason's associate talks about. +3029 Dennis's tutors arrive at. +3030 Those grandmothers of Grace couldn't find. +3031 Monet sells. +3032 Carol's supervisors have observed. +3033 Edward's associates escaped from. +3034 Rachel wasn't arguing about. +3035 Those guests would descend. +3036 Joel hadn't climbed down. +3037 Jennifer isn't curing. +3038 Nancy's friends disagreed with. +3039 Robert didn't sell. +3040 George's students examined. +3041 Edward's father cared for. +3042 Charles's hairdresser is investigating. +3043 Most adults hadn't talked about. +3044 Those people remember. +3045 Karen wasn't skated around. +3046 Emily watches. +3047 Tiffany's doctor descended. +3048 Janice can't cure. +3049 All fathers of Erin ran around. +3050 The Impressionists care for. +3051 Brad dislikes. +3052 Carol visited. +3053 Ruth helps. +3054 The cashier had seen. +3055 All patients' granddaughter would run around. +3056 Wendy did remember. +3057 Omar's grandsons bought. +3058 Tina climbed up. +3059 Most cats shouldn't resemble. +3060 Dan's brothers boasted about. +3061 Heather might argue about. +3062 Rachel won't observe. +3063 Craig hadn't seen. +3064 Stephen isn't wearing. +3065 Diana's partners went to. +3066 Kathleen hadn't helped. +3067 Alexander exits. +3068 Kirsten descended. +3069 Walter's senators investigate. +3070 Larry observes. +3071 Marie can walk through. +3072 Rachel resembled. +3073 Some doctor descends. +3074 Nicole wasn't returning to. +3075 Derek's guests had seen. +3076 All sons of April run around. +3077 Every actor's lawyers worked with. +3078 Most nieces of Kayla clean. +3079 The Impressionists talked about. +3080 Craig found. +3081 The schools forget. +3082 Those actresses were passing. +3083 A lot of waitresses talked about. +3084 Lisa's girlfriend doesn't argue about. +3085 Candice has walked through. +3086 Brad was leaving. +3087 William liked. +3088 Holly wasn't curing. +3089 Brett would see. +3090 Tara's ex-girlfriends do visit. +3091 An actress 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Emily would pass. +3124 Lisa's ex-wife hadn't seen. +3125 Mark found. +3126 Kendra reveals. +3127 Cynthia doesn't climb down. +3128 Angela is dropping by. +3129 Homer admires. +3130 Eva climbs up. +3131 Rachel's ex-wives listened to. +3132 Debra has ascended. +3133 Rodney hasn't admired. +3134 Andrew hasn't left. +3135 Tina does know. +3136 Keith's electrician exited. +3137 Barbara isn't attacking. +3138 A lot of granddaughters of Peter walked through. +3139 Some banks buy. +3140 Tammy's grandsons concealed. +3141 This waitress admires. +3142 Elaine's classmate toured. +3143 Eva isn't ascending. +3144 A man did cure. +3145 Linda exits. +3146 Barbara bought. +3147 Nicole's grandmothers were looking like. +3148 Those senators know. +3149 This adult examined. +3150 Joseph isn't passing. +3151 Spain talked to. +3152 Tracy buys. +3153 Candice is watching. +3154 Peter hadn't toured. +3155 Jill's ex-girlfriends haven't bought. +3156 Jennifer's teachers have remembered. +3157 Some women looked 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Adam had watched. +3192 Erin observes. +3193 A lot of grandsons of Tara have helped. +3194 Stephen wasn't watching. +3195 Stacy hasn't looked like. +3196 Nancy's boss could complain about. +3197 Amelia's hairdresser reveals. +3198 Sabrina wouldn't remember. +3199 Bethany can cure. +3200 The daughters of Robert are leaving. +3201 Holly attacks. +3202 Holly went to. +3203 Dan wasn't leaving. +3204 The waiters won't pass. +3205 Kevin climbs down. +3206 Many teenagers' sister doesn't skate around. +3207 Naomi resembled. +3208 All dancers had observed. +3209 Sandra wasn't watching. +3210 Marie did admire. +3211 Most dancers don't tour. +3212 Some friend of Gregory hasn't remembered. +3213 Some sister of Kathleen has loved. +3214 Dana's granddaughter can't sell. +3215 These senators aren't buying. +3216 Aaron's grandmothers liked. +3217 This lady's French teacher sees. +3218 Jeffrey's lawyers had worked with. +3219 Alan toured. +3220 Rose ascends. +3221 A senator won't argue about. +3222 Many guys weren't selling. +3223 Most friends of Ann talked about. +3224 Anne hasn't toured. +3225 Most brothers of Irene disliked. +3226 Every bank did remember. +3227 Some actress has observed. +3228 Keith loved. +3229 Susan escapes from. +3230 Raymond couldn't attack. +3231 Erin has escaped from. +3232 Amelia wouldn't ascend. +3233 Every actress shouldn't know. +3234 Monica's brothers clean. +3235 Ellen's spouse remembers. +3236 Derek will pass. +3237 Becca's associate is climbing down. +3238 Alan exited. +3239 Tina's boyfriend was cleaning. +3240 Rhonda is climbing up. +3241 Tamara exits. +3242 Bill walks through. +3243 Tracy left. +3244 This dancer has gone to. +3245 These customers' wife isn't helping. +3246 Carol's fathers concealed. +3247 April was observing. +3248 Some person's dentist forgets. +3249 Michael escapes from. +3250 Karla worked with. +3251 Julie knows. +3252 Jacqueline wasn't visiting. +3253 Carol isn't climbing down. +3254 Elaine cured. +3255 Some girl remembers. 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Jacqueline isn't admiring. +3352 Mary had referenced. +3353 Cashiers approached. +3354 Wayne sees. +3355 These ladies descend. +3356 Mary's colleague attacks. +3357 The teenager wasn't approaching. +3358 Most teenagers haven't disliked. +3359 Judy won't walk through. +3360 Many schools forget. +3361 Colleen was helping. +3362 Liam exits. +3363 Robert might climb down. +3364 Julia cared for. +3365 Mark's nieces tour. +3366 Guys have helped. +3367 That doctor wasn't cleaning. +3368 Liam's classmates observe. +3369 A lot of daughters of Melinda hadn't talked about. +3370 Scott wears. +3371 A lot of grandsons of Roger sound like. +3372 Ann's cousin will talk to. +3373 A student wasn't walking through. +3374 The patient had helped. +3375 Sheila didn't see. +3376 Monet had run around. +3377 Stephen's teacher visited. +3378 This friend of Derek knows. +3379 Kirsten's sisters remember. +3380 The cashiers aren't exiting. +3381 Derek has listened to. +3382 Robert visited. +3383 Becca's teacher 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Ella was walking through. +3479 Sonia's mother had passed. +3480 Rhonda wasn't buying. +3481 Todd walked through. +3482 Julie's contractors hadn't complained about. +3483 Rodney's senators remember. +3484 William observes. +3485 Kirsten leaves. +3486 Tonya doesn't sell. +3487 Some women ascend. +3488 Joseph's classmates resembled. +3489 These nephews of Amanda had complained about. +3490 Brad had passed. +3491 That waiter skated around. +3492 Jason toured. +3493 Valerie wears. +3494 The Borgias have exited. +3495 Ellen's granddaughter wasn't walking through. +3496 Natalie forgot. +3497 Carol hasn't exited. +3498 Steve hadn't talked about. +3499 Kristin's partners climb down. +3500 Becky is climbing up. +3501 Allison's mentors talked about. +3502 Theodore is going to. +3503 Paula lifts. +3504 Karla approaches. +3505 Liam can lift. +3506 This daughter of Bradley has cared for. +3507 Sabrina hadn't complained about. +3508 Mary is finding. +3509 Victoria has left. +3510 Most senators walk 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Richard returned to. +3644 Steve wouldn't leave. +3645 Helen can climb up. +3646 Some cashiers clean. +3647 A lot of daughters of Erin descend. +3648 Elaine isn't approaching. +3649 Wendy doesn't climb down. +3650 Caroline has forgotten. +3651 The cashier had approached. +3652 Martha's wife wears. +3653 Galileo's colleague concealed. +3654 Margaret is visiting. +3655 That granddaughter of Linda had run around. +3656 George has helped. +3657 Donna wouldn't exit. +3658 Those customers attacked. +3659 Valerie helps. +3660 Every boy observes. +3661 Ella's French teachers go to. +3662 Marcus wouldn't cure. +3663 Cats hadn't found. +3664 Valerie climbed down. +3665 Veronica explores. +3666 Marcus had left. +3667 Grace's piano teachers run around. +3668 John isn't leaving. +3669 Donna knows. +3670 Bruce's senator didn't climb up. +3671 All waiters listened to. +3672 Those men's senators leave. +3673 Kristen wasn't dropping by. +3674 Roger's piano teacher loves. +3675 Jane ran around. +3676 Tonya talked about. +3677 Meredith had ascended. +3678 Many nieces of Rebecca weren't buying. +3679 The Impressionists wear. +3680 Gregory hasn't seen. +3681 Anna's ex-husband won't exit. +3682 Tanya lifted. +3683 Paula isn't observing. +3684 Marie had listened to. +3685 Gary's mother saw. +3686 Hamsters look like. +3687 Larry can't skate around. +3688 The actors leave. +3689 Aaron was referencing. +3690 Sheila's dentists look like. +3691 This customer's electrician won't go to. +3692 Kevin escaped from. +3693 A lot of actresses could forget. +3694 Sara's colleagues resemble. +3695 Grace should complain about. +3696 Cindy hasn't passed. +3697 Dancers liked. +3698 Erin remembers. +3699 Kristin should know. +3700 A lot of teachers have talked about. +3701 A student likes. +3702 Michelle has remembered. +3703 A lady hadn't walked through. +3704 Many doctors go to. +3705 Some girl cures. +3706 Ronald's piano teacher hadn't known. +3707 The customer went to. +3708 Kevin hadn't exited. +3709 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Vincent's ex-girlfriend learns. +4794 Jerry's bosses argue. +4795 Nicole's sons will wake up. +4796 Gina's granddaughter isn't getting fired. +4797 Rose isn't existing. +4798 All shawls did dry. +4799 Ruth succeeds. +4800 Those shirts scare. +4801 Tracy will depart. +4802 Jason hasn't gotten a job. +4803 A lot of boys can cook. +4804 A lot of committees hadn't admired. +4805 Lori hadn't revealed. +4806 Jennifer talks about. +4807 A door hasn't flung open. +4808 That book had looked like. +4809 Michael will argue about. +4810 Some scarf dries. +4811 William's ex-wives returned to. +4812 Aaron has hunted. +4813 Charles's dermatologist chuckles. +4814 This computer looks like. +4815 The Borgias' daughters fall asleep. +4816 This university hadn't referenced. +4817 The universities donated. +4818 A closet couldn't close. +4819 All guys' sisters hug. +4820 That lady whispers. +4821 Rodney's sons respected. +4822 The cart can't rust. +4823 Janet writes. +4824 The pepper ripened. +4825 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Dan's mentor wasn't investigated by most actresses. +6248 Those legislatures are conferred by the water. +6249 A man is disturbed by literature. +6250 Debra's senators weren't annoyed by Patricia. +6251 Becky is watched by Dawn's students. +6252 These waiters weren't attacked by Bethany's doctors. +6253 Some cashier isn't hurt by some cafe. +6254 Some university wasn't competed by Marie. +6255 Lucille was described by Carl's father. +6256 Gerald isn't stunned by the tomato. +6257 Alicia isn't grinned by some sister of Andrea. +6258 Spain was negotiated by a lot of pedestrians' associates. +6259 Elaine's French teachers aren't praised by Rebecca's lawyer. +6260 Lisa wasn't bothered by those doors. +6261 These waitresses were seen by that woman. +6262 Jane was disturbed by these universities. +6263 A woman is cared for by Heidi. +6264 These organizations were upset by the pants. +6265 Carla isn't attacked by Teresa. +6266 Rebecca wasn't impressed by Larry. +6267 Most cousins of Victoria 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Margaret is remembered by Stephen's associates. +6288 Some essays about Jesus weren't concealed by Kathleen. +6289 Some waiters weren't retaliated by Roger. +6290 Naomi's French teachers are loved by drivers' cousin. +6291 Maria isn't disgusted by Roger. +6292 Gina isn't criticized by Marcus. +6293 A lot of legislatures aren't clashed by most blouses. +6294 Rhonda isn't upset by Jodi's spouse. +6295 Larry isn't talked about by these universities. +6296 Alan wasn't hated by Brian. +6297 Kathleen's partner wasn't kissed by Carlos. +6298 Those guys' bosses weren't conferred by Marcus. +6299 Dana isn't bothered by Christina's grandson. +6300 This lady was described by Colleen. +6301 Some organization is annoyed by a lot of bicycles. +6302 The Borgias weren't corresponded by all senators' grandfathers. +6303 These organizations weren't bothered by this coat. +6304 Jill isn't concealed by Veronica. +6305 Ronald's grandmothers were aggravated by this painting of Samuel. +6306 All apples 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Grandsons of Jason were profited. +6772 Kayla's brothers weren't retaliated. +6773 Those men weren't forgotten. +6774 Some hospital wasn't retaliated. +6775 Many cashiers were aggravated. +6776 Omar is worried. +6777 These mothers of many pedestrians were disturbed. +6778 Dennis isn't attacked. +6779 Some legislature was confused. +6780 Kristin was blinked. +6781 Meredith was fallen asleep. +6782 Men weren't irritated. +6783 Phillip wasn't gotten fired. +6784 That bank isn't stunned. +6785 Kathleen was investigated. +6786 Gerald's classmate was criticized. +6787 Those teachers weren't impressed. +6788 Grandfathers of Candice were cared for. +6789 The movie about music is disagreed. +6790 The museum wasn't cooperated. +6791 Nancy's father wasn't left. +6792 Tonya is aggravated. +6793 Kristen is seen. +6794 Barbara's grandfathers aren't profited. +6795 Those teenagers are forgotten. +6796 All waitresses aren't referenced. +6797 Marla's ex-husband is compromised. +6798 Jacqueline wasn't 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Renee's employees are referenced. +6885 Tanya wasn't hired. +6886 Renee isn't impressed. +6887 A son of Samuel is discussed. +6888 Derek was trained. +6889 Regina's senators weren't embarrassed. +6890 Many ladies aren't kissed. +6891 Amelia's senator wasn't compromised. +6892 Some schools are disturbed. +6893 A driver's fathers were testified. +6894 A customer's teachers aren't forgotten. +6895 Some pedestrians are profited. +6896 Heidi was blinked. +6897 Most patients aren't interacted. +6898 Michelle's boyfriend wasn't confused. +6899 Theodore's senators weren't fired. +6900 Carla was hurt. +6901 All granddaughters of Vanessa aren't impressed. +6902 Those waiters weren't disgusted. +6903 A lot of waitresses weren't conspired. +6904 Kenneth wasn't referenced. +6905 Many women were communicated. +6906 A lot of nieces of Tamara weren't clashed. +6907 Deborah's plumbers are referenced. +6908 Erin isn't embarrassed. +6909 Karla wasn't scared. +6910 Jennifer's colleagues are approached. 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Pamela's sister wasn't seen. +6969 Many guests aren't remembered. +6970 Michael isn't boasted. +6971 Brett was fired. +6972 A guest isn't upset. +6973 This man wasn't respected. +6974 A son of all students was referenced. +6975 Kevin's plumbers weren't negotiated. +6976 Julie is left. +6977 These girls were irritated. +6978 Some men weren't astounded. +6979 Winston Churchill's dentists weren't worried. +6980 Marie is wept. +6981 Many actors' lawyer is remembered. +6982 A brother of a lot of guys is respected. +6983 Nancy's sons weren't scared. +6984 The man's dermatologist was visited. +6985 Michael's guests are communicated. +6986 Veronica isn't forgotten. +6987 The cashiers were blinked. +6988 The Borgias are profited. +6989 This apple isn't bought. +6990 This bank is retaliated. +6991 All pedestrians are astounded. +6992 That library wasn't passed. +6993 All banks aren't answered. +6994 Christine wasn't fired. +6995 Rachel was criticized. +6996 Those teachers are criticized. +6997 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Rebecca's brothers aren't cooperated. +7144 Richard wasn't laughed. +7145 A lot of actors weren't remembered. +7146 Some mushrooms were seen. +7147 Pamela's dentist was confused. +7148 The granddaughters of Tonya aren't helped. +7149 Jessica's sister was respected. +7150 Many students are praised. +7151 Regina isn't left. +7152 The Borgias are hated. +7153 This actor was visited. +7154 Every apple was lifted. +7155 Lori wasn't joked around. +7156 Ann's daughters weren't negotiated. +7157 Some hospital isn't respected. +7158 Boys were spoken up. +7159 These boys' friend wasn't visited. +7160 Teresa isn't kissed. +7161 Caroline's students aren't admired. +7162 Sonia is left. +7163 Angela's teachers were hated. +7164 Nephews of Amelia weren't communicated. +7165 Some guests were retaliated. +7166 These teachers are discussed. +7167 A pedestrian wasn't distracted. +7168 Holly's sons are chatted. +7169 Charles isn't answered. +7170 That guest's daughters were embarrassed. +7171 Wayne's 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Janice isn't helped. +7199 Sabrina isn't worried. +7200 Natalie's contractors are chatted. +7201 Some lady is seen. +7202 Teresa was alarmed. +7203 Bill's supervisors are kissed. +7204 Aaron isn't upset. +7205 Some niece of Heather was complained. +7206 Jill was kissed. +7207 Tonya is embarrassed. +7208 Some hospital isn't stunned. +7209 A lot of customers weren't retaliated. +7210 A museum is interacted. +7211 Most associations weren't irritated. +7212 Mark was joked around. +7213 That hospital isn't left. +7214 Nina wasn't stunned. +7215 A lot of children aren't cooperated. +7216 Bruce was shocked. +7217 Many brothers of Denise weren't retaliated. +7218 Connie wasn't murmured. +7219 Mark's husband was embarrassed. +7220 Amy wasn't impressed. +7221 The guest wasn't joked around. +7222 Carla was trained. +7223 Benjamin's best friends were impressed. +7224 Phillip wasn't remembered. +7225 Nancy wasn't fired. +7226 Bethany wasn't talked about. +7227 That granddaughter of Catherine isn't 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The grandfather of these guys wasn't hired. +7343 Wendy's dentists are testified. +7344 Brenda was disagreed with. +7345 Jerry was upset. +7346 Some spinach is sold. +7347 Some turtles come here Kimberley. +7348 Diane watched Alan. +7349 This cousin of Theodore buys some mushroom. +7350 Rhonda was arguing about this nephew of Dana. +7351 A lot of actresses' nieces have toured that art gallery. +7352 Chad's daughters embarrasses Erin's girlfriend. +7353 The pedestrians wave some people. +7354 Vanessa has gone to a lot of college campuses. +7355 Those committees listened to Kristen. +7356 Most pedestrians' father might climb up the slope. +7357 Theresa shouldn't scan this essay. +7358 A lot of girls' son hadn't insulted Jerry. +7359 Denise has fallen Marie's grandsons. +7360 Grace is investigating a lot of girls' ex-wife. +7361 Donna talked about Anna's friends. +7362 Many sisters of some guest forget Eva. +7363 The truck hadn't astounded a bank. +7364 Phillip isn't grinning Winston Churchill. +7365 Bill explores the bank. +7366 These grandmothers of Rebecca remembered Angela. +7367 Many universities reacted Samantha. +7368 A lot of grandmothers of Melanie go to the mall. +7369 Ella's tutor can't talk every adult. +7370 Christopher researched most public parks. +7371 Melinda explores those closets. +7372 Most nieces of Jacqueline worry Phillip. +7373 Some mother of Diane wasn't distracting a lot of students' ex-girlfriends. +7374 Robert hasn't escaped from Brenda. +7375 Marcus wasn't running around that high school. +7376 Monet hadn't examined most commentaries. +7377 Many actresses escape from Cynthia. +7378 Amy's cousins distracts a lot of women. +7379 Many senators' niece admires Brian's ex-husband. +7380 Carmen's father isn't cleaning a lot of cups. +7381 Rose couldn't go fishing Liam's employees. +7382 Daniel's ex-husband resembles a lot of prints of actresses. +7383 Heidi sells every banana. +7384 Phillip's senators shouted the driver. +7385 The actress can 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Christine's doctors were laughing many shawls. +7678 Those men are investigating Janice. +7679 Many boys noticed Allison. +7680 Computers were falling Daniel. +7681 Most sketches bother Emily. +7682 That movie theater alarmed Kathleen's students. +7683 Vincent's sons cooperate Andrew's teachers. +7684 Carlos did reference Tiffany. +7685 Some actors fix this truck. +7686 Tammy's associates go to this high school. +7687 Sharon had watched Julia's grandmothers. +7688 A lot of grandfathers of Dana liked some carriage. +7689 The grandson of Valerie wasn't hiding Stephanie. +7690 Cindy wouldn't clean those museums. +7691 Joseph was complaining about Kenneth's nephew. +7692 The waiter should astound Rebecca's bosses. +7693 A high school shocked Cindy. +7694 Jane does aggravate Debra's dermatologist. +7695 Many adults haven't hired that dancer. +7696 Colleen discovers most newspaper articles. +7697 A student retaliates Jacqueline's sons. +7698 Martin has messed up some movie theaters. +7699 Nicole's guest wouldn't disagree with a dancer. +7700 Sandra looked like that photograph. +7701 Some guy does like these cakes. +7702 Those women do research some university. +7703 Liam's mentor wasn't disagreeing with Tracy. +7704 Marie didn't pass the glaciers. +7705 Rhonda examined many commentaries. +7706 Pamela's lawyers weren't leaving Cheryl. +7707 David complained about Amelia. +7708 Michael isn't talking about some play. +7709 The girls' nieces attacked Patrick. +7710 Theodore hadn't boasted Karla. +7711 Bradley slumped over that person. +7712 Monica climbs up some slope. +7713 Timothy found rivers. +7714 Melissa had questioned Leslie. +7715 Dawn hugged Becky. +7716 Sherry shouldn't tour some bank. +7717 Allison might buy some movie theaters. +7718 That actor's partner cried Homer. +7719 Some adult finds some bicycle. +7720 All men had exited these grocery stores. +7721 Phillip had visited those daughters of Candice. +7722 Some teacher has left Timothy. +7723 Those pedestrians 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Lucille had healed Donna. +7926 Sally had worked with a teacher. +7927 Eva might boycott every art gallery. +7928 Jacqueline's French teacher had hated Cheryl's mother. +7929 Donald's sisters didn't fix most computers. +7930 All guys buy that box. +7931 Curtis wasn't climbing up most mountains. +7932 Most cashiers resemble every sketch of Helen. +7933 Jessica appreciates science. +7934 Ann's senators discuss Judy. +7935 Many waitresses skate around some mountain. +7936 Alicia's piano teachers could conceal this fish. +7937 Renee's lawyers sell many hospitals. +7938 Melissa couldn't buy that shawl. +7939 Diane has died a lot of boys. +7940 A lot of pictures looked like that bicycle. +7941 Erin hadn't cleaned some couch. +7942 Some boy was chuckling Heidi. +7943 Chad's son knows Guy's best friends. +7944 Those newspaper articles about science look like that picture of Frank. +7945 Colleen's associate was concealing every print of that person. +7946 Tanya's bosses clean the cafes. +7947 The boys embarrasses Paula. +7948 Ella could buy some car. +7949 Regina hadn't approached Rhonda's handyman. +7950 Danielle had disliked Andrew. +7951 Renee's friend boasts Jill. +7952 Some pedestrians disagree with Irene. +7953 Tina won't remember Bill. +7954 Every son of Sheila is questioning Kirsten. +7955 Vanessa's lawyers hurt Jessica's grandfathers. +7956 Tiffany isn't appreciating Stacey. +7957 Naomi didn't conceal Candice. +7958 Timothy's ex-wives retaliated Rose. +7959 Ruth returned to Marie. +7960 Colleen laughed Larry. +7961 Some government couldn't conspire Charles. +7962 Those senators' tutors stun all sisters of all boys. +7963 This projector falls all waiters' husband. +7964 Veronica's associates hadn't run around that river. +7965 That grandmother of Wayne can't return to a lot of sisters of Bill. +7966 Meredith had irritated Tonya. +7967 Many companies hate Galileo. +7968 Some fathers of Carolyn described Eric. +7969 That pedestrian hadn't cured Brian. +7970 Some 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Many organizations compete Susan. +8016 Matt littered a lot of hospitals. +8017 Bradley's nieces negotiated every unicycle. +8018 Martin discovered Charles. +8019 Heidi fires Deanna. +8020 Irene's wife has run around all glaciers. +8021 Joel argued about Christopher's students. +8022 Every photograph looked like that library. +8023 The drivers were replying waitresses. +8024 That guy isn't fleeing from Walter. +8025 Those doctors heal Naomi. +8026 A hospital talked about friends of Keith. +8027 Cheryl's fathers find all actors. +8028 Those horses have bothered Sandra. +8029 A waiter remembers Jill. +8030 Sonia's mothers weren't nodding this association. +8031 Many nieces of Alicia praise Paula. +8032 All teachers' teacher hadn't kissed Tiffany. +8033 Janice smiles Donna. +8034 Rebecca fires this teacher. +8035 Raymond had seen most rivers. +8036 Kendra has worked with Andrea. +8037 Noah buys some restaurants. +8038 Stephanie struggles Eric's grandfather. +8039 Stephanie's dentists conferred Adam's senator. +8040 This teacher disturbs Suzanne. +8041 Naomi boasted these closets. +8042 All nieces of Beverly talked that granddaughter of Janet. +8043 Paintings look like most sketches. +8044 Monet questioned Karen. +8045 Nina attacks Marla. +8046 Many drivers knew Grace. +8047 Paula hadn't questioned Wendy's daughters. +8048 Some university distracts Kathleen. +8049 Irene explores some movie theater. +8050 Christopher was escaping from Amanda. +8051 Those mothers of Kayla complain about Adam's husband. +8052 Many guys competed Beth. +8053 Nicole's colleagues respect the pedestrian. +8054 Wayne wasn't going to that high school. +8055 A guy has slumped over most daughters of Benjamin. +8056 Lucille is fixing some planes. +8057 Karen might notice Michael. +8058 These actors were nodding every closet. +8059 Angela does aggravate Peter. +8060 Wayne will embarrass Stacey. +8061 Beth hasn't argued about Jeffrey. +8062 Kristin wore some hat. +8063 Some grandson of Homer examined the nephew of Becky. +8064 The print of every student is looking like some mother of all actresses. +8065 Those sketches hadn't upset Tammy's hairdresser. +8066 Those shows about Monet hadn't irritated Tamara. +8067 Leslie describes a lot of teenagers. +8068 That commentary aggravates the ladies. +8069 The student loves Amelia. +8070 Linda notices some ladies. +8071 John talks Christina. +8072 Jill wouldn't help Stacy's associates. +8073 Adam runs around many cafes. +8074 Carmen shouldn't find Carmen. +8075 Many nieces of Michelle sighed Melinda. +8076 Phillip's nieces compromise Kendra. +8077 That customer's doctors are buying that photograph of a carriage. +8078 Paul's sisters escaped Deborah. +8079 Every skateboard hasn't fallen Adam. +8080 Pamela boasts Derek. +8081 Most brothers of Michelle explore these closets. +8082 Carolyn exits that mall. +8083 Meredith visits many adults. +8084 Danielle's associate responds every guy. +8085 This truck aggravates Keith. +8086 Stephanie's students break the mirror. +8087 These pedestrians talked to Renee. +8088 Beth's ex-boyfriend shocked a grandson of Cheryl. +8089 Carmen won't find the pedestrian's mother. +8090 Danielle's employee shouldn't notice that pedestrian. +8091 All fathers of Stacy chatted Keith. +8092 Curtis would impress some granddaughter of Patrick. +8093 The customers' sons exit these grocery stores. +8094 Some fathers of this waitress have laughed Mary. +8095 A pedestrian's partners are arriving at all closets. +8096 Some drawing of these dishes can look like Ruth. +8097 Bill complains about Jill. +8098 Bradley's dermatologist left Frank. +8099 Janet was insulting these dancers. +8100 Most pedestrians shouldn't research Nancy's husband. +8101 Many doctors hadn't corresponded Gregory. +8102 Regina hadn't remembered some guy's daughters. +8103 Plays about Harvard University disagree Holly. +8104 The nose hadn't fallen Roger. +8105 All brothers of Regina remember Phillip's friends. +8106 Matt boasted Irene. +8107 Some man's employee has hated Sharon. +8108 Wayne's friends hadn't hated some men. +8109 The Lutherans kiss Victoria's associate. +8110 Eric praises Diane. +8111 Brett won't mutter this sketch of those steps. +8112 Naomi's brothers cooperated this cafe. +8113 This bicycle bored Maria. +8114 Valerie's mothers haven't admired Janet's contractor. +8115 Renee hasn't revealed women. +8116 Dawn should research this documentary about Jesus. +8117 Jeffrey isn't breaking a vase. +8118 Mary's piano teacher responds Gina's partner. +8119 Paul shouldn't conceal that waitress's guests. +8120 Wayne's students went to the grocery store. +8121 Lisa's colleague is messing up these hospitals. +8122 Liam is exploring this movie theater. +8123 Ellen cleaned the mushroom. +8124 Kenneth's plumbers wouldn't appreciate the dancer. +8125 Many people had bought this unicycle. +8126 Deanna hadn't driven to all rivers. +8127 Elaine hasn't driven to some mountain. +8128 Some book has sounded like some senator's nephews. +8129 Sonia is concurring Liam. +8130 Some mouths scare Kimberley. +8131 Homer fell Janet. +8132 Paul blinked the cars. +8133 Becca had died the glaciers. +8134 The cashiers communicate Carmen's friends. +8135 Gina's students know Jerry. +8136 Hamsters aggravated Barbara. +8137 Patricia's ex-boyfriend has toured a grocery store. +8138 Elizabeth sounds like Emily. +8139 This boy watched Nicole. +8140 Christopher won't explore a lot of high schools. +8141 Many cousins of a lot of customers walk through the banks. +8142 Thomas did hug this grandmother of Meredith. +8143 All drivers fired Benjamin. +8144 Jason will dislike Julia's classmate. +8145 A skateboard resembled these photographs. +8146 The documentary about the Borgias shocks Tiffany. +8147 Stacey's piano teachers shouldn't climb up most mountains. +8148 Bradley's electrician worked with a teenager's grandmother. +8149 Sally hadn't blinked Cheryl. +8150 Erin is scanning Clyde's supervisor. +8151 Most cars fall Galileo. +8152 Some person annoyed this patient. +8153 Thomas didn't talk to a customer. +8154 Some car hasn't bothered Carmen's hairdressers. +8155 Martin's employee won't exit the college campus. +8156 Karen's hairdressers were escaping from Judy. +8157 The Borgias hadn't shocked all granddaughters of Kathleen. +8158 Julie is shrugging that customer's granddaughters. +8159 Sandra was disgusting Vincent. +8160 Roger's French teachers irritated Allison. +8161 All brothers of Melanie bike to Harvard University. +8162 The Impressionists chatted a lot of malls. +8163 Alice laughed Gregory. +8164 A lot of drivers weren't admiring Dana. +8165 Dana approached Carrie. +8166 Those governments conferred Charles. +8167 Beverly arrives at many rivers. +8168 Many documentaries would disagree Carrie. +8169 A lot of teachers' mothers climb down all hills. +8170 These guests notice every hamster. +8171 Larry fell asleep Samuel. +8172 Sandra's tutors have insulted Peter. +8173 Most actors drive to some bank. +8174 Galileo kissed that doctor's daughter. +8175 Rachel was driving to most museums. +8176 Wendy couldn't disgust Scott. +8177 Vincent was discussing Erin. +8178 Nicole's contractors admire every door. +8179 Every waiter's ex-girlfriends listened to Richard's nephews. +8180 This child researched some woman's students. +8181 This grandson of Brenda isn't returning to the restaurant. +8182 Judy had some hat. +8183 Leslie hadn't argued about an actress. +8184 Dan had shouted George. +8185 Scott is driving to Harvard University. +8186 Cindy is wearing a scarf. +8187 Those cousins of Sonia argued about the people. +8188 Lori wasn't helping Beverly's senator. +8189 Nicole cures Cindy. +8190 Joseph can't praise Larry's friend. +8191 Bethany would upset Regina. +8192 Nancy's best friends have this glacier. +8193 All drivers fall Paul's French teachers. +8194 Cindy's husband had littered Jennifer. +8195 Lori isn't grinning some bicycle. +8196 Heather won't take those grocery stores. +8197 Every girl compromised the grocery stores. +8198 Some art galleries had fallen Lori. +8199 The story hadn't disagreed with these actresses. +8200 Kevin hadn't exited the restaurant. +8201 That daughter of Becca shouldn't arrive at this high school. +8202 Adam's ex-girlfriends haven't driven to the hospital. +8203 Stephanie arrives at that grocery store. +8204 Stephen finds all guests. +8205 Every slope disturbs Alexander. +8206 The government can't bother Gregory's dentists. +8207 Karen's guests had listened to Kenneth's friend. +8208 Those patients describe Amanda. +8209 A lot of shawls upset Martin's bosses. +8210 A lot of guys' grandfathers messed up a cafe. +8211 Some cashier mutters a pedestrian. +8212 Curtis will help a man. +8213 Some school is communicating Kirsten. +8214 Samuel's students arrive at all glaciers. +8215 A lot of lakes will embarrass Lucille. +8216 A print distracted Jennifer. +8217 Dan will explore the high school. +8218 Vanessa's granddaughters did clean the shoes. +8219 Rebecca didn't respect the hill. +8220 All shawls fall students. +8221 Many computers distract that niece of Martin. +8222 Most children are researching many art galleries. +8223 Some grandmothers of Tammy could conceal Tanya. +8224 Naomi's doctors investigate Sherry. +8225 Donna had ascended some mountain. +8226 Candice littered most companies. +8227 Rhonda wouldn't hire Amy. +8228 Kendra retaliates mountains. +8229 Rhonda's guests embarrassed Jill's supervisor. +8230 Galileo didn't investigate every child. +8231 Wayne sees Christina's classmates. +8232 Derek exits every glacier. +8233 Steve is boasting about actresses. +8234 Ronald wasn't coming here pork. +8235 Vincent's plumbers clean this skirt. +8236 Veronica's father wasn't watching a lot of grandfathers of Vanessa. +8237 Marla buys these blouses. +8238 Cynthia talked about Jacqueline. +8239 Some daughters of Becca had testified Paula. +8240 Charles sold the print. +8241 Most organizations retaliate Sally. +8242 That grandfather of Amanda should work with those cashiers. +8243 William didn't clean every museum. +8244 This doctor could talk about Denise. +8245 A lot of students' ex-wife hadn't climbed up this hill. +8246 That documentary about Winston Churchill disagreed Alexander's dentist. +8247 Valerie hadn't broken this skateboard. diff --git a/zeroshot/binding/eval_results.json b/zeroshot/binding/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..525e6347cb11311055a1b280326ee483f2016206 --- /dev/null +++ b/zeroshot/binding/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.6813594538438705} \ No newline at end of file diff --git a/zeroshot/binding/predictions.txt b/zeroshot/binding/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..754a0344dd2d250c062282f4710ce3a3b32788a9 --- /dev/null +++ b/zeroshot/binding/predictions.txt @@ -0,0 +1,6739 @@ +index prediction +0 A lot of patients who can sell some couch didn't investigate themselves. +1 A lot of actresses that thought about Alice healed herself. +2 A guy that has seen the wheelbarrow notices itself. +3 The children that were complaining about Gregory hated themselves. +4 Every boy that isn't selling the grocery stores isn't healing himself. +5 The hospitals that talk to Ann talked to herself. +6 Many skirts that stun some waitress are boring themselves. +7 All men that might fire Tiffany have worked with themselves. +8 That actress who admired the cup praises herself. +9 This lady who is healing Charles wasn't hiding herself. +10 Every woman that was hiring the Borgias argued about herself. +11 Every lady who does walk through a lot of movie theaters hadn't hugged herself. +12 These doctors who do leave Melissa fled from herself. +13 These ladies that hadn't forgotten Victoria fled from themselves. +14 The customers who embarrass Kevin are discussing themselves. +15 Some steak that scares Stacy doesn't shock herself. +16 All ladies who were bothering Dan observed themselves. +17 The woman who hadn't taken a screen should respect herself. +18 Most women who were arriving at every hospital fired themselves. +19 Some hill that could distract Barbara is alarming herself. +20 A lot of senators who climbed down the hill hired themselves. +21 The dancers who haven't shocked Candice aren't appreciating themselves. +22 A legislature that wouldn't criticize Patrick hasn't helped himself. +23 Most guests who were revealing Colleen weren't complaining about themselves. +24 A guy who hasn't annoyed Laura argued about himself. +25 That company that had known Sandra annoyed herself. +26 Those senators who had a slope have cared for themselves. +27 A cat that hasn't impressed Joseph couldn't distract himself. +28 Some association that had forgotten Claire insults herself. +29 That plane that was scaring Wendy has disgusted herself. +30 The dancer who has brought those fish could care for herself. +31 Some boy that disturbed Jill could argue about herself. +32 Some woman that was exploring some hill can't think about herself. +33 All actors that disgust Veronica escaped from herself. +34 Some museum that doesn't like a waitress was praising herself. +35 These pies that were embarrassing Jennifer are annoying themselves. +36 The book that is disgusting dancers would irritate themselves. +37 The drivers who attack Jodi haven't revealed themselves. +38 A lot of adults that scared Stacey talked to themselves. +39 A girl that wouldn't watch Omar questions himself. +40 The men who weren't confusing Valerie were kissing themselves. +41 Some guests who didn't discover Melissa were visiting themselves. +42 A lot of waiters who weren't boring Joseph would heal themselves. +43 Some organizations that haven't respected Nicole hadn't worked with herself. +44 Some dancer who isn't admiring mushrooms thinks about herself. +45 These actors who think about Russell won't remember themselves. +46 All cashiers who will respect Florence criticized themselves. +47 This book that hurt Kirsten has disagreed with itself. +48 These adults that won't listen to Janice impressed themselves. +49 The actors that were escaping from Carrie had cured themselves. +50 Every government that boasted about Amanda wasn't embarrassing herself. +51 Some cucumbers that can embarrass Amanda disgusted herself. +52 Some association that would astound Gregory had scared himself. +53 The customers that descend every hill can praise themselves. +54 That woman who had noticed John fled from herself. +55 Some beef that had annoyed this dancer is stunning herself. +56 These guests that haven't discovered Caroline had seen herself. +57 A library that has stunned Samantha criticized itself. +58 Every actress who observed Eric kissed herself. +59 Every girl that hadn't disgusted Travis can't approach herself. +60 Every woman that noticed some library respects herself. +61 Senators who disagree with Thomas can't heal themselves. +62 The dancers that argue about Beverly observed themselves. +63 The turtles that were boring Christine disgusted themselves. +64 These doctors that didn't flee from Michael visited himself. +65 Some guy who is visiting some lakes cares for himself. +66 Those students that looked like this print fired themselves. +67 Some girl who researches committees boasts about herself. +68 All customers who would drive to every river haven't returned to themselves. +69 That dog that wasn't boring Jason scared himself. +70 That woman that hated Scott hadn't talked about herself. +71 Some women that would approach Pamela had cured herself. +72 This committee that is referencing Gina works with herself. +73 The rabbits that won't look like a painting disturbed themselves. +74 A lot of boys who hadn't appreciated some river were working with themselves. +75 The river that impressed Cindy is impressing herself. +76 Most children who astounded Larry have seen themselves. +77 Customers who love Russell had investigated themselves. +78 Some company that discussed James discusses himself. +79 That couch that shouldn't aggravate all women was annoying themselves. +80 Wheelbarrows that shocked the company distracted themselves. +81 These shoes that irritate Omar hurt themselves. +82 That fish that astounded this actress hadn't scared herself. +83 That lady that was examining the movies remembers themselves. +84 Most waitresses that couldn't think about some vase had disliked itself. +85 Most boys who have confused Kristin hadn't concealed themselves. +86 Most doctors that are running around this library did love itself. +87 The adults that scared Mary are approaching themselves. +88 The customers who distracts the guy would investigate himself. +89 Most cars that astound Catherine irritated herself. +90 The woman who loves Theodore notices herself. +91 Most women that passed the hill won't conceal themselves. +92 A girl that is fleeing from Derek wasn't discovering herself. +93 Some children who leave Erin are observing themselves. +94 Most waitresses that praised Ruth aren't appreciating themselves. +95 Some legislatures that haven't argued about Jeffrey worked with themselves. +96 The organization that has disliked many books insults themselves. +97 Those fish that are embarrassing Catherine aren't disgusting themselves. +98 This girl who boasted about some book could hate herself. +99 A lot of banks that weren't talking to Kayla will forget herself. +100 These committees that don't work with Frank will dislike himself. +101 An actress that wasn't boasting about Todd isn't discovering himself. +102 Some companies that were hurting David don't criticize himself. +103 All dancers who passed the college campus can hate itself. +104 All universities that aggravated Carmen shouldn't astound themselves. +105 This unicycle that shouldn't hurt Suzanne will disturb itself. +106 That waitress who talked about many malls observed herself. +107 That eggplant that astounded Douglas disturbs itself. +108 Every girl that should bike to some hospitals had attacked herself. +109 All teenagers that wouldn't sell that vase discovered itself. +110 People who weren't discussing Laurie didn't sound like themselves. +111 A lot of boys that don't fix a cup remembered themselves. +112 Senators that conceal Danielle haven't forgotten themselves. +113 Dancers who kiss Bradley have astounded themselves. +114 The boy that would listen to Tiffany had escaped from himself. +115 Some teenagers that haven't confused Eva can reveal themselves. +116 That school that was distracting Renee appreciates herself. +117 This slope that should stun Lori isn't astounding herself. +118 Some libraries that were buying the sketch hadn't worked with themselves. +119 Most cashiers who conceal Bruce would reveal himself. +120 This car that impressed Frank hadn't disgusted himself. +121 This boy who will fire Pamela appreciated herself. +122 Some essay that hurt Peter has sounded like himself. +123 The documentaries that worried Susan aggravated herself. +124 This legislature that respected Karen doesn't describe herself. +125 Many pedestrians that confuses Rhonda weren't revealing themselves. +126 Every company that hired Travis remembers himself. +127 All governments that scared Larry do fire themselves. +128 These fish that were worrying Joseph scared himself. +129 Some library that could confuse Julie respects herself. +130 A hospital that had remembered Andrea praised herself. +131 Some woman that does embarrass Samuel questions himself. +132 That cup that has astounded Lisa astounded herself. +133 Many actresses that can find an apple won't astound itself. +134 This girl that was noticing Alexander leaves herself. +135 That actress who would complain about Phillip has complained about herself. +136 Those actresses who respect George approached themselves. +137 The lamp that isn't disgusting James can't worry himself. +138 A lot of shirts that hadn't alarmed Wayne embarrassed himself. +139 This association that listened to Rhonda isn't working with itself. +140 Some lady that cures Clyde had approached herself. +141 Every school that isn't discussing Marla had distracted herself. +142 The actress that wouldn't visit some teachers would reveal themselves. +143 The pedestrians who referenced Candice haven't complained about themselves. +144 These doctors that ascend a hill had discovered itself. +145 A lot of schools that resemble some painting hadn't appreciated themselves. +146 Some car that will irritate Mark hadn't embarrassed himself. +147 Some boy who concealed an actress isn't describing herself. +148 Customers that have hated Tammy weren't healing themselves. +149 That girl who cured girls does complain about herself. +150 A woman that has most photographs disagrees with herself. +151 That lady who hadn't worn every glove could heal herself. +152 A woman who hadn't looked like some photograph sees herself. +153 Those students that should aggravate Paula did cure herself. +154 The legislatures that should praise Victoria worked with themselves. +155 A museum that talks about Katherine talks about herself. +156 Many sketches that aren't boring Jill had hurt herself. +157 Many art galleries that have impressed Lawrence hadn't distracted himself. +158 Most adults who didn't attack Brett investigated themselves. +159 Some adults that do shock Mitchell won't think about themselves. +160 A lot of guys that aren't arguing about Derek were helping himself. +161 That grocery store that hurt Alice should aggravate herself. +162 A man that can help Nancy hugs himself. +163 Most dresses that distract Steve can hurt himself. +164 Some university that will distract Teresa wasn't helping itself. +165 Every boy that was disturbing Sherry watched himself. +166 This organization that wouldn't know Sharon admired itself. +167 Those guests who hadn't returned to Patrick didn't approach themselves. +168 That actress who wouldn't take this screen questions herself. +169 Every man that isn't approaching Kirsten is attacking himself. +170 Those teenagers who hadn't seen some photograph don't boast about themselves. +171 This dancer who is curing some patients criticizes herself. +172 Some shirt that hadn't disgusted Kimberley should worry herself. +173 Those waitresses who clean some face have revealed itself. +174 A boy that doesn't climb up a ladder returned to himself. +175 The glass that looked like many photographs bothered itself. +176 The girl that discovered most associations remembers herself. +177 A lot of drivers that went to this public park can watch itself. +178 Every company that hadn't appreciated Becky can hire herself. +179 A lot of people who forget Christina aren't referencing themselves. +180 Many computers that won't disturb Becky can astound themselves. +181 Some lady that was exiting a restaurant leaves itself. +182 Some mouths that would upset Florence did astound themselves. +183 Some nose that worries these patients hasn't embarrassed itself. +184 All students that research Harvard University won't question themselves. +185 Those women that had confused every boy had irritated themselves. +186 That guy that hadn't astounded a lot of libraries might hug himself. +187 A boy that descends some steps doesn't attack himself. +188 Some universities that alarmed Carolyn have talked about themselves. +189 Some actress that fled from some closet will respect herself. +190 Many essays that bore Michelle scared themselves. +191 This movie that hasn't impressed Meredith might aggravate herself. +192 A lot of pedestrians that did astound Becky had discovered themselves. +193 Every scarf that had bored Benjamin wasn't boring himself. +194 Most actors who bores Todd didn't love themselves. +195 Most legislatures that aren't insulting Catherine aren't discussing themselves. +196 These projectors that are impressing Jason astounded himself. +197 That man who arrives at the museum wasn't hurting himself. +198 The paintings that bored Helen were aggravating themselves. +199 The skateboard that didn't stun Gregory has astounded itself. +200 An actress who upset all drivers wasn't observing herself. +201 An essay that won't worry Tiffany should distract itself. +202 These companies that bore Tammy haven't fired themselves. +203 Teenagers that are discussing Andrea weren't criticizing themselves. +204 Some drivers that hadn't broken the window aren't talking to themselves. +205 Many cats that have ascended the ladder aren't irritating themselves. +206 Most people who had attacked Deanna have disgusted herself. +207 Many pedestrians that hadn't hurt Lisa aren't finding themselves. +208 The teachers who aren't disagreeing with Caroline hadn't listened to themselves. +209 A lot of cashiers who mess up this college campus questioned themselves. +210 The lights that aggravated Sabrina aren't shocking themselves. +211 Many cashiers who can heal Guy sounded like himself. +212 A lot of adults who have irritated Tanya discussed themselves. +213 Some horse that hasn't confused these students impressed themselves. +214 Some men that don't escape from a bank were helping themselves. +215 Some waitress that was climbing up all hills had insulted themselves. +216 This boy who annoyed Laurie disagrees with himself. +217 The pedestrians that exit this museum weren't fleeing from themselves. +218 Those pedestrians who had examined this play hadn't watched itself. +219 A lady who isn't bringing the shawls returns to herself. +220 Many chairs that shock Heidi haven't stunned themselves. +221 A scarf that has embarrassed Carolyn wasn't disgusting herself. +222 Many pedestrians that distracts Lisa investigated herself. +223 Most waitresses who weren't concealing John have approached themselves. +224 Dancers who hadn't concealed some hospital were discovering themselves. +225 Many museums that work with Mitchell couldn't help himself. +226 This museum that wasn't taking the mouths is talking to themselves. +227 Some guy that does help Laurie leaves himself. +228 Children that aren't irritating David might flee from themselves. +229 That actress that won't discover that plate watches itself. +230 A lot of associations that will remember Ruth do work with themselves. +231 The doors that haven't scared Amy wouldn't distract themselves. +232 This company that wasn't talking to Heather talks about herself. +233 That dancer who thinks about a car isn't hugging herself. +234 These glasses that disturbed Bradley can't aggravate himself. +235 These guys who disturb Sarah criticized themselves. +236 Most waiters that were disagreeing with Omar hadn't respected themselves. +237 Teachers who brought that car wouldn't disagree with themselves. +238 All mushrooms that hurt William disgusted himself. +239 This mountain that would distract Heidi can stun itself. +240 Most women who didn't disturb Stacey are investigating themselves. +241 Some girl who hasn't climbed up the hill wasn't watching herself. +242 Some waitress that hadn't cured Daniel flees from himself. +243 Many committees that haven't sold this bicycle do talk to itself. +244 Those teenagers that didn't climb up a slope weren't disgusting themselves. +245 A guy who scares some committees healed himself. +246 A lot of movie theaters that had bored Phillip aggravated himself. +247 Most girls that listen to Christopher have visited themselves. +248 Some museums that shouldn't know George hadn't talked to himself. +249 Some dancer who had dropped by a lot of movie theaters isn't working with herself. +250 That boy who had hated Paula attacks himself. +251 Every high school that annoys Emily does impress herself. +252 Some bank that knows Patrick helps himself. +253 Some students who walk through the library were curing themselves. +254 The wheelbarrows that should bore Sonia irritated themselves. +255 The plates that have irritated Victoria were alarming themselves. +256 Some picture that had hurt Elizabeth upset herself. +257 This woman that had lifted some cats thought about herself. +258 A lot of cashiers who go to a mountain admired themselves. +259 Patients who might impress Laurie haven't hired themselves. +260 All associations that aren't boring Peter weren't discussing themselves. +261 All senators who could walk through this library hadn't cured themselves. +262 The association that doesn't embarrass Noah references itself. +263 Some man that insults Kristin approached herself. +264 That actress who hurt a lot of women liked herself. +265 Every organization that is boring Bill wouldn't fire himself. +266 The customers who could worry Sharon knew themselves. +267 Some people who annoy William are watching themselves. +268 All hospitals that scared Kirsten will reference themselves. +269 That boy that hated Pamela cares for herself. +270 Every school that couldn't know Stacy describes itself. +271 All waitresses who weren't hurting Jason might discover themselves. +272 Most customers who are impressing Laurie have found themselves. +273 Some cars that embarrassed this boy aren't scaring themselves. +274 Some carriage that isn't annoying Cheryl bored herself. +275 Some cats that resemble that painting had impressed itself. +276 A lot of icicles that can confuse Alice haven't impressed herself. +277 The lady who has impressed Homer had discussed herself. +278 All cashiers that had driven to the library hugged themselves. +279 Some shawl that bored Mitchell is impressing himself. +280 A lot of waiters who have broken a window might visit themselves. +281 These cakes that don't aggravate Ruth do scare themselves. +282 Pedestrians who hadn't loved Brian cured himself. +283 Some doctors that aren't describing Thomas revealed themselves. +284 A lot of actors who haven't fired Theodore haven't respected themselves. +285 Some drivers that hated Nicole knew themselves. +286 This waitress that drove to every river had hurt herself. +287 All committees that can't boast about Wayne don't irritate themselves. +288 Some man who had bought every fork referenced himself. +289 An organization that could bore Tiffany helps itself. +290 The waitresses that concealed Karla disliked herself. +291 These guests that do notice Derek aren't boasting about themselves. +292 Every museum that helped a man could hate himself. +293 A waitress that is messing up many glasses helped herself. +294 A commentary that was irritating these legislatures disagrees with themselves. +295 The dancer who stunned Russell approached herself. +296 A museum that has astounded Eva didn't hate herself. +297 The senators that had hugged Deanna cured herself. +298 These banks that appreciated Derek referenced himself. +299 All teachers who won't take some movie theater wouldn't complain about themselves. +300 This rug that hadn't stunned the doctors embarrassed themselves. +301 Every library that was distracting Carol describes itself. +302 The girls that stun Lucille would conceal themselves. +303 All teachers that scare some waitress noticed themselves. +304 All guests who could reveal Gerald haven't observed themselves. +305 Some casserole that resembled the photographs can't disgust themselves. +306 Every school that hasn't confused Carrie helped herself. +307 Every girl who toured some hill loves herself. +308 All pedestrians that do embarrass Christine weren't revealing themselves. +309 Some bicycle that looks like most sketches worried itself. +310 Those actresses that were curing this dancer are escaping from herself. +311 A guy who shouldn't forget all waitresses wasn't approaching himself. +312 Some committee that hasn't remembered Kimberley isn't criticizing herself. +313 Every waitress that exits a cafe escapes from herself. +314 A lot of legislatures that would reference that man have hired himself. +315 Most jackets that bothered Carlos had disgusted himself. +316 Most waiters that see Daniel have forgotten himself. +317 The dancer that embarrassed this committee thinks about herself. +318 A dancer that researched this art gallery fled from herself. +319 The men who bother Carlos haven't hired themselves. +320 Every actress who has bored a lot of teachers hasn't boasted about themselves. +321 These people who hired Wayne don't complain about themselves. +322 The women who embarrassed Laura haven't kissed herself. +323 All plates that were upsetting Derek shouldn't bore themselves. +324 That guy who wasn't describing most guys describes himself. +325 Some people who wouldn't discuss Tonya won't boast about herself. +326 This boy who isn't fleeing from Melanie isn't returning to herself. +327 All stairs that haven't impressed Peter do stun themselves. +328 Every river that didn't scare April had annoyed itself. +329 The car that did resemble some pictures astounded itself. +330 That woman that doesn't return to a man noticed herself. +331 Every lady who hired these cashiers kisses themselves. +332 People who left every man do describe himself. +333 Some bikes that aren't scaring Sherry aren't worrying themselves. +334 Some boys who had toured the bank aren't observing themselves. +335 Some man that is hiding Dana hasn't described himself. +336 Some girl that should praise the Lutherans attacks themselves. +337 That waitress that left most students knows themselves. +338 All sweaters that had resembled a print did astound itself. +339 Customers that referenced John concealed himself. +340 Most guests that were hugging Daniel hadn't attacked himself. +341 These guests who won't boycott that association hurt themselves. +342 These children who wore that shawl are finding themselves. +343 These ladies that ascended some ladder were fleeing from themselves. +344 Banks that can't resemble that drawing have disliked itself. +345 Some boy that is revealing a bicycle was escaping from himself. +346 A waitress that annoyed Joseph insults himself. +347 These ladies that researched that school loved themselves. +348 Most waitresses that climb down every ladder hadn't healed itself. +349 Many snakes that worried Dawn have bored themselves. +350 Every bird that might distract Todd has disgusted himself. +351 Every cup that hasn't bothered Deanna hadn't upset herself. +352 A lot of actresses who exit some public park hadn't hired themselves. +353 Every government that remembers Sheila has talked about herself. +354 Pies that irritate the bank weren't stunning themselves. +355 That library that knows Jennifer helped herself. +356 Some plate that worried those companies impressed themselves. +357 These waiters that flee from Teresa can visit themselves. +358 The cashiers that were breaking some couch would argue about themselves. +359 That actress that shouldn't praise Homer hates herself. +360 The shirts that astound Lisa aggravated herself. +361 This actress that returns to Bradley kisses herself. +362 Those children that were descending the slope didn't think about themselves. +363 The man who wasn't driving to a lot of mountains has escaped from himself. +364 Most patients who notice Amy have complained about herself. +365 All waiters who aren't scaring Amy haven't referenced themselves. +366 Many universities that irritated Barbara will describe themselves. +367 Some dancers that bike to this cafe were noticing themselves. +368 That slope that had disturbed James can't scare himself. +369 A man who hasn't annoyed Catherine complains about himself. +370 Most hospitals that respect the lake couldn't discuss itself. +371 Those adults who impresses Colleen will admire themselves. +372 Some guy who won't explore some rivers listens to himself. +373 Every association that hadn't disturbed Candice boasts about itself. +374 The girl who would hate Stephen would notice herself. +375 Most hospitals that distracted Sharon listened to herself. +376 A lady who was boycotting a lot of cafes references herself. +377 All people who confused Kendra had talked about themselves. +378 These carts that haven't confused Daniel have stunned himself. +379 Some company that can listen to Robert couldn't appreciate himself. +380 Ladies that had lifted every shoe had disliked itself. +381 Every boy who had shocked Deanna observes himself. +382 The documentary that irritates Eva sounded like herself. +383 That girl who climbed down many stairs visits herself. +384 A man who sounds like Pamela wasn't hiring herself. +385 Ladies that skated around a glacier did see themselves. +386 Cashiers who were noticing Teresa escaped from themselves. +387 All cups that don't alarm Karla did upset herself. +388 This boy that ascends the slope can care for himself. +389 The newspaper article that embarrassed Connie isn't disagreeing with herself. +390 Every university that annoys Lawrence did remember himself. +391 This committee that shouldn't fire the guys had discussed themselves. +392 That waitress that sells this cafe boasts about herself. +393 That truck that didn't irritate Christopher hurt himself. +394 A lot of doctors who embarrasses some boy had seen himself. +395 A legislature that is hurting Jason hasn't hired himself. +396 A lot of universities that bothered Holly weren't upsetting herself. +397 A girl that notices some movie theaters doesn't know herself. +398 That boy who complains about stories leaves himself. +399 The actors that weren't climbing up every slope hired themselves. +400 Most people who are criticizing Jason weren't kissing themselves. +401 Children who will drop by this mountain liked themselves. +402 Some teachers that hated Noah have cured himself. +403 Some girl who hadn't fled from Thomas praises herself. +404 This lady who has scanned all plays is approaching herself. +405 Most senators who do climb down every hill hadn't described themselves. +406 A lot of projectors that impressed Joel weren't confusing themselves. +407 A lot of senators that could aggravate Theodore have fled from themselves. +408 Those 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Melissa explained that she drives to college campuses. +1577 Rhonda hadn't explained that she talks about most commentaries. +1578 Richard thinks that he isn't appreciating Samantha. +1579 Richard hadn't thought that he disturbs the teacher. +1580 Sara imagines that she would descend many mountains. +1581 Julie was imagining that she left every customer. +1582 Sharon has explained that she messed up a skirt. +1583 Lori hadn't explained that she runs around every college campus. +1584 Jeffrey hasn't explained that he healed Gerald. +1585 Elizabeth imagined that she returned to every hospital. +1586 Janet wasn't explaining that she does annoy those companies. +1587 Judy thinks that she reveals Richard. +1588 Those women had explained that they did break every car. +1589 Marla hasn't said that she had listened to Robert. +1590 Maria explained that she can drive to Harvard University. +1591 Some woman thought that she doesn't escape from Stephen. +1592 Maria wasn't thinking that she lifts 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Teresa did say that she can't conceal Nancy. +1856 All actors explain that they healed Nina. +1857 Tiffany said that she wasn't investigating Matt. +1858 Waiters will think that they were breaking some mirrors. +1859 Many senators hadn't said that they had dropped by Spain. +1860 Rebecca says that she researched art. +1861 Many women didn't imagine that they haven't described Bethany. +1862 Most drivers were imagining that they wouldn't forget every cashier. +1863 All patients will explain that they were returning to Florence. +1864 Debra thinks that she sold that slope. +1865 Janice says that she references this girl. +1866 Frank thought that he does cure Julie. +1867 Brad imagined that he returned to Tara. +1868 Kimberley imagines that she would remember George. +1869 Claire explained that she hasn't healed all men. +1870 Kayla explains that she isn't buying that beef. +1871 Raymond is imagining that he isn't listening to Mitchell. +1872 Beverly wouldn't say that she examines these 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Margaret was thinking about herself buying those dresses. +2809 Kevin had imagined himself confusing Ella. +2810 Anna hadn't thought about herself revealing this actress. +2811 A lot of actors think about themselves alarming Leslie. +2812 Richard hasn't forgotten about himself exiting most schools. +2813 Alan won't imagine himself worrying those doctors. +2814 Noah hadn't thought about himself approaching Kirsten. +2815 Melinda can forget about herself escaping from Paula. +2816 Melissa thought about herself observing most actors. +2817 The Lutherans aren't thinking about themselves working with a cashier. +2818 Ella hadn't thought about herself observing the senator. +2819 Carl forgets about himself breaking some car. +2820 Some women think about themselves talking to Gerald. +2821 Beverly forgot about herself worrying Jacqueline. +2822 Craig forgets about himself returning to Daniel. +2823 Margaret isn't thinking about herself hiding those hospitals. +2824 Ella thought about herself skated around many glaciers. +2825 Dana won't forget about herself biking to the lake. +2826 Ronald will forget about himself going to Spain. +2827 Mitchell has thought about himself scans a story. +2828 Theresa didn't think about herself fires Kenneth. +2829 Alice thinks about herself fires Deanna. +2830 Gary was imagining himself praising the boy. +2831 Melinda was thinking about herself breaking these bikes. +2832 Christine should think about herself looking like many pictures. +2833 Tammy thinks about herself finding those paintings. +2834 Sara isn't thinking about herself embarrassing an association. +2835 That bank thought about itself looked like this photograph. +2836 The people forget about themselves revealing every man. +2837 The Lutherans forget about themselves passing Spain. +2838 Rose hadn't thought about herself arriving at the hill. +2839 Paul thinks about himself cleaning many art galleries. +2840 Alice imagined herself confusing the student. +2841 A woman imagines 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drivers. +2858 Barbara is imagining herself thinking about the customers. +2859 All customers forgot about themselves concealing that essay. +2860 Aaron imagines himself listening to Brian. +2861 This university has thought about itself talking about Candice. +2862 Marla wasn't thinking about herself confused Nicole. +2863 Sabrina hasn't thought about herself exploring the river. +2864 Stacey imagines herself helping boys. +2865 William did imagine himself complaining about Andrea. +2866 Dan imagines himself observing Bruce. +2867 Barbara thinks about herself embarrassed Omar. +2868 Phillip thinks about himself healing Amanda. +2869 The Impressionists think about themselves aggravating Anne. +2870 Many guests imagine themselves questioning the men. +2871 Anna had thought about herself working with Angela. +2872 Christine thinks about herself attacking Alicia. +2873 Sabrina thought about herself messing up schools. +2874 Some man had thought about himself watching a pedestrian. +2875 Renee hadn't thought about herself watching Scott. +2876 Kayla might forget about herself breaking all bicycles. +2877 Some cashiers think about themselves astounding Veronica. +2878 Stacey forgets about herself visiting Julie. +2879 Many people think about themselves bringing a lot of bananas. +2880 Carla had explained that Samuel has discussed her. +2881 Alice hadn't said that some people weren't escaping from her. +2882 Carl imagines that Maria does leave him. +2883 Those waiters think that Robert flees from them. +2884 Florence did think that Walter stunned her. +2885 Carlos said that Lori helped him. +2886 All people think that Tamara investigated them. +2887 A lot of people imagined that Sabrina had aggravated them. +2888 Emily is thinking that Samuel will love her. +2889 A lot of children say that Allison hadn't approached them. +2890 Paul wasn't thinking that Marie had distracted him. +2891 A lot of actors imagined that Kenneth loves them. +2892 A waitress hasn't thought that 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Carlos wasn't saying that these pedestrians sound like himself. +2963 Lori was imagining that a hospital likes her. +2964 Those teenagers will think that Donald fled from them. +2965 Derek can explain that the lady couldn't discover him. +2966 Vanessa is thinking that John couldn't bore her. +2967 Susan thought that the unicycle isn't impressing her. +2968 Melinda does imagine that these candles bothered her. +2969 The teenagers think that Vincent is arguing about them. +2970 Christopher said that Galileo cured him. +2971 Nicole explained that Frank hasn't returned to her. +2972 Stacy says that Brett discovered herself. +2973 These men say that every skateboard won't shock them. +2974 These drivers think that some public park embarrassed them. +2975 Beverly imagined that some organization hires herself. +2976 Gina explains that some newspaper article has sounded like her. +2977 Many customers explain that Marie boasts about them. +2978 Christina thinks that Alan would remember her. 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her. +2998 Peter will think that Stacy criticized him. +2999 These people explain that Michael isn't discovering them. +3000 All dancers imagine that Paula would return to them. +3001 Kirsten is saying that Bradley cares for her. +3002 Some guys haven't thought that Ronald is criticizing them. +3003 Erin imagines that Robert appreciated her. +3004 William hasn't explained that these glaciers disturbed him. +3005 Rose is imagining that Timothy has forgotten her. +3006 Raymond explained that Laura referenced him. +3007 Derek has thought that Mary praised him. +3008 The Lutherans think that Winston Churchill can notice them. +3009 Craig wasn't explaining that Sarah attacks him. +3010 Jason can't explain that Elizabeth wouldn't dislike him. +3011 Michael thought that the Impressionists would discuss himself. +3012 Naomi doesn't imagine that that grocery store had irritated her. +3013 Karla had said that Edward hasn't impressed her. +3014 Some senators had said that this dog was finding them. +3015 Guys could say that Anne fires them. +3016 Sarah is saying that these men aren't concealing herself. +3017 Nicole will imagine that that committee admires herself. +3018 Denise hasn't imagined that Gregory could talk about her. +3019 A lot of teachers explain that Jennifer would discover themselves. +3020 Susan wasn't saying that that organization criticized her. +3021 Rachel has said that Jason hasn't investigated herself. +3022 Some guys explain that Timothy leaves them. +3023 That girl can't say that Jesus hires herself. +3024 The guys think that Janet talked to them. +3025 Erin has said that pedestrians aren't noticing her. +3026 Alexander had imagined that some bank had respected him. +3027 Jacqueline thinks that Craig escapes from her. +3028 Many actresses would say that this play disagreed with them. +3029 Sheila explained that Walter concealed her. +3030 Jacqueline doesn't say that this commentary disturbs her. +3031 These patients haven't imagined that some skirt 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that Jason forgets her. +3067 That boy hadn't imagined that Lucille can conceal himself. +3068 Donna wasn't explaining that Jerry isn't noticing her. +3069 Sabrina hasn't imagined that every ladder will bore her. +3070 Becca has said that Monet referenced her. +3071 Vincent hasn't explained that all eyes weren't bothering him. +3072 Linda won't explain that Bill watched her. +3073 These waitresses might think that Ellen hadn't hugged them. +3074 A waitress says that Bradley hasn't impressed herself. +3075 Timothy thinks that some children disagree with him. +3076 Amy wasn't saying that that boy noticed her. +3077 Becca can imagine that John hasn't watched her. +3078 Robert imagined that most patients have returned to him. +3079 Lawrence will imagine that Gina was thinking about him. +3080 Many children wouldn't say that Tara wasn't escaping from them. +3081 Chad thought that Erin attacks him. +3082 Catherine imagined that Brad should hire her. +3083 A woman says that some organizations 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hires herself. +3119 The Borgias imagine that that actress can't escape from them. +3120 Craig didn't explain that Tracy forgets him. +3121 Veronica had imagined that Eric concealed her. +3122 Martha has imagined that Alexander watches her. +3123 Customers think that Charles couldn't heal them. +3124 Jerry was explaining that Natalie wasn't appreciating him. +3125 These men say that Gina wasn't concealing them. +3126 George hasn't explained that Valerie can't criticize himself. +3127 Rhonda would say that every boy is arguing about her. +3128 Wayne was explaining that Janet has hired himself. +3129 Travis said that Christine remembers him. +3130 Steve explained that Suzanne has approached him. +3131 Ella imagines that Aaron could fire her. +3132 Jacqueline isn't explaining that Donald investigated herself. +3133 Most senators thought that every plane did hurt them. +3134 A lot of doctors think that Wayne can discuss them. +3135 Some actress thought that Omar hasn't hired herself. +3136 Janet didn't imagine that those actors loved her. +3137 Derek didn't imagine that Heidi is attacking him. +3138 Ronald is thinking that that organization was praising himself. +3139 Stacey explains that that organization discusses her. +3140 Caroline imagines that Andrew helps her. +3141 Catherine thought that Todd can watch herself. +3142 Samuel imagined that this waitress has hated him. +3143 Suzanne hadn't imagined that that university hired herself. +3144 Dawn explained that Brian talks to her. +3145 Allison had said that some cucumber was confusing her. +3146 Many senators think that Sally revealed them. +3147 Carolyn did think that Noah could escape from her. +3148 James does think that many teachers hated him. +3149 Scott thinks that Barbara had returned to him. +3150 Matt would imagine that Heidi hasn't known himself. +3151 Eric imagined that some pedestrians cured him. +3152 Tina thought that some pedestrians do disagree with her. +3153 Raymond wasn't explaining that Tamara 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Kirsten doesn't hurt himself. +3172 Ruth couldn't think that the doctors haven't attacked her. +3173 Aaron hadn't imagined that the skirts aggravated him. +3174 Brian does explain that Rachel dislikes him. +3175 Becky thinks that Dan wouldn't visit her. +3176 Lori hasn't explained that a face upset her. +3177 Catherine imagines that that pork can stun her. +3178 Andrea thought that every man disagrees with her. +3179 John does imagine that the Borgias can't upset him. +3180 Edward says that all teenagers have fled from him. +3181 Craig hasn't explained that Michelle forgot him. +3182 Daniel hasn't imagined that a casserole isn't shocking himself. +3183 Holly should explain that John isn't insulting her. +3184 All patients don't explain that some water has aggravated them. +3185 Brian had thought that Samantha likes him. +3186 Stacy didn't say that Phillip shouldn't alarm her. +3187 Bruce isn't thinking that this government hasn't upset himself. +3188 Kristen should think that some 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Timothy said that Claire attacked him. +3207 Tonya wasn't thinking that Carlos returns to her. +3208 Some teachers thought that Edward didn't shock them. +3209 Judy hasn't thought that the Impressionists aren't curing her. +3210 Robert is imagining that Carol has attacked him. +3211 Mitchell can't explain that all museums listened to him. +3212 Judy hadn't imagined that Paul is insulting herself. +3213 Susan did say that many pedestrians were discussing her. +3214 Teresa had thought that Jesus won't cure her. +3215 Carolyn is saying that a lot of guys hid her. +3216 Barbara said that many mirrors alarmed her. +3217 Janet hadn't thought that Guy is appreciating her. +3218 Sarah isn't saying that Winston Churchill was firing herself. +3219 The lady didn't think that Benjamin had talked about her. +3220 Those dancers said that George wasn't observing them. +3221 A man says that Ellen notices him. +3222 Kirsten doesn't say that Frank hid her. +3223 Sharon imagines that Matt isn't 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that Alan was revealing herself. +3241 Rose could explain that Paul concealed her. +3242 Cindy had explained that Harvard University praised her. +3243 Scott would explain that Mary wasn't praising him. +3244 Ronald won't say that these guys watched him. +3245 Noah says that Diana annoyed him. +3246 Raymond won't imagine that Denise was attacking him. +3247 Phillip hasn't thought that most boys were disagreeing with him. +3248 Sarah thinks that Walter questions her. +3249 Rodney won't imagine that Deborah visited him. +3250 William hasn't imagined that Elaine left him. +3251 Benjamin says that most grocery stores aggravated himself. +3252 Stephanie will think that all bananas will stun her. +3253 Holly explains that Chad remembers her. +3254 Steve had explained that Catherine should respect him. +3255 Gerald thinks that a glass wasn't scaring him. +3256 Some pedestrians imagine that Suzanne hasn't discovered them. +3257 Christopher isn't thinking that Bethany has respected him. +3258 Gregory is explaining that some association remembers him. +3259 Those cashiers imagine that Meredith hadn't referenced them. +3260 Helen explained that Alexander revealed her. +3261 Larry was saying that Stephanie describes him. +3262 Christopher won't imagine that the pants shocked him. +3263 Bruce couldn't explain that Carol respects him. +3264 Tiffany thinks that Daniel discovers her. +3265 The Impressionists imagine that Alice bothered them. +3266 Martha imagined that Steven hated her. +3267 Vincent explained that Sabrina was embarrassing himself. +3268 Pamela explained that Jerry had respected her. +3269 Maria explained that those guys won't respect her. +3270 Travis said that Renee might know him. +3271 Mary wasn't saying that Gregory disturbs her. +3272 Charles thinks that Gina fired him. +3273 Sally thought that the guy criticized her. +3274 Rodney did say that Wendy did annoy him. +3275 Patrick isn't thinking that Anna kisses him. +3276 Nancy hasn't thought that Homer wasn't questioning her. +3277 Russell thinks that Jill could complain about him. +3278 Janet imagined that Curtis cures her. +3279 David imagines that politics hasn't stunned him. +3280 Anne explained that Douglas attacked her. +3281 Craig says that Eva would worry himself. +3282 Dancers will say that Nicole wasn't approaching them. +3283 Travis hasn't said that some museum wouldn't confuse him. +3284 The Impressionists won't imagine that Amelia won't like them. +3285 Carmen is imagining that that committee remembers her. +3286 Carlos isn't imagining that Heidi was healing himself. +3287 The actors imagine that Craig confused them. +3288 Caroline says that Russell wasn't distracting her. +3289 All teachers imagine that Heather approached them. +3290 Rebecca imagines that Samuel embarrassed her. +3291 Joel says that Tina wasn't hugging him. +3292 Donna has thought that Douglas will care for her. +3293 Naomi explained that Steven scared her. +3294 Todd said that Anne talks about him. +3295 Dawn shouldn't think that gates have upset her. +3296 The Borgias explained that Cynthia sounds like them. +3297 Liam explains that Sharon cared for him. +3298 The Lutherans imagine that Tanya finds them. +3299 Omar said that Sara cared for him. +3300 Cindy has thought that Martin shouldn't notice her. +3301 Rose had explained that Homer admires her. +3302 Laurie can't explain that that turtle shocked her. +3303 Those girls imagine that Donna had found them. +3304 A lot of men imagined that Thomas couldn't escape from them. +3305 Natalie said that Aaron hadn't talked to her. +3306 Becca says that Derek is curing her. +3307 Danielle thinks that Christopher had hugged her. +3308 Julia had imagined that that hill shouldn't annoy her. +3309 The teenagers think that this guy disgusted them. +3310 Tamara is explaining that the hat doesn't astound her. +3311 Jessica was saying that Scott was praising her. +3312 Dawn is saying that many movies were disturbing her. +3313 Many teenagers say that 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thinks that Tara should leave him. +3349 Mark could explain that Becky leaves him. +3350 Janet was imagining that these patients wouldn't visit her. +3351 Christina doesn't think that Mark has attacked her. +3352 Kenneth wouldn't say that Lucille liked him. +3353 John says that Jill had disliked him. +3354 Craig hasn't said that Margaret wasn't observing him. +3355 Anna has thought that many movie theaters weren't shocking herself. +3356 All pedestrians explain that April would return to them. +3357 Julie thought that Andrew worried her. +3358 Craig imagines that this sock scares him. +3359 Kendra would think that Michael appreciates her. +3360 Tiffany can say that Raymond should see her. +3361 Connie will imagine that a public park does hurt her. +3362 Edward isn't explaining that Kayla wasn't visiting him. +3363 Anne shouldn't imagine that Christopher criticized her. +3364 Sandra is thinking that glasses might shock her. +3365 Ann hasn't said that these vases irritated her. +3366 Robert explained that Dana will leave him. +3367 Beth was saying that Alan wasn't noticing her. +3368 Janet says that those senators have remembered her. +3369 A lot of actors imagine that Brett was attacking them. +3370 Julie explained that Richard praised her. +3371 Those waitresses had said that Patricia fired them. +3372 These girls say that Jill thought about them. +3373 Most children explain that Roger is kissing them. +3374 Those people imagine that Pamela hasn't hurt them. +3375 Sally says that Curtis talks about her. +3376 All doctors imagine that some movie might upset them. +3377 All children were imagining that Christopher had fired them. +3378 These teenagers think that Kristin hadn't thought about them. +3379 Grace thought that the newspaper articles weren't alarming herself. +3380 Some customers can't imagine that Becca wasn't listening to them. +3381 Some children say that Elizabeth didn't see them. +3382 Most waitresses say that Omar confused them. +3383 Melissa wasn't 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Amy might imagine that those mountains couldn't irritate her. +3454 Some men hadn't said that Valerie investigated them. +3455 Alexander hadn't thought that Christina had loved him. +3456 Walter imagines that Danielle isn't impressing him. +3457 Jacqueline had thought that Derek talks to her. +3458 Deanna couldn't explain that a lot of libraries are boasting about her. +3459 The senators think that Sandra has remembered them. +3460 Samantha says that the Impressionists weren't admiring her. +3461 Bruce wouldn't imagine that these grocery stores are aggravating himself. +3462 Lori isn't thinking that those children can conceal herself. +3463 Steve explained that Caroline can't upset himself. +3464 Jason wasn't explaining that Julia might return to him. +3465 Candice said that Carlos did praise her. +3466 The Lutherans imagine that the convertible is hurting them. +3467 Todd could say that some dancers haven't hidden himself. +3468 All drivers say that Thomas complains about them. +3469 Sherry couldn't say that Paul had fled from her. +3470 Melissa is saying that Galileo wasn't hiring her. +3471 Caroline shouldn't imagine that Alan has upset herself. +3472 Kristin could explain that these muffins have hurt her. +3473 Sabrina has said that Daniel hugged her. +3474 William hadn't imagined that Galileo escapes from him. +3475 Lisa might imagine that Steve thought about her. +3476 Joseph would say that Elaine hasn't concealed him. +3477 Those doctors say that Tracy can't see them. +3478 The woman explained that Wayne had criticized her. +3479 Most doctors were explaining that April has attacked them. +3480 Scott said that Margaret isn't helping him. +3481 Those drivers explain that Gary wouldn't help them. +3482 Some lady has explained that Vincent could help her. +3483 Tanya wasn't saying that this apple hasn't disgusted her. +3484 Sharon hasn't imagined that Curtis complains about her. +3485 Leslie won't explain that Clyde escaped from her. +3486 Kathleen had said that Stephen isn't finding her. +3487 Those actresses had explained that Todd doesn't distract them. +3488 Homer is imagining that Sally does care for him. +3489 Timothy wasn't explaining that Deborah hasn't aggravated himself. +3490 Julia was thinking that Michael leaves her. +3491 That dancer was explaining that Samuel doesn't love her. +3492 Donna was thinking that Douglas shouldn't visit her. +3493 Irene isn't imagining that Joseph notices her. +3494 Helen thinks that Liam has remembered her. +3495 Beth isn't explaining that Scott is describing her. +3496 Chad hasn't said that Andrea hurt him. +3497 Patrick wasn't explaining that Carol hasn't seen him. +3498 Carolyn had imagined that Stephen isn't discovering her. +3499 Claire didn't explain that this legislature disliked her. +3500 Tammy thought that George isn't hiding her. +3501 The people imagine that Tammy has watched them. +3502 Grace would imagine that Richard investigated her. +3503 Larry explains that Sherry cures him. +3504 Most dancers think that Alicia didn't discover them. +3505 The Impressionists had thought that Theresa has investigated themselves. +3506 Dan explained that Florence approached him. +3507 Jill is thinking that Stephen shouldn't admire her. +3508 These ladies hadn't explained that Gregory had praised them. +3509 An actress imagined that Brett hugged her. +3510 Charles hasn't thought that these guys have returned to him. +3511 The Borgias explain that Debra wasn't talking to them. +3512 John has thought that Deborah complained about him. +3513 Teresa can't imagine that a fish didn't embarrass her. +3514 Danielle hadn't imagined that Liam concealed her. +3515 The boys explain that the plane has scared them. +3516 Patients have imagined that Travis shouldn't complain about them. +3517 Angela did explain that Jeffrey thinks about her. +3518 Meredith said that the Borgias respected her. +3519 Carolyn is imagining that Russell likes her. +3520 Stephen imagined that Florence hasn't fled from 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Brian would explain that some girl respected him. +3539 Most girls say that every slope had aggravated them. +3540 A lot of senators explain that Curtis didn't appreciate them. +3541 This girl thinks that Omar can like her. +3542 Daniel was imagining that a lot of steps have disgusted him. +3543 Gary thought that Stacey hated him. +3544 Timothy would explain that Diana forgets him. +3545 Noah thinks that Carrie isn't arguing about him. +3546 These senators imagine that every book worried themselves. +3547 Sabrina explains that most museums criticized her. +3548 All pedestrians did explain that the essay was disagreeing with them. +3549 Melanie wasn't imagining that these drivers disagree with her. +3550 A lot of drivers say that Mitchell described them. +3551 Theresa is thinking that these actors haven't referenced her. +3552 Gerald is imagining that Carrie is appreciating him. +3553 Stephanie explains that a lot of children had referenced her. +3554 Carmen doesn't explain that Wayne is leaving her. +3555 Most patients hadn't thought that Leslie hasn't forgotten them. +3556 Amanda wasn't saying that Walter wasn't annoying her. +3557 Carrie was imagining that all pedestrians didn't talk about her. +3558 Natalie hadn't said that that screen hasn't irritated her. +3559 Todd hadn't thought that the hair isn't impressing him. +3560 Valerie will think that Jesus cared for her. +3561 Many waiters explain that Bradley returns to them. +3562 Many ladies explain that Samantha cared for them. +3563 Some actors think that that skirt impressed them. +3564 Samantha won't explain that the governments do listen to her. +3565 Some drivers think that an actress wasn't describing them. +3566 Victoria imagines that every university has concealed her. +3567 Those senators think that Wendy has talked to them. +3568 All men have said that Tracy respected them. +3569 A lot of teachers say that Jane hasn't irritated them. +3570 Sara hasn't imagined that some movie theater does annoy her. 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boring himself. +3588 Alice had imagined that Theodore will watch her. +3589 The waitress wasn't explaining that Donald isn't approaching her. +3590 Patients say that this truck has scared them. +3591 Many actresses imagine that that glass couldn't astound them. +3592 Mitchell doesn't explain that Denise irritated him. +3593 Melinda thought that Brad hasn't hugged her. +3594 Tara would say that James has disliked her. +3595 Timothy thought that Carmen is hurting him. +3596 Most patients say that Karla was talking about them. +3597 Joel isn't thinking that Veronica hasn't questioned himself. +3598 Girls explain that Vincent sounds like them. +3599 Dana has imagined that guys cured her. +3600 Dan imagined that music would worry himself. +3601 Diana wasn't explaining that Paul referenced her. +3602 A lot of boys say that Rebecca isn't observing them. +3603 Those students say that Tina questions themselves. +3604 These senators say that Thomas escaped from them. +3605 Katherine wasn't thinking that schools hadn't loved her. +3606 Gary hadn't imagined that Sarah is investigating him. +3607 All waitresses imagine that Derek has thought about them. +3608 Cynthia thinks that Frank complained about her. +3609 Margaret said that Carlos fires her. +3610 Andrea was saying that Steve would admire her. +3611 Tara imagined that Richard talked about her. +3612 The girl was saying that some women discussed her. +3613 Rebecca imagines that Robert wasn't boring her. +3614 The girls say that Bill was praising them. +3615 The teachers explained that Samantha couldn't attack themselves. +3616 Cynthia hadn't said that the hills are confusing her. +3617 Some children imagine that that shawl hurt them. +3618 The Borgias say that Patrick forgot them. +3619 Larry had said that Tara watched him. +3620 Sarah says that every company helped her. +3621 Most girls explain that Kevin hasn't disgusted them. +3622 Becca explains that some museums hired herself. +3623 Matt said that all governments are describing him. +3624 Phillip is explaining that Rebecca helped him. +3625 Stephanie imagined that Omar respected her. +3626 Richard can't explain that Lucille has visited him. +3627 Douglas was imagining that Diana is healing himself. +3628 Grace isn't thinking that Frank had noticed her. +3629 Alice could think that Donald was astounding her. +3630 Beth has explained that Kevin wouldn't appreciate her. +3631 Grace shouldn't imagine that Alan has hidden herself. +3632 The Lutherans hadn't said that Guy might embarrass themselves. +3633 Beverly has said that many customers have fled from her. +3634 The teenagers say that Karen isn't curing them. +3635 Teachers would imagine that Carl might hurt them. +3636 Stacy has explained that Aaron is escaping from her. +3637 The Lutherans think that Charles could worry themselves. +3638 Phillip couldn't explain that Denise insults himself. +3639 Gregory hadn't imagined that Ruth describes himself. +3640 Most teenagers weren't explaining that Benjamin sees them. +3641 Wayne couldn't think that Barbara admires himself. +3642 Margaret said that many actresses hadn't worried her. +3643 Gregory had imagined that Natalie loves him. +3644 Derek hasn't explained that Andrea can't escape from him. +3645 Benjamin hadn't explained that Stephanie remembers him. +3646 Brett wasn't imagining that Deanna has fired him. +3647 A lot of girls had thought that Brad had astounded them. +3648 Anna might think that Dennis wasn't working with her. +3649 Many people would say that Sonia should find them. +3650 Daniel should explain that this legislature alarmed him. +3651 Patricia thought that Dan irritated her. +3652 Veronica had thought that Carl worried her. +3653 Bruce says that Suzanne observed him. +3654 Barbara explained that Omar hadn't talked about her. +3655 Meredith hasn't said that museums loved her. +3656 The students thought that Kristen observed themselves. +3657 Aaron did imagine that Marie visits him. +3658 Sabrina hadn't thought that Richard should know her. +3659 Carl had thought that a lot of women weren't investigating him. +3660 Marcus has thought that a cucumber won't shock him. +3661 Stephanie hadn't explained that Travis returns to her. +3662 Jane explains that every museum knew her. +3663 Curtis has imagined that Carrie was questioning him. +3664 Connie thinks that Timothy didn't discover her. +3665 Homer will say that that government won't praise him. +3666 Brenda wouldn't say that Christopher leaves her. +3667 Many people aren't imagining that the woman wasn't returning to them. +3668 The adults don't think that Ruth can't listen to them. +3669 Ellen could imagine that Monet didn't disgust her. +3670 Gary thought that many guys haven't found him. +3671 Anna hasn't said that a lot of cashiers hadn't boasted about her. +3672 Craig thought that Tammy escaped from him. +3673 The senators say that Brian sees them. +3674 Lisa thinks that the companies helped her. +3675 All waiters say that Rose was insulting themselves. +3676 Renee isn't imagining that Scott shouldn't hide her. +3677 Many actors say that Sherry can worry them. +3678 A lot of girls say that Alexander was observing them. +3679 Linda could say that Ronald heals herself. +3680 These men imagine that Samantha stunned them. +3681 Brenda couldn't say that Phillip hadn't scared her. +3682 Michelle can't say that that plate hadn't bothered her. +3683 Some pedestrians think that Kathleen hasn't hidden them. +3684 Russell imagined that the cup had shocked him. +3685 Andrew hasn't explained that Janet has attacked him. +3686 Omar hasn't thought that these girls would visit him. +3687 Andrew hasn't thought that a dancer returned to him. +3688 Beth imagines that Gregory couldn't see her. +3689 Peter thought that Kayla boasts about him. +3690 The dancers imagine that Nicole isn't thinking about them. +3691 A lot of girls thought that every restaurant shouldn't hurt them. +3692 The Lutherans haven't imagined that Noah didn't hide them. +3693 Many boys imagine that that movie might stun them. +3694 A boy is imagining that Eva confused him. +3695 Danielle is explaining that Mitchell does watch her. +3696 These guests explain that Timothy approached them. +3697 Joel had thought that the cilantro is upsetting himself. +3698 Tanya was imagining that Mitchell hugs herself. +3699 Some people think that Mary wasn't concealing them. +3700 Becca imagines that some senators weren't hiring her. +3701 Clyde doesn't explain that Catherine won't annoy him. +3702 Tanya says that Omar is observing her. +3703 Melissa says that Patrick leaves her. +3704 Heidi was imagining that Travis wasn't referencing her. +3705 Customers think that Bruce healed them. +3706 Lawrence was thinking that Ella discovered him. +3707 Some girls explain that David wasn't shocking themselves. +3708 Tamara wouldn't imagine that these children have fled from her. +3709 Sandra doesn't think that Carl distracted her. +3710 Dennis wasn't saying that Vanessa isn't returning to him. +3711 Stacy imagined that Russell had escaped from her. +3712 Tiffany thinks that Joseph references her. +3713 Homer imagined that Tina returns to him. +3714 Patricia did say that Raymond is referencing her. +3715 Rodney thought that Rose is upsetting him. +3716 Brett imagines that Ruth hadn't loved him. +3717 A lot of patients say that Liam alarmed them. +3718 Douglas thinks that that mouth could astound him. +3719 Jennifer didn't think that some screens weren't irritating her. +3720 Some girls explain that Caroline left them. +3721 Deborah imagined that every spotlight scares her. +3722 Cindy hadn't explained that Steven helps her. +3723 Andrea isn't thinking that every shirt astounded her. +3724 Renee is imagining that Samuel hasn't discovered herself. +3725 Julia won't say that this bird had bothered her. +3726 Rose isn't thinking that Theodore did visit her. +3727 These waiters explain that Derek hadn't annoyed them. +3728 Walter imagines that Laurie can bother him. +3729 Every man isn't explaining that Stacy reveals himself. +3730 Jessica might say that most girls have seen her. +3731 Some lady thought that Samuel attacks her. +3732 Wendy has said that Alan thought about her. +3733 Some lady is saying that some museums did stun her. +3734 April thought that Lawrence is scaring her. +3735 Patrick hasn't said that the woman sounds like him. +3736 Emily thinks that Clyde will hire her. +3737 Kathleen explains that Steven won't observe her. +3738 Ann wasn't thinking that this cat isn't stunning herself. +3739 Diana thinks that Peter discusses her. +3740 All adults explain that David doesn't hurt them. +3741 The Impressionists explain that Rodney hid them. +3742 Rachel was thinking that these men couldn't disturb her. +3743 Richard imagines that Jacqueline is concealing him. +3744 The patients imagine that Janet has discussed them. +3745 Dawn wouldn't say that Mitchell has disturbed her. +3746 Meredith isn't imagining that Bill hasn't watched her. +3747 Diana doesn't explain that the museums can discuss her. +3748 Joel says that Beth helps him. +3749 George might think that Sharon hasn't observed him. +3750 Ella was imagining that Stephen wouldn't stun her. +3751 Jodi hasn't thought that Martin aggravated her. +3752 Irene imagines that a lot of schools can annoy her. +3753 Alicia has thought that Edward hides herself. +3754 Eva isn't imagining that Alexander could annoy her. +3755 Emily wouldn't think that Aaron concealed her. +3756 Stephanie wasn't imagining that the river doesn't aggravate herself. +3757 Ann has explained that this library can't aggravate herself. +3758 These boys imagine that Mitchell didn't cure them. +3759 The waitress imagines that Noah praised her. +3760 Many pedestrians imagine that Todd talks to them. +3761 Kristen thought that that sock hadn't shocked her. +3762 Colleen said that candles aren't astounding herself. +3763 Some people aren't thinking that Tammy hadn't cared for them. +3764 Martin explained that Kimberley worried himself. +3765 Robert thought that Meredith likes him. +3766 Caroline can think that Charles was revealing herself. +3767 Cheryl is thinking that Jerry thinks about her. +3768 James hadn't explained that Julie is helping him. +3769 Becca hasn't said that Galileo cares for herself. +3770 Cashiers will say that Stacy talks about them. +3771 Erin can't explain that Russell could conceal herself. +3772 Every dancer has imagined that some boy has complained about her. +3773 A lot of ladies explain that Sherry investigated them. +3774 Kendra explains that Gary fires herself. +3775 Many women imagine that Tara was curing themselves. +3776 Suzanne isn't explaining that Edward would boast about her. +3777 Most doctors do think that Rodney hugs them. +3778 Dan is explaining that April had noticed him. +3779 Kathleen says that all banks hadn't upset her. +3780 Ruth said that Curtis wouldn't fire her. +3781 Cashiers explain that Emily will reference them. +3782 Jill said that Jason is approaching her. +3783 Suzanne said that all patients praised her. +3784 Larry imagines that Sandra observes him. +3785 Cheryl shouldn't say that some dancers wouldn't notice her. +3786 Teresa thinks that Homer isn't watching her. +3787 Marla explained that the customers were appreciating her. +3788 The teenagers explain that Tammy approaches them. +3789 Gary hadn't thought that Kendra was attacking him. +3790 The patients imagine that Kayla hadn't questioned them. +3791 Janet said that Chad references her. +3792 The Impressionists say that Kristen upset them. +3793 Helen would think that these windows will worry her. +3794 Kevin isn't thinking that Tonya works with him. +3795 Raymond would say that Claire wouldn't embarrass him. +3796 Theodore wasn't imagining that Gina isn't insulting himself. +3797 Holly was explaining that Adam isn't concealing her. +3798 Leslie could say that the cafes aren't shocking herself. +3799 Many teenagers say that Emily couldn't sound like them. +3800 Tracy isn't imagining that Clyde wasn't hugging her. +3801 Stephen would imagine that Sabrina might find him. +3802 Carla explained that many bananas are confusing herself. +3803 Ronald said that Alicia alarmed him. +3804 These students think that Russell can distract them. +3805 Clyde imagined that Heather had concealed him. +3806 Some dancers explain that Melissa was admiring themselves. +3807 Craig explains that Melinda returns to him. +3808 Ladies say that Noah hadn't worried them. +3809 Irene says that Galileo talks to her. +3810 Many actors say that Sarah hasn't impressed them. +3811 Kenneth hadn't said that Irene worked with him. +3812 Karen thought that every man describes herself. +3813 Theresa says that the women were talking to her. +3814 Samantha explained that most rivers stunned her. +3815 A lot of senators thought that Margaret was working with them. +3816 Sheila hasn't explained that Jerry praises her. +3817 The cashiers had said that a company isn't praising them. +3818 Bill thought that Cheryl escapes from him. +3819 Joel hadn't explained that many newspaper articles hadn't sounded like him. +3820 Many waitresses say that Travis cares for themselves. +3821 Joel thought that those windows had hurt him. +3822 Theodore thought that Carla boasts about him. +3823 Eva shouldn't think that some face has disgusted her. +3824 Theresa explained that Monet is talking about herself. +3825 Frank explains that Valerie cured him. +3826 Theresa hadn't imagined that Monet knew her. +3827 Vincent thought that Samantha hated him. +3828 Julia was imagining that a lot of doctors wouldn't conceal herself. +3829 This boy was saying that Allison dislikes him. +3830 Irene explained that Vincent can investigate her. +3831 Linda says that Craig doesn't discuss herself. +3832 Frank wasn't saying that Deanna was questioning himself. +3833 Teresa explains that Carlos helped her. +3834 Jeffrey explained that Patricia isn't distracting him. +3835 Connie hadn't said that Patrick criticized her. +3836 Stacey had said that a report is disturbing her. +3837 Paula imagined that Noah cured her. +3838 Donald can imagine those college campuses are boring themselves. +3839 Gerald was explaining Patricia was revealing himself. +3840 Mark imagines Erin might admire himself. +3841 Gary imagined most reports upset himself. +3842 A lot of doctors hadn't imagined Denise notices herself. +3843 Edward didn't think some teachers weren't discussing themselves. +3844 The Borgias explain Scott had talked to himself. +3845 Valerie doesn't think Russell sounds like himself. +3846 Craig hasn't thought Jessica praised himself. +3847 Holly would say some slope didn't irritate herself. +3848 Sally wouldn't explain Rodney confused herself. +3849 The dancer said Jeffrey doesn't find herself. +3850 Beverly had said Homer was concealing himself. +3851 The Impressionists think that window distracted itself. +3852 Those waitresses say Curtis hadn't alarmed himself. +3853 Guy does explain Judy argued about herself. +3854 James says Kayla helped himself. +3855 Joel can't explain some girl can't visit herself. +3856 Victoria thinks Harvard University isn't firing herself. +3857 Alice has said Craig did upset herself. +3858 Allison has imagined Christopher had concealed himself. +3859 Jerry said the rabbit had annoyed himself. +3860 Rodney is saying Renee hadn't fled from herself. +3861 Many patients aren't saying Roger kisses themselves. +3862 Rachel explains Matt thinks about herself. +3863 The children weren't imagining Anna knows themselves. +3864 Vincent would say many patients can research himself. +3865 Karla was saying the Impressionists were leaving themselves. +3866 Diana wasn't explaining Lawrence wasn't curing herself. +3867 Beth isn't imagining Dennis won't listen to herself. +3868 Every actress imagines Raymond shouldn't care for herself. +3869 Michelle was thinking Joel was healing himself. +3870 Caroline has explained Stephen is working with herself. +3871 Nancy imagines this book hasn't impressed itself. +3872 Aaron thinks Anna upset himself. +3873 Those students have explained Valerie hides themselves. +3874 Erin says Scott is kissing herself. +3875 Rebecca thought Rodney will praise herself. +3876 Teresa won't think Timothy bored himself. +3877 Some dancers explain Sonia didn't distract themselves. +3878 Many teenagers explain Valerie reveals herself. +3879 John has said April loved himself. +3880 Adults think Clyde approaches himself. +3881 Susan won't say that organization admired herself. +3882 Many children explain Derek should work with himself. +3883 Mark explains Pamela should forget herself. +3884 Clyde said Caroline shouldn't argue about herself. +3885 Meredith was explaining Steven returns to himself. +3886 Maria thinks many shoes shocked themselves. +3887 Those teachers think Walter remembers himself. +3888 Some men say Alexander praises themselves. +3889 Dana says Carl works with himself. 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+3978 All actors say Kirsten has disliked themselves. +3979 Heather imagines Walter had watched herself. +3980 Stephen thinks Irene was boycotting herself. +3981 Steve has said Deborah cares for himself. +3982 Mark thought Kendra had talked to himself. +3983 Dennis wasn't imagining Tamara investigated himself. +3984 All waitresses explain Vanessa hadn't found herself. +3985 Customers can't explain Rose is watching herself. +3986 Alan hadn't imagined Diana sees herself. +3987 Alexander might imagine Vanessa has helped himself. +3988 Rhonda couldn't imagine Wayne worried himself. +3989 Sarah has imagined Gerald should listen to herself. +3990 Kristen didn't say Theodore bothered himself. +3991 James wasn't imagining Sharon hasn't respected himself. +3992 Alexander hadn't thought Dawn hadn't talked about himself. +3993 A waitress hadn't thought Lawrence hides herself. +3994 Travis can't explain that lady revealed herself. +3995 Tanya is explaining Phillip remembered himself. +3996 Sabrina explained all shirts weren't irritating themselves. +3997 Ellen hasn't said Donald hasn't astounded himself. +3998 Colleen thinks Martin cared for himself. +3999 The waitresses say Tina had shocked herself. +4000 Curtis should think those projectors could aggravate himself. +4001 The Borgias say Jessica talks about herself. +4002 Sandra is thinking this guy was concealing himself. +4003 Rebecca wasn't imagining the school won't admire itself. +4004 All actresses think Teresa hires herself. +4005 Michelle thinks Joel respects himself. +4006 Roger is explaining Gina could think about himself. +4007 Holly had imagined the museum can't talk to herself. +4008 Jodi imagines those actors boasted about themselves. +4009 Sandra is thinking Matt notices himself. +4010 Valerie imagines Homer is helping herself. +4011 Kirsten explains these cafes hurt themselves. +4012 Brett thinks Barbara can see herself. +4013 Sonia isn't imagining Charles hadn't hated himself. +4014 Gerald was thinking photographs aren't hurting themselves. +4015 Some lady imagines Walter loves herself. +4016 Barbara thought Travis was visiting herself. +4017 Kirsten is thinking the governments argued about themselves. +4018 James said Danielle had praised himself. +4019 Lori thought Derek doesn't investigate himself. +4020 Heather is explaining Liam hires himself. +4021 Some waiters think Naomi wasn't aggravating herself. +4022 Dennis would say Brenda insults himself. +4023 Many women imagine Rebecca wasn't criticizing herself. +4024 Actresses say Raymond has escaped from himself. +4025 Children think Victoria can't talk about herself. +4026 Rachel said Noah didn't observe himself. +4027 Travis hasn't said Emily appreciates herself. +4028 Kayla hadn't explained Peter hadn't argued about himself. +4029 Gary isn't explaining Deanna respected himself. +4030 Bruce hadn't thought Kendra sounded like himself. +4031 Angela was imagining some mouths did disgust themselves. +4032 Janet explains some pasta 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herself. +4050 Many guys say Alicia isn't noticing herself. +4051 Kenneth was thinking Teresa should insult herself. +4052 These girls imagine Carl won't research themselves. +4053 Most actresses say Keith will hire himself. +4054 Bruce can think Jane has loved himself. +4055 Amelia said Homer couldn't help himself. +4056 James imagines banks haven't described himself. +4057 Mark imagines Becca had revealed himself. +4058 Most ladies say Nicole won't admire themselves. +4059 Judy says the skateboards should aggravate herself. +4060 Emily won't explain Andrew can boast about herself. +4061 Laura had said Omar appreciated herself. +4062 Some men don't explain Pamela is firing themselves. +4063 Kayla said Gary could forget himself. +4064 George hasn't explained the woman hasn't cared for herself. +4065 Colleen imagined Aaron helped himself. +4066 Jessica imagines Vincent investigated herself. +4067 Debra had said Kenneth criticized himself. +4068 Barbara isn't explaining Carl hasn't 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disagreed with themselves. +4088 Liam was explaining these companies haven't forgotten himself. +4089 Eric thought Deborah can respect himself. +4090 Clyde has explained these actresses hadn't impressed themselves. +4091 Omar says Marla kissed herself. +4092 Tina did think Liam might hide herself. +4093 Lucille had imagined Keith was helping himself. +4094 Erin is explaining Craig should scare himself. +4095 Alicia might say that mall worried herself. +4096 Kimberley thought Rodney researched himself. +4097 John had said Monica has boasted about himself. +4098 Scott hadn't imagined Karla hasn't attacked himself. +4099 Elaine was imagining some government appreciates herself. +4100 Curtis imagined Brenda talked about himself. +4101 Stephanie would think this legislature could hire herself. +4102 Craig explains some stairs can aggravate himself. +4103 Daniel might imagine Heather concealed himself. +4104 The ladies explain Cindy irritated themselves. +4105 Tamara isn't explaining children healed herself. +4106 Alice doesn't explain Richard would scare herself. +4107 Candice hadn't thought Scott escaped from himself. +4108 The man explained Elizabeth kisses herself. +4109 Many cashiers imagine Travis worried himself. +4110 Mark says Amelia is concealing himself. +4111 Homer isn't explaining Carmen won't leave herself. +4112 Carrie says Dan reveals himself. +4113 Amy says Roger saw himself. +4114 Some girls had imagined Dawn researched herself. +4115 Marla explains teachers are confusing themselves. +4116 Michelle imagined Gary boasts about himself. +4117 Some actresses say Andrea won't dislike herself. +4118 Many teenagers imagine Gerald isn't escaping from himself. +4119 John explained Samantha concealed himself. +4120 Elaine said Travis cared for himself. +4121 Susan wasn't imagining some closet does embarrass herself. +4122 Christopher does think that school hadn't respected himself. +4123 Sabrina wasn't saying every light wasn't boring itself. +4124 Aaron 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themselves. +4142 Florence wouldn't think Gerald thinks about himself. +4143 Sandra explains Craig is thinking about himself. +4144 Amanda will explain many glasses aggravated herself. +4145 Meredith might think Walter was revealing himself. +4146 Those cashiers hadn't thought Edward hasn't questioned himself. +4147 Sherry wasn't thinking Ronald has hidden herself. +4148 Marla imagined Noah is leaving himself. +4149 Colleen isn't explaining this apple couldn't aggravate itself. +4150 Most pedestrians imagine an icicle impressed itself. +4151 Brian is imagining Becca watches himself. +4152 Sherry says jackets will distract themselves. +4153 Sally was saying Bradley hadn't seen herself. +4154 Kirsten thinks Stephen has sounded like himself. +4155 Eva isn't imagining Gary discovered himself. +4156 A lot of customers think Elaine isn't approaching herself. +4157 Those guests say Candice returned to themselves. +4158 Many cashiers say Sally hasn't investigated herself. +4159 The Impressionists might think Cynthia hated themselves. +4160 This man should think Samantha hugs himself. +4161 Michael imagined Katherine can find herself. +4162 Leslie has explained every school couldn't listen to itself. +4163 Melinda won't think Vincent knows himself. +4164 Candice imagines Paul can distract himself. +4165 Benjamin said Stacy was listening to himself. +4166 Andrew wouldn't say Cindy isn't helping herself. +4167 Rebecca doesn't think every hospital fired herself. +4168 Frank says Barbara isn't talking about herself. +4169 The Borgias think Andrea hasn't revealed herself. +4170 Nina thinks that boy works with herself. +4171 Maria isn't thinking all waiters weren't disturbing themselves. +4172 Tracy wasn't thinking Steven was revealing himself. +4173 A lot of cashiers say Laurie hadn't escaped from themselves. +4174 Benjamin wasn't thinking Andrea has cared for himself. +4175 Patricia will think Alexander visits herself. +4176 Sabrina thinks some play was disagreeing 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himself. +4195 Alexander imagines Melissa couldn't hurt himself. +4196 Some children explain Scott had alarmed himself. +4197 Naomi wasn't thinking Paul had found himself. +4198 Mitchell says Debra cares for himself. +4199 Many guys should say Patrick visited himself. +4200 All girls aren't explaining Jodi has fired herself. +4201 Dancers have said this guy appreciated himself. +4202 James won't say some adults can impress themselves. +4203 Linda wasn't thinking Scott researched himself. +4204 Matt said these senators observed themselves. +4205 All children weren't explaining Karen was hiring herself. +4206 Eric can imagine Melinda should conceal himself. +4207 Alicia wasn't imagining Jerry had praised himself. +4208 Heidi was explaining Mitchell hasn't loved himself. +4209 Theodore might imagine many trucks had irritated themselves. +4210 Some boy explains Michelle disagrees with herself. +4211 Tammy wasn't saying Dennis isn't boasting about herself. +4212 Jason isn't thinking Tanya should remember himself. +4213 The lady thinks this broccoli hadn't disgusted herself. +4214 Noah might say all teachers hadn't sounded like themselves. +4215 Carolyn was imagining that report annoyed itself. +4216 Jodi did say Rodney forgets himself. +4217 Many cashiers say Eric was firing themselves. +4218 James thought Florence has referenced himself. +4219 Erin has imagined the broccoli won't confuse itself. +4220 Jodi did explain the museums can't disgust themselves. +4221 Sandra thought that pepper bored herself. +4222 Timothy thinks Cheryl left himself. +4223 Katherine might say most high schools have scared themselves. +4224 Brett imagined Rebecca loved herself. +4225 A lot of dancers say Theodore has hated themselves. +4226 Theresa has thought that man did work with herself. +4227 Omar says Anna hates herself. +4228 Candice wouldn't say Curtis escapes from herself. +4229 Patricia said Stephen wasn't hiring herself. +4230 Amelia explained Kenneth aggravated himself. +4231 Melissa explained Russell likes himself. +4232 Carlos has said Irene has kissed herself. +4233 The Lutherans hadn't thought Tonya concealed themselves. +4234 Elizabeth wasn't thinking Brian sounds like herself. +4235 Jennifer explains this restaurant did disgust herself. +4236 Patricia explains the Impressionists have concealed themselves. +4237 Deanna explains Donald boasts about himself. +4238 Patrick thinks Paula did fire herself. +4239 Brad isn't explaining most pants can alarm themselves. +4240 Elizabeth had imagined a lot of patients disgusted herself. +4241 Those girls say Florence wouldn't reveal themselves. +4242 Alan isn't imagining Spain boasts about himself. +4243 Some teenagers say Gina scared themselves. +4244 Sally didn't explain some people complained about themselves. +4245 Walter wasn't thinking some waiters have disagreed with himself. +4246 Margaret explains the Impressionists questioned themselves. +4247 All customers hadn't explained Irene can't hug herself. +4248 Girls imagine every actress fled from herself. +4249 Most waiters imagine Diana hid herself. +4250 Dan imagines Rhonda will forget himself. +4251 Most doctors imagine Omar doesn't complain about himself. +4252 Drivers have thought Omar couldn't praise himself. +4253 Waiters explain Irene respected herself. +4254 Beth said Martin concealed himself. +4255 The senators explain Rhonda scares themselves. +4256 William does think Jill disturbed himself. +4257 Florence imagined Vincent would criticize himself. +4258 Alan is explaining Sheila is talking about herself. +4259 Raymond imagined Monica described himself. +4260 Gary said all documentaries are worrying themselves. +4261 Paula shouldn't think a documentary would disagree with herself. +4262 Tara says the snake had embarrassed herself. +4263 Many guests thought Barbara isn't upsetting herself. +4264 Sabrina did explain Craig bored himself. +4265 Kendra had thought Derek hadn't forgotten himself. +4266 Sharon says these cars had shocked themselves. +4267 Patrick thinks Becky had forgotten himself. +4268 Most waiters were explaining the cat wouldn't worry themselves. +4269 Carolyn thinks Gregory hasn't criticized himself. +4270 Steven would think these computers did impress themselves. +4271 Sandra has said Mitchell referenced himself. +4272 Patrick hasn't imagined Sherry has admired himself. +4273 Frank explains those waitresses observed himself. +4274 These waitresses explain Carla leaves herself. +4275 Karla explained Donald does leave himself. +4276 Martin imagines Janet wasn't discovering himself. +4277 Lisa could imagine this grocery store is scaring herself. +4278 Every woman should imagine these hills scared themselves. +4279 Marla imagined Liam discovered himself. +4280 Adults explain Bruce had kissed himself. +4281 Judy can imagine these lights had disgusted themselves. +4282 Bradley wasn't thinking Marie knows herself. +4283 Tanya would think Benjamin researched himself. +4284 Lori explained Daniel complained about himself. +4285 Most guys say Robert healed himself. +4286 Lucille shouldn't think that movie theater should worry itself. +4287 Jerry imagined Julia is discovering himself. +4288 Heather said Bradley approaches himself. +4289 Brian isn't thinking Victoria thinks about himself. +4290 Kristin does imagine Gary concealed himself. +4291 These guys imagine this commentary wasn't disagreeing with themselves. +4292 The Borgias don't say Joel sounded like himself. +4293 Gary is thinking Gina investigated himself. +4294 The Borgias were imagining this water hadn't hurt itself. +4295 The Impressionists say Chad sounds like themselves. +4296 All men explain Teresa hadn't praised themselves. +4297 Gerald imagined Christina disagrees with herself. +4298 Sally imagines Roger works with himself. +4299 Walter hadn't imagined Melanie doesn't argue about himself. +4300 Becca imagined Todd hadn't appreciated himself. +4301 Elaine thinks the commentary disgusted itself. +4302 Russell isn't thinking Ann had attacked herself. +4303 Timothy explained Ellen hasn't appreciated himself. +4304 These actors had thought Julie likes herself. +4305 Patients imagine Kendra hadn't kissed herself. +4306 These teenagers did imagine Claire can leave herself. +4307 Benjamin is thinking Debra hated himself. +4308 Some girls explain Jill could observe themselves. +4309 Alice thinks Peter isn't boycotting herself. +4310 Vanessa does say that legislature might describe itself. +4311 Alice wasn't imagining Patrick had hidden herself. +4312 Jane is saying Jerry discusses himself. +4313 William was explaining Lori has fled from himself. +4314 Kimberley did say screens wouldn't disgust themselves. +4315 Beth would imagine Russell would help himself. +4316 Most adults imagine a window couldn't aggravate itself. +4317 Derek hasn't imagined Sharon might astound himself. +4318 Jason should imagine Lucille complains about himself. +4319 Marla thinks Stephen was discovering himself. +4320 Jason would explain boys were disagreeing with themselves. +4321 Stacey isn't saying Mitchell admired himself. +4322 Karen wasn't imagining all hills hadn't disturbed herself. +4323 Those people imagined this high school is embarrassing themselves. +4324 Teresa doesn't say Samuel cures himself. +4325 Liam hasn't said Jennifer shouldn't criticize herself. +4326 These guys said this glove disgusted itself. +4327 Jeffrey said Julie talked about herself. +4328 Some actresses explain a waitress astounded herself. +4329 Curtis has thought some door bothered himself. +4330 Connie wasn't thinking Peter had praised himself. +4331 Denise imagines Steve had hugged herself. +4332 Holly isn't explaining Marcus will hurt herself. +4333 Most waiters haven't explained Mark should stun himself. +4334 Alan isn't saying Theresa shouldn't bore herself. +4335 Marla had thought the people are healing themselves. +4336 Amanda explains the book irritated herself. +4337 Edward had explained the car would scare himself. +4338 This dancer has explained George isn't concealing herself. +4339 Danielle imagined some beef can stun itself. +4340 Connie imagines that guy knows himself. +4341 The waitresses wouldn't say Heather didn't remember themselves. +4342 Valerie might think many children aren't astounding themselves. +4343 Katherine explains many pedestrians would reference herself. +4344 All guys imagine Alexander forgot himself. +4345 Richard says Katherine wasn't boring herself. +4346 That guy had explained some screen was confusing himself. +4347 Those men say Jeffrey hadn't upset himself. +4348 These guests imagine Carlos was stunning himself. +4349 James said this library has described itself. +4350 Rhonda hadn't said Craig was questioning himself. +4351 Emily was explaining that boy wasn't worrying himself. +4352 Linda hadn't thought Craig hid herself. +4353 All doctors think Tanya will describe themselves. +4354 Many pedestrians say Frank won't criticize himself. +4355 Jacqueline imagines those prints are astounding herself. +4356 Those cashiers imagine a man has forgotten himself. +4357 Grace wouldn't say Jeffrey would fire herself. +4358 Carol wasn't saying Alexander could forget herself. +4359 Cindy isn't imagining a lot of girls had left herself. +4360 These ladies think Ellen impressed themselves. +4361 Martha hadn't thought Steve heals himself. +4362 Carlos had said Kristin discovers himself. +4363 Karen didn't explain Peter cares for herself. +4364 Erin could say a turtle had irritated herself. +4365 Ellen explains Carl fired herself. +4366 Jill has imagined Patrick referenced herself. +4367 Walter explains Wendy hadn't listened to himself. +4368 Erin has thought Andrew returns to himself. +4369 The Lutherans explain William sees themselves. +4370 Donald wasn't saying Danielle loved himself. +4371 Thomas said Debra has boasted about himself. +4372 Jeffrey explained Catherine isn't boring herself. +4373 Tiffany explained Robert was helping himself. +4374 Sara says Richard wasn't noticing himself. +4375 Helen wasn't explaining Homer has sounded like himself. +4376 Carmen hasn't said Guy did praise himself. +4377 Ann doesn't say Timothy approaches himself. +4378 Sherry does say the hats aggravated themselves. +4379 Frank can't imagine patients were healing himself. +4380 A lot of ladies say Amanda did hug herself. +4381 Those students explain Alicia bored themselves. +4382 Waiters explain Dana wasn't complaining about herself. +4383 Jacqueline won't think Jeffrey has boycotted herself. +4384 Those students imagine a print disgusted itself. +4385 Becca has imagined a spotlight upset itself. +4386 Martin does say most actors haven't upset themselves. +4387 Andrew was thinking those adults shouldn't criticize themselves. +4388 April hadn't imagined Aaron observes himself. +4389 These pedestrians imagine Jason would kiss himself. +4390 Keith did say Julia hadn't aggravated himself. +4391 Phillip doesn't think Tiffany forgot himself. +4392 Many students think Rachel hasn't sounded like herself. +4393 Those cashiers think Rebecca doesn't respect herself. +4394 Children explain this bicycle was shocking itself. +4395 Tonya hadn't said some women haven't kissed herself. +4396 This girl explained Matt shouldn't cure herself. +4397 April imagines Clyde cures himself. +4398 Catherine had explained Carl fires herself. +4399 Christina shouldn't think Bruce visited himself. +4400 Marie says the Impressionists had healed themselves. +4401 Most patients imagine Wendy aggravated herself. +4402 All people say Elizabeth doesn't love herself. +4403 Craig imagines Deanna likes himself. +4404 Kendra is imagining those guests might love themselves. +4405 A lady could explain Brett wasn't appreciating herself. +4406 Roger explains some commentary might disagree with himself. +4407 Patricia thought Matt is listening to himself. +4408 Some girl has said Travis did criticize herself. +4409 Sally said every man embarrassed himself. +4410 Carmen hadn't explained most children wouldn't think about themselves. +4411 Those women think Jill fires herself. +4412 These customers say Paula worried herself. +4413 Every dancer was thinking Paul researches himself. +4414 Waitresses haven't explained Maria worried herself. +4415 Donna said drivers haven't complained about herself. +4416 Ella can think Russell has seen himself. +4417 Brenda will imagine Thomas has fled from himself. +4418 Beverly explains Peter did aggravate himself. +4419 Anna is explaining Rodney does remember herself. +4420 Many ladies think Thomas hasn't worked with himself. +4421 Mark hasn't said Christine is thinking about himself. +4422 Charles shouldn't say Nina appreciates herself. +4423 Naomi had thought Kevin will reference himself. +4424 Bradley did imagine Rachel had helped herself. +4425 Charles wasn't imagining Wendy couldn't hide himself. +4426 Todd had said Carrie is firing himself. +4427 Most women explain Sherry has remembered herself. +4428 Julie explained Adam escaped from herself. +4429 Paul imagines those drivers aren't attacking themselves. +4430 George hadn't thought the print scares himself. +4431 Brett wasn't saying Valerie wasn't observing himself. +4432 A lot of girls think Laurie can think about themselves. +4433 Becky imagines some bicycle couldn't confuse itself. +4434 Jeffrey is saying Candice has fled from herself. +4435 Liam hadn't said Kristin hadn't healed himself. +4436 Most adults have thought Beth sees themselves. +4437 Those pedestrians explain James has discovered himself. +4438 That actress doesn't say Keith hasn't visited herself. +4439 Charles hadn't thought Alicia was criticizing himself. +4440 Some teenagers hadn't imagined Susan hasn't returned to herself. +4441 Those women say Connie returns to herself. +4442 Paul would imagine a lot of adults have returned to himself. +4443 Pamela wasn't explaining Dan respected himself. +4444 Most teachers explain Marie has appreciated herself. +4445 Sandra should think those hospitals will talk to herself. +4446 An actress would say Kenneth cures herself. +4447 Jill says Michael returns to himself. +4448 Heidi thinks Larry wouldn't escape from himself. +4449 Those patients say Andrea hasn't watched herself. +4450 Most boys imagine Connie argued about themselves. +4451 Some woman said this broccoli disturbs herself. +4452 Brenda thought Douglas has found himself. +4453 The waitress hasn't thought Ronald couldn't conceal herself. +4454 Jill imagined Larry would return to herself. +4455 Many waiters explain Carmen could bother themselves. +4456 Victoria says some school praises herself. +4457 Angela has said this dog shouldn't shock herself. +4458 Chad wouldn't imagine a mouth will worry himself. +4459 Most patients think Suzanne didn't disgust herself. +4460 Karen said that unicycle confused herself. +4461 Many boys aren't thinking that glacier might bore themselves. +4462 This waitress can think Gary was discussing himself. +4463 Edward thought Karen was leaving himself. +4464 Anne hadn't thought Gerald has hidden herself. +4465 Tamara wasn't explaining every bank didn't aggravate herself. +4466 The guys imagine Dennis hasn't insulted himself. +4467 Those customers said Tamara won't work with herself. +4468 Edward says Denise wouldn't conceal himself. +4469 Ruth explained Peter flees from himself. +4470 Veronica had said Charles hasn't concealed himself. +4471 Tanya isn't thinking many boys hadn't concealed themselves. +4472 William wouldn't say Christine disgusted himself. +4473 Victoria wasn't explaining those men remembered themselves. +4474 Becky wasn't imagining that snake is bothering herself. +4475 Dan could say all boys have known themselves. +4476 William can't explain Helen does reference himself. +4477 A lot of teenagers say Brenda wasn't stunning herself. +4478 Melissa has imagined Gerald can flee from herself. +4479 All waitresses said Tina questioned herself. +4480 Jennifer thinks Ronald has kissed herself. +4481 The Borgias imagined Walter hadn't investigated himself. +4482 All men say Ella has embarrassed herself. +4483 Valerie thinks these eyes hadn't shocked themselves. +4484 Mark could say these teachers are firing themselves. +4485 Catherine isn't explaining Alexander will fire herself. +4486 Travis explains these stories disagreed with themselves. +4487 Those women imagine Amanda praised herself. +4488 Daniel isn't thinking most libraries were irritating themselves. +4489 A man imagined Lucille isn't hiring herself. +4490 Travis has imagined Elaine noticed himself. +4491 Anne was imagining the public park hurt herself. +4492 Rebecca says these projectors confused themselves. +4493 Rebecca imagined many universities helped herself. +4494 Andrew says Margaret listened to himself. +4495 A lot of waitresses imagine Elizabeth hasn't cared for herself. +4496 Cindy is thinking Steven heals himself. +4497 Helen isn't thinking most companies haven't admired themselves. +4498 These girls imagine Tonya knows themselves. +4499 Melissa isn't imagining Craig isn't worrying herself. +4500 Katherine is saying Frank sounds like herself. +4501 The Lutherans explain Gerald was appreciating himself. +4502 Bruce had explained Debra did dislike himself. +4503 Jerry wasn't saying Pamela revealed himself. +4504 Anna isn't thinking a snake isn't irritating herself. +4505 Grace hadn't said a projector does worry herself. +4506 Victoria thinks many teachers couldn't argue about themselves. +4507 That actress hadn't explained these plays have aggravated themselves. +4508 Jennifer isn't imagining Russell hasn't shocked himself. +4509 A lot of pedestrians don't explain Amelia can see themselves. +4510 Catherine thought most adults revealed themselves. +4511 Clyde wasn't explaining this pasta hadn't scared himself. +4512 Dawn thinks that bicycle might bother herself. +4513 Ann has explained a print won't upset itself. +4514 Michael thinks Rose won't talk to himself. +4515 The Impressionists think Kathleen couldn't worry herself. +4516 Claire wouldn't say Kenneth isn't boring herself. +4517 These teenagers should imagine this report would impress themselves. +4518 Edward hasn't thought those senators aren't discussing themselves. +4519 Naomi imagined some museum talks about herself. +4520 Deanna thought most windows were boring themselves. +4521 Robert is saying Cindy would shock himself. +4522 Connie thinks Ronald fired himself. +4523 Jason was explaining the fish did hurt himself. +4524 Walter can't imagine Elizabeth appreciated herself. +4525 Jeffrey hadn't said Jessica is helping herself. +4526 Paula explains Bill is thinking about himself. +4527 Stephen might explain many teachers were scaring themselves. +4528 Waiters think Tonya doesn't research herself. +4529 The Impressionists explain Richard is concealing himself. +4530 Andrea doesn't explain Brad wasn't helping herself. +4531 Victoria had explained some pedestrians hadn't described themselves. +4532 Becky is explaining Rodney has thought about himself. +4533 Dawn thought Ronald talks about herself. +4534 Brenda was thinking David isn't curing himself. +4535 Matt explains Pamela insulted herself. +4536 The waitresses think Anna hasn't insulted herself. +4537 Teachers explain Chad has observed himself. +4538 All customers have said Larry would attack themselves. +4539 The Borgias say Nicole is admiring herself. +4540 Danielle explained that university didn't forget itself. +4541 Martin imagined Suzanne would sound like himself. +4542 Joel hadn't explained Alicia hires himself. +4543 Rhonda had explained Michael visits himself. +4544 Some customers say Martin has disliked himself. +4545 The waitress thought Samuel wasn't boycotting herself. +4546 Grace thinks Spain wasn't referencing herself. +4547 Matt is thinking most dancers hadn't researched himself. +4548 Alice might explain Peter fires himself. +4549 Rachel thinks some guests wouldn't return to themselves. +4550 Martin was explaining some university can't listen to himself. +4551 Lucille hasn't said this guy concealed himself. +4552 Tanya should explain Wayne hires himself. +4553 Some adults explain Anne shouldn't reveal themselves. +4554 Susan hadn't imagined many people haven't disagreed with herself. +4555 Eric explains Tiffany hadn't praised himself. +4556 Wayne said associations aggravated himself. +4557 A lot of girls do explain Rose left herself. +4558 Anne is explaining some print hadn't scared herself. +4559 The children were saying Matt insulted themselves. +4560 Most guests won't think Dan worked with himself. +4561 Lawrence would explain most libraries had boasted about themselves. +4562 Wendy imagines Chad wasn't complaining about himself. +4563 These adults imagined Amanda hides herself. +4564 Those doctors did explain Debra hires herself. +4565 Martin imagines Rhonda hadn't cured himself. +4566 Wendy said Spain isn't discussing itself. +4567 Carol is thinking Adam respected himself. +4568 Theresa thinks some sweater had impressed herself. +4569 Anne imagines the Borgias referenced herself. +4570 Guy might explain Rhonda cured himself. +4571 The Borgias thought Bill discusses himself. +4572 David had explained this girl was noticing herself. +4573 Andrea imagines that sketch isn't bothering herself. +4574 Jennifer says Curtis hid herself. +4575 Many customers think Holly did work with herself. +4576 Most dancers explain Tracy cures themselves. +4577 The pedestrians might explain Bruce works with himself. +4578 Derek isn't thinking this broccoli aggravated itself. +4579 Curtis shouldn't explain Nina wouldn't forget herself. +4580 Ronald wasn't saying a lot of boys do appreciate themselves. +4581 Katherine said Mitchell discusses himself. +4582 Diane explained that water hadn't alarmed itself. +4583 Julie imagines James confused herself. +4584 Ellen was imagining some governments wouldn't hate themselves. +4585 Rhonda imagined most waiters might discuss themselves. +4586 The women can say Clyde wasn't working with themselves. +4587 Joel wasn't explaining Sabrina had admired himself. +4588 Heather explained Joseph dislikes himself. +4589 Patricia should say Roger is watching herself. +4590 Peter isn't saying Sandra helped herself. +4591 Many people hadn't said Helen hasn't listened to themselves. +4592 Kenneth explained Veronica wasn't observing himself. +4593 Every dancer can't say some committee loves herself. +4594 Scott has imagined that woman appreciated herself. +4595 A lot of boys thought some light shocked themselves. +4596 Joel thought Sharon hadn't boycotted herself. +4597 Tanya does say Donald criticized himself. +4598 Alan had thought Beth discusses himself. +4599 Jeffrey thought Tiffany boasted about himself. +4600 Some cashiers think a dancer had found herself. +4601 Anna thinks Clyde respected herself. +4602 Rodney is imagining Jane remembered himself. +4603 Florence isn't imagining a lot of turtles have embarrassed themselves. +4604 Brenda thought Scott complained about himself. +4605 All senators are explaining some dancer had discussed themselves. +4606 Lisa wasn't thinking Jason described herself. +4607 Clyde did think those ladies approached themselves. +4608 Marie hadn't explained Lawrence hugs herself. +4609 Carol thinks Noah escapes from himself. +4610 Erin says Lawrence discusses himself. +4611 Waitresses imagine Julia was astounding herself. +4612 The Lutherans imagine Anne shouldn't remember themselves. +4613 Daniel won't imagine a lot of screens had embarrassed himself. +4614 Thomas explained all hats haven't aggravated himself. +4615 Suzanne had imagined Timothy had found himself. +4616 Ruth explained many dogs have shocked themselves. +4617 Michelle hadn't explained Aaron won't notice herself. +4618 Some students think Heather boycotted themselves. +4619 Thomas explains Monica isn't attacking himself. +4620 Veronica imagined Frank hasn't stunned himself. +4621 Kendra has imagined Spain wasn't talking about itself. +4622 The senators think Gary boasted about themselves. +4623 Christine explains Todd found herself. +4624 Homer had explained Kayla knew herself. +4625 Tracy can't think a lot of teachers had attacked themselves. +4626 Valerie could say Brian fired himself. +4627 Melinda isn't thinking Jerry hasn't seen himself. +4628 Amy explains Douglas was firing himself. +4629 Diana has explained Roger shouldn't insult herself. +4630 Most guys imagine travel would alarm themselves. +4631 Tanya isn't imagining the Borgias hadn't questioned herself. +4632 Travis is imagining Heather talked to himself. +4633 April thought Larry can visit himself. +4634 Larry imagined Lisa praised himself. +4635 Guy explained universities had argued about themselves. +4636 Margaret has said Craig discusses himself. +4637 James imagines Brenda might notice himself. +4638 The guests explain Jacqueline does escape from herself. +4639 Bruce imagines Kayla isn't alarming himself. +4640 Deborah imagined Jerry doesn't visit himself. +4641 Sara explains a grocery store couldn't irritate herself. +4642 Gina hadn't explained a lot of actresses hadn't discovered herself. +4643 Many students should think Brett hasn't left himself. +4644 Melinda hadn't said Ronald was disturbing himself. +4645 Scott said this girl approached herself. +4646 Bill does imagine those waiters boycotted himself. +4647 Dawn imagined Eric healed himself. +4648 Kimberley has imagined Travis was appreciating himself. +4649 Actresses say Sherry had watched herself. +4650 Melanie says most art galleries would hurt themselves. +4651 Rose is thinking Charles discusses himself. +4652 Ellen should imagine some men were talking about themselves. +4653 Stacey is explaining Martin wasn't concealing himself. +4654 Bethany explained Stephen cures himself. +4655 Alan doesn't explain Judy flees from herself. +4656 Christina hadn't said every apple isn't disgusting itself. +4657 Walter didn't imagine some fish disturbs himself. +4658 A lot of actresses had explained Karla wasn't working with herself. +4659 Angela thinks that spinach has impressed herself. +4660 Those teenagers think Lori astounded themselves. +4661 Guests think Gary wasn't questioning himself. +4662 Wayne imagined Alicia wasn't watching herself. +4663 Most adults explain Carl shocked themselves. +4664 People hadn't explained Sherry hasn't questioned herself. +4665 Customers thought Tammy sounded like herself. +4666 Patrick explains Laura was shocking herself. +4667 Curtis isn't saying this actress hadn't escaped from herself. +4668 Beverly has thought most students do talk about themselves. +4669 A lot of waitresses say Dan cured himself. +4670 Richard isn't saying Holly was stunning himself. +4671 Holly hasn't thought Theodore insulted himself. +4672 That boy hasn't imagined legislatures are arguing about himself. +4673 Nina explains Gerald isn't criticizing herself. +4674 Jacqueline said banks hired herself. +4675 Keith said Jacqueline attacks herself. +4676 Most people explain Debra could alarm themselves. +4677 Timothy wouldn't imagine Carol criticizes himself. +4678 Heather shouldn't think Keith hasn't loved himself. +4679 Bethany thinks Michael knows himself. +4680 Elaine imagined Gregory does flee from himself. +4681 Teachers imagine Tanya will boast about themselves. +4682 Dawn hasn't said Robert hadn't watched himself. +4683 Todd imagines Suzanne finds herself. +4684 Theresa has thought Curtis hadn't talked to herself. +4685 Victoria doesn't say Michael works with himself. +4686 Vanessa explains every pepper is confusing herself. +4687 Some boy doesn't think Renee wasn't observing himself. +4688 Those pedestrians imagine George didn't scare himself. +4689 Mary said Bruce was insulting himself. +4690 Denise wouldn't imagine Frank sounds like himself. +4691 All doctors explained Adam won't notice himself. +4692 Dana said many guys hadn't criticized themselves. +4693 A girl hadn't thought Aaron hadn't boycotted himself. +4694 A lot of people explain some pie can impress themselves. +4695 Kristen imagines the ladies should listen to themselves. +4696 Emily said this school is criticizing itself. +4697 Tara was explaining Charles has cared for himself. +4698 Lucille says Dennis could see himself. +4699 Veronica has thought Joel can find himself. +4700 Angela isn't saying an apple aggravated itself. +4701 Valerie is imagining Mitchell insulted himself. +4702 These senators explained Timothy hasn't concealed himself. +4703 Rodney imagined some commentaries weren't disagreeing with themselves. +4704 Judy thought Martin criticizes herself. +4705 Women have imagined this government wouldn't talk to themselves. +4706 Some guys explain Janice hasn't aggravated herself. +4707 These people think Kirsten described themselves. +4708 Brian thought Gina distracted himself. +4709 Derek won't imagine Cindy knew herself. +4710 Samuel is explaining Monica doesn't scare herself. +4711 George hadn't said Teresa appreciates himself. +4712 All people say Ann disagrees with themselves. +4713 Kristen would explain Richard knows himself. +4714 Actresses shouldn't imagine every man argued about himself. +4715 Some senators say Candice found themselves. +4716 Susan can think most socks weren't shocking themselves. +4717 Danielle wasn't imagining Walter stunned himself. +4718 Phillip did explain Carolyn cures himself. +4719 Rebecca is saying David will notice herself. +4720 Dan wasn't thinking Rose might approach himself. +4721 Laura should say Gary heals himself. +4722 A lot of people shouldn't think Catherine discovers herself. +4723 Wendy explains Paul should forget himself. +4724 Anna is saying the association has astounded itself. +4725 Stacy has said most drivers investigated themselves. +4726 These teenagers think Jill insulted herself. +4727 Clyde hadn't thought all hamsters had disgusted himself. +4728 Suzanne can imagine the Balkans were disturbing themselves. +4729 Grace hadn't thought Timothy has concealed himself. +4730 Bruce says Amelia hasn't hugged herself. +4731 Laura hadn't thought Raymond didn't care for herself. +4732 These guys weren't thinking Victoria had hated themselves. +4733 Dancers think some story had distracted themselves. +4734 Lisa is thinking that carriage irritated herself. +4735 Sally explains apples hadn't stunned herself. +4736 Christine would imagine Keith wasn't healing himself. +4737 Patrick hadn't said Florence hasn't investigated himself. +4738 Many senators thought some committee isn't discussing themselves. +4739 A lot of dancers shouldn't say Carla thought about themselves. +4740 The boy says Deborah hadn't disliked himself. +4741 All senators say every sock isn't upsetting themselves. +4742 Leslie said all ladies can question themselves. +4743 Elizabeth might explain this guy wasn't concealing himself. +4744 Most teachers say Phillip has praised himself. +4745 Allison hadn't imagined Dennis doesn't leave herself. +4746 Paul did imagine Barbara referenced himself. +4747 These teachers imagine Jane doesn't alarm themselves. +4748 Robert says hills aren't shocking themselves. +4749 A lot of guests explain Gary was arguing about himself. +4750 Women explain Holly works with herself. +4751 Karen wasn't thinking these committees talked about themselves. +4752 Kristin was explaining Joseph isn't researching himself. +4753 The adults imagine Candice cured themselves. +4754 Gina shouldn't explain Bill remembered himself. +4755 Tina hasn't thought Andrew couldn't care for herself. +4756 Vincent says that documentary could alarm itself. +4757 Emily had thought Joseph astounded herself. +4758 Christina will think Noah hugs himself. +4759 Many actresses explain Connie isn't talking to herself. +4760 These patients think the lamp was worrying themselves. +4761 A lot of teenagers say Kristin thinks about herself. +4762 Gary has thought the students couldn't cure themselves. +4763 Michael has said the Borgias are appreciating themselves. +4764 Joel said this organization hadn't upset himself. +4765 Stephanie thought these glasses shouldn't confuse themselves. +4766 Tara had explained the guy isn't revealing himself. +4767 Kirsten thinks many paintings weren't irritating themselves. +4768 Jennifer explains Craig wasn't discussing himself. +4769 Kristen said Bill wouldn't attack himself. +4770 Dana isn't saying many men couldn't praise themselves. +4771 Deborah hasn't imagined David has watched himself. +4772 Grace hasn't said many rugs were annoying herself. +4773 All waitresses think Elizabeth left herself. +4774 Timothy does say Michelle loves herself. +4775 Thomas hasn't explained Ella hadn't worried himself. +4776 Jodi imagines the Impressionists haven't visited themselves. +4777 Many guests say Carla confused themselves. +4778 The Lutherans imagine Sally thinks about themselves. +4779 Kenneth might say Lori was annoying himself. +4780 Those pedestrians imagine Deborah was concealing themselves. +4781 Alexander was explaining these doctors weren't talking to themselves. +4782 Catherine isn't imagining Homer likes himself. +4783 Larry isn't explaining those waitresses haven't loved themselves. +4784 Jason said some public park wouldn't stun himself. +4785 Michael hasn't said Janice appreciated himself. +4786 The Impressionists think Homer is hiding themselves. +4787 Natalie couldn't think Benjamin wasn't complaining about himself. +4788 A lot of patients think Stephanie wasn't criticizing herself. +4789 Carmen had said Gerald wouldn't talk about himself. +4790 Grace can explain most libraries discussed themselves. +4791 A lot of boys explain Barbara doesn't notice herself. +4792 Lori would imagine Guy works with himself. +4793 Many doctors think Rose leaves herself. +4794 The Impressionists explain the glacier scared itself. +4795 Thomas imagines Sabrina loved himself. +4796 Michael has imagined Meredith talks about himself. +4797 Jennifer is saying Carl revealed himself. +4798 Alicia hasn't said Benjamin isn't curing herself. +4799 Kayla explains Steven won't hurt herself. +4800 Gina explains Alan fires himself. +4801 Paul did think Alice hugs herself. +4802 Beverly imagines Carla's ex-boyfriend hates himself. +4803 Tonya explains Kevin would boast about himself. +4804 Adam can explain Katherine will describe herself. +4805 Nancy could say every guy hides himself. +4806 Lawrence says Claire attacks himself. +4807 Donna explains Gerald could boast about himself. +4808 Steve did explain Rachel couldn't praise himself. +4809 Jeffrey would imagine Caroline watches herself. +4810 Bill imagines Maria praises himself. +4811 Jill could explain Ronald will hide himself. +4812 Teresa imagines Liam didn't distract himself. +4813 Andrew's father couldn't imagine Melanie could conceal herself. +4814 Carl explains Carmen might approach herself. +4815 Amelia should explain George kisses himself. +4816 Susan says Eric helps himself. +4817 Diane explains Laurie's boyfriend should remember himself. +4818 Tanya thinks Sandra's boyfriend talks about himself. +4819 Stacey doesn't explain that man doesn't help himself. +4820 Winston Churchill's son couldn't say Kayla loves himself. +4821 Brenda can't imagine Adam did leave himself. +4822 Elaine thinks Aaron cares for himself. +4823 Tanya thinks Guy wouldn't sound like herself. +4824 Laurie imagines Andrew can't conceal himself. +4825 Dana could say Bradley will like herself. +4826 Diane should say Russell notices herself. +4827 Kathleen says Samuel won't like himself. +4828 Donna does imagine some boy could flee from himself. +4829 Christina should imagine Gerald might know himself. +4830 Grace thinks Travis doesn't embarrass himself. +4831 Ronald couldn't think Debra listens to herself. +4832 Renee thinks Frank might work with himself. +4833 Sarah does explain Rodney didn't think about himself. +4834 Pamela will think Andrew complains about herself. +4835 Douglas thinks Frank's ex-girlfriend complains about himself. +4836 Paula's nephew can think Samuel's niece should annoy himself. +4837 Sara can't imagine David cures herself. +4838 Bradley imagines Florence's grandmother shouldn't stun himself. +4839 Cheryl will imagine Kenneth visits himself. +4840 Carlos won't explain Leslie works with himself. +4841 Guy explains Holly describes himself. +4842 Teresa explains Alan should reference himself. +4843 Samuel didn't think Leslie can care for herself. +4844 Heather imagines Jason could praise herself. +4845 Carolyn's husband can explain David's sister didn't bother herself. +4846 Phillip says Amelia didn't embarrass himself. +4847 Veronica didn't explain Guy could remember himself. +4848 George thinks Mary remembers herself. +4849 Sarah should imagine Brad might embarrass herself. +4850 Timothy imagines Ella does return to herself. +4851 Catherine explains Frank might argue about herself. +4852 Curtis would imagine Elizabeth hides himself. +4853 Janice won't explain Craig observes himself. +4854 Craig explains every woman wouldn't respect himself. +4855 Derek explains Ruth talks about herself. +4856 That guy imagines Angela's ex-wife doesn't listen to himself. +4857 Keith doesn't think Diana sounds like himself. +4858 Melissa imagines Vincent could respect himself. +4859 Alexander does say Holly does visit himself. +4860 Kathleen's ex-boyfriend didn't say that actress cures himself. +4861 Stacey thinks Mitchell couldn't astound himself. +4862 Melanie says Benjamin will attack herself. +4863 Noah says that woman won't worry himself. +4864 Katherine can't explain Omar fires herself. +4865 Sara doesn't think Bradley dislikes himself. +4866 Guests' ex-girlfriend imagines Heidi's ex-husband didn't disagree with herself. +4867 Melinda doesn't imagine Peter will fire himself. +4868 Some actress imagines Richard disturbs herself. +4869 Marcus should imagine Candice wouldn't flee from himself. +4870 Derek might explain Sally leaves himself. +4871 Martha will think Eric can upset himself. +4872 Carla won't imagine Kevin did find himself. +4873 Tara thinks Richard won't admire himself. +4874 Todd can imagine Gina helps herself. +4875 Anna imagines Stephanie's grandfather hides herself. +4876 Adam says Elizabeth reveals herself. +4877 Gary doesn't explain Tiffany will hug himself. +4878 Cynthia can't think Carlos watches himself. +4879 Alan says Florence escapes from himself. +4880 Patrick's son couldn't say Lori shouldn't shock himself. +4881 Aaron will say Meredith cares for himself. +4882 Brian says some girl boasts about herself. +4883 Paul can't imagine Danielle dislikes himself. +4884 Karen can say John sounds like herself. +4885 Mark might explain a woman won't approach himself. +4886 Karla thinks Edward flees from himself. +4887 Daniel couldn't think Diana won't respect himself. +4888 Elizabeth thinks Brett couldn't stun himself. +4889 Vanessa should imagine Dan attacks himself. +4890 Phillip thinks Carmen listens to himself. +4891 Steven could think Angela would discover herself. +4892 This lady might imagine Alan observes herself. +4893 Richard might say Caroline didn't sound like himself. +4894 Jill's nephew wouldn't imagine Julia should hurt herself. +4895 Raymond does think Danielle hides herself. +4896 Karla can't say James criticizes himself. +4897 Karla didn't say Robert can appreciate herself. +4898 Stephanie thinks a man heals himself. +4899 Stacey imagines Lucille's grandson didn't respect himself. +4900 Bruce imagines Pamela flees from himself. +4901 David might say Danielle admires himself. +4902 This actress won't think Scott does heal himself. +4903 Gary did say Jessica did fire himself. +4904 Rebecca explains Steven wouldn't impress himself. +4905 Victoria's nephew explains every girl won't praise herself. +4906 Florence's niece won't say Lawrence cures himself. +4907 Matt wouldn't say Suzanne won't talk to himself. +4908 Patricia should say Jerry criticizes himself. +4909 Christina wouldn't explain Matt talks to himself. +4910 Elaine didn't think Russell escapes from herself. +4911 Katherine says Guy likes himself. +4912 Bradley thinks Lori didn't admire himself. +4913 Dennis can't imagine Valerie discovers herself. +4914 Donna says Benjamin should hurt himself. +4915 Laurie thinks Omar does admire herself. +4916 Stephen can say Sandra did hide himself. +4917 Amy imagines Bill describes himself. +4918 Sherry wouldn't imagine Richard describes himself. +4919 Matt explains Dana doesn't embarrass himself. +4920 Kristen thinks James approaches himself. +4921 Gary should explain Grace could complain about himself. +4922 Douglas thinks Connie can listen to himself. +4923 Kendra couldn't think Todd shouldn't reference herself. +4924 Holly did say Jacqueline's nephew can describe himself. +4925 Holly imagines actresses' grandson doesn't confuse himself. +4926 Noah says Nina kisses herself. +4927 Melanie should explain Samuel would return to herself. +4928 Teresa explains Noah talks about herself. +4929 Christine imagines Gary can visit himself. +4930 Clyde thinks Nina references himself. +4931 Ruth might say Brett knows himself. +4932 Samuel says Elizabeth respects herself. +4933 Erin's son didn't explain Kevin's wife didn't irritate himself. +4934 Edward says Carol won't describe himself. +4935 Mitchell thinks Samantha approaches himself. +4936 Gerald would explain Ann does listen to herself. +4937 Tanya did think Liam would disturb himself. +4938 Diana explains Scott's brother will cure himself. +4939 Donald explains the dancer should love himself. +4940 Suzanne's father says Carmen might watch herself. +4941 Marla can say Charles praises herself. +4942 Adam didn't say Theresa's girlfriend insults herself. +4943 Kayla thinks Mark doesn't conceal herself. +4944 A lot of teachers' grandson thinks Valerie hugs herself. +4945 Tamara imagines Scott did shock himself. +4946 Veronica's mother imagines Noah doesn't approach herself. +4947 Regina didn't imagine Robert couldn't disturb himself. +4948 Rhonda can't say Robert will stun herself. +4949 Martin says Christina works with herself. +4950 Stephanie won't imagine Jeffrey does flee from himself. +4951 Thomas thinks Becca can't hide herself. +4952 Elaine explains Jason couldn't love himself. +4953 Benjamin thinks Tina loves himself. +4954 Gina explains Brad should astound himself. +4955 Travis couldn't imagine Cheryl shouldn't insult herself. +4956 Kevin explains Sara questions herself. +4957 Larry explains Alicia shouldn't confuse herself. +4958 Wayne explains Jill will reference himself. +4959 Kayla says Richard likes himself. +4960 Connie could say Noah might worry himself. +4961 Carla imagines Brian thinks about herself. +4962 Dan thinks Noah's daughter will disgust herself. +4963 Susan thinks Kevin does forget herself. +4964 Kristen says George's grandfather can attack himself. +4965 Katherine might think Brian can conceal herself. +4966 Anna says Charles admires herself. +4967 Paul should imagine Rebecca could bore herself. +4968 Carolyn didn't say Kevin would annoy herself. +4969 Vincent can't think Julia shouldn't approach himself. +4970 Carlos explains Valerie admires herself. +4971 Carlos imagines Anne helps himself. +4972 Marcus imagines Melinda could impress himself. +4973 Craig thinks Lori can't bore himself. +4974 Daniel shouldn't say Rachel didn't visit himself. +4975 Becca does say Christopher cares for himself. +4976 Kristin shouldn't explain Brett doesn't confuse himself. +4977 Ruth explains Brett should annoy herself. +4978 Dennis did say Tara wouldn't like himself. +4979 Douglas says Pamela's girlfriend finds herself. +4980 Guy imagines that woman couldn't care for himself. +4981 Jill says every student's ex-husband wouldn't care for himself. +4982 Stacey thinks Clyde didn't disgust herself. +4983 Russell imagines Colleen appreciates himself. +4984 William thinks Marie loves himself. +4985 Sara explains every boy would appreciate himself. +4986 Lawrence imagines Victoria didn't argue about himself. +4987 Diana can think Stephen won't watch himself. +4988 Susan's niece explains Kenneth wouldn't discuss herself. +4989 Tammy shouldn't think Larry should alarm herself. +4990 Martha shouldn't explain Bruce might worry himself. +4991 James didn't explain Heather doesn't research himself. +4992 Cynthia does say David heals herself. +4993 Homer could explain Maria won't return to himself. +4994 Charles can't say Nina disturbs himself. +4995 Jeffrey imagines some lady won't insult herself. +4996 Jeffrey says Andrea finds himself. +4997 Stacy can't imagine Richard couldn't talk about himself. +4998 Bruce thinks Jodi visits herself. +4999 Alexander does think Emily cures himself. +5000 Eric explains Tara respects herself. +5001 Rachel couldn't think Dennis questions herself. +5002 Some dancer might imagine Mark can't shock himself. +5003 Clyde wouldn't say Alice knows himself. +5004 Beverly's niece might think Gregory did remember himself. +5005 Martha's sister doesn't think James finds himself. +5006 Sharon thinks Homer could watch himself. +5007 Matt thinks Rachel did fire himself. +5008 Carolyn did think a man scares herself. +5009 Bradley says Laura sounds like himself. +5010 Amelia thinks Roger complains about himself. +5011 Kathleen doesn't say Michael didn't hurt himself. +5012 Gregory's boyfriend could explain Diane thinks about himself. +5013 Ella can imagine Jason sees himself. +5014 Margaret didn't say Liam does alarm himself. +5015 Stacey can't think Monet's ex-boyfriend can flee from himself. +5016 Raymond thinks Melissa did see himself. +5017 Homer thinks Sandra doesn't respect herself. +5018 Beth thinks Craig won't approach herself. +5019 Mark shouldn't say Natalie can't see himself. +5020 Phillip explains Eva loves herself. +5021 Wayne could say Emily will disturb himself. +5022 Gary imagines Julia wouldn't find himself. +5023 Kathleen explains Homer will visit himself. +5024 Gary says Valerie appreciates himself. +5025 Holly wouldn't say Aaron talks about herself. +5026 Craig thinks an actress returns to herself. +5027 Nancy's ex-husband imagines Andrea will research herself. +5028 Dawn shouldn't think Clyde praises himself. +5029 Kirsten imagines Bruce can think about himself. +5030 Christina didn't think Larry shouldn't distract himself. +5031 That lady should think Liam talks about himself. +5032 Samantha couldn't say Noah can aggravate himself. +5033 Sara says Chad's brother notices himself. +5034 Omar says Kirsten cares for herself. +5035 Maria explains Daniel leaves himself. +5036 Matt won't say Tamara would hire himself. +5037 Vincent thinks Deanna could reference himself. +5038 Russell says Rose attacks himself. +5039 Tamara couldn't explain Curtis sounds like himself. +5040 Kendra did say Paul can hate herself. +5041 George explains Tiffany escapes from herself. +5042 Claire thinks Patrick fires herself. +5043 Bruce thinks Maria shouldn't confuse himself. +5044 Douglas imagines Rachel wouldn't complain about herself. +5045 Guy thinks Anna complains about herself. +5046 Theresa explains Keith hides himself. +5047 Dawn shouldn't explain Liam shouldn't conceal himself. +5048 Kenneth thinks Sheila hugs himself. +5049 Omar explains Dawn could forget herself. +5050 Travis thinks Bethany won't worry herself. +5051 Judy did imagine Frank respects herself. +5052 Holly thinks Wayne could notice himself. +5053 Nina should say George might admire himself. +5054 Scott can't think Maria boasts about himself. +5055 Marcus says Valerie couldn't escape from himself. +5056 Samantha explains Helen's husband escapes from herself. +5057 Catherine says Brett will insult herself. +5058 Karen should think James does conceal herself. +5059 Kathleen imagines Benjamin shouldn't admire himself. +5060 Michael does explain Bill's grandmother can bother himself. +5061 Christina might say Adam fires himself. +5062 Michael won't explain Debra's ex-girlfriend might bore himself. +5063 Lisa imagines Brett should shock himself. +5064 Melinda can't say Carl might complain about herself. +5065 Monica's sister should imagine some boy does watch himself. +5066 Keith says Donna heals herself. +5067 Brian couldn't say Amy can't sound like herself. +5068 Craig says Angela thinks about herself. +5069 Gregory didn't say Maria shouldn't forget herself. +5070 Heather thinks Marcus hires herself. +5071 Joseph thinks Maria returns to himself. +5072 Beth imagines Timothy loves himself. +5073 Phillip says an actress did question herself. +5074 Julie doesn't imagine Paul did investigate himself. +5075 Lawrence explains Tamara could embarrass himself. +5076 Rebecca's husband explains Elizabeth scares himself. +5077 Adam wouldn't say Catherine will care for herself. +5078 Brian doesn't say Patricia talks to himself. +5079 Marcus thinks Rachel should research himself. +5080 Melissa might imagine Aaron didn't attack himself. +5081 Carla might explain Ronald shouldn't care for himself. +5082 Travis did explain Rebecca couldn't listen to herself. +5083 Sabrina won't explain Aaron could appreciate himself. +5084 This boy says that waitress thinks about himself. +5085 Joel imagines Vanessa does reference himself. +5086 Vincent imagines Michelle should approach herself. +5087 Margaret explains Carl could scare himself. +5088 Karla couldn't imagine Kevin doesn't annoy himself. +5089 Natalie would imagine Jeffrey shouldn't disturb himself. +5090 Liam's husband won't think Connie sees himself. +5091 Kristen did say Brad reveals herself. +5092 Gary imagines Rebecca flees from himself. +5093 Grace's nephew says Cindy might flee from himself. +5094 Alicia should imagine Rodney couldn't impress himself. +5095 Carol says Brett didn't astound himself. +5096 Todd thinks Andrea describes herself. +5097 Richard explains Homer's ex-girlfriend does think about himself. +5098 Paul's boyfriend would think Stacy sees himself. +5099 Some actor's granddaughter imagines Larry might disagree with herself. +5100 Elaine explains Curtis might astound himself. +5101 Eric imagines Laura talks about herself. +5102 Naomi says Kevin can sound like himself. +5103 Andrew imagines Carolyn talks about himself. +5104 Laurie imagines Vincent does return to himself. +5105 Michael imagines Heather might flee from himself. +5106 Rachel might think Aaron observes herself. +5107 Timothy shouldn't say Lucille observes himself. +5108 Irene didn't explain Paul does research himself. +5109 Tonya could explain Gregory respects herself. +5110 Becky thinks this boy couldn't approach himself. +5111 Tammy thinks Denise's son can listen to himself. +5112 The actress might imagine Jeffrey escapes from herself. +5113 Marcus didn't think Eva admires himself. +5114 Christine explains Stephen leaves himself. +5115 Dawn can explain Gerald does disturb himself. +5116 Sarah could imagine Benjamin shouldn't hate himself. +5117 Kathleen thinks Scott hires himself. +5118 Bethany won't imagine Richard criticizes herself. +5119 Naomi couldn't imagine Adam doesn't leave himself. +5120 Paul shouldn't say Vanessa can't disturb himself. +5121 Carla imagines Martin wouldn't upset herself. +5122 Douglas couldn't say Sarah helps himself. +5123 Jerry can say Donna cares for himself. +5124 Beth imagines Lawrence reveals herself. +5125 Steven would explain Irene talks to herself. +5126 Sally would explain George criticizes herself. +5127 Brenda will say Guy watches himself. +5128 Derek will think Debra hugs herself. +5129 Thomas can't explain Christina hugs herself. +5130 Ann thinks some guy questions himself. +5131 Melissa's brother does imagine Susan watches himself. +5132 Michelle thinks Eric listens to herself. +5133 Scott thinks Debra might respect himself. +5134 Alice could think Dan can hurt himself. +5135 Amelia should explain Thomas researches himself. +5136 Sonia thinks Liam could admire herself. +5137 Marcus imagines Tamara attacks herself. +5138 Thomas will think Tracy does escape from himself. +5139 Carolyn can explain Travis disturbs herself. +5140 Bradley says Deanna thinks about herself. +5141 Eva's grandmother can imagine Russell knows himself. +5142 Laura can explain Kevin doesn't talk to himself. +5143 Jodi explains Brian's son listens to himself. +5144 Colleen's ex-boyfriend imagines Diana wouldn't discuss herself. +5145 Karen can think Carlos fires himself. +5146 Robert wouldn't say this driver's ex-wife finds herself. +5147 Amanda thinks this senator's ex-boyfriend watches himself. +5148 Monica's mother thinks Marcus respects himself. +5149 Elaine's daughter imagines Dennis might escape from himself. +5150 Christopher imagines Debra should heal himself. +5151 Christine says William wouldn't criticize herself. +5152 Kristin thinks Todd loves herself. +5153 Omar thinks Karla can't research himself. +5154 Barbara imagines Paul disturbs himself. +5155 Eva thinks Frank didn't confuse herself. +5156 Emily explains Russell insults himself. +5157 Samuel doesn't explain Bethany describes herself. +5158 Theodore should explain Jane admires himself. +5159 Julia says Brett notices himself. +5160 Craig's ex-boyfriend says Colleen reveals himself. +5161 David will imagine some adult's granddaughter helps himself. +5162 Ann could explain Curtis reveals himself. +5163 Thomas says Cheryl doesn't research herself. +5164 Michael should say every actress should heal himself. +5165 Mitchell explains Sandra criticizes herself. +5166 Meredith explains the guy should irritate herself. +5167 Christina did say Phillip wouldn't hug himself. +5168 Jill should explain John wouldn't attack himself. +5169 Helen shouldn't say Joseph references himself. +5170 Richard imagines that actress boasts about himself. +5171 Steven wouldn't say Theresa does praise himself. +5172 Vincent won't say Margaret remembers herself. +5173 Kathleen thinks Andrew discusses himself. +5174 Theresa says Guy couldn't kiss himself. +5175 Cynthia thinks Lawrence can't question himself. +5176 Christopher's grandson could imagine Catherine can criticize herself. +5177 Steven does imagine Heather respects himself. +5178 Christine might explain Robert will research herself. +5179 Janice wouldn't imagine Joseph didn't think about himself. +5180 Ruth didn't imagine Charles won't describe herself. +5181 Natalie explains Carlos's brother sees himself. +5182 Nina doesn't think Brett approaches herself. +5183 Carolyn says Brad did irritate herself. +5184 Katherine doesn't explain Joel observes himself. +5185 Linda says Ronald won't shock himself. +5186 Carmen doesn't imagine Christopher would discover himself. +5187 Tiffany says William will approach himself. +5188 Amelia might imagine Samuel notices himself. +5189 Monica thinks Charles cares for herself. +5190 Frank should think Samantha should visit himself. +5191 Scott won't think Leslie won't listen to himself. +5192 Sarah says the boy listens to herself. +5193 Gary imagines Cheryl should see herself. +5194 Keith doesn't imagine Julia praises herself. +5195 Travis might imagine Rose couldn't listen to herself. +5196 Christine imagines Martin can't know herself. +5197 Julie explains Natalie's grandson will approach himself. +5198 Joseph doesn't explain Marie will reveal himself. +5199 Erin explains Liam knows herself. +5200 Benjamin explains Heather couldn't scare herself. +5201 Paul's wife didn't say Brad should escape from herself. +5202 Karla won't think Matt sounds like himself. +5203 Matt thinks Carolyn did discuss himself. +5204 Suzanne does think Mitchell can appreciate himself. +5205 Diana could imagine Gregory returns to himself. +5206 Gerald should explain Deanna might flee from himself. +5207 Sonia says Phillip doesn't forget himself. +5208 Larry wouldn't say Anna could heal himself. +5209 Danielle imagines every boy talks to himself. +5210 Brian will explain Stacy talks about himself. +5211 Paul wouldn't imagine Rose knows herself. +5212 Monica does say Roger leaves himself. +5213 Mark wouldn't imagine Nancy can't kiss himself. +5214 Suzanne says Rodney should leave herself. +5215 Catherine explains Homer attacks herself. +5216 Alice says Donald doesn't remember himself. +5217 Sandra won't think Brad can escape from himself. +5218 Lisa wouldn't think Jerry should hug himself. +5219 Travis thinks Catherine will worry himself. +5220 The waitress can explain Frank discovers himself. +5221 Frank will explain Laura can't aggravate himself. +5222 Grace could say a boy can't approach himself. +5223 Bradley thinks Andrea would disgust himself. +5224 Mitchell says Amy hugs himself. +5225 Beverly's ex-husband couldn't say Kendra didn't scare herself. +5226 Julie will say Guy finds himself. +5227 Vanessa can't explain Donald can't shock himself. +5228 Paula explains Peter should praise herself. +5229 Tonya can think Charles will kiss himself. +5230 Liam did explain Sheila hates herself. +5231 Paula won't imagine Kevin does approach himself. +5232 Benjamin did say Victoria won't argue about himself. +5233 Wayne thinks Julie questions himself. +5234 Douglas wouldn't explain Anna doesn't research himself. +5235 Gary didn't say Todd's granddaughter sounds like himself. +5236 Peter does say Deanna talks to herself. +5237 Maria's nephew can't think Jennifer flees from himself. +5238 Susan says Kenneth will praise himself. +5239 Gina didn't imagine the man would dislike himself. +5240 Connie's son should think Amanda doesn't scare himself. +5241 Bruce couldn't think Mary visits himself. +5242 Naomi could think Jerry heals himself. +5243 Melinda's ex-boyfriend won't think Lucille should hire himself. +5244 Leslie explains William can't watch herself. +5245 Barbara says Rodney shouldn't describe herself. +5246 Teresa will imagine Alan should stun himself. +5247 Grace might explain Brian will complain about herself. +5248 Dan does explain Heidi can admire himself. +5249 Sara says Michael observes himself. +5250 Bethany says Brett does cure himself. +5251 Allison thinks Robert's son works with himself. +5252 Tina says Andrew shouldn't leave herself. +5253 Alice didn't think Chad would hire himself. +5254 Thomas can't think Eva will respect himself. +5255 Gary does say Candice should research himself. +5256 Matt thinks Andrea could leave himself. +5257 Tammy imagines Andrew can't disturb himself. +5258 Ronald thinks Jessica discusses himself. +5259 Wendy wouldn't imagine Joseph admires himself. +5260 Naomi will imagine Carlos shouldn't escape from herself. +5261 Stephen imagines Dana forgets herself. +5262 Douglas imagines Monica could hide herself. +5263 Paul does say Nina could cure himself. +5264 Aaron says Sonia reveals herself. +5265 Wayne thinks Denise couldn't approach herself. +5266 Vanessa says Eric approaches himself. +5267 Donald's brother says Laura remembers herself. +5268 Sherry thinks Joseph reveals himself. +5269 Jennifer doesn't imagine Samuel would kiss himself. +5270 Russell would imagine Sharon leaves herself. +5271 Candice would imagine Craig might see herself. +5272 Sharon would say Curtis should observe herself. +5273 Dawn says Chad didn't help herself. +5274 Tina thinks David should flee from himself. +5275 Samantha says Andrew shouldn't irritate himself. +5276 Christine couldn't think Bradley could escape from himself. +5277 Sara couldn't think Liam won't argue about himself. +5278 Tiffany might imagine Roger does conceal himself. +5279 Lisa should think Donald could help himself. +5280 Rodney's ex-husband imagines Martin's sister complains about himself. +5281 Brian explains Tara hires himself. +5282 Helen thinks Guy talks to herself. +5283 Carla doesn't imagine Marcus didn't return to herself. +5284 Chad doesn't say Diane could boast about himself. +5285 Kathleen can't say Dan forgets himself. +5286 Tracy couldn't think Edward watches himself. +5287 Chad thinks Rose heals herself. +5288 Paul explains Ella could work with herself. +5289 Christine would explain Steven did worry himself. +5290 Kenneth explains Sabrina might confuse himself. +5291 Benjamin explains Janice does aggravate herself. +5292 Connie explains a guy's son doesn't bother himself. +5293 Natalie explains Paul could aggravate himself. +5294 Tanya will think James references himself. +5295 Samantha should imagine Walter notices himself. +5296 Timothy can say Tara visits herself. +5297 Christopher says Kirsten praises himself. +5298 Rhonda's niece didn't imagine Benjamin can't reference herself. +5299 Tonya thinks Steve cures herself. +5300 Claire thinks Frank's ex-husband will forget herself. +5301 Joseph did think Theresa might talk to himself. +5302 Michael thinks Stacy would help himself. +5303 Rodney might explain Karla will aggravate himself. +5304 Grace explains Robert could upset himself. +5305 Travis shouldn't think Samantha does remember herself. +5306 Dawn did think Alexander shouldn't alarm herself. +5307 Nicole's father could imagine Stacey loves herself. +5308 Bradley imagines Natalie remembers himself. +5309 Paul imagines Jodi hugs herself. +5310 Beverly can't explain Travis can't attack himself. +5311 Dan imagines Carmen will worry himself. +5312 Gerald thinks Linda doesn't forget herself. +5313 Theodore explains Diana will praise himself. +5314 Anne imagines Mark didn't criticize herself. +5315 Guy explains Jill can embarrass himself. +5316 Nina explains Michael will think about himself. +5317 Samuel says Jane sounds like himself. +5318 Roger imagines that girl appreciates himself. +5319 Omar could explain Naomi hugs himself. +5320 Ann says Dan respects himself. +5321 Tracy should say Brian could approach himself. +5322 Robert did say Beverly does shock himself. +5323 Alan wouldn't say Monica wouldn't dislike himself. +5324 Margaret wouldn't say Jason's son can't discover himself. +5325 Vanessa imagines those guys' grandson admires himself. +5326 Jason explains Beth did worry himself. +5327 Bill wouldn't think Nina would fire herself. +5328 Monica didn't explain Peter couldn't hurt himself. +5329 Edward imagines Jacqueline might appreciate himself. +5330 Kevin could explain Lisa boasts about himself. +5331 Anne can't say Omar can discuss himself. +5332 Patricia says Andrew should observe himself. +5333 Walter can say Heather watches himself. +5334 Irene's father would explain Nancy will complain about herself. +5335 Jane did explain Bill can't distract herself. +5336 Sara says Samuel observes himself. +5337 Alan imagines Andrea couldn't reveal himself. +5338 Melinda can't say Noah discovers herself. +5339 Beth can imagine Rodney shouldn't research herself. +5340 Tanya thinks Christopher kisses himself. +5341 Robert should say Marla hides herself. +5342 Keith says that waitress shouldn't see himself. +5343 Monica might think Todd can't worry himself. +5344 Matt can say a lot of boys' daughter might like himself. +5345 Florence can't explain Curtis shouldn't know himself. +5346 Brad can't explain Paula hates himself. +5347 Susan can't imagine Stephen praises himself. +5348 Kenneth thinks Allison could listen to himself. +5349 Every man does say a dancer criticizes himself. +5350 Veronica imagines Gary could help himself. +5351 Matt shouldn't explain Teresa did hurt himself. +5352 Gina says a guy shouldn't hate himself. +5353 A girl shouldn't think Jerry references himself. +5354 Suzanne imagines Lawrence fires himself. +5355 Raymond won't explain Marla shouldn't hide himself. +5356 Suzanne doesn't imagine Dan couldn't discover himself. +5357 Rose thinks Melissa's ex-husband appreciates himself. +5358 Homer can say Susan notices herself. +5359 Ronald might say Kendra hates himself. +5360 Rhonda didn't think Steven cares for herself. +5361 Regina couldn't say Donald's grandson could talk about himself. +5362 Joel's niece imagines Roger might astound himself. +5363 Denise couldn't say Bill might criticize herself. +5364 Cynthia explains Kevin might care for himself. +5365 Patricia wouldn't explain Joseph wouldn't hide herself. +5366 Kathleen explains Gerald describes herself. +5367 Theresa can't explain Jeffrey will describe herself. +5368 Bruce's brother can explain Jill won't hurt himself. +5369 Kristin's ex-boyfriend did say Mary shouldn't disagree with himself. +5370 Mark thinks Stacy leaves himself. +5371 Martin does explain Heather listens to herself. +5372 Kirsten could explain Raymond might find herself. +5373 Victoria thinks Paul could cure himself. +5374 Catherine imagines Jerry appreciates himself. +5375 Tiffany says Peter couldn't visit himself. +5376 Gregory did imagine Anne couldn't astound herself. +5377 Carlos says Christine praises himself. +5378 Ann won't say Thomas could scare herself. +5379 Curtis thinks Sally shouldn't annoy himself. +5380 Dennis explains Heidi would help himself. +5381 Liam could think Florence complains about himself. +5382 Amanda could say Kevin's brother remembers herself. +5383 Lisa will imagine Vincent knows himself. +5384 Tanya's boyfriend says this girl admires himself. +5385 Meredith should imagine James shouldn't notice herself. +5386 Denise does imagine Mitchell could hire himself. +5387 Larry says Caroline hugs himself. +5388 George did think Judy knows herself. +5389 Anne imagines Erin's grandfather might reference himself. +5390 Mary couldn't explain Scott does listen to herself. +5391 Carla explains Dan would confuse himself. +5392 Sherry wouldn't imagine Brett might investigate herself. +5393 Noah imagines Marla knows herself. +5394 Diana does say Brad questions himself. +5395 Dawn can't think Joel insults herself. +5396 Gina would explain Vincent sees himself. +5397 Karla says Steven should watch himself. +5398 This guy didn't explain Jill talks about herself. +5399 Beverly says Eric admires himself. +5400 Lisa can think Stephen would question herself. +5401 Grace didn't say Timothy doesn't know herself. +5402 Stacy doesn't imagine Craig's ex-husband notices himself. +5403 Emily says Mark reveals himself. +5404 Vincent thinks Michelle shouldn't conceal herself. +5405 Regina says Stephen praises herself. +5406 Julie explains Robert will notice himself. +5407 Erin wouldn't say William should watch himself. +5408 Carla thinks this guy might conceal himself. +5409 Homer won't think Valerie wouldn't work with herself. +5410 Regina will imagine Matt might discover herself. +5411 Gary imagines Elizabeth loves himself. +5412 Kristin thinks Richard can't criticize himself. +5413 Sarah did imagine Carlos shouldn't observe himself. +5414 Cynthia thinks Bill didn't describe himself. +5415 Aaron says Melissa's girlfriend might hire himself. +5416 Diane can't say Alexander hires himself. +5417 Alan should say Janice remembers himself. +5418 Kirsten imagines every guy cares for herself. +5419 Kevin imagines Kimberley won't criticize himself. +5420 Judy didn't explain Richard can alarm herself. +5421 Elaine could explain Marcus observes herself. +5422 Vincent couldn't think Angela does distract himself. +5423 Carlos says Matt's grandmother won't question herself. +5424 Gina's nephew did think Nina praises himself. +5425 Becca explains Samuel would confuse herself. +5426 Linda won't explain Carlos can return to himself. +5427 Kenneth imagines Sara couldn't talk about himself. +5428 Christina says Dan did talk about himself. +5429 Martin can't imagine Ann would alarm himself. +5430 Florence might explain Dan researches herself. +5431 Margaret's father could think Janet shouldn't astound herself. +5432 Timothy might explain Lori might dislike himself. +5433 Erin couldn't explain a guy talks to herself. +5434 Ronald explains Teresa should cure himself. +5435 Paula says Thomas can't cure himself. +5436 Carl could think some woman notices himself. +5437 Erin can explain Joseph did embarrass himself. +5438 Tiffany thinks Joel shouldn't listen to himself. +5439 Theodore does say Kayla should disgust himself. +5440 Victoria does say Homer will like himself. +5441 Bradley did explain Tammy sees herself. +5442 Russell couldn't imagine Alice researches himself. +5443 Brian thinks Dana remembers himself. +5444 Jennifer's ex-husband thinks Sara should approach herself. +5445 Kristin wouldn't say Gregory admires himself. +5446 Benjamin's grandson does imagine Monica hates herself. +5447 Eric didn't explain Monica did dislike herself. +5448 Eva thinks Joseph will conceal himself. +5449 Timothy explains Karen attacks herself. +5450 Gregory explains some actress did question himself. +5451 Carmen doesn't say Gary watches himself. +5452 Catherine explains Donald cures herself. +5453 Bradley thinks Claire loves herself. +5454 Richard explains Sheila could bore himself. +5455 Sherry explains Guy couldn't dislike himself. +5456 Aaron thinks Judy insults herself. +5457 Erin explains Kristin's grandson likes herself. +5458 Andrew thinks Lucille won't upset himself. +5459 Keith can't think Jacqueline will notice himself. +5460 Angela should imagine Derek did cure himself. +5461 Vincent shouldn't imagine Regina talks to herself. +5462 Omar imagines Caroline praises herself. +5463 Patrick's grandfather says Lori kisses herself. +5464 Deborah shouldn't imagine Lawrence won't visit himself. +5465 Bill won't think Andrea did notice herself. +5466 Nicole imagines this boy shouldn't return to himself. +5467 Irene explains Roger wouldn't know himself. +5468 Patricia explains Mark couldn't escape from himself. +5469 Kenneth explains Renee doesn't kiss himself. +5470 Curtis thinks Kathleen could talk to herself. +5471 Marcus won't explain an adult's daughter can remember himself. +5472 Timothy imagines Wendy might alarm himself. +5473 Natalie couldn't say Mitchell hates himself. +5474 Emily explains Travis likes himself. +5475 Patrick might say Martha might hide himself. +5476 Nicole thinks Roger will reveal herself. +5477 Lawrence will imagine Heidi wouldn't bother herself. +5478 Sharon explains Guy could boast about herself. +5479 Jennifer couldn't explain Brett wouldn't discuss herself. +5480 Vincent thinks Theresa boasts about himself. +5481 Kevin imagines Rachel could conceal himself. +5482 Samantha's sister explains Donald's ex-boyfriend didn't flee from himself. +5483 Marie's grandmother explains Jodi's father won't dislike herself. +5484 Jeffrey should say Meredith won't describe herself. +5485 Michelle will explain James shouldn't forget herself. +5486 Claire explains Liam's brother doesn't irritate himself. +5487 Rodney thinks Jennifer likes himself. +5488 Charles would say Karla could distract himself. +5489 Craig does say Kayla wouldn't love himself. +5490 Kristin says Paul finds herself. +5491 Naomi can explain Benjamin would work with himself. +5492 Joel shouldn't think Carol won't leave himself. +5493 Chad should think Donna could listen to himself. +5494 Kathleen thinks Kevin helps himself. +5495 Mitchell's nephew can't imagine Mary won't respect himself. +5496 Teachers' ex-wife thinks William listens to herself. +5497 Bill won't explain Martha likes himself. +5498 Timothy imagines Michelle didn't hug himself. +5499 Mitchell wouldn't imagine Julia describes himself. +5500 Steven shouldn't explain Carla scares himself. +5501 Lawrence thinks Jessica wouldn't question himself. +5502 Anna will explain Thomas couldn't hurt himself. +5503 Joel thinks Cheryl will admire himself. +5504 Russell says Margaret discovers himself. +5505 Emily thinks Robert references himself. +5506 Beth can explain Keith doesn't describe herself. +5507 Thomas says Pamela can embarrass himself. +5508 Marie explains Richard can't annoy himself. +5509 Chad can't say that dancer sounds like himself. +5510 Karla imagines every guy can't forget herself. +5511 Jerry imagines Meredith should think about himself. +5512 Brian explains Maria won't care for herself. +5513 Gerald did think Cindy doesn't scare himself. +5514 Eva says George's boyfriend observes himself. +5515 Tonya couldn't explain Adam questions herself. +5516 Bruce's husband could imagine Grace questions himself. +5517 Connie's wife can't imagine Peter returns to himself. +5518 Raymond says Cheryl doesn't bore himself. +5519 Martha didn't explain Theodore hires herself. +5520 Amelia didn't think Samuel cures herself. +5521 Edward shouldn't say Julie shouldn't irritate himself. +5522 Tracy could imagine George didn't dislike himself. +5523 Keith thinks Rachel didn't respect herself. +5524 Stacey's girlfriend can't say Kenneth won't talk about herself. +5525 Gerald imagines Anne talks to himself. +5526 Patients' grandson explains Debra wouldn't work with herself. +5527 Roger says Dawn insults herself. +5528 Janet thinks Candice's boyfriend finds herself. +5529 Gary would say Naomi returns to herself. +5530 Marie wouldn't think Deborah's father can upset herself. +5531 Jeffrey will say Melinda didn't return to himself. +5532 Cindy explains Liam notices himself. +5533 Steven shouldn't explain Alicia does disturb herself. +5534 Karen's girlfriend does explain Jeffrey won't disagree with herself. +5535 Paul says Anne dislikes himself. +5536 James did imagine Carol does hurt himself. +5537 Barbara explains Alexander did find himself. +5538 Brenda explains Bruce would conceal himself. +5539 Gary imagines Tamara talks about himself. +5540 Christine says Bradley couldn't question himself. +5541 Sabrina explains Joel approaches himself. +5542 Nicole thinks every man references himself. +5543 Tammy imagines Lawrence might hurt herself. +5544 Theodore explains Jessica helps herself. +5545 Victoria didn't imagine Russell does discover himself. +5546 Anna thinks Charles reveals himself. +5547 Jodi doesn't explain Curtis could scare himself. +5548 Homer did think Renee insults herself. +5549 Christopher says Debra could distract herself. +5550 Tamara shouldn't explain Christopher can aggravate herself. +5551 Stephen will imagine Heidi escapes from himself. +5552 Nicole explains Noah wouldn't like himself. +5553 Daniel thinks Eva won't praise himself. +5554 Ella should think the Borgias' ex-husband admires himself. +5555 Gerald explains Paula hates himself. +5556 Kimberley could think Derek couldn't visit himself. +5557 Charles thinks Carol fires himself. +5558 Marcus explains Benjamin's mother could talk about himself. +5559 Marcus explains Laura discovers himself. +5560 Anne explains Curtis leaves herself. +5561 Jason thinks Emily could visit himself. +5562 Melinda will explain Mitchell wouldn't describe himself. +5563 Bradley says Diana couldn't research himself. +5564 Dan explains the waitress might discover herself. +5565 Christine can say Martin's nephew won't astound herself. +5566 Patrick imagines Elaine won't upset himself. +5567 Mary explains Russell can argue about himself. +5568 Jerry imagines Linda can't appreciate himself. +5569 Beverly didn't imagine Eric describes himself. +5570 Adam could explain Karla can annoy himself. +5571 Julie imagines Anne's brother can visit himself. +5572 Patrick says an actress will sound like herself. +5573 Joel imagines Deborah hates herself. +5574 Amelia imagines Richard attacks himself. +5575 Margaret imagines Brad can't worry himself. +5576 Jacqueline thinks this man fires herself. +5577 A lot of senators' nephew thinks Tara shouldn't think about herself. +5578 Guy doesn't explain Carla thinks about herself. +5579 Alan says Andrea cares for himself. +5580 Jennifer imagines Derek likes himself. +5581 Stacy explains Brett's son discovers himself. +5582 Kendra wouldn't think Craig did appreciate himself. +5583 Kenneth says Julia does bore himself. +5584 Kristin can say Benjamin's grandson forgets himself. +5585 Deborah wouldn't think Derek does kiss herself. +5586 Bradley imagines Renee finds himself. +5587 Jill thinks Richard should distract herself. +5588 Larry might imagine Rebecca does leave himself. +5589 Pamela thinks Matt should impress herself. +5590 Thomas explains Naomi's girlfriend will hire himself. +5591 James will think Jane researches himself. +5592 Barbara can imagine Steven didn't impress herself. +5593 Karen could think David will see himself. +5594 Jeffrey's ex-girlfriend thinks Thomas should describe himself. +5595 Alan thinks Carrie could forget herself. +5596 Lawrence says Sabrina doesn't heal himself. +5597 Bruce imagines Amanda might annoy himself. +5598 Maria's wife might explain Keith couldn't bore himself. +5599 Craig imagines Teresa won't hide himself. +5600 Tammy doesn't think John would watch himself. +5601 Thomas wouldn't think the woman complains about herself. +5602 Katherine will imagine Peter leaves herself. +5603 Sabrina might think Joseph complains about himself. +5604 Nicole imagines Scott can conceal himself. +5605 Derek can imagine Allison reveals himself. +5606 Theodore thinks Tina will escape from himself. +5607 Donna would explain Ronald didn't shock himself. +5608 Patricia says Guy might scare himself. +5609 Vanessa will think Mark did discuss himself. +5610 April explains Bradley scares himself. +5611 David doesn't think Sonia might hate himself. +5612 Gerald thinks Margaret would heal himself. +5613 Linda might think a man did scare herself. +5614 Rhonda will say Ronald doesn't notice himself. +5615 Carolyn explains Douglas should hurt himself. +5616 Kendra might say Scott would bore himself. +5617 Beverly thinks Marie's son will complain about herself. +5618 Ronald says Kristin talks about herself. +5619 Matt won't think Stacy references himself. +5620 Bradley thinks Kevin's ex-wife might disagree with herself. +5621 Sarah will imagine Thomas didn't worry herself. +5622 Edward thinks Eva criticizes himself. +5623 Gina would explain Theodore thinks about himself. +5624 Irene thinks Liam shouldn't embarrass himself. +5625 William won't imagine Alice attacks herself. +5626 Curtis thinks Jacqueline didn't distract himself. +5627 Brad explains Stacey could talk to himself. +5628 Christina thinks George can listen to himself. +5629 Larry can't imagine Caroline doesn't praise herself. +5630 Ronald should say Kirsten wouldn't alarm himself. +5631 Nicole thinks Travis did conceal himself. +5632 Catherine thinks Liam does observe herself. +5633 Becca explains Steven thinks about himself. +5634 Gerald can't say some dancer forgets himself. +5635 Kayla should think Dawn's ex-husband helps herself. +5636 Eric says Andrea knows himself. +5637 Katherine does think Ruth's boyfriend did respect herself. +5638 Raymond imagines that woman likes himself. +5639 Alexander thinks Tracy respects himself. +5640 Carlos says Bethany can't appreciate herself. +5641 Craig thinks Nina couldn't question himself. +5642 The Lutherans' grandson can't imagine Catherine could know herself. +5643 Nicole can say Douglas won't shock himself. +5644 Travis imagines Tiffany's daughter flees from herself. +5645 Diane won't think John wouldn't worry himself. +5646 Kristen thinks Andrew references himself. +5647 Teresa might think the boy insults herself. +5648 Dan imagines Debra references herself. +5649 Natalie's grandmother will explain Lawrence does scare herself. +5650 Timothy will say Marie notices himself. +5651 Steve explains April hugs himself. +5652 Steven says Claire wouldn't listen to herself. +5653 Tracy explains Rodney reveals himself. +5654 Stephanie can think Claire's son couldn't boast about himself. +5655 Erin explains Marcus finds herself. +5656 Timothy says Nina discusses herself. +5657 Michelle's grandfather explains Monica might talk to himself. +5658 Tammy says Robert's boyfriend describes himself. +5659 Gary says Amanda can't annoy herself. +5660 Liam wouldn't explain Samantha could confuse himself. +5661 Steven imagines some woman questions himself. +5662 Heidi didn't imagine Rodney should cure himself. +5663 Robert's daughter explains Jessica's ex-boyfriend will watch himself. +5664 Douglas did explain Alice sounds like himself. +5665 Sheila doesn't say Pamela's son works with himself. +5666 Ruth imagines Edward can hide himself. +5667 Beth explains Rodney dislikes himself. +5668 Benjamin did imagine Sonia can't sound like herself. +5669 Gregory thinks the teenager's daughter would watch himself. +5670 Patrick imagines Danielle references herself. +5671 Sonia didn't imagine Lawrence describes herself. +5672 Steve might think Rose hates herself. +5673 Lawrence imagines Elizabeth approaches himself. +5674 Phillip says Bethany does know himself. +5675 Marcus could imagine Alice couldn't bore himself. +5676 Melissa could imagine Noah criticizes himself. +5677 Robert shouldn't say Carol hides himself. +5678 Diane does explain Steve didn't leave himself. +5679 Craig shouldn't say Theresa helps himself. +5680 Rhonda's wife won't imagine Gerald fires himself. +5681 Tiffany did imagine Donald won't talk about himself. +5682 Teresa imagines the customers' son shouldn't conceal himself. +5683 Alicia thinks Peter talks to himself. +5684 Diana explains Judy's grandson should astound herself. +5685 Homer won't think Michelle would attack himself. +5686 Gerald says Diane couldn't bore herself. +5687 Barbara thinks every senator's ex-husband kisses himself. +5688 Cynthia wouldn't say Alexander will scare herself. +5689 Vanessa says Chad hates himself. +5690 Alice imagines Eric approaches himself. +5691 Benjamin shouldn't say Victoria would appreciate himself. +5692 Marie says Patrick can aggravate himself. +5693 Jeffrey thinks Susan might heal herself. +5694 Jacqueline explains Noah boasts about herself. +5695 Russell thinks Debra doesn't astound herself. +5696 Barbara says Aaron respects himself. +5697 Marcus can explain Susan remembers herself. +5698 Kristin might imagine Robert wouldn't admire himself. +5699 Kendra might imagine Gary dislikes himself. +5700 Carl thinks Kirsten hates himself. +5701 Larry shouldn't think Gina shouldn't hate himself. +5702 Martin didn't explain Julie's ex-wife will attack himself. +5703 Ann thinks Rose's son discusses himself. +5704 Scott would think Cheryl did hire himself. +5705 Craig imagines Jill appreciates himself. +5706 Sherry does think Dennis could forget herself. +5707 Guy thinks Leslie cares for herself. +5708 Alan thinks Grace fires herself. +5709 Colleen couldn't explain Jill's nephew remembers herself. +5710 Christina does explain Eric leaves herself. +5711 Allison imagines Noah likes herself. +5712 Joel did explain Sonia will fire himself. +5713 Kevin imagines Elizabeth shouldn't listen to herself. +5714 Mark wouldn't imagine Marla couldn't reference herself. +5715 Florence could say Liam won't forget himself. +5716 Homer imagines Cindy scares herself. +5717 Theresa imagines the drivers' grandfather doesn't forget himself. +5718 Gary's granddaughter imagines Carl scares himself. +5719 Gary's nephew might say Emily can't stun himself. +5720 Anne's husband would explain Sonia could question himself. +5721 Steve will imagine Candice might reference himself. +5722 Helen would say this man hires himself. +5723 Todd imagines Erin couldn't bore himself. +5724 Jessica's grandson says Allison approaches herself. +5725 Beth imagines this guy can work with himself. +5726 Stephen says this dancer cares for himself. +5727 Kimberley did explain Walter loves himself. +5728 Cynthia won't explain James could heal himself. +5729 Tracy explains Robert complains about himself. +5730 Carrie shouldn't imagine Danielle's boyfriend will argue about himself. +5731 Nicole does explain Kenneth wouldn't help himself. +5732 Samantha imagines Gerald couldn't cure herself. +5733 Teresa says Gregory attacks himself. +5734 Matt explains Kendra will return to himself. +5735 Holly says a girl's grandfather should disagree with himself. +5736 Jennifer explains Dan cares for herself. +5737 Cheryl says Liam sees herself. +5738 Thomas didn't imagine Caroline attacks himself. +5739 Omar imagines Vanessa might visit herself. +5740 Diana says Dennis hates himself. +5741 Cheryl says this waiter's grandson can criticize himself. +5742 Anne can't imagine Rodney hides himself. +5743 Dennis couldn't imagine Donna will heal himself. +5744 Donald imagines Nancy shouldn't confuse himself. +5745 Samantha says Roger talks to himself. +5746 Timothy thinks Erin did describe himself. +5747 Renee says this guy didn't hug himself. +5748 Stephanie might explain John did remember himself. +5749 Judy can't explain Joseph leaves himself. +5750 Florence explains Scott does kiss himself. +5751 Curtis thinks Jennifer kisses herself. +5752 Nina imagines Thomas couldn't argue about herself. +5753 April should imagine Brad researches himself. +5754 Karen's brother would say April could conceal himself. +5755 Colleen explains Steven can't praise himself. +5756 Jason didn't think Ann couldn't find himself. +5757 Kristen doesn't imagine Samuel will question himself. +5758 It's himself that attacked this cashier. +5759 It's herself that attacked Andrea. +5760 It's herself that Sharon talked about. +5761 It's himself that Todd described. +5762 It's herself who criticized Karen. +5763 It's itself that some ice isn't stunning. +5764 It's himself who discusses Robert. +5765 It's herself who wasn't firing Judy. +5766 It's herself that Carmen won't stun. +5767 It's herself who did work with Gina. +5768 It's itself that would stun that pork. +5769 It's itself that has referenced this committee. +5770 It's herself that couldn't insult Allison. +5771 It's themselves that haven't disgusted many college campuses. +5772 It's himself that Guy likes. +5773 It's himself who astounded Bill. +5774 It's herself that Rose had talked to. +5775 It's himself who attacked Robert. +5776 It's herself who stunned Jodi. +5777 It's themselves that weren't distracting all dishes. +5778 It's themselves that can't astound some legislatures. +5779 It's itself that the cake isn't alarming. +5780 It's herself who can talk about Tamara. +5781 It's herself who had argued about Andrea. +5782 It's herself that Linda wasn't describing. +5783 It's himself who didn't hide Russell. +5784 It's themselves who hadn't disagreed with the Lutherans. +5785 It's himself who isn't hurting Gerald. +5786 It's himself who visits Steve. +5787 It's herself who should boast about Sabrina. +5788 It's himself that Wayne was questioning. +5789 It's herself who had bored Stacy. +5790 It's herself that Veronica was curing. +5791 It's itself that has disturbed that commentary. +5792 It's themselves that all men knew. +5793 It's herself who respected Sally. +5794 It's herself who hasn't impressed Denise. +5795 It's itself that some print hurt. +5796 It's himself that hasn't irritated Donald. +5797 It's herself who attacks Denise. +5798 It's herself that alarmed Jane. +5799 It's herself who was insulting Margaret. +5800 It's himself who hated Scott. +5801 It's itself that impressed a cart. +5802 It's herself who couldn't bore Stacey. +5803 It's herself that some actress was fleeing from. +5804 It's himself that flees from Steve. +5805 It's herself that Judy argued about. +5806 It's himself that David complained about. +5807 It's herself who Brenda wasn't firing. +5808 It's themselves that most pedestrians were complaining about. +5809 It's herself who hasn't seen Caroline. +5810 It's herself that Tina is hiding. +5811 It's himself that William is attacking. +5812 It's herself that visits Carmen. +5813 It's herself that Laura has investigated. +5814 It's himself who Douglas was listening to. +5815 It's themselves that the people disagree with. +5816 It's herself who was irritating Judy. +5817 It's herself who forgets Nina. +5818 It's herself that cured Rachel. +5819 It's himself who hates Kevin. +5820 It's herself that was finding Kendra. +5821 It's herself who isn't insulting Cynthia. +5822 It's herself who Sally should think about. +5823 It's herself that Sandra did leave. +5824 It's himself that is insulting Jerry. +5825 It's themselves that most ladies returned to. +5826 It's herself that every waiter can escape from. +5827 It's herself that Sherry was praising. +5828 It's itself that the cafe is 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It's himself who returned to Omar. +5852 It's himself who watched Bruce. +5853 It's herself who hadn't hated Samantha. +5854 It's herself who hasn't talked to Tara. +5855 It's itself that some organization talks about. +5856 It's himself that David had left. +5857 It's herself who has helped Maria. +5858 It's herself that investigated Patricia. +5859 It's himself that was irritating Bradley. +5860 It's herself who likes Meredith. +5861 It's himself that cures Joseph. +5862 It's herself who has boasted about Anna. +5863 It's herself who approaches Victoria. +5864 It's herself who couldn't leave Sabrina. +5865 It's themselves that the teachers were concealing. +5866 It's itself that isn't confusing the door. +5867 It's themselves who some actors might leave. +5868 It's herself who is praising Candice. +5869 It's herself who helps Monica. +5870 It's herself who visits Carol. +5871 It's herself who didn't describe Nancy. +5872 It's herself who wasn't referencing Elizabeth. +5873 It's herself who returned to Debra. +5874 It's herself who wasn't attacking some girl. +5875 It's herself who isn't watching Candice. +5876 It's themselves that all governments described. +5877 It's themselves that a lot of drivers are investigating. +5878 It's herself that had investigated Danielle. +5879 It's herself who Nancy did remember. +5880 It's herself who listens to Holly. +5881 It's herself that has bothered Claire. +5882 It's herself that didn't cure Florence. +5883 It's itself that scared a scarf. +5884 It's herself that Rose was working with. +5885 It's themselves that most stories haven't disagreed with. +5886 It's himself that shouldn't attack Steven. +5887 It's herself who kisses Janice. +5888 It's himself that won't describe Jeffrey. +5889 It's herself that Christine did discuss. +5890 It's herself who isn't returning to Tamara. +5891 It's herself that hasn't investigated Katherine. +5892 It's herself who wasn't referencing every customer. +5893 It's herself who knew Vanessa. +5894 It's itself that annoyed the blouse. +5895 It's itself that scares some shirt. +5896 It's herself that works with Kayla. +5897 It's himself that Phillip criticizes. +5898 It's herself that Jill couldn't think about. +5899 It's herself that doesn't discuss Tiffany. +5900 It's herself that can shock Rachel. +5901 It's himself that listens to Guy. +5902 It's herself that Dawn wouldn't dislike. +5903 It's herself who hadn't distracted that dancer. +5904 It's herself that fled from Carol. +5905 It's himself who won't escape from Benjamin. +5906 It's herself that Amy had talked to. +5907 It's herself that questioned Irene. +5908 It's himself that can sound like Douglas. +5909 It's himself who Keith was noticing. +5910 It's himself that Christopher talks about. +5911 It's himself that insulted Kevin. +5912 It's herself who has annoyed Debra. +5913 It's himself who hasn't insulted Mitchell. +5914 It's themselves that weren't aggravating those screens. +5915 It's themselves that the men healed. +5916 It's herself that every actor appreciates. +5917 It's herself that some lady shouldn't see. +5918 It's herself who annoyed that patient. +5919 It's himself who hadn't appreciated Marcus. +5920 It's herself that helps Jessica. +5921 It's herself who did fire Sharon. +5922 It's himself who had healed Raymond. +5923 It's herself who bothered some doctor. +5924 It's himself that Bradley isn't irritating. +5925 It's himself that John liked. +5926 It's herself who likes Karla. +5927 It's itself that impressed travel. +5928 It's herself that Cynthia should reference. +5929 It's themselves that have concealed many pedestrians. +5930 It's herself that insulted Nancy. +5931 It's themselves who most waitresses had boasted about. +5932 It's herself that some girl wasn't listening to. +5933 It's itself that every report shouldn't annoy. +5934 It's herself that Martha wasn't appreciating. +5935 It's themselves who respected most teachers. +5936 It's themselves that guests were 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+5958 It's herself that Julie cared for. +5959 It's himself that Raymond should complain about. +5960 It's herself who hasn't helped a girl. +5961 It's themselves who these patients haven't cared for. +5962 It's themselves that the reports disagree with. +5963 It's himself that referenced that student. +5964 It's herself that Carrie boasted about. +5965 It's herself that Maria found. +5966 It's herself who visits Stacey. +5967 It's herself that hadn't escaped from Christina. +5968 It's themselves that the screens aren't disgusting. +5969 It's themselves that were referencing a lot of schools. +5970 It's herself who saw Danielle. +5971 It's herself who wasn't annoying Sally. +5972 It's themselves that all governments do remember. +5973 It's herself who appreciated Barbara. +5974 It's herself that Janice sees. +5975 It's herself who wouldn't kiss Laura. +5976 It's herself that isn't appreciating Martha. +5977 It's herself who cares for Tanya. +5978 It's himself that Matt worked with. +5979 It's himself that could alarm Omar. +5980 It's themselves that shocked the mushrooms. +5981 It's herself that wasn't curing Kristin. +5982 It's herself who some student might visit. +5983 It's herself that Natalie does complain about. +5984 It's herself who will attack Sarah. +5985 It's himself that this boy hasn't visited. +5986 It's herself who couldn't hate Rebecca. +5987 It's himself who shouldn't distract Adam. +5988 It's herself that Kristin hadn't described. +5989 It's himself that some teacher was concealing. +5990 It's himself that heals Michael. +5991 It's herself who Renee notices. +5992 It's herself who cures Ella. +5993 It's themselves that all paintings will embarrass. +5994 It's himself that Guy complained about. +5995 It's herself who complains about Diane. +5996 It's himself that Thomas was helping. +5997 It's himself that Douglas hadn't hated. +5998 It's herself who didn't leave Sandra. +5999 It's herself who hasn't aggravated Eva. +6000 It's himself that every pedestrian is embarrassing. +6001 It's herself who admires Marie. +6002 It's himself that loved Jerry. +6003 It's herself that Renee dislikes. +6004 It's themselves that hadn't disgusted these cakes. +6005 It's himself who complains about Bill. +6006 It's herself that does question Margaret. +6007 It's herself who isn't concealing Debra. +6008 It's herself that doesn't hire Patricia. +6009 It's herself who Erin wasn't revealing. +6010 It's themselves that many ladies had disagreed with. +6011 It's herself who doesn't return to Renee. +6012 It's themselves that these dancers weren't revealing. +6013 It's herself that Diane is questioning. +6014 It's themselves who sound like most teenagers. +6015 It's herself that Suzanne revealed. +6016 It's itself that scares every lake. +6017 It's himself who observes Kevin. +6018 It's himself who references Richard. +6019 It's himself who doesn't hire Homer. +6020 It's themselves that confused the high schools. +6021 It's herself who returned to Rose. +6022 It's himself who criticizes Derek. +6023 It's himself that escapes from Alexander. +6024 It's itself that isn't hurting the plane. +6025 It's herself who questioned Carmen. +6026 It's herself who isn't hiding Deanna. +6027 It's herself who left Donna. +6028 It's himself who has questioned Walter. +6029 It's himself that does question Alexander. +6030 It's herself that Carrie isn't watching. +6031 It's himself who has visited James. +6032 It's himself who watched Kenneth. +6033 It's herself who Angela did talk about. +6034 It's himself that this waiter talks about. +6035 It's herself that Heather is arguing about. +6036 It's herself who should talk to Tara. +6037 It's itself that references the legislature. +6038 It's herself that Rose does think about. +6039 It's themselves that the men will remember. +6040 It's themselves who are hugging a lot of children. +6041 It's himself who talked to Brad. +6042 It's herself that Christina listened to. +6043 It's himself who didn't watch Timothy. +6044 It's itself that some hospital couldn't argue about. +6045 It's herself that discovers Jacqueline. +6046 It's herself who was appreciating Kirsten. +6047 It's himself that Theodore had shocked. +6048 It's himself that Brett hadn't admired. +6049 It's himself who does find Noah. +6050 It's herself that appreciated Monica. +6051 It's herself who does stun Allison. +6052 It's himself that fired Guy. +6053 It's herself that Allison had watched. +6054 It's himself that flees from Liam. +6055 It's himself who hadn't healed Frank. +6056 It's himself who Marcus had cared for. +6057 It's herself that Emily hadn't hidden. +6058 It's herself that described Leslie. +6059 It's himself that David was noticing. +6060 It's themselves that might scare these hills. +6061 It's himself who disturbed Clyde. +6062 It's himself who could notice Frank. +6063 It's herself that had confused Melanie. +6064 It's himself that Rodney talked about. +6065 It's himself who had kissed Mitchell. 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It's herself that boasts about Tamara. +6089 It's themselves that most sweaters are embarrassing. +6090 It's himself who Rodney had healed. +6091 It's himself who Wayne heals. +6092 It's themselves that many guests won't embarrass. +6093 It's themselves that should shock those committees. +6094 It's himself that dislikes Peter. +6095 It's herself who was disturbing Rachel. +6096 It's itself that the skirt wasn't shocking. +6097 It's himself that Patrick wasn't listening to. +6098 It's itself that every book hasn't disagreed with. +6099 It's himself that Joel should like. +6100 It's himself that should attack Bradley. +6101 It's herself who sounded like a woman. +6102 It's herself that is confusing Melinda. +6103 It's himself who distracted Paul. +6104 It's himself who sees Raymond. +6105 It's herself who cared for Carol. +6106 It's himself that is worrying Chad. +6107 It's himself that Eric would notice. +6108 It's herself who notices Tanya. +6109 It's themselves that a lot of teenagers were thinking about. +6110 It's herself that Colleen has concealed. +6111 It's himself who questions Brian. +6112 It's itself that has impressed this screen. +6113 It's themselves who many actresses weren't hiding. +6114 It's herself who questions Pamela. +6115 It's itself that a sweater didn't disturb. +6116 It's himself that hasn't sounded like Matt. +6117 It's themselves that some ladies hadn't sounded like. +6118 It's himself who fires Kevin. +6119 It's herself that flees from Sally. +6120 It's itself that every glass wasn't irritating. +6121 It's herself that Jill hadn't noticed. +6122 It's herself who will heal an actress. +6123 It's himself who Derek might appreciate. +6124 It's itself that wasn't irritating a drawing. +6125 It's himself who doesn't attack Scott. +6126 It's himself who has worried the pedestrian. +6127 It's herself who escaped from Carol. +6128 It's himself who should observe Daniel. +6129 It's himself who Wayne won't describe. +6130 It's themselves who aren't watching the Borgias. +6131 It's herself who was concealing Kristin. +6132 It's herself that some waitress discovered. +6133 It's himself who is criticizing Noah. +6134 It's herself who isn't talking to Cheryl. +6135 It's himself that Russell should appreciate. +6136 It's herself who finds Candice. +6137 It's herself that hires Rose. +6138 It's herself who was scaring Lisa. +6139 It's himself that Walter had hired. +6140 It's themselves who sound like a lot of boys. +6141 It's himself who returns to Todd. +6142 It's herself that Samantha was hugging. +6143 It's themselves who many dancers aren't worrying. +6144 It's herself who remembers Brenda. +6145 It's himself who Carl hadn't thought about. +6146 It's herself that sounded like Karen. +6147 It's herself that Rhonda wasn't discussing. +6148 It's herself that Florence is talking to. +6149 It's himself that discussed Thomas. +6150 It's herself who isn't boring Suzanne. +6151 It's himself who hasn't bothered Bradley. +6152 It's itself that sounds like this play. +6153 It's herself that hasn't questioned Dana. +6154 It's himself who that man shouldn't worry. +6155 It's herself that Brenda hasn't talked about. +6156 It's herself that Kimberley hadn't described. +6157 It's himself who irritated that customer. +6158 It's himself who wouldn't reveal Larry. +6159 It's herself who wouldn't admire Victoria. +6160 It's herself who found a student. +6161 It's herself that could shock Cheryl. +6162 It's herself that doesn't kiss Sheila. +6163 It's himself who could worry Peter. +6164 It's herself that Judy hadn't seen. +6165 It's himself that David observes. +6166 It's himself who knew Omar. +6167 It's himself who is admiring Chad. +6168 It's herself that Janet wasn't praising. +6169 It's themselves who these boys disagree with. +6170 It's himself that Andrew has talked about. +6171 It's herself who can watch Samantha. +6172 It's itself that upset that mirror. +6173 It's themselves that the pictures do bore. +6174 It's herself who was visiting a senator. +6175 It's himself that hasn't impressed this man. +6176 It's herself who can complain about Sabrina. +6177 It's themselves that legislatures haven't boasted about. +6178 It's himself that can't worry George. +6179 It's himself who Stephen couldn't boast about. +6180 It's themselves that boys haven't argued about. +6181 It's herself who does worry Susan. +6182 It's herself who doesn't escape from Rebecca. +6183 It's herself that Emily should like. +6184 It's herself who investigated every cashier. +6185 It's herself who hadn't attacked Jacqueline. +6186 It's himself that Eric isn't approaching. +6187 It's herself that is irritating Suzanne. +6188 It's himself who wouldn't help Curtis. +6189 It's themselves who had attacked a lot of women. +6190 It's herself who upset Florence. +6191 It's himself who every adult wasn't thinking about. +6192 It's himself that will like Noah. +6193 It's herself that Rhonda had approached. +6194 It's themselves who weren't escaping from the waiters. +6195 It's themselves that those museums are concealing. +6196 It's herself that forgets Jacqueline. +6197 It's himself who wasn't discovering Phillip. +6198 It's himself that this teenager didn't notice. +6199 It's herself who will conceal Patricia. +6200 It's themselves who aren't firing some senators. +6201 It's himself who would return to Joseph. +6202 It's herself who dislikes Nina. +6203 It's themselves that most schools are disgusting. +6204 It's herself that bored Tiffany. +6205 It's himself that Phillip referenced. +6206 It's herself who can hire Valerie. +6207 It's herself that irritated Stephanie. +6208 It's themselves that confused a lot of banks. +6209 It's herself who won't remember Sheila. +6210 It's himself that criticizes Scott. +6211 It's themselves that all pedestrians disagreed with. +6212 It's himself that hides Theodore. +6213 It's himself who didn't talk about the customer. +6214 It's herself that describes Jennifer. +6215 It's herself that Claire thought about. +6216 It's herself that Victoria would argue about. +6217 It's herself who helps Cynthia. +6218 It's herself who won't describe Ruth. +6219 It's herself that this dancer could watch. +6220 It's themselves that most rugs hadn't annoyed. +6221 It's herself that some pedestrian has noticed. +6222 It's herself who can't remember Wendy. +6223 It's himself that Rodney remembers. +6224 It's herself that can help Jane. +6225 It's themselves that bored those committees. +6226 It's himself who hasn't argued about Dan. +6227 It's itself that an organization should know. +6228 It's herself who isn't insulting Carolyn. +6229 It's herself that attacks Pamela. +6230 It's himself that a person has approached. +6231 It's herself who wasn't boring every student. +6232 It's herself who saw Karla. +6233 It's herself that Stephanie didn't alarm. +6234 It's himself who insults Theodore. +6235 It's himself that couldn't annoy this customer. +6236 It's herself that wasn't observing Jennifer. +6237 It's herself who isn't upsetting Tanya. +6238 It's herself that every doctor wasn't embarrassing. +6239 It's herself who hugs Veronica. +6240 It's itself that criticized some association. +6241 It's himself that helps Christopher. +6242 It's herself who left that teenager. +6243 It's himself who hated every person. +6244 It's itself that every company should boast about. +6245 It's themselves that most cups haven't worried. +6246 It's itself that dislikes that organization. +6247 It's himself who does sound like Paul. +6248 It's herself who appreciates Carolyn. +6249 It's herself that returned to Jennifer. +6250 It's herself who is worrying Lucille. +6251 It's herself who has respected Stephanie. +6252 It's herself that Kimberley isn't kissing. +6253 It's itself that respects an association. +6254 It's himself who couldn't question Lawrence. +6255 It's himself that an actor returns to. +6256 It's themselves who those girls talked about. +6257 It's herself that didn't conceal Julia. +6258 It's herself that Lori had aggravated. +6259 It's himself who insulted Craig. +6260 It's himself who notices Wayne. +6261 It's herself that could sound like Sally. +6262 It's himself who sounds like Bradley. +6263 It's herself who had left Rachel. +6264 It's herself who does watch Donna. +6265 It's themselves who hadn't escaped from these students. +6266 It's herself that Claire was admiring. +6267 It's himself that Brad won't listen to. +6268 It's herself that Becca isn't revealing. +6269 It's themselves that wouldn't insult legislatures. +6270 It's himself who discusses Bill. +6271 It's himself that Andrew is returning to. +6272 It's herself who won't insult Lucille. +6273 It's herself that argued about Irene. +6274 It's himself who healed every senator. +6275 It's herself who discovers Jill. +6276 It's themselves that many waiters won't love. +6277 It's herself who wasn't fleeing from Monica. +6278 It's herself that disliked Martha. +6279 It's themselves who aren't concealing senators. +6280 It's himself that Martin has noticed. +6281 It's themselves who were embarrassing the girls. +6282 It's herself that helps Veronica. +6283 It's himself who hired Travis. +6284 It's herself that fires Kimberley. +6285 It's herself who sounds like Kristen. +6286 It's herself that has irritated Naomi. +6287 It's himself that reveals Lawrence. +6288 It's himself that Steven isn't finding. +6289 It's herself that Elaine complained about. +6290 It's herself that sounds like April. +6291 It's themselves that hospitals could admire. +6292 It's himself who was arguing about Homer. +6293 It's himself who insulted a cashier. +6294 It's herself who loved Danielle. +6295 It's itself that talks about that company. +6296 It's herself that visits a dancer. +6297 It's herself that Bethany has listened to. +6298 It's herself who hated Melinda. +6299 It's herself who kisses Carla. +6300 It's himself that impressed Peter. +6301 It's herself who is working with Ruth. +6302 It's himself that wasn't curing Thomas. +6303 It's themselves that those girls weren't thinking about. +6304 It's herself that Lucille was hugging. +6305 It's himself who hadn't worried that student. +6306 It's herself that some dancer hasn't approached. +6307 It's himself that wasn't admiring Kenneth. +6308 It's herself that helps Becky. +6309 It's herself who attacked Susan. +6310 It's herself who the person disagrees with. +6311 It's herself that hasn't visited Kristin. +6312 It's herself who referenced Becky. +6313 It's itself that the newspaper article might bother. +6314 It's himself that Russell approaches. +6315 It's himself that appreciates Bruce. +6316 It's herself who was hurting Veronica. +6317 It's herself that Kayla was hugging. +6318 It's herself that disagrees with Christina. +6319 It's themselves that rabbits weren't upsetting. +6320 It's herself that escapes from Kayla. +6321 It's herself who is firing Tamara. +6322 It's themselves who those teenagers did 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themselves that those children aggravated. +6345 It's himself who hugs Peter. +6346 It's herself who might cure Natalie. +6347 It's herself who won't listen to Amy. +6348 It's herself that the lady wouldn't respect. +6349 It's himself that kissed Guy. +6350 It's herself that Tanya wasn't hiding. +6351 It's herself who knew Patricia. +6352 It's themselves who aren't disagreeing with the doctors. +6353 It's himself who noticed Russell. +6354 It's themselves that stunned skateboards. +6355 It's herself who Renee didn't appreciate. +6356 It's herself that the doctor approaches. +6357 It's herself who isn't questioning Angela. +6358 It's herself that Janet has cared for. +6359 It's herself who loved Donna. +6360 It's themselves that don't praise these legislatures. +6361 It's herself who isn't hurting Janet. +6362 It's himself who didn't approach Andrew. +6363 It's himself that Thomas could see. +6364 It's herself who loves Kendra. +6365 It's itself that every turtle alarmed. +6366 It's herself who embarrassed Patricia. +6367 It's herself that Caroline shouldn't discuss. +6368 It's herself who wasn't talking about Natalie. +6369 It's herself who shouldn't admire Natalie. +6370 It's herself that has respected Lisa. +6371 It's herself that Kristin observed. +6372 It's herself who works with this actor. +6373 It's herself who forgot Meredith. +6374 It's herself who visited Kendra. +6375 It's themselves that a lot of committees aren't describing. +6376 It's himself who respects Marcus. +6377 It's herself that isn't talking to Kendra. +6378 It's herself that can reference Sonia. +6379 It's himself that Donald hadn't concealed. +6380 It's himself that Paul was returning to. +6381 It's themselves that didn't upset the coats. +6382 It's itself that upset music. +6383 It's themselves who most guests wouldn't disagree with. +6384 It's himself who hugged Samuel. +6385 It's herself that had fired Irene. +6386 It's herself that Rhonda observes. +6387 It's herself who was discussing Heather. +6388 It's herself who hasn't stunned Sarah. +6389 It's herself that Tina hasn't attacked. +6390 It's herself who has insulted Suzanne. +6391 It's itself that every association isn't worrying. +6392 It's themselves that the women disagreed with. +6393 It's himself that wasn't criticizing Scott. +6394 It's herself that this waitress forgets. +6395 It's themselves that distracted a lot of pants. +6396 It's herself who complained about Valerie. +6397 It's himself who complains about Curtis. +6398 It's themselves that didn't hurt the Impressionists. +6399 It's himself who had scared James. +6400 It's herself who hasn't argued about Sheila. +6401 It's themselves that all trucks hurt. +6402 It's himself that cures Todd. +6403 It's himself that Peter is thinking about. +6404 It's himself that cures Omar. +6405 It's herself that sounds like Samantha. +6406 It's herself who wasn't impressing Leslie. +6407 It's himself who had stunned Peter. +6408 It's herself who did conceal Paula. +6409 It's himself who cares for William. +6410 It's herself that hadn't worked with Melanie. +6411 It's itself that aggravated that commentary. +6412 It's herself that discovers Anna. +6413 It's herself who helps Catherine. +6414 It's itself that that company isn't listening to. +6415 It's herself that isn't aggravating Teresa. +6416 It's himself who is returning to that cashier. +6417 It's herself who hasn't worried Monica. +6418 It's herself that Rebecca boasted about. +6419 It's herself who had questioned Naomi. +6420 It's herself who Kirsten is thinking about. +6421 It's herself that wasn't hugging Amanda. +6422 It's herself that an adult hasn't thought about. +6423 It's themselves that many steps are bothering. +6424 It's herself who fired a guest. +6425 It's itself that disturbed some lake. +6426 It's herself that fires Meredith. +6427 It's herself that Carolyn might watch. +6428 It's herself who has described Angela. +6429 It's himself who Curtis was discovering. +6430 It's herself who had criticized Rose. +6431 It's himself that Larry is kissing. +6432 It's herself that Caroline dislikes. +6433 It's herself who wasn't discovering Amanda. +6434 It's themselves that had stunned some chairs. +6435 It's himself that wasn't aggravating Peter. +6436 It's herself who cared for the waiter. +6437 It's herself that returned to Suzanne. +6438 It's themselves that most teenagers have criticized. +6439 It's herself who was questioning Deanna. +6440 It's himself that hadn't bothered John. +6441 It's itself that disturbs that dress. +6442 It's himself who will hide Craig. +6443 It's himself that wasn't helping Lawrence. +6444 It's herself who sees Karla. +6445 It's herself that Allison wasn't watching. +6446 It's herself that fired Nancy. +6447 It's itself that some dress isn't annoying. +6448 It's himself that Clyde will notice. +6449 It's himself who isn't talking about Noah. +6450 It's herself who did fire Theresa. +6451 It's themselves that some muffins do distract. +6452 It's themselves who aren't noticing dancers. +6453 It's himself that Guy has noticed. +6454 It's himself that Martin was embarrassing. +6455 It's themselves that the boxes have distracted. +6456 It's herself that Vanessa listened to. +6457 It's himself that Eric has concealed. +6458 It's herself who has kissed Sara. +6459 It's himself who should conceal Bruce. +6460 It's themselves who shouldn't leave many waitresses. +6461 It's herself that isn't attacking Mary. +6462 It's herself who wasn't listening to Tanya. +6463 It's itself that has embarrassed some jacket. +6464 It's himself that will conceal George. +6465 It's himself who flees from Bruce. +6466 It's themselves that a lot of people had insulted. +6467 It's herself who worked with Beth. +6468 It's himself who did confuse the driver. +6469 It's himself who hasn't healed Bruce. +6470 It's herself that criticized Judy. +6471 It's herself that wasn't attacking Patricia. +6472 It's itself that impressed that hat. +6473 It's himself who could boast about Gary. +6474 It's herself that concealed Heather. +6475 It's herself that discusses Irene. +6476 It's themselves that the ladies might work with. +6477 It's herself who hadn't remembered Susan. +6478 It's itself that Harvard University had criticized. +6479 It's himself who wouldn't talk about Theodore. +6480 It's herself that works with Heidi. +6481 It's herself that Melissa knew. +6482 It's himself who finds Derek. +6483 It's herself that worried Elizabeth. +6484 It's himself who couldn't hire Donald. +6485 It's herself that wasn't aggravating Catherine. +6486 It's himself that is praising Eric. +6487 It's himself that isn't confusing Daniel. +6488 It's herself that was talking to Lisa. +6489 It's himself that hadn't bothered Bradley. +6490 It's himself that hides Gerald. +6491 It's herself that cures that pedestrian. +6492 It's herself who wasn't annoying Stacey. +6493 It's itself that would impress this commentary. +6494 It's herself that hides Elizabeth. +6495 It's herself who would hide Meredith. +6496 It's herself that is admiring Rachel. +6497 It's herself that sounds like Dawn. +6498 It's herself who criticizes Heidi. +6499 It's himself that Patrick had disliked. +6500 It's himself who this guest will discuss. +6501 It's themselves that irritated the shawls. +6502 It's himself that Daniel will like. +6503 It's themselves that most cakes are embarrassing. +6504 It's herself who works with Amelia. +6505 It's herself who can't kiss Florence. +6506 It's herself who concealed Rebecca. +6507 It's himself who finds Phillip. +6508 It's itself that Harvard University wasn't describing. +6509 It's herself that Sherry shouldn't discover. +6510 It's herself who wasn't hiding Melanie. +6511 It's himself who is criticizing Brett. +6512 It's herself who fires Martha. +6513 It's themselves that these men liked. +6514 It's herself who disagrees with Jodi. +6515 It's herself that cured Michelle. +6516 It's himself who hasn't concealed Travis. +6517 It's herself who did admire Eva. +6518 It's himself who hired Edward. +6519 It's herself that aggravated Dana. +6520 It's herself that Karen could see. +6521 It's themselves that a lot of teenagers aren't astounding. +6522 It's herself who won't discover Catherine. +6523 It's herself that Diana is arguing about. +6524 It's herself that kisses Maria. +6525 It's himself who loves Todd. +6526 It's himself who heals Roger. +6527 It's himself who hadn't thought about Mark. +6528 It's himself that concealed that guest. +6529 It's herself that Carrie wouldn't hug. +6530 It's himself who complained about Guy. +6531 It's herself that disgusted Marie. +6532 It's themselves that will scare some snakes. +6533 It's herself who wasn't distracting the dancer. +6534 It's herself who hadn't annoyed Monica. +6535 It's themselves who disagreed with many patients. +6536 It's themselves that a lot of cucumbers are aggravating. +6537 It's herself that didn't aggravate that waitress. +6538 It's himself that Michael didn't investigate. +6539 It's themselves that all students haven't healed. +6540 It's himself that Eric can appreciate. +6541 It's herself who hugged Leslie. +6542 It's herself that hadn't hidden Cheryl. +6543 It's herself that discusses Julia. +6544 It's himself that Marcus has watched. +6545 It's herself who might hide Nancy. +6546 It's herself that Cynthia appreciates. +6547 It's themselves that most plays hadn't worried. +6548 It's himself who should boast about Roger. +6549 It's themselves that the Great Lakes can irritate. +6550 It's herself that Christina wasn't kissing. +6551 It's herself who Beth is leaving. +6552 It's himself that should disgust Michael. +6553 It's herself who has disliked Carol. +6554 It's itself that that committee should forget. +6555 It's herself that scared Rhonda. +6556 It's herself that Sharon couldn't appreciate. +6557 It's herself who shouldn't attack Denise. +6558 It's himself that doesn't astound Omar. +6559 It's herself who leaves Becca. +6560 It's herself that Carla did talk to. +6561 It's herself who fled from Denise. +6562 It's himself who hadn't found James. +6563 It's herself that a patient discusses. +6564 It's themselves that the mirrors aren't shocking. +6565 It's itself that every skateboard wouldn't upset. +6566 It's herself who hasn't boasted about Melissa. +6567 It's itself that has upset some skateboard. +6568 It's herself who did observe Christine. +6569 It's herself that Nancy shouldn't admire. +6570 It's himself who hugged Joel. +6571 It's herself that Tamara wasn't concealing. +6572 It's herself who isn't alarming the driver. +6573 It's herself who hasn't sounded like Anna. +6574 It's himself that Edward was concealing. +6575 It's herself that stunned Andrea. +6576 It's himself who is watching Bruce. +6577 It's herself that watches Gina. +6578 It's herself that some guest knew. +6579 It's herself who Nicole couldn't appreciate. +6580 It's herself that that adult is investigating. +6581 It's themselves who those students disagree with. +6582 It's himself who praises Matt. +6583 It's himself who escaped from Keith. +6584 It's himself who isn't discovering Ronald. +6585 It's herself that discusses Sherry. +6586 It's herself who approaches Karen. +6587 It's themselves that these girls hadn't cared for. +6588 It's themselves that some planes stunned. +6589 It's themselves who shouldn't visit a lot of adults. +6590 It's himself who has described that teenager. +6591 It's herself who Linda had listened to. +6592 It's himself that Carlos escaped from. +6593 It's himself that Lawrence had boasted about. +6594 It's herself who was irritating Emily. +6595 It's themselves that helped banks. +6596 It's himself that Rodney cared for. +6597 It's herself that Carla has hated. +6598 It's themselves that the men would return to. +6599 It's himself who every boy was thinking about. +6600 It's himself who hadn't visited Brett. +6601 It's himself that Joseph has watched. +6602 It's itself that every essay does hurt. +6603 It's himself that might sound like Raymond. +6604 It's herself who couldn't find Christina. +6605 It's herself that wouldn't forget Nina. +6606 It's herself that hid Dana. +6607 It's themselves who had fired most senators. +6608 It's themselves that many carts couldn't disturb. +6609 It's themselves that many students weren't praising. +6610 It's himself who kissed David. +6611 It's himself that Steve discussed. +6612 It's himself that discusses Larry. +6613 It's herself who had approached Margaret. +6614 It's herself who hired Emily. +6615 It's herself who talks to Janet. +6616 It's himself that bored Dan. +6617 It's herself that Brenda was kissing. +6618 It's themselves who all patients saw. +6619 It's herself that Melinda would worry. +6620 It's herself that alarmed Elaine. +6621 It's herself that revealed Catherine. +6622 It's himself that fires Frank. +6623 It's himself who is disturbing Craig. +6624 It's herself who had forgotten Jessica. +6625 It's herself that is revealing Rose. +6626 It's herself that Natalie has investigated. +6627 It's himself that Scott wasn't alarming. +6628 It's itself that some window will aggravate. +6629 It's themselves who had concealed a lot of actresses. +6630 It's herself that notices Patricia. +6631 It's herself who had healed Allison. +6632 It's herself who Beverly will discuss. +6633 It's himself that Keith hasn't listened to. +6634 It's herself that Pamela would see. +6635 It's himself that every doctor won't argue about. +6636 It's herself who couldn't find Kristen. +6637 It's himself who fires Michael. +6638 It's himself who finds Dennis. +6639 It's herself who boasts about Kendra. +6640 It's himself who embarrassed William. +6641 It's herself that Beth might talk to. +6642 It's herself that Caroline had investigated. +6643 It's herself who can't admire Kayla. +6644 It's themselves who have attacked actresses. +6645 It's herself that returns to Tammy. +6646 It's themselves that some actors have sounded like. +6647 It's herself that some patient finds. +6648 It's herself that fires some lady. +6649 It's herself who dislikes Deborah. +6650 It's themselves who hated all guests. +6651 It's herself who might work with Samantha. +6652 It's herself who wouldn't flee from Sheila. +6653 It's himself that Bradley had boasted about. +6654 It's herself that Kendra hadn't kissed. +6655 It's himself that praises Walter. +6656 It's themselves that many patients have cured. +6657 It's herself that helps Ann. +6658 It's themselves that all commentaries shouldn't astound. +6659 It's themselves that malls might worry. +6660 It's themselves that those wheelbarrows were alarming. +6661 It's herself that returns to Naomi. +6662 It's himself that Brian can hate. +6663 It's himself that disgusted Mitchell. +6664 It's himself that Andrew listened to. +6665 It's herself who shouldn't appreciate Sarah. +6666 It's himself that Larry won't discover. +6667 It's himself that found Craig. +6668 It's herself that Kristin didn't see. +6669 It's itself that hasn't distracted that spinach. +6670 It's herself that concealed Wendy. +6671 It's themselves who argued about the Lutherans. +6672 It's himself who cured Martin. +6673 It's herself that approaches Meredith. +6674 It's herself who wasn't hugging Becky. +6675 It's herself who respects Marla. +6676 It's herself that the woman fired. +6677 It's themselves that all patients can't insult. +6678 It's herself that thinks about Janice. +6679 It's himself who escaped from Paul. +6680 It's themselves that those senators are shocking. +6681 It's herself who attacked Dana. +6682 It's himself who Edward was disturbing. +6683 It's itself that wasn't referencing that legislature. +6684 It's itself that the computer isn't irritating. +6685 It's themselves that bothered many shirts. +6686 It's himself that Ronald should astound. +6687 It's herself that scares Tamara. +6688 It's himself that isn't distracting Alexander. +6689 It's herself who is disgusting Vanessa. +6690 It's himself that escaped from Joseph. +6691 It's themselves that hadn't irritated all skateboards. +6692 It's himself who will hire Stephen. +6693 It's herself who won't see Diane. +6694 It's themselves that a lot of ladies worked with. +6695 It's himself that Samuel wasn't revealing. +6696 It's herself who has astounded Anne. +6697 It's themselves that some boys escaped from. +6698 It's themselves that shocked most teachers. +6699 It's himself that fires Joseph. +6700 It's herself who kissed Mary. +6701 It's herself who wasn't returning to Katherine. +6702 It's himself who talked to Brett. +6703 It's herself who referenced Donna. +6704 It's herself that Christine will visit. +6705 It's herself who Stacey was boasting about. +6706 It's herself who won't hire Tanya. +6707 It's herself who fires Allison. +6708 It's herself who shouldn't dislike the teacher. +6709 It's himself that fired Peter. +6710 It's herself who loved Stacey. +6711 It's himself that has impressed Joel. +6712 It's herself that Stacey shouldn't think about. +6713 It's itself that some essay isn't distracting. +6714 It's himself that returns to Carlos. +6715 It's himself who has listened to Daniel. +6716 It's herself who sees Patricia. +6717 It's himself that William hadn't disliked. +6718 It's herself that some dancer complained about. +6719 It's itself that bothered every muffin. +6720 It's herself who investigated Jane. +6721 It's herself that Amelia hadn't discussed. +6722 It's itself that a turtle hasn't annoyed. +6723 It's itself that every committee wasn't helping. +6724 It's himself who hadn't respected Gerald. +6725 It's herself who does love Martha. +6726 It's themselves that these blouses haven't bored. +6727 It's herself who hires Christina. +6728 It's herself who isn't firing Carol. +6729 It's herself that won't scare Sandra. +6730 It's herself that approached Kristin. +6731 It's herself who works with Sarah. +6732 It's himself that Alexander had revealed. +6733 It's himself that shocked Marcus. +6734 It's itself that irritated that documentary. +6735 It's herself that insults Monica. +6736 It's himself who helped Joel. +6737 It's themselves who haven't disagreed with a lot of children. diff --git a/zeroshot/control_raising/eval_results.json b/zeroshot/control_raising/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..c390f10351cf429040f23e179f3a4c2aba9fe3e8 --- /dev/null +++ b/zeroshot/control_raising/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.6564295183384887} \ No newline at end of file diff --git a/zeroshot/control_raising/predictions.txt b/zeroshot/control_raising/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d651a5ae721455203ec88f400e3bb29dbaf0c50 --- /dev/null +++ b/zeroshot/control_raising/predictions.txt @@ -0,0 +1,4527 @@ +index prediction +0 William has declared there to be no guests getting fired. +1 Richard believed there to be many boxes stunning Ellen. +2 Teresa finds there to be many bananas stunning Claire. +3 Mary can entreat there to be some ladders falling. +4 An actress wouldn't declare there to be a print looking like that ice. +5 Gregory is declaring there to be a movie irritating Douglas. +6 Dana hasn't ordered there to be a unicycle dropping. +7 Frank judged there to be a photograph of Michael looking like Sherry. +8 Sarah was judging there to be a cashier exiting that mountain. +9 Cynthia believed there to be a lot of apples boring Kevin. +10 Many organizations have believed there to be no stories distracting some company. +11 Ann hadn't believed there to be no birds disappearing. +12 Anna's best friend prompted there to be more than three cashiers helping Bruce. +13 Steven's tutors wouldn't expect there to be a father of some women pressing that waitress to study. +14 Some dancer advises there to be a light scaring Scott. +15 Some cousins of Debra haven't spurred there to be fewer than three governments inducing Kendra to argue. +16 Victoria advised there to be commentaries confusing a lot of customers. +17 Every association had needed there to be many grocery stores stunning Colleen. +18 Kathleen hadn't found there to be a committee describing Kristen. +19 Irene's granddaughters prefer there to be more than three plates falling. +20 The library didn't discover there to be an art gallery disappearing. +21 Most banks believed there to be a lot of waiters discussing Phillip. +22 Dawn can declare there to be few waitresses working hard. +23 David had spurred there to be a lot of mouths irritating some girls. +24 The museums persuade there to be an icicle condensing. +25 The customers needed there to be an art gallery irritating Stacy. +26 An organization will oblige there to be a drawing looking like every dog. +27 Danielle didn't find there to be a cashier compromising. +28 Some teacher's plumbers implored there to be some slopes wasting away. +29 Some boys advise there to be a cashier swaying a lot of boys' mothers to conspire. +30 This child ascertained there to be no documentaries about Winston Churchill hurting Douglas. +31 Donna hasn't expected there to be a plate worrying Alice. +32 Most fathers of Frank prefer there to be some dogs breaking the computer. +33 Most grandsons of Christina declared there to be a commentary about the Impressionists worrying Irene. +34 Some library required there to be few pictures of Nancy hurting Winston Churchill. +35 Guy isn't ascertaining there to be no associations appearing to boycott all cafes. +36 A lot of museums needed there to be a lot of drivers lifting that fish. +37 Craig's lawyers were motivating there to be fish irritating a lot of actors. +38 Gregory has expected there to be some skirts loosening. +39 Edward didn't need there to be a company arguing. +40 Julie won't want there to be fewer than three legislatures irritating Brad. +41 Danielle obligated there to be many cups impressing Lucille. +42 Libraries needed there to be a woman persuading those girls to learn. +43 Alexander hadn't ordered there to be a library hurting Marie. +44 Every waiter hadn't believed there to be some eyes aggravating Erin. +45 Kathleen's electricians were expecting there to be a company persuading Joseph's ex-wife to compromise. +46 John encourages there to be a casserole shocking Samantha. +47 Cheryl isn't asking there to be a college campus existing. +48 Todd wanted there to be a closet worrying William. +49 The Borgias' brothers need there to be more than three cafes existing. +50 Those sisters of Tammy wanted there to be fewer than three companies tempting some pedestrian to drive to that library. +51 All nephews of Teresa judged there to be a commentary confusing Daniel. +52 Lawrence hasn't wanted there to be no shows about Spain astounding Robert. +53 Colleen's mothers oblige there to be a lot of screens vanishing. +54 James hadn't anticipated there to be some pictures stunning Brad. +55 Sherry begs there to be some children listening to Lawrence. +56 Lawrence will entreat there to be many shoes twisting. +57 Jerry's grandsons wanted there to be a car worrying Rhonda. +58 This boy's bosses prefer there to be no drivers falling asleep. +59 A lot of guests' contractor prompted there to be many pedestrians anticipating an association to reply. +60 Alexander was imploring there to be more than three prints of that muffin irritating those students. +61 Donna has declared there to be a niece of Alice appearing to embrace. +62 The Borgias' nieces need there to be a lot of grandmothers of Sonia admiring Colleen. +63 Bill's senator had ascertained there to be a lot of carts stopping. +64 Dan's boyfriend predicted there to be fewer than three grocery stores wasting away. +65 Dennis pushes there to be some lakes scaring Irene. +66 Chad's ex-wives weren't imploring there to be a restaurant astounding Tiffany. +67 Amelia will expect there to be a lot of women waving. +68 Marie wasn't advising there to be steps vanishing. +69 Dancers' senators need there to be a lot of commentaries scaring Lawrence. +70 Some senators' tutor does consider there to be few horses stunning Andrew. +71 Some cousins of Wayne might expect there to be some guests lifting the projector. +72 Dawn advised there to be some teenagers meeting. +73 Caroline discovers there to be an apple burning. +74 Victoria's wife wasn't considering there to be a hill stunning Bill. +75 Donna is wanting there to be no high schools alarming Kayla. +76 Some grandmother of Nancy had needed there to be a person hoping to clean some art gallery. +77 Russell isn't expecting there to be a drawing of Tonya shocking Heidi. +78 Janice's students had judged there to be a picture worrying some driver. +79 Mitchell is considering there to be bread appearing. +80 Bill's senator was preferring there to be a report boring Brenda. +81 Natalie's lawyers expect there to be few teenagers profiting. +82 These waiters declare there to be a mountain worrying those banks. +83 Andrew hasn't expected there to be a screen annoying Bill. +84 Many friends of Ellen haven't discovered there to be many shawls folding. +85 Bill isn't advising there to be pork stunning Kendra. +86 The friends of Allison are entreating there to be a lot of mountains astounding Monet. +87 Andrea's brother is finding there to be a cucumber alarming Kirsten. +88 Sabrina couldn't consider there to be a grocery store alarming Samantha. +89 Some customers won't judge there to be few gates alarming Gina. +90 Stephen did consider there to be no mirrors bothering every dancer. +91 Kendra has found there to be a face falling. +92 Eva encourages there to be fewer than three cakes aggravating this organization. +93 Helen found there to be a mountain stunning Clyde. +94 Victoria hadn't wanted there to be spinach annoying Wendy. +95 This mother of Bill can't find there to be a convertible falling. +96 Sandra is discovering there to be a guest discovering who has brought this horse. +97 All nephews of Eva haven't spurred there to be a lot of trucks existing. +98 This cashier's students require there to be few grandfathers of Christine confusing Martha. +99 Theresa finds there to be few adults bringing many restaurants. +100 Wayne was finding there to be a mall alarming Bradley. +101 Winston Churchill expects there to be a cousin of Marla believing Becca to resemble a lot of sketches. +102 Jill considered there to be a lot of books worrying many senators. +103 This granddaughter of Kevin wants there to be a carriage slipping. +104 The patient can't oblige there to be a high school embarrassing Thomas. +105 Melanie hasn't wanted there to be hats tightening. +106 Benjamin's ex-boyfriend convinces there to be more than three sketches distracting Keith. +107 Amelia required there to be a book alarming a lot of schools. +108 Stephen predicted there to be some cats coming here. +109 Caroline's electricians believe there to be a story aggravating these museums. +110 Some child doesn't find there to be a man figuring out who would confer. +111 Tammy has judged there to be many apples scorching. +112 These granddaughters of Kayla declared there to be an actress bothering to cry. +113 Every actress declared there to be fewer than three legislatures communicating. +114 Diana hadn't begged there to be some snakes embarrassing Becky. +115 Brad is urging there to be many rabbits looking like some photograph. +116 Most associations encouraged there to be a cafe aggravating Debra. +117 Kayla's hairdresser can want there to be few nephews of Dan declaring some skirts to warp. +118 A mother of a lot of doctors can believe there to be many restaurants annoying a child. +119 Andrew finds there to be a play bothering Stacy. +120 Becky's classmates found there to be fewer than three grandsons of Beverly upsetting Mark. +121 Patrick's niece ordered there to be many sweaters soaking. +122 A hospital wasn't urging there to be few steps appearing. +123 A brother of that waitress considered there to be more than three daughters of every waitress motivating Derek's handyman to tour this high school. +124 Becky hasn't wanted there to be a university boring a lot of ladies. +125 Alicia does force there to be fewer than three cafes disturbing Carlos. +126 Samantha's employees haven't ordered there to be a book boring Edward. +127 Jill was finding there to be more than three dishes scaring Cynthia. +128 Omar preferred there to be few essays impressing Brenda. +129 Some drivers couldn't want there to be some bicycles annoying most doctors. +130 The banks found there to be a customer investigating what prod Gerald to paint. +131 Harvard University doesn't want there to be many sweaters twisting. +132 Adam's sons are advising there to be no art galleries falling. +133 All sisters of Donna implored there to be a print of that coat existing. +134 Paul's doctor discovers there to be a window bothering Gerald. +135 Most grandmothers of Tonya expected there to be a documentary about the Borgias irritating Sonia. +136 Carrie should declare there to be a granddaughter of Travis sneezing. +137 Many students implored there to be few gates vanishing. +138 Those legislatures consider there to be a book upsetting Melanie. +139 Jennifer is judging there to be a newspaper article about Winston Churchill looking like the sketches. +140 Michael obliged there to be more than three movies about Galileo aggravating Phillip. +141 Edward has induced there to be no waiters conferring. +142 Martin shouldn't anticipate there to be a commentary embarrassing that girl. +143 All offspring shouldn't want there to be a lot of jackets warping. +144 These nieces of some actors discover there to be an essay about literature aggravating the dancer. +145 Harvard University prompts there to be some eyes shocking these organizations. +146 Jodi needed there to be a lot of public parks appearing. +147 Caroline declared there to be a sweater tightening. +148 Mark shouldn't anticipate there to be ice cream impressing Roger. +149 Michelle hasn't ascertained there to be some museums managing to benefit. +150 Kenneth is asking there to be more than three books about Jesus disagreeing. +151 Carmen was anticipating there to be a committee interacting. +152 Leslie would find there to be bread shocking every teenager. +153 Walter is judging there to be an association anticipating every dancer to complain about a lot of people. +154 Heidi couldn't consider there to be an eye shocking Kristen. +155 That legislature will need there to be fewer than three screens boring Melanie. +156 Carmen predicted there to be a waiter imploring Elaine to leave. +157 Andrew considered there to be fewer than three horses boring Colleen. +158 Tanya's classmates anticipate there to be a lot of commentaries disturbing the guest. +159 Karen has expected there to be granddaughters of Marcus finding Karla to drop by Spain. 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There were sure to be no planes boring Craig. +1646 There shouldn't continue to be beef scorching. +1647 There isn't ready to be a skateboard stunning that patient. +1648 There happened to be no snakes vanishing. +1649 There wasn't apt to be a photograph wasting away. +1650 There would attempt to be a ladder distracting Sara. +1651 There wasn't apt to be a blouse worrying Peter. +1652 There are unlikely to be more than three sons of Homer disappearing. +1653 There were certain to be more than three malls looking like a lot of prints. +1654 There can appear to be fewer than three horses appearing. +1655 There weren't likely to be windows worrying all teachers. +1656 There wasn't certain to be an actor thinking about what was hurting Marcus. +1657 There wasn't soon to be fish existing. +1658 There hadn't appeared to be a photograph appearing. +1659 There had looked to be a door worrying Becky. +1660 There were unlikely to be cashiers protesting. +1661 There were apt to be a lot of 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There wasn't sure to be a projector vanishing. +1803 There have longed to be boxes wasting away. +1804 There shouldn't seem to be few cars impressing every woman. +1805 There can't seem to be many chairs disappearing. +1806 There might manage to be a hat shrinking. +1807 There look to be no shows about art winning. +1808 There isn't bound to be a photograph alarming Jane. +1809 There isn't certain to be a university changing. +1810 Tara would ascertain it to be noteworthy that Kenneth didn't wash. +1811 Carla can convince it to be not so important that these doctors observe Rhonda. +1812 Nina obligated it to be nice that Jacqueline exists. +1813 Some granddaughter of Bruce did find it to be expected that a lot of legislatures try to conceal Colleen. +1814 Charles's brothers hadn't considered it to be not so amusing that every man exercised. +1815 Maria discovers it to be not so good that Karla couldn't need Carlos to question Candice. +1816 Walter considers it to be lucky that Angela 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Ann's wife hasn't discovered it to be worth mentioning that many pants wrinkle. +1933 Julie has judged it to be acceptable that some actresses think about who retaliates. +1934 Anne was finding it to be unusual that the museums reference Diane. +1935 Theses considered it to be sad that these students conceal who compels Caroline's nephew to scare Curtis. +1936 Ronald wanted it to be strange that Renee has concealed what had reacted. +1937 Winston Churchill was prompting it to be not so surprising that Allison fell. +1938 Clyde had induced it to be clear that many patients disagree. +1939 Joel swayed it to be not so weird that Karla had appreciated the pedestrians. +1940 That company hadn't expected it to be unlucky that Daniel is touring most hospitals. +1941 Donald's doctors encourage it to be amusing that the Borgias require Candice's son to flee from Tiffany. +1942 Some grandfathers of the teenager were expecting it to be unfortunate that this fish bakes. +1943 Sandra was declaring 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This teenager is finding it to be not so pleasant that the planes roll. +1969 Curtis needed it to be unexpected that those schools consider Lisa to like Tara. +1970 The Lutherans haven't found it to be unlucky that Bill is bothering most universities. +1971 Some actress's boyfriend hasn't preferred it to be obvious that this popsicle can melt. +1972 The cashier's employees need it to be strange that some convertible steers. +1973 Michael's son has wanted it to be not so pleasant that Stephanie won't command Jill to murmur. +1974 These pedestrians expect it to be unpleasant that Gina wasn't concealing who isn't regretting that adult was watching Kevin. +1975 Donald's colleagues find it to be unlucky that Wendy works hard. +1976 Those theses judge it to be not so nice that a lot of skirts warp. +1977 Sally is urging it to be not so bad that all apples were growing. +1978 Larry's grandson is finding it to be not so normal that Kayla does watch William. +1979 Danielle isn't declaring it to 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reacted. +4197 Aaron is sure to listen to Ruth. +4198 Every dancer isn't enjoyable to cry. +4199 These adults aren't unlikely to mutter. +4200 Beverly's daughters weren't hard to praise Kenneth. +4201 Kayla isn't apt to commission some waiter to fire Andrea. +4202 Carl's father is boring to observe Vincent. +4203 Every son of a lot of guests was hard to talk. +4204 Ellen's mothers weren't bound to respond. +4205 Michael's friends are sure to fail to heal this snake. +4206 The Borgias were certain to compromise. +4207 Helen is certain to descend these mountains. +4208 Every child isn't bound to see these peppers. +4209 All museums aren't easy to hire Phillip. +4210 Linda's mothers are pleasant to litter. +4211 Christopher's piano teachers weren't soon to walk through this public park. +4212 Monica wasn't about to boast about Dana. +4213 Rhonda's electrician isn't apt to happen to compete. +4214 Sisters of Helen are bound to retaliate. +4215 Susan was about to describe David. +4216 Adam isn't certain to drop by all museums. +4217 The offspring wasn't sure to escape. +4218 Cheryl's electricians aren't sure to interact. +4219 Tammy was certain to hope to write. +4220 Martin isn't bound to slump over. +4221 Phillip's granddaughter isn't apt to motivate Erin to perform. +4222 That nephew of Kristin is about to leave George. +4223 Margaret is difficult to prevent Mark from irritating the guy. +4224 A doctor's senators were unlikely to interact. +4225 Most brothers of Stacy were soon to confer. +4226 Kenneth's sons aren't apt to question Becca. +4227 Kirsten's French teacher is apt to urge Julia to examine the plays. +4228 Eric's supervisor isn't apt to yearn to murmur. +4229 Roger was unlikely to praise Mary. +4230 David's ex-wives aren't about to reply. +4231 This glove wasn't about to annoy Catherine. +4232 Grandmothers of Galileo aren't pleasant to dislike these adults. +4233 Kirsten wasn't pleasant to complain. +4234 Sonia's grandson isn't soon to prod Jodi to drive to some lake. +4235 Pamphlets about politics are about to hurt Laurie. +4236 That waitress's supervisors are about to grin. +4237 Colleen was sure to shout. +4238 Craig is certain to implore Katherine to admire chicken. +4239 Katherine was apt to expect the skirts to wrinkle. +4240 The nephews of Ann are tough to fix a lot of glasses. +4241 Alan isn't about to dislike Walter. +4242 Some bank wasn't hard to testify. +4243 Samuel was likely to mutter. +4244 These dancers weren't exciting to appear to collaborate. +4245 Those students weren't apt to intend to argue about current events. +4246 Wendy isn't apt to regret some girls suffer. +4247 Students' fathers weren't sure to figure out who wasn't climbing down many hills. +4248 Those offspring aren't hard to criticize Naomi. +4249 Alan's ex-girlfriends aren't unlikely to drop by movie theaters. +4250 James's spouse isn't unlikely to require Larry to grin. +4251 All drivers were easy to cooperate. +4252 Wayne wasn't pleasant to like that ice. +4253 Derek's sister isn't about to complain about all customers. +4254 A restaurant isn't bound to impress Lori. +4255 Many grandfathers of actresses are unlikely to reply. +4256 A grandfather of Noah was fun to discover every shirt. +4257 Some man isn't certain to hug Edward. +4258 Peter was certain to learn who shocks Susan. +4259 The Lutherans are apt to care for Kimberley. +4260 Debra was about to conceal Paul. +4261 Rebecca is bound to sound like some senator. +4262 Most patients' electrician is easy to oblige Carolyn to drop by the cafes. +4263 Some waiters weren't sure to disagree. +4264 Ronald's ex-wives aren't unlikely to bother to react. +4265 Marla is easy to hire Teresa. +4266 Elizabeth isn't unlikely to like to talk to Jennifer. +4267 Those drivers' hairdresser was apt to expect Tonya to wave. +4268 These grandfathers of Carolyn were sure to cry. +4269 Lisa isn't unlikely to turn out to learn. +4270 Deanna isn't boring to know who concealed Stacy. +4271 Susan is soon to plan to escape. +4272 Todd is certain to dislike Renee. +4273 Douglas's ex-wives were apt to predict those drivers to surrender. +4274 Most men were likely to explain Larry couldn't nod. +4275 Many schools were enjoyable to cope. +4276 Lucille was sure to need to respect Spain. +4277 Donna was bound to escape from these dancers. +4278 Keith was apt to find out who is getting a job. +4279 Katherine isn't apt to try to scratch. +4280 Sara's dentists weren't easy to negotiate. +4281 Christine's partner was bound to annoy the museum. +4282 The people were likely to remember Anne. +4283 Michael is about to worry Alice. +4284 Michelle was likely to find universities to have eyes. +4285 Noah was tough to find Linda. +4286 The people aren't likely to long to alarm some people. +4287 Those analyses weren't apt to irritate Catherine. +4288 Alice was about to pressure Kristin to astound most associations. +4289 That driver's piano teachers weren't about to figure out what confuses Janet. +4290 Daniel wasn't about to discover most skirts to distract Theresa. +4291 Karla is bound to bring the cilantro. +4292 Daniel was likely to encourage Walter to work with most men. +4293 That adult's nephew was unlikely to believe Richard to dislike Matt. +4294 Sherry is apt to encourage some patients to disagree. +4295 This driver wasn't soon to prompt an analysis to worry Heidi. +4296 All ladies aren't pleasant to anticipate Brett to forfeit. +4297 Naomi was certain to admire gates. +4298 Jane's senator is apt to know who hasn't concealed Lori. +4299 That brochure about Jesus isn't apt to bother Noah. +4300 Ann's dermatologist was apt to buy those planes. +4301 Some people weren't soon to chuckle. +4302 Stephanie wasn't likely to mess up every cup. +4303 Adam was likely to doubt Larry was commissioning Jill to exist. +4304 Melinda isn't likely to clean a hat. +4305 Eric was apt to threaten to see Andrea. +4306 Some men are likely to prove to cry. +4307 A lot of offspring aren't about to sound like many students. +4308 Jill wasn't about to reference Pamela. +4309 Teresa wasn't apt to want to wake up. +4310 Adam isn't bound to threaten to complain. +4311 Carla isn't about to know Samuel. +4312 Most peppers were likely to rot. +4313 All grandfathers of Kevin were bound to hide this bicycle. +4314 Some niece of Veronica isn't sure to watch Rodney. +4315 Liam's dermatologist wasn't certain to threaten to tour some restaurants. +4316 That drawing was hard to shock Judy. +4317 Vincent's employees aren't soon to cope. +4318 Tammy isn't about to talk to George. +4319 Some nuclei weren't unlikely to resemble most paintings. +4320 Brenda isn't about to try to argue. +4321 Michelle's bosses were certain to clash. +4322 Steven isn't about to expect all men to complain about Melanie. +4323 Tina was sure to work with Tracy. +4324 Victoria was soon to intend to dislike the waiters. +4325 Becca is unlikely to go fishing. +4326 Daniel wasn't likely to wish to perform. +4327 Roger's students aren't apt to forget Carl. +4328 The people aren't sure to investigate Paul. +4329 Florence was likely to drive to this closet. +4330 A grandson of Dawn isn't bound to annoy Amelia. +4331 Denise's fathers were difficult to doubt the adult hasn't admired Barbara. +4332 The library is about to confuse a lot of actors. +4333 Jill's brothers aren't soon to litter. +4334 Gregory's lawyers are apt to confer. +4335 Carla was apt to kiss Eva. +4336 Stacy was exciting to clean those grocery stores. +4337 Some boys weren't bound to go fishing. +4338 Guy wasn't unlikely to run around a museum. +4339 The bank wasn't soon to hope to investigate Emily. +4340 Those children were likely to persuade Peter to scream. +4341 Curtis wasn't likely to grin. +4342 Dennis's mothers are apt to boast. +4343 The sister of these waiters isn't interesting to shock some libraries. +4344 Deborah is easy to climb up that mountain. +4345 Some pedestrian is sure to remember Joseph. +4346 A lot of patients' contractors weren't bound to hurt Homer. +4347 All teachers are difficult to respond. +4348 Maria was sure to prevent Janice from skated around all art galleries. +4349 Jeffrey wasn't interesting to skate around that high school. +4350 Noah isn't bound to drive to most art galleries. +4351 Some dancers weren't difficult to flirt. +4352 Kristin is apt to leave Naomi. +4353 That waitress was apt to take that ice. +4354 Most sisters of every boy were unlikely to wonder who begs Joel to notice Janet. +4355 Lucille isn't certain to bring mushrooms. +4356 Elizabeth is bound to flee from Laurie. +4357 Beth isn't sure to push Deborah to question some child. +4358 Keith wasn't sure to sigh. +4359 A dish isn't easy to disturb every woman. +4360 Danielle wasn't soon to consider most girls to break every skateboard. +4361 A lot of alumni were soon to bring cilantro. +4362 Benjamin's dentist isn't apt to prevent those committees from joking around. +4363 Monet wasn't easy to disturb David. +4364 Caroline was sure to care for Diane. +4365 The girls' lawyer isn't bound to cure every actress. +4366 Clyde isn't apt to drive to a school. +4367 Keith isn't likely to like Jennifer. +4368 Libraries are likely to motivate Debra's grandmother to know Melissa. +4369 Erin wasn't unlikely to explore the hospitals. +4370 That friend of Martha was about to discuss Todd. +4371 Regina is unlikely to alarm a bank. +4372 The driver is about to fix that computer. +4373 Janice is about to laugh. +4374 Adam isn't irritating to threaten to wake up. +4375 Kimberley isn't easy to shrug. +4376 Stephen was likely to prove to heal Derek. +4377 Some dancers were certain to cope. +4378 Cynthia wasn't likely to learn what bothers Kathleen. +4379 Kenneth's mother wasn't about to return to a lot of doctors. +4380 This government is fun to interact. +4381 Stephen's ex-boyfriend isn't likely to examine these movies. +4382 A lot of associations aren't about to badger Gina to run around the museums. +4383 Many girls weren't easy to react. +4384 Veronica was difficult to attack Matt. +4385 Samantha isn't easy to spur those students to astound Nicole. +4386 Claire isn't sure to wish to weep. +4387 The Borgias were about to boycott every public park. +4388 Maria was apt to anticipate Tiffany to alarm the boys. +4389 All children aren't difficult to react. +4390 Gerald is likely to convince most teenagers to investigate Janet. +4391 Vanessa wasn't certain to notice Christine. +4392 An actor wasn't bound to propose to drive to these banks. +4393 That driver was sure to clean some cup. +4394 Florence's granddaughters were difficult to alarm Kimberley. +4395 Sherry isn't certain to reveal who shouldn't order Janet's contractors to sit down. +4396 Meredith was apt to return to Sarah. +4397 Wendy's granddaughters are likely to fail to evaporate. +4398 Tonya's granddaughter isn't likely to annoy Julia. +4399 Stephanie's partner was tough to talk about Wendy. +4400 The peppers weren't bound to rot. +4401 That guest's sisters weren't bound to collaborate. +4402 Beverly wasn't about to astound many teachers. +4403 Colleen is hard to take a bird. +4404 Danielle isn't about to prefer Wayne to remember Danielle. +4405 Grace wasn't apt to require Melinda to wash. +4406 All scarves weren't bound to hurt Richard. +4407 John's bosses are interesting to collaborate. +4408 Brian is soon to reveal who walk through every restaurant. +4409 Brenda's boyfriend wasn't certain to descend a lot of steps. +4410 Becky was hard to conceal Beverly. +4411 David wasn't certain to escape. +4412 George wasn't enjoyable to boycott the grocery store. +4413 Richard's electrician was fun to skate around most lakes. +4414 Ruth's associate isn't soon to flee from Andrea. +4415 A lot of grandsons of Dan are about to escape from every movie theater. +4416 Natalie's tutors weren't easy to cope. +4417 Angela isn't sure to regret some cafe isn't impressing Kathleen. +4418 The mothers of Ruth were apt to escape from Clyde. +4419 The ladies aren't sure to entice patients to cope. +4420 Angela's brothers weren't likely to know Jane. +4421 Jeffrey isn't soon to resemble this photograph. +4422 Wendy wasn't about to joke around. +4423 Renee's partners are sure to prevent science from irritating Stephanie. +4424 Charles isn't likely to chuckle. +4425 The hospitals aren't exciting to entice Todd's partner to study. +4426 Every waiter was soon to talk to Susan. +4427 Some cashiers are tough to ascend the ladder. +4428 Stephanie isn't bound to tempt that woman to die. +4429 Stephanie's brothers weren't bound to try to disgust some actor. +4430 Every actor is interesting to observe Beverly. +4431 Carol is unlikely to hide couches. +4432 Nancy wasn't about to believe Guy to protest. +4433 A lot of students aren't enjoyable to bike to the high school. +4434 Benjamin's classmates weren't tough to smile. +4435 Martin isn't apt to descend the ladder. +4436 Every dancer was about to mutter. +4437 Jeffrey's guest isn't sure to sway Mary to sigh. +4438 A lot of pants were about to irritate Donna. +4439 This association wasn't boring to realize who talk. +4440 The teachers were likely to nod. +4441 Kimberley isn't sure to look to discuss Carol. +4442 Aaron was tough to nod. +4443 Sheila's ex-girlfriends aren't certain to clash. +4444 April is likely to lie. +4445 The Borgias weren't sure to communicate. +4446 Some grandfathers of Larry weren't pleasant to find some cakes to disturb this committee. +4447 Noah's best friend wasn't likely to refuse to aggravate some guy. +4448 Stacey wasn't easy to admire Stephanie. +4449 Drivers are likely to negotiate. +4450 Marcus isn't easy to chuckle. +4451 Jodi's fathers were bound to smile. +4452 Benjamin was unlikely to explore a grocery store. +4453 Douglas wasn't exciting to lift some cats. +4454 Homer was about to require Judy to reference Susan. +4455 Nicole isn't easy to skate around the cafes. +4456 Maria isn't bound to smile. +4457 Maria isn't certain to encourage every organization to take a lot of books. +4458 Some son of this senator was apt to escape from that pedestrian. +4459 Carl is soon to ask Stacey's daughter to find Elizabeth. +4460 Elaine isn't about to continue to flee from Jerry. +4461 Tracy's ex-girlfriends aren't apt to examine every play. +4462 Every goose is soon to bore Janet. +4463 Monica wasn't bound to entice Cheryl to observe many boys. +4464 Naomi is apt to press Carol to bother Alice. +4465 Tammy's supervisor was apt to conceal what could flatten. +4466 Sheila isn't about to think about Deborah. +4467 Sandra's supervisor was unlikely to discover what are bothering Carlos. +4468 Sarah is about to advise Brenda to lie. +4469 Brett wasn't likely to fall. +4470 Many ladies weren't sure to think some committee reacts. +4471 Valerie is exciting to push many guys to kiss every dancer. +4472 Valerie is enjoyable to talk about Douglas. +4473 Patricia was unlikely to turn out to twirl. +4474 Grace's friend was sure to talk. +4475 The person's grandfathers are likely to think Florence embarrassed Florence. +4476 Teresa is about to fail to complain about Florence. +4477 Connie is interesting to litter. +4478 Stacy's guests weren't sure to hire every waiter. +4479 Every commentary is certain to annoy Holly. +4480 Bethany's employees are soon to exit a lot of mountains. +4481 Rhonda is sure to insult Stephanie. +4482 These guys' sons are apt to remember Deanna. +4483 Linda isn't unlikely to prefer that sweater to tighten. +4484 That dancer's mothers aren't likely to reply. +4485 Beth is likely to examine all plays. +4486 Rebecca is unlikely to descend a lot of mountains. +4487 This synopsis wasn't unlikely to irritate Dana. +4488 These museums were about to hurt Veronica. +4489 Diana isn't bound to notice Rebecca. +4490 Sharon's partners were unlikely to prod the customers to blink. +4491 Some girl is easy to worry Paul. +4492 Teresa is sure to bring some windows. +4493 A school was unlikely to compete. +4494 A lot of drivers are soon to clash. +4495 Rachel is soon to command Nina's mothers to hide away. +4496 Some child is sure to boycott public parks. +4497 Ruth wasn't likely to fail to slump over. +4498 Deanna was interesting to hide some plates. +4499 Dan wasn't about to explore every public park. +4500 Gregory's student wasn't soon to ascend this mountain. +4501 Jerry wasn't likely to reveal Rhonda. +4502 Lori's ex-girlfriend was hard work to attack that boy. +4503 Naomi is apt to induce a hospital to fall. +4504 Mark is about to conceal Carrie. +4505 Douglas is certain to prompt those museums to upset Valerie. +4506 Many actresses are apt to push Heather to cough. +4507 Martha's grandmothers are soon to wave. +4508 That story is bound to aggravate every girl. +4509 Sara wasn't apt to forget about who would attempt to bore Helen. +4510 Kathleen wasn't about to need Eric to sing. +4511 Every man is bound to ask a lot of drivers to disagree with a lot of actresses. +4512 Claire was likely to find some socks. +4513 Jerry was sure to wish to boast about Janet. +4514 The people are sure to confer. +4515 Naomi was unlikely to climb down these hills. +4516 Those women are unlikely to collaborate. +4517 Michelle is sure to discover Gregory. +4518 Jerry's sister wasn't easy to lift every photograph. +4519 Most governments were bound to pressure Rodney to alarm Melanie. +4520 Helen was soon to tour this hill. +4521 Frank isn't about to cry. +4522 A lot of cashiers are unlikely to chat. +4523 Bradley is unpleasant to compromise. +4524 Brad wasn't unlikely to declare Regina to hate Allison. +4525 Amelia's plumbers were sure to reveal who is dropping by libraries. diff --git a/zeroshot/determiner_noun_agreement/eval_results.json b/zeroshot/determiner_noun_agreement/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..9bbfad7871a1a4b70085b5b40a6352f1d2fb0eff --- /dev/null +++ b/zeroshot/determiner_noun_agreement/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.8969769291964996} \ No newline at end of file diff --git a/zeroshot/determiner_noun_agreement/predictions.txt b/zeroshot/determiner_noun_agreement/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..07ec8df402950ac164dccaeaa3337d85721b48da --- /dev/null +++ b/zeroshot/determiner_noun_agreement/predictions.txt @@ -0,0 +1,7543 @@ +index prediction +0 Raymond is selling this sketch. +1 Craig explored that grocery store. +2 Eva has scared these children. +3 Marcus would conceal that pamphlet. +4 Carmen hadn't shocked these customers. +5 The Impressionists were walking through this library. +6 Some men weren't touring those cafes. +7 Leslie isn't firing that actresses. +8 Jennifer concealed those cups. +9 A waitress does take those hospitals. +10 The man walks through those hospitals. +11 This sock disturbs that customer. +12 William described this movie. +13 Monet dropped by those libraries. +14 Elaine was biking to this bank. +15 These senators wear that jacket. +16 The Lutherans notice that drawing. +17 Cheryl is hurting that teacher. +18 Russell might break those cups. +19 A lot of patients resemble these pictures. +20 Vanessa can talk to these doctors. +21 Janice had worn this shawl. +22 A lot of waitresses find those jackets. +23 Every dancer hasn't admired this skirt. +24 The adult doesn't hide those malls. +25 Samuel criticizes that art gallery. +26 A teacher annoyed those senators. +27 A teacher talked to that customer. +28 Mark discovered this face. +29 Carmen toured these restaurants. +30 Eva toured this school. +31 Travis would tour that hospital. +32 Some stories aggravated those libraries. +33 These cashiers hate that guests. +34 Alan listened to these guests. +35 Most children examine this pamphlet. +36 Stacey will know these patients. +37 Jessica sold this rabbit. +38 Daniel couldn't hate those cashiers. +39 Those ladies kiss those adults. +40 Diane works with these waiters. +41 Most women conceal that blouses. +42 Senators can't disagree with that newspaper article. +43 Jane has concealed this book. +44 Marla took this pamphlet. +45 The skirt wasn't annoying those dancers. +46 Most guys conceal this driver. +47 David wasn't lifting those hats. +48 The dancers are driving to those art galleries. +49 Tracy might cure those patients. +50 Heather cleans that prints. +51 Rose won't reference these movies. +52 Many reports irritate these senators. +53 Dan couldn't walk through this public park. +54 Rose hated this girl. +55 Melinda hires that teacher. +56 Joseph healed these customers. +57 A door doesn't distract those students. +58 Theodore skated around that art gallery. +59 These tomatoes shock these dancers. +60 Guys observe these children. +61 Some guests flee from those dancers. +62 Linda isn't embarrassing these waitresses. +63 Walter hides these dancers. +64 Sharon investigated those waiters. +65 Bradley should explore this high school. +66 Tanya wears these socks. +67 That cat had bored those ladies. +68 Stephen had cleaned this bank. +69 Waiters have left that driver. +70 Heather wasn't criticizing these teachers. +71 Brian wasn't concealing those cafes. +72 Some trucks weren't boring that cashier. +73 Children aren't boring this lady. +74 Karla hadn't brought these pictures. +75 Janice hasn't cared for that waitress. +76 A lot of doctors exit those art galleries. +77 Thomas should hug this waiter. +78 Candice hasn't attacked that boy. +79 Katherine wasn't taking that public park. +80 Amelia loves this waiter. +81 Caroline hasn't known this boy. +82 Sheila isn't passing that cafe. +83 Claire had cleaned that picture. +84 The people exit these movie theaters. +85 Bruce won't mess up that restaurant. +86 Margaret hasn't bought that blouses. +87 Some waitresses remember that driver. +88 Timothy loved this boy. +89 A lot of adults help that guy. +90 Steven has broken that cup. +91 Patricia loved those cashiers. +92 Many drivers fix that glass. +93 A lot of cashiers mess up that library. +94 Waitresses drive to that school. +95 Roger should sound like that waitress. +96 The Impressionists do listen to this waitresses. +97 Theresa bought this drawing. +98 The Impressionists walk through this cafes. +99 Thomas can boycott these committees. +100 Alan had boasted about those girls. +101 Sally isn't escaping from these boys. +102 Jesus can run around this high school. +103 Amy wasn't confusing those companies. +104 Roger ran around this school. +105 These teachers escape from those restaurants. +106 Maria was talking to this guest. +107 Rachel has hugged those boys. +108 The students fire this patient. +109 Jennifer helped those guests. +110 Some plays disgust this legislature. +111 Jerry had hidden these cups. +112 Martin has concealed this boy. +113 That person hadn't bothered those girls. +114 Carla has hired those patients. +115 Noah talks about this doctor. +116 Rachel ran around those high school. +117 All waiters run around these malls. +118 All students didn't buy these schools. +119 Patricia bikes to those malls. +120 Vanessa hadn't taken those grocery stores. +121 Bethany walked through this school. +122 Patricia had argued about those children. +123 Julie did flee from this waitress. +124 Melinda fired this customer. +125 The Borgias are biking to these college campuses. +126 Marcus will watch that dancers. +127 These waitresses aggravate those guests. +128 Those teachers sell this shoe. +129 Mark isn't worrying those libraries. +130 Elaine didn't visit that restaurant. +131 Veronica is hugging those boys. +132 Kayla has passed these art galleries. +133 Janet hasn't attacked these customers. +134 The children investigate that waiter. +135 Margaret has discussed those boys. +136 These hamsters irritated this doctor. +137 Some banks annoy these doctors. +138 Leslie was firing these adults. +139 Teenagers disagreed with that students. +140 Some doctor is breaking those couches. +141 Larry disturbed this waitress. +142 Julie leaves that cafe. +143 Every waiter hasn't driven to that high school. +144 Those ladies exit these schools. +145 Valerie hadn't annoyed this customer. +146 Marla hadn't forgotten this cashier. +147 Amanda explored those high schools. +148 Mitchell has investigated this school. +149 A commentary has stunned these libraries. +150 Catherine scanned those books. +151 Many planes look like that photograph. +152 The women break that plate. +153 Jesus does worry those dancers. +154 Most companies forget these actors. +155 Stephanie wasn't talking to that teenagers. +156 Every lady did hate these brochures. +157 Sabrina resembles this sketch. +158 Julia might conceal this actresses. +159 Benjamin isn't praising these cashiers. +160 Some boys notice this skirt. +161 Some documentaries confused those dancers. +162 Travis approached this doctor. +163 Donna does scan those newspaper articles. +164 Sheila hasn't forgotten that dancer. +165 The Lutherans criticize this restaurant. +166 Many doctors might visit those restaurants. +167 Teresa has irritated those boys. +168 Becky had seen those gloves. +169 Veronica might cure these actors. +170 Florence is referencing those teachers. +171 Larry hugged that teacher. +172 Julie has boasted about these reports. +173 All schools talk to those actors. +174 The pedestrians could praise that pamphlet. +175 Jane wouldn't clean that dog. +176 That student remembered that play. +177 All teenagers cure that patient. +178 Prints have looked like those dogs. +179 Lori wasn't selling these shirts. +180 This woman sees these eyes. +181 April is observing that adults. +182 Bradley cleaned that glass. +183 Todd wasn't concealing those doctors. +184 Christina should drive to that museum. +185 Eric wasn't returning to those boys. +186 Kendra does hide this rug. +187 These tomatoes irritate those committees. +188 Larry doesn't hate that play. +189 This driver wasn't breaking these dishes. +190 Many doctors bike to these malls. +191 Monet has these birds. +192 The pasta hadn't astounded this library. +193 Thomas wouldn't watch those boys. +194 Those dancers wear that shoes. +195 Some convertible hasn't hurt these guests. +196 Carla didn't fix these chairs. +197 A lot of waiters haven't appreciated these children. +198 A lot of drivers return to those restaurants. +199 Margaret saw those senators. +200 Catherine bought these hamsters. +201 Laura heals that patient. +202 Many screens disturb that lady. +203 Stacy had referenced those plays. +204 Those actresses aren't escaping from these drivers. +205 Samuel visits those adults. +206 Brett complains about this story. +207 Florence doesn't heal these guests. +208 All cashiers bother that school. +209 Erin would hug those cashiers. +210 George described this lady. +211 Many adults do attack this dancer. +212 Some customers could disturb this actor. +213 Katherine discusses this newspaper article. +214 The organization loved these adults. +215 William criticizes this student. +216 Rodney had researched those hospitals. +217 Sherry hasn't talked about that malls. +218 Melissa couldn't question these committees. +219 All screens aren't looking like this sketch. +220 Keith has complained about those waitresses. +221 Alice does boast about this senators. +222 Many associations have this shoe. +223 Jill has sold these public parks. +224 Sara had healed those boys. +225 All lakes don't astound those legislatures. +226 Some college campuses had resembled that photograph. +227 Many grocery stores wouldn't shock these adults. +228 Every waiter was concealing this turtle. +229 Joel complains about those drivers. +230 A dancer walks through that library. +231 Victoria can't care for these drivers. +232 Denise had bored those senators. +233 Vanessa discovers these drawings. +234 Janet had hidden these libraries. +235 A lot of actresses distract that teenagers. +236 Denise bought these cats. +237 Martha is confusing those universities. +238 Lori hasn't distracted those drivers. +239 Tanya hadn't talked to this teenager. +240 The pedestrian healed this guest. +241 These hills should stun this patient. +242 Liam is returning to these teenagers. +243 Jessica hadn't cleaned these hospitals. +244 A lot of dancers cured this boy. +245 Tiffany hadn't distracted this patient. +246 Students sell that rugs. +247 Christopher concealed this art gallery. +248 These guys admired that school. +249 Rugs astound that association. +250 Robert wasn't scaring these museums. +251 A lot of ladies arrive at that museum. +252 Some pedestrians drive to this movie theater. +253 Tonya hasn't remembered that brochure. +254 Museums hadn't disturbed that patient. +255 Every person left that actresses. +256 Sandra should disgust that actor. +257 Amelia is disgusting these ladies. +258 Carla isn't taking those sketches. +259 Florence was investigating these actors. +260 Many cashiers disagree with those cashiers. +261 These committees argue about this commentary. +262 Brian sees those horses. +263 Candice hasn't explored this art gallery. +264 Aaron walks through those art galleries. +265 Some books alarm that cashier. +266 Raymond wasn't kissing that guest. +267 Some cashier might boycott that art gallery. +268 Winston Churchill hadn't hidden this adult. +269 Jennifer skates around this public park. +270 Ruth doesn't flee from those malls. +271 Some waiters have known those guests. +272 Patricia thinks about that legislature. +273 Every patient shouldn't drive to these schools. +274 Most boys hadn't disagreed with those books. +275 Mark fled from those guys. +276 Grace should break these cups. +277 Carrie passes those restaurants. +278 Anne was returning to these guys. +279 Craig could disturb those teenagers. +280 Linda has this rabbit. +281 Christopher does pass this hospital. +282 Mitchell wasn't embarrassing these adults. +283 These senators clean these grocery stores. +284 Cynthia should complain about those actresses. +285 Curtis hasn't praised these boys. +286 Florence listened to that patient. +287 Jodi might break these chairs. +288 Sherry forgot that play. +289 Clyde hasn't loved these boys. +290 A lot of hats aggravate that legislature. +291 Ellen will look like those prints. +292 April wasn't examining those reports. +293 Diana isn't selling that car. +294 Christina knew this doctor. +295 Catherine wouldn't research these universities. +296 Those customers weren't researching this government. +297 All doctors know that students. +298 Tonya has insulted that play. +299 Those documentaries were disagreeing with that lady. +300 An organization had respected that doctor. +301 Timothy explores this restaurant. +302 Sarah hires this waiter. +303 Kendra exits this art gallery. +304 Natalie concealed this hospital. +305 Amelia was impressing this company. +306 Wendy hadn't scared that bank. +307 Mark will cure those guys. +308 Spain was describing those actors. +309 Allison talked about those schools. +310 William buys 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Some customer is firing that person. +3187 Russell loved that man. +3188 Melanie wasn't going to this oasis. +3189 Todd doesn't talk to that child. +3190 Amy should bore those women. +3191 Peter shouldn't reference this woman. +3192 Deanna explores this oasis. +3193 William is disgusting that women. +3194 Janice has that goose. +3195 Laurie would criticize those oases. +3196 Pamela wouldn't talk to this woman. +3197 That actress lifted these teeth. +3198 Lawrence returned to that person. +3199 All students bike to those oasis. +3200 These guys flee from these women. +3201 Joel skated around those oases. +3202 Christina has shocked that child. +3203 Drivers distracts that person. +3204 Matt notices that stimulus. +3205 Some fork wasn't distracting these children. +3206 Some broccoli was confusing that man. +3207 Most cashiers like these teeth. +3208 Many people complain about those oases. +3209 Caroline did fire this child. +3210 A lot of guys sounded like those synopsis. +3211 Stephen 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Paul couldn't find this ox. +3468 Some customers skate around this oasis. +3469 These commentaries can't bother this man. +3470 Amy disagrees with that man. +3471 Omar fires that people. +3472 That high school is boring this child. +3473 Those men worry that woman. +3474 The Lutherans have scanned these synopsis. +3475 Patricia hadn't arrived at that oasis. +3476 Tiffany cared for this woman. +3477 Amanda appreciated these theses. +3478 Jason wouldn't flee from that woman. +3479 Liam hasn't admired those nuclei. +3480 Peter respects those men. +3481 Every print hasn't bored this man. +3482 Brenda hurt that alumnus. +3483 Every glove irritated this man. +3484 Jessica respects those children. +3485 Every senator hasn't kissed these alumni. +3486 This pedestrian cares for that woman. +3487 Most guys leave that oasis. +3488 Emily explores those oases. +3489 Many cakes shocked that person. +3490 A tomato will upset those women. +3491 Timothy wouldn't like that nuclei. +3492 Brian hated this 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upset that lost lady. +7385 Marcus hasn't examined that concealed newspaper article. +7386 Donald complains about that important play. +7387 Patricia isn't talking about these determined dancers. +7388 These students will confuse those rough libraries. +7389 Florence had researched that important museum. +7390 Amanda praised that hopeful student. +7391 The Lutherans hadn't praised those ill patients. +7392 Lisa doesn't notice those blue restaurants. +7393 The bank is talking about this good driver. +7394 Jeffrey went to those cheap schools. +7395 Those teenagers aren't leaving that hidden cashier. +7396 Most women sound like that blue brochure. +7397 Scott attacks those uncertain boys. +7398 This company wasn't talking about this lost pedestrians. +7399 Every lamp aggravated these busy cashiers. +7400 The bananas alarmed those fast dancers. +7401 The boys weren't discussing that good pedestrians. +7402 Marie broke those new glasses. +7403 That boy is alarming these popular governments. +7404 This newspaper article wasn't astounding those hidden girls. +7405 The Lutherans know these important drivers. +7406 Larry has confused that busy senators. +7407 This committee isn't describing this good pamphlet. +7408 Benjamin is revealing this serious teacher. +7409 All patients investigate that standing girl. +7410 Most waitresses talk about these gray grocery stores. +7411 Robert went to those gray movie theaters. +7412 The people left these cheap banks. +7413 Christine isn't talking to this concealed pedestrians. +7414 Some children fire that nice guy. +7415 Benjamin messes up these black hospitals. +7416 Fish find this popular rug. +7417 Daniel scanned those bad essays. +7418 Curtis dislikes these orange cafes. +7419 David investigated that troubled actors. +7420 Maria isn't breaking those whole dishes. +7421 Stacy concealed this important newspaper article. +7422 Dan had known that unlucky girl. +7423 That girl hadn't irritated this white school. +7424 Katherine is scanning that concealed book. +7425 Marie hadn't broken these brown dishes. +7426 A teacher could bring this clean hospital. +7427 Many cashiers dropped by those dirty hospitals. +7428 Karla admired these out in the open forks. +7429 Those people hugged that hidden cashier. +7430 A cashier was passing this black movie theater. +7431 Many girls insult that concealed waiter. +7432 A lot of reports alarm these out in the open banks. +7433 April was touring that concealed public park. +7434 These women shouldn't hate this good story. +7435 Charles was taking that bad turtles. +7436 Jeffrey has talked to these troubled students. +7437 The Impressionists annoyed these serious guys. +7438 A lot of pedestrians arrive at these hard libraries. +7439 Those doctors hugged these happy actresses. +7440 That painting is looking like this delighted boy. +7441 Alexander was running around these popular museums. +7442 Theodore hadn't scared this unsure waiters. +7443 Caroline was healing those good teachers. +7444 Beth had fixed these orange couches. +7445 Many guests argue about that uncertain girl. +7446 Some lady should dislike these excited waiters. +7447 These boys ran around this rough public park. +7448 Many waiters have worn that black coat. +7449 That waiter isn't arriving at these rough restaurants. +7450 These snakes have hurt these young waitresses. +7451 Keith does hide that popular dog. +7452 Kenneth shouldn't drive to these hard grocery stores. +7453 Russell visits these confident dancers. +7454 David walks through this blue hospital. +7455 Alan couldn't criticize these fast girls. +7456 Julia skated around that popular cafe. +7457 A lot of boys conceal those rough couches. +7458 These patients were criticizing those unlucky pedestrians. +7459 Katherine can clean that small dress. +7460 This actor was breaking those out in the open cups. +7461 Mary returned to those important customers. +7462 These people think about that popular story. +7463 Jason hasn't hired those happy children. +7464 The museums weren't criticizing this lost waitresses. +7465 The Lutherans weren't worrying that young teenagers. +7466 Tammy walked through that messy restaurant. +7467 All people were cleaning those green cafes. +7468 Gina had listened to these displeased drivers. +7469 Jodi argued about this black pamphlet. +7470 Every sketch looks like that red nose. +7471 Michael isn't finding these smooth prints. +7472 Colleen didn't embarrass this good cashier. +7473 Karla tours this new movie theater. +7474 Ella hadn't walked through that important malls. +7475 A lot of actors discover that gray shoes. +7476 Those customers disagreed with these troubled students. +7477 Some banks describe these hopeful boys. +7478 Dennis looks like this exposed painting. +7479 This girl couldn't insult that smart girl. +7480 Kevin does drop by those cheap malls. +7481 Alicia had that orange horse. +7482 Every cashier dislikes those employed dancers. +7483 Jeffrey hasn't known that nice boy. +7484 All girls dropped by that red hospital. +7485 Ann had taken this black turtle. +7486 The Borgias have examined these bad newspaper articles. +7487 Some woman isn't talking to this sick waiter. +7488 Every actor hadn't observed those determined customers. +7489 Patrick can escape from those new schools. +7490 Renee isn't embarrassing those lost teachers. +7491 That customer wasn't attacking those exposed actresses. +7492 That light astounded that important organization. +7493 A patient isn't leaving these big customers. +7494 This turtle does disturb those rough schools. +7495 Mark references those young guys. +7496 Many boys approach this popular teenager. +7497 Nancy talked about this popular boy. +7498 Colleen won't see these sick boys. +7499 All adults sell these hard grocery stores. +7500 Sharon did flee from this hard grocery store. +7501 Some hospital embarrasses those orange hospitals. +7502 Those mouths worried these small waiters. +7503 Erin observes those hidden actresses. +7504 This customer worked with these displeased waiters. +7505 Joel has fixed those dirty dishes. +7506 Patrick should notice these black rugs. +7507 Omar hasn't left this lost boy. +7508 Heidi healed that exposed teenagers. +7509 Linda finds those gray hospitals. +7510 Carla is firing those young waiters. +7511 Paul isn't talking about those scared pedestrians. +7512 The school won't praise those standing waitresses. +7513 Suzanne wouldn't tour this old mall. +7514 Scott had discussed this hidden brochure. +7515 Monica worked with these unlucky waiters. +7516 Christina dropped by that yellow college campus. +7517 Danielle has disturbed this fast doctor. +7518 Christine has hugged these troubled teachers. +7519 Michael isn't biking to those black hospitals. +7520 Amelia examines this popular report. +7521 A bike shouldn't bother those important schools. +7522 Becky distracted this important hospital. +7523 John disturbed these good committees. +7524 Lucille goes to those old movie theaters. +7525 Carlos cleaned those concealed birds. +7526 Jerry walked through those purple grocery stores. +7527 Rebecca leaves that standing waiters. +7528 The Borgias observe that busy actresses. +7529 The actor didn't forget these long movies. +7530 Christine exited this important public park. +7531 Some teenagers exit that purple hospital. +7532 Suzanne brought that new glove. +7533 Cheryl has complained about those busy children. +7534 Julia researches that popular high school. +7535 These actresses discuss those good schools. +7536 Those teachers lift those small hospitals. +7537 Kristin praised this good documentary. +7538 Christine wasn't bringing these out in the open chairs. +7539 A dancer has upset these nice senators. +7540 Galileo would fire those exposed guys. +7541 All actresses are embarrassing that exposed hospitals. diff --git a/zeroshot/ellipsis/eval_results.json b/zeroshot/ellipsis/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..77c7f6dc33a427151c49147c3e5f96bda38f3f8f --- /dev/null +++ b/zeroshot/ellipsis/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.7904157043879908} \ No newline at end of file diff --git a/zeroshot/ellipsis/predictions.txt b/zeroshot/ellipsis/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..e7d2ace42042c42afde7d4b7afc6cf211c935b70 --- /dev/null +++ b/zeroshot/ellipsis/predictions.txt @@ -0,0 +1,1733 @@ +index prediction +0 Dawn's ex-husband wasn't going to one rough grocery store and Becca wasn't going to many. +1 Most men irritates two happy students and a snake irritates three. +2 A pedestrian's brothers had loved one smart dancer and Phillip's mother had loved few. +3 Brad passed one big museum and Eva passed several. +4 The daughters of Michelle weren't hurting one displeased ex-wife and Stephanie's classmates weren't hurting three. +5 Most ladies can appreciate one good essay and Matt's dermatologist can appreciate two. +6 That lady has resembled one hidden print and Guy's colleague has resembled four. +7 Every actress's piano teachers skate around five messy college campuses and Carolyn's dentists skate around four. +8 This print scares a lot of busy senators and Benjamin scares a few. +9 The customers don't walk through one orange public park and Ellen might walk through few. +10 Amy's lawyers examine one upset grandson and a lot of cashiers examine few. +11 Jerry was cleaning few public parks and some grandson of Ann was cleaning many gray. +12 Jerry fired a few hopeful employees and Daniel's sons fired several. +13 A slope isn't shocking few lost colleagues and horses aren't shocking many. +14 Claire could sound like one college campus and Daniel could sound like some good. +15 Donald criticizes three important customers and Carrie criticizes more. +16 Connie's fathers find one determined spouse and all teenagers find four. +17 The son of Danielle will bore several confident cousins and these grandfathers of Carrie will bore a few. +18 These grandmothers of Martin hadn't resembled two yellow photographs of and some reports about music hadn't resembled three. +19 Many bikes embarrassed no French teachers and the organizations embarrassed some excited. +20 Suzanne's grandfathers talk to five grandsons and many schools talk to four unconvinced. +21 Kenneth's brother revealed one purple restaurant and Karen revealed more. +22 Kristin's piano teachers see one rough school and Florence's electricians see two. +23 A lot of governments describe a few unsure senators and Meredith's lawyers describe several. +24 Laurie cares for four ill sisters and every cashier cares for five. +25 Tiffany was stunning a few displeased nieces of and bananas are stunning several. +26 This actress observes three students and Monica observes more busy. +27 Jesus flees from three determined granddaughters of and Gerald flees from fewer. +28 Dennis talks to a few good students and Monet talks to several. +29 The libraries bought three green eyes and Colleen bought two. +30 Sharon hasn't concealed one sketch and Theodore hasn't concealed at least as many exposed. +31 Maria's senator scanned two upset contractors and this guy scanned three. +32 Colleen's students hadn't sounded like three long essays and Mark hadn't sounded like almost as many. +33 Those men wear few new shawls and most cousins of the Impressionists wear many. +34 That book bored many troubled sons and Theresa bored few. +35 Catherine is visiting one confident colleague and every teacher is visiting some. +36 Debra should praise few nice mothers of and Becky should praise many. +37 Stephanie examines one small mother and Richard's grandfather examines some. +38 Ella was stunning one exposed sister of and this dress was stunning several. +39 Anne's doctor cleans one important book and Stacey cleans a few. +40 This cake impressed one hospital and most essays impressed four rough. +41 Peter should scare one hidden girlfriend and Stephanie should scare three. +42 The sisters of Keith disturbed three nieces and Edward disturbed almost as many exposed. +43 Jill has climbed down one hill and Phillip has climbed down more out in the open. +44 Tara had revealed no delighted ladies and the lady's electricians had revealed some. +45 Many daughters of Sarah were exiting one rough library and Renee wasn't exiting three. +46 Some guest has distracted one important organization and Curtis's hairdresser has distracted four. +47 Every actor's ex-girlfriend disturbs one hidden daughter of and this closet disturbs at least as many. +48 Glasses might scare one content ex-girlfriend and Noah might scare some. +49 A guest's tutors aggravate one unsure contractor and these glasses aggravate three. +50 The bikes have bored three ladies and Mary hadn't bored almost as many happy. +51 Denise saw a lot of unlucky guests and Christine's electricians saw a few. +52 Kirsten couldn't climb up one hidden mountain and Irene couldn't climb up few. +53 This brother of Walter is distracting three good governments and Danielle's grandsons aren't distracting almost as many. +54 Some banks hires one grandmother of and Brad's ex-husband hires a lot employed. +55 Those grandsons of Larry irritates one old grandmother and some gate irritates three. +56 These mothers of Lori haven't helped one good teenager and Sharon had helped four. +57 Those schools will bother one senator and that dress will bother three displeased. +58 Stephanie should kiss one excited friend of and Omar should kiss more. +59 Beth isn't hurting four daughters and some school isn't hurting five out in the open. +60 Derek's associates clean one old hospital and senators clean a lot. +61 Many slopes will resemble one yellow photograph of and Danielle will resemble two. +62 A man wasn't shocking one boy and Donna's nephew wasn't shocking some determined. +63 Julia hasn't remembered one son and Tammy's boyfriend hasn't remembered several determined. +64 Ronald's father cared for one young woman and that teenager cared for many. +65 Keith's contractors can mess up two small shoes and Jodi can mess up three. +66 That cilantro had annoyed three adults and some shoes had annoyed two out in the open. +67 Jerry questions five bad French teachers and Karen questions four. +68 Diana has one rough car and the lady has three. +69 Victoria appreciated few concealed employees and Craig's brothers appreciated many. +70 Aaron hasn't helped one lucky father of and Samuel hasn't helped at least as many. +71 Curtis bikes to one important glacier and Kevin bikes to two. +72 Renee had noticed one fast waiter and a mother of Erin had noticed three. +73 The Impressionists concealed three exposed snakes and Elizabeth concealed at least as many. +74 A sketch is looking like one good unicycle and some drawing of the malls is looking like more. +75 Caroline shocked a few determined grandmothers and some pasta shocked several. +76 These fathers of Brad helped four unemployed classmates and Andrea helped five. +77 Fathers of Jesus worries one patient and literature worries some lucky. +78 A glacier aggravated several old waitresses and Heather's daughters aggravated a few. +79 Danielle messes up few good hospitals and Amy messes up many. +80 Diane's supervisors haven't broken a few purple bicycles and Jerry hasn't broken several. +81 Judy can't wear a few old sweaters and Travis can't wear several. +82 That box annoys one granddaughter and every jacket annoys a lot delighted. +83 Winston Churchill shouldn't remember one good story and Amy shouldn't remember few. +84 Melanie didn't run around one purple cafe and Rose didn't run around a lot. +85 Jerry has aggravated three lawyers and Laura has aggravated almost as many excited. +86 Some shoes hadn't upset one unsure sister of and Dennis's dermatologist hadn't upset more. +87 These students hadn't exited no hidden cafes and Eva hadn't exited some. +88 Brenda had broken one cup and Donald had broken some white. +89 Sabrina wasn't exiting three new closets and Sarah wasn't exiting more. +90 Natalie's daughters tour one green hill and all teenagers tour two. +91 David's teacher can't complain about one troubled brother and Katherine can't complain about five. +92 Ellen does fix two chairs and Eric does fix three orange. +93 A lot of prints looked like one casserole and all drawings of a pie looked like more blue. +94 Benjamin will boycott four bad committees and Jodi will boycott five. +95 Sabrina loves three clever grandsons and the guest loves almost as many. +96 The cashiers could hurt one important waitress and Brad could hurt a lot. +97 Winston Churchill is distracting one driver and Alicia is distracting a few scared. +98 Some child doesn't wear one white dress and Renee doesn't wear many. +99 Paul hadn't astounded one ex-wife and some painting hadn't astounded several serious. +100 This high school confuses one hard bank and most teachers confuses some. +101 Andrew wasn't working with a few excited sons of and Tamara wasn't working with several. +102 Larry walked through one dirty museum and every senator walked through many. +103 Theresa has two small banks and Timothy has three. +104 All people's dermatologist can watch one unsure cousin of and all nephews of Kathleen can watch a few. +105 Beverly has investigated one long wife and that driver has investigated a few. +106 Catherine climbs up three smooth slopes and Wayne climbs up fewer. +107 Elizabeth didn't admire a few exposed shirts and Rodney didn't admire a lot. +108 Regina's hairdresser might wear five dirty gloves and a child's lawyer might wear four. +109 That horse had embarrassed few fast supervisors and all friends of Kayla had embarrassed many. +110 The horses were descending one exposed ladder and Andrea wasn't descending two. +111 Janet discusses one red river and Debra discusses at least as many. +112 The grandfathers of women fix one old bicycle and many granddaughters of Jessica fix at least as many. +113 Ronald's student is discovering one father and every cousin of Becky is discovering some hopeful. +114 That picture of Stacy shocks a lot of excited classmates and a play shocks a few. +115 Sonia examines one nephew of and an actress examines two displeased. +116 Many pedestrians wear one bad glove and these teenagers wear a lot. +117 Janet discusses three uncertain contractors and the niece of pedestrians discusses at least as many. +118 Some adult's nephew couldn't argue about few smart piano teachers and Naomi's cousin couldn't argue about many. +119 George is going to one purple mall and Katherine is going to five. +120 Heather hasn't approached one small doctor and Homer hasn't approached some. +121 Melissa has skated around three bad cafes and Denise has skated around at least as many. +122 Guy wears one soft blouse and Wendy wears several. +123 John's daughter walks through one messy library and Mary walks through several. +124 Those actors' electrician has descended three blue stairs and all guests' employee has descended almost as many. +125 Theodore's granddaughters observe a lot of mentors and many nieces of Kirsten observe a few hopeful. +126 Ruth's electrician concealed one hopeful teenager and Heidi concealed several. +127 Travis breaks one brown convertible and Roger breaks several. +128 Martha scared one big supervisor and every book about art scared three. +129 Some grandmothers of Beth bike to no college campuses and Tara's dentists bike to some gray. +130 Naomi complains about a few ill customers and Larry complains about a lot. +131 Nicole was concealing five smooth casseroles and that person was concealing four. +132 Vanessa hadn't questioned one old niece and Charles hadn't questioned five. +133 Many sons of those teachers forget three confident nephews and all brothers of Denise forget almost as many. +134 The jacket bores one classmate and some library bores several unconvinced. +135 Rose drove to three important libraries and Daniel drove to at least as many. +136 Gerald breaks one skateboard and Kimberley breaks five white. +137 Most guys clean one small couch and that boy's nephews clean few. +138 The Impressionists break one yellow computer and these adults break a few. +139 That sister of the Impressionists revealed three smart nieces of and Matt revealed more. +140 The Lutherans climb down three blue ladders and Heather's partners climb down fewer. +141 The book resembled no photographs and these jackets resembled some orange. +142 A lot of people might kiss three bosses and Jessica might kiss almost as many unconvinced. +143 Jesus hired three serious mentors and Carrie's contractor hired fewer. +144 Derek loves one nephew and Allison loves some unemployed. +145 Those actresses scanned several big classmates and Katherine scanned a few. +146 The story about Spain is hurting one excited guy and that doctor is hurting five. +147 Dan remembers four old art galleries and Sally remembers five. +148 That print distracts one hopeful lady and this lamp distracts several. +149 Many cashiers hadn't disliked a few hopeful lawyers and Rose hadn't disliked several. +150 Many sketches looked like one lucky daughter of and this painting of the candles looked like three. +151 These eyes distract a few scared guests and the teenagers distract several. +152 That high school disgusted one piano teacher and that fish disgusted four scared. +153 Tonya sounds like one ill daughter and Allison sounds like several. +154 Theodore won't talk about one mentor and Bradley won't talk about few content. +155 Alan hadn't thought about one out in the open wheelbarrow and Bill's granddaughter hadn't thought about few. +156 Many legislatures admire several windows and teachers admire a few exposed. +157 Aaron doesn't find many delighted French teachers and Edward's sisters don't find few. +158 Every drawing of many glasses should look like one good dog and those paintings of most books should look like many. +159 Most rivers disgust one ex-boyfriend and the gates disgust many content. +160 Mitchell explores one glacier and Denise explores many exposed. +161 Some father of Sonia hadn't toured a few new art galleries and Diana hadn't toured several. +162 Clyde's supervisors question several employed ladies and Allison's mothers question a few. +163 Richard wasn't attacking four long senators and some sisters of this lady were attacking five. +164 Brad noticed one nice friend and Eva's hairdresser noticed a lot. +165 Alexander's associates hug one employed friend of and some guys hug at least as many. +166 Brian listens to several ladies and Angela listens to a few content. +167 Amy isn't complaining about one bad commentary about and Frank isn't complaining about four. +168 The mothers of some dancers are questioning five serious fathers and Tanya wasn't questioning four. +169 Leslie had explored one old closet and Douglas's sisters had explored two. +170 James's friends ascend three small mountains and a lot of patients ascend more. +171 Angela's tutors don't describe a few dancers and Brian could describe several out in the open. +172 Erin's nephews haven't thought about one gray computer and Lisa has thought about four. +173 Ronald's hairdressers are complaining about one upset wife and Sheila wasn't complaining about few. +174 Ann walks through two rough high schools and Caroline walks through three. +175 Edward didn't reference one confident brother of and April's piano teachers didn't reference five. +176 Kayla shouldn't listen to one nice customer and Martha's guests shouldn't listen to some. +177 Many guests looked like one brown sketch and Noah looked like at least as many. +178 A lady's lawyers had passed several rough schools and Brenda had passed a few. +179 Jessica has talked to a lot of big nieces and Lucille has talked to a few. +180 Craig questions one standing patient and Kirsten questions more. +181 Carolyn's daughters were walking through few glaciers and Bradley isn't walking through many concealed. +182 Phillip sees a few purple sweaters and Steven sees a lot. +183 Marcus can confuse one niece and Katherine can confuse more fast. +184 The boys had approached one actress and that grandson of Holly had approached many delighted. +185 Stacey goes to one mall and Samuel goes to a lot concealed. +186 A lot of actresses worry one good best friend and many chairs worry a few. +187 Thomas has concealed one lucky handyman and Grace has concealed some. +188 Melinda distracted one big brother of and every spotlight distracted three. +189 Alan's teachers argue about a lot of unsure partners and all grandsons of that dancer argue about a few. +190 Brothers of those teachers listen to one uncertain grandfather and these drivers listen to four. +191 All men wouldn't wear one important skirt and Natalie wouldn't wear two. +192 Those associations have a lot of yellow shawls and all drivers have a few. +193 Monica could disturb two clever associates and Naomi could disturb three. +194 Tina cleaned one old public park and Kristen cleaned three. +195 Every dress won't look like four orange paintings of and Victoria's guest won't look like five. +196 That patient might discover one best friend and some guys might discover several upset. +197 Adam cleans a lot of forks and Russell cleans a few exposed. +198 A friend of Grace knows three hidden women and Christina knows two. +199 Douglas's contractor should lift one hidden convertible and this nephew of Larry should lift a lot. +200 Some grandsons of Maria are annoying few confident plumbers and all commentaries about politics are annoying many. +201 Victoria isn't buying a lot of old hills and Benjamin's associates are buying a few. +202 A lot of reports about Winston Churchill could disagree with one clever grandfather and these essays could disagree with a lot. +203 A sister of Curtis skates around a lot of high schools and Jason skates around a few brown. +204 Noah can't climb down three yellow stairs and Mary's plumbers can't climb down more. +205 Amy could boycott one hidden school and Alicia could boycott at least as many. +206 Sheila doesn't scare five smart mentors and Steven doesn't scare four. +207 Russell's doctors weren't dropping by one yellow river and Sonia isn't dropping by three. +208 Sketches are looking like one unlucky mentor and this drawing was looking like at least as many. +209 Caroline leaves a lot of lost sons of and Helen leaves a few. +210 A guy's students disagreed with three hidden nieces and a commentary about the Balkans disagreed with at least as many. +211 Laurie didn't skate around a few hidden banks and Stephen didn't skate around a lot. +212 Donna hated a few good plays and April hated several. +213 Janet had worried one popular organization and Chad had worried two. +214 Candice ascends three hard slopes and some woman's granddaughter ascends at least as many. +215 Emily cleaned one cheap bank and Jeffrey's partners cleaned at least as many. +216 Some sons of Janice dislike three good French teachers and Matt's contractors dislike at least as many. +217 Steven hadn't walked through four brown art galleries and Candice hadn't walked through five. +218 Melinda's colleague hadn't described one unemployed woman and Christopher hadn't described several. +219 Rhonda escapes from one new lake and Ella's contractor escapes from a lot. +220 Current events embarrass one popular legislature and the teachers embarrass a lot. +221 Some pedestrians passed several cafes and Christina's brother passed a few purple. +222 A lot of sketches of Margaret alarm few good universities and the Borgias alarm many. +223 The waitress's doctor talks about one lost dermatologist and Melanie talks about more. +224 Melanie was appreciating a few big hills and Benjamin's girlfriend was appreciating several. +225 The cat confuses one confident colleague and a teenager's doctors confuses two. +226 Ann didn't pass one cafe and Cheryl didn't pass a few hidden. +227 Julia scans one good handyman and Lisa scans four. +228 Those drivers astound several long electricians and most bicycles astound a few. +229 Ruth fires three lucky waiters and Patricia fires more. +230 Bradley hides two contractors and Diane hides three concealed. +231 Mitchell's grandsons hid one important banana and Rhonda hid several. +232 Bethany wasn't discovering one white library and many guests' electricians weren't discovering many. +233 Jason lifts a few books and Bethany lifts several purple. +234 The teenagers stun one doctor and most trucks stun a lot fast. +235 Barbara might conceal one white book and Brenda might conceal three. +236 Theodore's tutors visit one displeased brother of and Craig's ex-girlfriends visit five. +237 Andrew is arguing about three mothers and a lot of sisters of Russell aren't arguing about at least as many hopeful. +238 Theodore's supervisors healed a few hopeful brothers and Mary healed a lot. +239 Martin returned to one hidden river and a lot of grandsons of Todd returned to some. +240 Russell's associates skate around several hospitals and many grandsons of Alicia skate around a few exposed. +241 Every man might appreciate one troubled pedestrian and Vincent might appreciate at least as many. +242 Carmen skates around one purple lake and guests' daughter skates around five. +243 Wayne might pass two malls and Marcus might pass three concealed. +244 A lot of people talk about one mentor and some actresses talk about many hopeful. +245 Dana goes to several important malls and Lori goes to a few. +246 Every grandmother of Andrew drives to a lot of purple malls and Sonia drives to a few. +247 Ella could break one gray carriage and John could break at least as many. +248 Rebecca criticizes one serious girl and Vincent criticizes a lot. +249 Joel isn't kissing one dermatologist and Judy isn't kissing four unsure. +250 Those icicles hadn't confused one hidden nephew of and this mountain hadn't confused many. +251 Brenda's friends skate around three brown libraries and many daughters of most children skate around more. +252 This cashier had helped three delighted electricians and Marcus's cousin had helped almost as many. +253 A school wasn't irritating one out in the open wife and Heidi wasn't irritating three. +254 Caroline examines two unlucky mothers and Sherry examines three. +255 Noah's nephew is disturbing one nice mother and some face is disturbing at least as many. +256 Christine's grandmothers appreciate one smart mother of and the Impressionists appreciate several. +257 Christine's grandson scanned one long show about and Tracy's sisters scanned three. +258 Gerald wasn't leaving three nice dancers and Galileo wasn't leaving almost as many. +259 Omar cleans three brown rugs and Janice cleans at least as many. +260 Monet hadn't cleaned one couch and Stephen hadn't cleaned five smooth. +261 The Borgias appreciated no exposed senators and Deanna appreciated some. +262 Ann's daughter describes one long story about and Rose's ex-boyfriend describes four. +263 Janice's teacher investigated three hidden boys and Ronald investigated almost as many. +264 Laura hadn't lifted one hidden drawing and those children hadn't lifted more. +265 Most daughters of Douglas explore one cheap bank and Ruth's plumbers explore at least as many. +266 Sherry bikes to one blue college campus and Bradley bikes to two. +267 Most women confuse one big cousin and all lakes confuse two. +268 Candice researches one brown photograph of and an actor researches a few. +269 Lori noticed one small cucumber and Pamela noticed three. +270 These actors' plumber might worry one important guest and the hospitals might worry a lot. +271 Jacqueline references one guest and Colleen references at least as many unsure. +272 Amelia will examine one happy nephew and Heather will examine five. +273 Eva worked with two busy cashiers and this grandfather of Rebecca worked with three. +274 Thomas has hidden few movie theaters and Alice has hidden many green. +275 A play isn't disagreeing with one hopeful plumber and most granddaughters of many teenagers weren't disagreeing with four. +276 Barbara didn't have one concealed couch and the guest didn't have some. +277 The niece of Theresa wasn't irritating one smart niece and Gregory wasn't irritating three. +278 Some people examine one uncertain brother of and Kenneth's fathers examine few. +279 Suzanne escaped from one dancer and Joel's guests escaped from a few sick. +280 Jeffrey's hairdresser climbs down one hidden ladder and that teenager climbs down four. +281 Andrew had bored two bad grandfathers and a lot of patients had bored three. +282 Some actress's mentors sounded like one standing colleague and Tonya sounded like five. +283 Marie was exploring many small banks and Sandra was exploring few. +284 A lot of actresses ascended five blue slopes and Theodore ascended four. +285 Phillip brought three blue windows and Eric brought at least as many. +286 Theresa hid a few grandmothers of and Claire hid several unconvinced. +287 The Lutherans' classmates remember three sisters of and Caroline's ex-girlfriends remember at least as many content. +288 Todd isn't escaping from one good father and a patient isn't escaping from three. +289 Valerie stunned one excited grandfather of and Claire stunned a lot. +290 Ellen hadn't talked about one man and Marie hadn't talked about more confident. +291 Janice's bosses attack one wife and sons of Martin attack many unemployed. +292 This cashier's classmates exited four new hills and Tonya exited five. +293 Rebecca's colleagues didn't question one long ex-girlfriend and Kayla didn't question five. +294 Eva won't boast about one hidden mother of and Jacqueline won't boast about many. +295 Frank respects one bad boyfriend and Patricia respects many. +296 Many students can't distract two unlucky supervisors and a library can't distract three. +297 Richard is cleaning one blue mall and Marcus is cleaning a lot. +298 The plates didn't resemble three gray drawings of and some books about the Balkans didn't resemble fewer. +299 Tammy visits one mother of and Vincent's employee visits more delighted. +300 Jane explores one big glacier and Florence explores few. +301 Ronald would discover one glass and the Lutherans would discover two yellow. +302 Todd disagrees with a few old mothers and Marie disagrees with a lot. +303 Those paintings of the windows might look like one concealed actor and the print of Barbara might look like a lot. +304 Every library was astounding several drivers and a girl was astounding a few ill. +305 Stephen had criticized one big newspaper article about and many nephews of Anna had criticized five. +306 Donna listens to one confident son and Alice listens to few. +307 Some cousins of Carl go to five rivers and Alexander's friends go to four blue. +308 Carlos would complain about many confident mentors and Bruce's doctor would complain about few. +309 Candice would cure one child and Rachel would cure few unconvinced. +310 Libraries haven't concealed one uncertain lawyer and Martin's colleague hasn't concealed three. +311 Some nephews of this man weren't lifting one black carriage and Naomi was lifting many. +312 Every boy's granddaughter had loved one big boss and every granddaughter of most students had loved four. +313 Erin doesn't hide one young dancer and Travis doesn't hide more. +314 Monet is describing three bad plays and Martin is describing two. +315 The actresses have observed one clever ex-boyfriend and Pamela's nieces have observed two. +316 Deborah's bosses hadn't lifted few blue cucumbers and Valerie hadn't lifted many. +317 Those actors kiss one exposed boyfriend and Leslie's fathers kiss a lot. +318 Those pedestrians fire one associate and the Borgias fire many lost. +319 Emily noticed many purple pictures and Joel noticed few. +320 That person's electricians had messed up one white glass and Craig had messed up at least as many. +321 Gerald resembles several black photographs and Andrea's best friend resembles a few. +322 Andrew's girlfriend explores three smooth hospitals and some brother of Meredith explores two. +323 Gary insulted one bad essay and that actor insulted four. +324 Theresa could alarm two sons of and Chad could alarm three concealed. +325 Douglas's friends can disagree with four excited children and Helen's fathers can disagree with five. +326 Roger's supervisors think about one gray cake and Naomi's friends think about many. +327 Many reports had distracted one lucky boy and all sweaters had distracted few. +328 Essays about the Lutherans distracted one hidden grandfather and Tammy distracted more. +329 The mushroom isn't aggravating one granddaughter of and that person isn't aggravating some delighted. +330 The Borgias think about one red eggplant and the Impressionists think about many. +331 Ellen can't think about a few standing guests and Tammy can't think about a lot. +332 Some essay has stunned one good associate and those ladies had stunned at least as many. +333 Mothers of Teresa toured three important cafes and the adult toured at least as many. +334 Harvard University listens to one displeased friend of and Brett listens to few. +335 Melissa is researching many delighted piano teachers and Ann is researching few. +336 The grandson of most guys returns to one fast student and Jeffrey returns to five. +337 Walter boasts about one uncertain cousin and that guest's friend boasts about several. +338 Brad's friends should care for one excited mentor and Denise should care for some. +339 Some shoes don't shock few upset partners and a lot of lamps don't shock many. +340 Derek cured one daughter and every student cured at least as many determined. +341 Claire's friend wouldn't pass one small mall and Gerald wouldn't pass several. +342 A doctor's ex-girlfriends left one 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Gerald would investigate many uncertain. +365 Every lady was fixing few bad screens and Dawn was fixing many. +366 That adult's guests observe no grandsons of and many people's classmates observe some smart. +367 Tracy is arriving at one hidden library and Tonya is arriving at more. +368 Scott's senator can arrive at a few big college campuses and Patrick can arrive at a lot. +369 Peter was referencing one old senator and Nancy was referencing several. +370 Kendra's piano teacher has sold five orange paintings of and Meredith has sold four. +371 A story about travel disturbed one big guest and that rabbit disturbed many. +372 Craig is boring one confident customer and Beth's dentist is boring several. +373 Cindy hadn't kissed one upset friend of and Dennis hadn't kissed three. +374 Russell is listening to three happy brothers and Laura's supervisors were listening to fewer. +375 Stephen couldn't have three new rivers and Guy couldn't have more. +376 That car shocks one waitress and the steak shocks some bad. +377 The Lutherans' cousins had watched one small guy and Paul had watched some. +378 Most ladies scan one smart niece and all grandfathers of David scan more. +379 Vincent's friends were kissing one lucky French teacher and Russell wasn't kissing some. +380 The Great Lakes aggravate a few messy museums and Nancy's hairdressers aggravate a lot. +381 Samuel was escaping from one green public park and the Impressionists' ex-girlfriends aren't escaping from a lot. +382 Caroline hadn't bored one fast father and Becky hadn't bored many. +383 Many sons of most actors worries one classmate and a lot of adults worries many unemployed. +384 Connie's lawyers were leaving a lot of nice children and Eva was leaving a few. +385 Jerry's boss has boycotted few bad organizations and Ann has boycotted many. +386 Nicole didn't sell a few green books and Brett's doctor didn't sell a lot. +387 Kayla's sons aggravate one nice girl and Barbara's tutors aggravate three. +388 Andrea listened to one fast grandmother and Ronald listened to some. +389 Some women were climbing down one important ladder and Sarah's hairdressers were climbing down more. +390 Lori hasn't criticized three displeased cousins of and the cashier's tutor hasn't criticized at least as many. +391 Robert's ex-girlfriends upsets one clever dermatologist and this projector upsets more. +392 Claire hated few popular movies and Allison hated many. +393 Regina remembers several delighted pedestrians and Rose's girlfriend remembers a few. +394 Kathleen might hide one happy boyfriend and Marcus might hide many. +395 Monet won't kiss three determined cashiers and Kristin's guest won't kiss fewer. +396 Marla is bringing one broken skateboard and Karla is bringing five. +397 Daniel would hire few scared students and that adult would hire many. +398 Those reports about the Great Lakes wouldn't impress one bad girl and this book wouldn't impress four. +399 Dan will investigate one unlucky husband and Bill's partners will investigate a lot. +400 Donald arrives at a few important glaciers and an actress arrives at a lot. +401 Russell cleans one yellow rug and every girl cleans a few. +402 Claire won't discover many sick French teachers and Ella won't discover few. +403 Joseph couldn't forget one bad daughter and Amanda couldn't forget five. +404 The niece of Ella is breaking one purple glass and Guy is breaking a lot. +405 Claire can't alarm a few important universities and Rachel can't alarm a lot. +406 Some grandsons of Karen upsets three lucky hairdressers and that public park upsets two. +407 Monet insults one young actor and Sonia's lawyer insults four. +408 Some company buys one red box and Tara buys four. +409 Kristen wasn't discovering two out in the open grandfathers and most nephews of Karla aren't discovering three. +410 A lot of guys' piano teacher notices one young best friend and Galileo's boyfriend notices three. +411 Catherine works with one wife and some teenager works with 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daughters of and Linda's senator didn't aggravate two unsure. +424 Pedestrians help three displeased nieces and a lot of grandmothers of Tracy help almost as many. +425 Water annoys a few displeased girls and this chair annoys a lot. +426 The actress's classmates know several sons and friends of Curtis know a few unsure. +427 Waitresses' colleague dropped by three new grocery stores and this granddaughter of Derek dropped by fewer. +428 Some senator cleans one skirt and a doctor cleans few good. +429 Tamara couldn't investigate a few clever mothers of and Eric's colleagues couldn't investigate a lot. +430 Most girls disagreed with two concealed grandfathers and some drivers disagreed with three. +431 These actors resemble two blue drawings and these grandsons of Debra resemble three. +432 Debra finds three popular glaciers and Lori finds two. +433 Noah could buy three green photographs of and the Lutherans could buy almost as many. +434 Some guest knows one nice doctor and Patricia knows two. +435 Some children hug one ex-husband and a lady's colleagues hug more serious. +436 Bradley kissed one unconvinced senator and Diane's teachers kissed four. +437 Elizabeth cares for three hidden best friends and Nicole cares for almost as many. +438 Diane's dentist cares for three lost grandsons and Edward cares for at least as many. +439 That customer had attacked five good waitresses and the men's partner had attacked four. +440 Jodi's son is disagreeing with one bad guest and Karen's dermatologist is disagreeing with three. +441 Those newspaper articles about science irritated three old contractors and the pictures of Maria irritated more. +442 These waitresses' associate wasn't boycotting few concealed lakes and Maria wasn't boycotting many. +443 Edward knew one man and these women knew more upset. +444 The man drove to one important closet and Natalie drove to a few. +445 Guy escaped from a few busy girls and the actor escaped from several. +446 A driver flees from one 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Jessica was breaking three. +458 Some fathers of this cashier aggravates one employed classmate and Matt's brothers aggravates three. +459 Leslie's mentors describe three yellow reports about and those waiters describe more. +460 Turtles were impressing no smart cousins of and some friends of Anna were impressing some. +461 Jeffrey sounds like one hidden waiter and every woman sounds like more. +462 Kayla's ex-girlfriends remember several content senators and Brad's grandmothers remember a few. +463 Dan hasn't discovered one snake and Laurie's classmate hasn't discovered many out in the open. +464 Customers were finding one cat and the bird is finding more popular. +465 Bruce can criticize few rough hospitals and Jennifer can criticize many. +466 Renee hasn't investigated one pedestrian and the boys hadn't investigated at least as many employed. +467 Becky could aggravate a lot of confident doctors and Kendra could aggravate a few. +468 Beverly's tutor has dropped by one popular art gallery and Paula has dropped by four. +469 Jessica isn't cleaning one old dress and Beth isn't cleaning more. +470 Some doctors worries one brother of and Dana's electricians worries some exposed. +471 Marcus is examining one clever grandmother of and the student is examining a lot. +472 All photographs hurt one big niece and granddaughters of Jesus hurt a lot. +473 A lot of girls were annoying one smart granddaughter of and a senator isn't annoying at least as many. +474 These guests might drop by few orange lakes and Catherine's father might drop by many. +475 Russell hasn't approached one actor and Christopher hasn't approached some uncertain. +476 Becca reveals five uncertain mothers and Paula reveals four. +477 The Lutherans hadn't argued about three unlucky friends of and Katherine hadn't argued about at least as many. +478 Kimberley can't clean one old shoe and Teresa can't clean at least as many. +479 Some dancer was descending four gray stairs and Veronica was descending five. +480 Gina climbed down a few white slopes and Barbara's electrician climbed down a lot. +481 Samantha examined a lot of exposed reports about and Brian examined a few. +482 Tonya was boycotting one cheap museum and Jill's French teachers aren't boycotting a lot. +483 Barbara has remembered one small school and Eric has remembered a lot. +484 Larry's teachers exit one closet and these waiters exit five smooth. +485 Rodney climbs up many hard hills and Tara climbs up few. +486 Janet has visited a few big mothers of and this patient has visited a lot. +487 Naomi hadn't returned to one old school and this dancer hadn't returned to two. +488 Every daughter of Lisa isn't investigating no young sons of and Leslie isn't investigating some. +489 Holly's piano teachers escape from one grandfather and the cashiers escape from at least as many out in the open. +490 Leslie was returning to no rough glaciers and Julie was returning to some. +491 Marie's nieces hire one bad ex-wife and Linda's French teachers hire several. +492 Dawn kisses a few unconvinced partners and Carol kisses a lot. +493 Those waitresses were scaring one happy sister and some rice isn't scaring five. +494 Guy's granddaughter ascended one slope and a lot of grandsons of Todd ascended two concealed. +495 Jane observed three uncertain sons and Keith observed almost as many. +496 Samantha's daughters think about three hard turtles and all grandsons of Jerry think about fewer. +497 Ann is lifting three white couches and a sister of many girls is lifting fewer. +498 Diana would know one contractor and Catherine would know five content. +499 Waitresses' cousins distracts three dancers and every hat distracts more employed. +500 Peter passed several banks and adults passed a few purple. +501 Rachel's doctors could aggravate three good associations and some hamsters could aggravate almost as many. +502 Judy's French teacher shouldn't describe five good movies about and a hospital shouldn't describe four. +503 Winston Churchill should walk through three public parks and Maria should walk through at least as many brown. +504 A girl helped three adults and most schools helped more important. +505 Katherine returned to one bad ex-boyfriend and Tanya returned to two. +506 Connie's employee had cleaned a few big eyes and Michael had cleaned a lot. +507 Andrew's lawyer would care for three happy sons and patients would care for fewer. +508 Becca's grandson couldn't scan one bad story about and that mother of Bruce couldn't scan many. +509 Those students will attack one hidden associate and a child will attack many. +510 That grandson of Alexander hadn't complained about two long essays about and Claire's ex-husband hadn't complained about three. +511 A lot of guests notice one popular sketch and Gina's colleagues notice three. +512 Most people were buying one red restaurant and Raymond wasn't buying few. +513 Susan's mentors listen to one hidden teacher and many teenagers listen to a lot. +514 Some ladies' best friend remembered several purple mountains and this brother of these senators remembered a few. +515 The Impressionists went to one black glacier and a nephew of Melanie went to three. +516 Anne's piano teachers won't go to one dirty hospital and Catherine won't go to some. +517 Frank will observe three standing cousins and Maria will observe more. +518 Kathleen isn't complaining about one scared customer and Chad isn't complaining about five. +519 Amy's nephews hire one good mentor and Tanya's doctors hire two. +520 Paul has three hidden hills and Natalie has almost as many. +521 Barbara's plumbers brought three small glasses and Amelia's partners brought two. +522 Dennis's French teachers sound like three grandmothers of and most guys sound like almost as many exposed. +523 Anna climbed down five white hills and Laura climbed down four. +524 Amanda complained about one good commentary about and Derek complained about few. +525 Vanessa could boast about a few electricians and Rebecca could boast about a lot fast. +526 Tracy disagrees with three lost drivers and Anne disagrees with almost as many. +527 Laurie didn't reveal one unlucky colleague and Julie's daughter didn't reveal a few. +528 Erin's partners haven't noticed three exposed horses and Clyde has noticed more. +529 A grandson of those children sold one high school and every daughter of Anna sold some blue. +530 Ella passes several mountains and William passes a few smooth. +531 Many men listen to one unlucky grandson of and a lot of pedestrians listen to five. +532 These senators mess up one clean grocery store and Diana's students mess up three. +533 Martha had irritated four fast brothers of and many nephews of David had irritated five. +534 Mary hadn't looked like no drawings of and Angela hadn't looked like some yellow. +535 Elizabeth's grandson shouldn't clean one red dog and Scott's grandfather shouldn't clean a lot. +536 Elizabeth descended one big ladder and Joel descended more. +537 Craig's guests should sell a lot of popular slopes and Suzanne should sell a few. +538 Brian wouldn't lift no red books about and Todd's cousin wouldn't lift some. +539 A lot of daughters of Amanda boast about several scared sons and a lot of patients boast about a few. +540 Paula's daughter liked one red carriage and Sandra liked a lot. +541 Travis attacks a few serious men and Carolyn attacks a lot. +542 All teachers research one uncertain doctor and Cheryl's colleagues research several. +543 Deborah's friend isn't visiting a lot of serious classmates and Alan isn't visiting a few. +544 Carolyn's spouse talked to one employed dentist and Cynthia talked to more. +545 Bethany upset two white banks and those high schools upset three. +546 All sisters of Gina hug three brothers of and Larry's contractors hug more fast. +547 Many women aren't breaking one smooth dish and Brian isn't breaking few. +548 This senator's grandfather has explored one brown bank and Joseph has explored two. +549 Carolyn talks about one grandson of and Rhonda talks about at least as many unsure. +550 Paul won't notice few nice sons of and Sally's sister won't notice many. +551 Most actors' granddaughters explore four clean college campuses and all cousins of Craig explore five. +552 Most waitresses disagreed with one happy son and Monet's granddaughter disagreed with four. +553 Regina examined two black newspaper articles and Russell's nieces examined three. +554 Most grandfathers of Lori exit a few bad schools and the sons of Gina exit several. +555 A sketch looks like one clever actress and a drawing of Allison looks like few. +556 Those cashiers walk through one blue glacier and the ladies walk through several. +557 This doctor didn't break five messy forks and Christina didn't break four. +558 These skateboards have hurt a few employed fathers of and Alice's handyman had hurt several. +559 All children go to one red school and cashiers go to at least as many. +560 Most children disagree with a few hidden sisters and a lot of commentaries disagree with a lot. +561 Some association was aggravating three clever ladies and some restaurant was aggravating at least as many. +562 Lawrence isn't revealing one out in the open mirror and Paul isn't revealing a few. +563 Monet sold one big unicycle and Donna sold more. +564 Those men skate around four gray closets and Leslie's sons skate around five. +565 Kristen's girlfriend discusses no serious grandmothers of and Irene discusses some. +566 Kimberley's girlfriend sees one small actor and Jennifer's wife sees four. +567 Alicia's grandmothers criticized one small granddaughter and Tammy criticized two. +568 Carolyn's supervisors scanned two bad commentaries about and Melinda's ex-wives scanned three. +569 Adam's electrician had escaped from three children and a student had escaped from almost as many delighted. +570 The doctor's grandfathers should hate one essay about and Emily should hate some good. +571 Pamela flees from three lost best friends and Amelia flees from two. +572 Many women were researching three orange books and Melanie's cousins were researching two. +573 That popsicle looked like one out in the open picture and Connie looked like five. +574 Laura's electricians had boycotted three red high schools and Tamara had boycotted fewer. +575 Richard isn't firing three unlucky patients and Marie isn't firing two. +576 Stacy sold three red paintings of and a dancer sold almost as many. +577 Kirsten had one gray lamp and Katherine had more. +578 Some cousins of Richard conceal one big dish and Carrie's grandmothers conceal many. +579 Regina finds few small ladders and Paul finds many. +580 Some library shouldn't distract four nice friends and a guy shouldn't distract five. +581 Gerald's bosses shouldn't walk through one rough hill and that friend of Rachel shouldn't walk through a lot. +582 All actors weren't alarming a few guys and all waitresses weren't alarming several unconvinced. +583 Raymond questions one excited ex-girlfriend and some actress questions four. +584 Donna watched one scared best friend and Barbara watched a few. +585 Jane skates around three hidden public parks and Brian skates around at least as many. +586 Carl has descended a lot of purple stairs and most grandmothers of Thomas hadn't descended a few. +587 Every senator's supervisors love few exposed sisters of and these grandmothers of Cynthia love many. +588 Jodi might criticize one confident plumber and David might criticize few. +589 Many women aren't selling three big peppers and some guests aren't selling fewer. +590 Martha hasn't driven to four gray grocery stores and Catherine's daughters have driven to five. +591 The niece of Pamela approached one child and Anna approached more popular. +592 Jane buys one bad closet and Tracy's teacher buys two. +593 Connie passes three yellow lakes and Winston Churchill passes more. +594 Omar arrives at many high schools and some senator arrives at few concealed. +595 Roger's nephews impress one troubled supervisor and the doctors impress several. +596 Anna has concealed a few fish and Stacy has concealed several ill. +597 Patricia hasn't helped three smart colleagues and Craig hasn't helped two. +598 Those drivers approach one employed child and Alicia's French teachers approach four. +599 Most companies don't criticize a lot of important movies and Amelia did criticize a few. +600 That nephew of Pamela thought about one red carriage and Robert thought about many. +601 The brother of Gregory isn't skated around three green libraries and Margaret isn't skated around more. +602 Tanya would watch one confident daughter and Omar would watch several. +603 Brenda doesn't run around one popular high school and Rebecca's daughter doesn't run around few. +604 Jessica's boyfriend drives to a few movie theaters and Heidi drives to a lot rough. +605 Stacey isn't cleaning many black socks and Anna isn't cleaning few. +606 Steve sells a few tomatoes and some doctor's nephew sells several out in the open. +607 Donald can argue about one hopeful lady and these fathers of Gregory can argue about a few. +608 Commentaries about literature impress one big museum and hills impress a lot. +609 Ellen criticizes four unlucky mothers and Marla criticizes five. +610 Some grocery stores aren't stunning one popular customer and those restaurants aren't stunning several. +611 Most waitresses had visited one troubled wife and Karla's daughters had visited at least as many. +612 Steve scanned one long essay about and Sonia scanned four. +613 Many patients were passing four important public parks and Derek's friends were passing five. +614 Anne's brother is disturbing one excited girl and Michael is disturbing three. +615 Some banks criticize one clever ex-girlfriend and Julia's granddaughters criticize three. +616 Ella's students ascended a few purple ladders and some ladies ascended a lot. +617 Libraries reference five confident men and Raymond's colleagues reference four. +618 Every niece of every actress bikes to several rivers and a grandmother of boys bikes to a few red. +619 Amelia can conceal one car and Samantha can conceal five orange. +620 Judy is observing five nephews of and Barbara's friends aren't observing four out in the open. +621 Jill's hairdresser wasn't biking to many orange hospitals and Suzanne wasn't biking to few. +622 A photograph couldn't irritate one upset waiter and Melinda couldn't irritate four. +623 Wayne respected one busy father of and Sonia respected two. +624 Some committees appreciate one old hill and these companies appreciate five. +625 Sandra forgot five bad shows about and Suzanne forgot four. +626 Jill hides one chair and Tammy hides more orange. +627 Every father of Kevin has kissed one nice actor and Paul has kissed few. +628 Ronald is firing five busy doctors and Douglas's mentor is firing four. +629 Kenneth's classmates hadn't exited one concealed closet and Tammy hadn't exited few. +630 Most people had disgusted one girl and some ladies had disgusted few out in the open. +631 All men's best friends weren't arguing about one contractor and Irene was arguing about three employed. +632 Tamara listens to three long mothers of and Janice listens to at least as many. +633 Sara hasn't noticed one happy father of and Tracy hasn't noticed a lot. +634 The mothers of Joseph are biking to one red restaurant and Melissa isn't biking to four. +635 A lot of doctors hadn't found several children and Frank hadn't found a few upset. +636 The association shocks a few popular committees and Russell's daughters shocks several. +637 Sandra boasts about three black books and Jason boasts about two. +638 These guests don't pass many good mountains and Catherine wouldn't pass few. +639 Regina complains about one exposed dentist and Cindy's grandson complains about some. +640 Donna argued about four unlucky ex-wives and Jeffrey's tutors argued about five. +641 The museums forget one standing contractor and Ruth's sons forget at least as many. +642 Nancy argued about four young piano teachers and Kristin argued about five. +643 Martha examined four long plays and Bradley's hairdresser examined five. +644 Richard's associate has one red glove and that university has four. +645 Tamara's son boycotted one good committee and Benjamin boycotted five. +646 Boys see three lucky plumbers and Vanessa's tutors see almost as many. +647 John's partners liked one bad best friend and this company liked two. +648 Alice distracted three best friends and a coat distracted more long. +649 Nina hadn't cared for few good grandmothers and Rodney hadn't cared for many. +650 Those waitresses skate around one brown river and the Lutherans skate around a lot. +651 Some pedestrians complained about one brother and Katherine's partners complained about more fast. +652 Jessica isn't helping one boss and Karla's classmates are helping a few unsure. +653 Kristin's grandfathers heal one hidden waiter and every patient's plumbers heal at least as many. +654 Sherry isn't escaping from one delighted lawyer and Scott isn't escaping from four. +655 Elizabeth's son won't stun one ill sister and a lot of dancers' daughter won't stun two. +656 All carts impress one hidden son and the Impressionists impress a lot. +657 Ann's bosses boycott one clean grocery store and this boy's guests boycott several. +658 Irene can reference one fast employee and most waitresses can reference three. +659 Kevin can return to a few employed sisters and Anne's ex-girlfriend can return to several. +660 Douglas hadn't admired one red gate and Karla's best friends hadn't admired a few. +661 Jessica wasn't breaking one glass and Samantha wasn't breaking two white. +662 Amanda isn't biking to one public park and John's dentists aren't biking to a few brown. +663 Brett can't praise one clever mentor and Becky's mentors can't praise few. +664 Christine hadn't forgotten four young mothers of and Elizabeth hadn't forgotten five. +665 Cheryl didn't reveal five small people and Paula didn't reveal four. +666 Every senator's teacher hasn't cured one sick actress and some actors' granddaughters hadn't cured a few. +667 Guy isn't touring one old lake and Jill isn't touring several. +668 Most customers admire a lot of important closets and a lot of doctors admire a few. +669 Galileo hadn't skated around one green grocery store and Ella's employee hadn't skated around several. +670 Alice's grandfathers aren't working with one electrician and Ann wasn't working with a few lost. +671 Karla isn't questioning a few concealed teachers and most dancers aren't questioning several. +672 Nicole's contractor lifted three good glasses and Janice lifted almost as many. +673 Steven skated around three restaurants and Lawrence skated around more out in the open. +674 Candice should complain about one concealed grandfather and Brett should complain about few. +675 Claire might escape from three smart mothers and a guy might escape from two. +676 These windows confuse one important company and the sketches confuse three. +677 Becky isn't discussing one lost French teacher and Pamela isn't discussing several. +678 Tara flees from one wife and Sharon flees from a lot exposed. +679 Todd breaks many black bikes and Vanessa breaks few. +680 That light had bored several serious grandfathers of and Dawn's French teachers had bored a few. +681 Meredith's doctor wouldn't forget three long senators and Steve's teacher wouldn't forget almost as many. +682 Doctors exit three good college campuses and this teenager's electricians exit more. +683 Marla shouldn't talk about one important documentary and Liam shouldn't talk about at least as many. +684 This woman isn't criticizing few bad documentaries about and that committee isn't criticizing many. +685 Stacey's piano teacher will hate a few bad essays and George's boyfriend will hate a lot. +686 Wayne's ex-wives do irritate one fast senator and Carmen should irritate some. +687 Linda's hairdresser won't notice three yellow planes and Tammy won't notice almost as many. +688 Every teacher scans three lucky lawyers and Patricia scans at least as many. +689 Cynthia boasts about one long show about and Becky boasts about few. +690 Chad sold one clean bank and the nephew of Donna sold few. +691 A lot of waitresses can wear three green shirts and Stephanie's wife can wear almost as many. +692 This waiter doesn't talk to a few small mothers and these people can talk to several. +693 Museums praise a few good stories about and Raymond's electricians praise several. +694 Stacy isn't observing many serious mothers and Ronald isn't observing few. +695 Beth's bosses hadn't revealed one bad man and Noah's French teacher hadn't revealed more. +696 This window would astound one bad legislature and Carlos would astound some. +697 The Borgias tour several green movie theaters and those fathers of Karla tour a few. +698 That friend of Julie isn't hiding a lot of mushrooms and Sabrina's best friend isn't hiding a few out in the open. +699 The Impressionists' brothers investigated several hidden dentists and Bethany investigated a few. +700 Phillip escapes from few granddaughters and Rachel escapes from many busy. +701 Rose wasn't admiring one patient and all governments weren't admiring many unconvinced. +702 Danielle is breaking one red convertible and rabbits weren't breaking a few. +703 People disturb one important legislature and all glasses disturb a few. +704 Becca hasn't taken one hidden window and Patrick's colleague hasn't taken more. +705 Homer has disgusted one important government and Christine has disgusted few. +706 The documentary about the Impressionists shocks few sick adults and this hair shocks many. +707 This niece of Raymond hasn't praised one big ex-boyfriend and Holly hasn't praised five. +708 Frank was going to several art galleries and Sandra was going to a few good. +709 A man's senators describe three delighted sons of and the lady's dentists describe fewer. +710 The plate upsets one delighted senator and those nephews of most actresses upsets few. +711 Stacey has brought three newspaper articles about and some patient's dermatologist has brought two exposed. +712 Tracy complains about one ex-husband and Patricia complains about a lot ill. +713 Deanna wasn't bothering one big guy and Jill wasn't bothering four. +714 Tina shouldn't take three mountains and Elizabeth's guests shouldn't take almost as many orange. +715 Veronica returns to four happy contractors and Sara returns to five. +716 Donald's ex-boyfriend was visiting three young actors and Carla was visiting at least as many. +717 The legislature didn't disgust one nephew and Raymond didn't disgust at least as many unsure. +718 Lawrence works with five small sisters of and Liam works with four. +719 The mothers of Julia explore three old malls and these sisters of Frank explore fewer. +720 These nephews 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legislature has three peppers and Charles has at least as many small peppers. +857 A picture does look like four shawls and the photographs of Douglas couldn't look like five red shawls. +858 Bill's associate fires a lot of adults and Galileo fires a few old adults. +859 Melissa's granddaughter complained about four friends of and some people complained about five big friends of. +860 Roger isn't exploring one college campus and some friend of Dan isn't exploring a lot red college campus. +861 Curtis's boss discussed four sons and Andrew discussed five sick sons. +862 Robert's best friend helped one dentist and some universities helped more displeased dentist. +863 Renee helps one girlfriend and Roger helps a few ill girlfriend. +864 That newspaper article about Winston Churchill astounded one waitress and Donald astounded several unemployed waitress. +865 Jodi's plumbers climb down no mountains and the snakes climb down some rough mountains. +866 A lady fixes a lot of bicycles and Bethany's spouse fixes a few concealed bicycles. +867 A lot of patients' girlfriend sells one red popsicle and Steve sells few concealed. +868 Allison's doctor didn't astound one associate and Vincent's mothers didn't astound a few concealed associate. +869 A patient's hairdressers wouldn't forget no unemployed brothers of and a lot of women's grandson wouldn't forget some serious. +870 Stephen's French teacher hasn't worried one important teenager and Roger hasn't worried a lot old. +871 A senator escapes from no plumbers and Sonia escapes from some confident plumbers. +872 Suzanne had climbed down one slope and Martha had climbed down three lost slope. +873 Most pedestrians' ex-wife doesn't arrive at one high school and these waiters' son doesn't arrive at two new high school. +874 Dennis wasn't boycotting one college campus and Laurie wasn't boycotting some blue college campus. +875 Holly disgusted one employee and mirrors disgusted some lucky employee. +876 Tonya isn't attacking 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Lutherans' daughters scan five grandfathers and these waiters scan four lucky grandfathers. +918 These pedestrians aren't arguing about one doctor and Bradley was arguing about some troubled doctor. +919 Judy wore four sweaters and Emily wore five big sweaters. +920 Some man's colleague goes to two schools and Gregory goes to three orange schools. +921 A lady cleans no hospitals and Amelia cleans some hidden hospitals. +922 Peter remembers one grandfather and Tanya remembers few concealed grandfather. +923 Many dishes hadn't impressed no waiters and some water hadn't impressed some unlucky waiters. +924 Jason was passing one museum and that woman was passing several dirty museum. +925 Patricia isn't complaining about one guest and Roger isn't complaining about five uncertain guest. +926 Amy has driven to one hospital and Carla has driven to some messy hospital. +927 Marie works with five people and Leslie works with four concealed people. +928 Marcus exited one hospital and Heidi 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newspaper articles and Heidi had two gray newspaper articles. +1001 Kathleen's piano teachers aren't selling five apples and most nieces of Theresa aren't selling four gray apples. +1002 Connie hadn't bothered many nieces and Jodi hadn't bothered few small nieces. +1003 That waiter's grandmothers aren't passing one grocery store and that driver's best friend is passing four important grocery store. +1004 Lori sees three gloves and Natalie sees at least as many hidden gloves. +1005 Every nephew of Carol has examined one purple book about and William's husband has examined more smooth. +1006 Diana isn't bothering several senators and that person isn't bothering a few clever senators. +1007 Those books about art hadn't upset one old granddaughter of and some cups hadn't upset some upset. +1008 Every bank helps several classmates and Mitchell's lawyer helps a few determined classmates. +1009 Eva has healed one ex-girlfriend and every student has healed more uncertain ex-girlfriend. +1010 Pamela examines one best friend and this dancer examines two happy best friend. +1011 Ladies had brought three prints of and Kayla had brought at least as many big prints of. +1012 Melinda hasn't noticed one employee and Florence hasn't noticed many long employee. +1013 Anna's dentists bores one lady and some banana bores four big lady. +1014 A lot of friends of Bill had investigated three standing boys and Gerald's girlfriend had investigated almost as many content. +1015 Gary's wife climbed up one mountain and these nieces of Robert climbed up three bad mountain. +1016 Lawrence sounds like a few essays about and some movie sounds like several good essays about. +1017 Amy can't look like a few photographs and Florence can't look like a lot smooth photographs. +1018 Erin arrived at several libraries and Richard's teachers arrived at a few popular libraries. +1019 A projector impresses one employed senator and that hat impresses more busy. +1020 Kendra's brothers walk through a few 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popular library. +1031 Heidi's sister loves a lot of contractors and Dennis loves a few serious contractors. +1032 Charles's partners do bring three rugs and Mark's students do bring more purple rugs. +1033 Karen isn't hiring two nieces of and the Lutherans are hiring three concealed nieces of. +1034 Timothy's senator isn't questioning one excited cousin of and Renee isn't questioning a lot employed. +1035 That guest kisses one partner and Mary kisses three long partner. +1036 That son of Heidi forgot one play and Karen's cousin forgot few popular play. +1037 That grocery store aggravates one husband and many teachers aggravates five busy husband. +1038 All girls' teacher is discovering one purple turtle and Chad is discovering a lot red. +1039 Andrea explores one lake and Kendra explores few rough lake. +1040 The picture looked like one tutor and this print looked like five displeased tutor. +1041 Clyde wouldn't disgust one brother and this ladder wouldn't disgust many confident 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dentists. +1450 A lot of nephews of Karen are criticizing one ex-husband and those brothers of Phillip are criticizing several unsure ex-husband. +1451 Mark hadn't toured many public parks and Jerry hadn't toured few orange public parks. +1452 This nephew of this senator has a lot of slopes and the brother of Julie has a few good slopes. +1453 Every eggplant hadn't impressed one unconvinced father and Katherine's hairdressers hadn't impressed a lot long. +1454 Those teachers ascended no stairs and Sara ascended some gray stairs. +1455 Liam isn't attacking one happy sister of and Danielle's students are attacking at least as many standing. +1456 Bethany fired four patients and Martha fired five unsure patients. +1457 Many grandmothers of some customers wear four coats and the students wear five old coats. +1458 Kristin was distracting one friend of and Maria was distracting some scared friend of. +1459 Current events annoy three fathers and Rachel's cousins annoy more long fathers. 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one muffin and Meredith discovered four important muffin. +1471 A print confused one unconvinced cousin of and some boys confused a few content. +1472 Some rug astounded many displeased cousins and those screens astounded few unconvinced. +1473 Julia is observing one big brother of and Marie is observing two smart. +1474 Becca's niece fixed three carriages and an actor fixed more blue carriages. +1475 Many gloves alarm one ex-wife and all boxes alarm several small ex-wife. +1476 Some girls hadn't worn many jackets and that grandson of Nancy hadn't worn few hidden jackets. +1477 Stacy couldn't care for one man and some teenagers' fathers couldn't care for a lot busy man. +1478 Steve hasn't found three dishes and Erin hasn't found at least as many clean dishes. +1479 Janice wasn't criticizing one long newspaper article and Brad wasn't criticizing three hard. +1480 Sarah dropped by many libraries and some ladies dropped by few old libraries. +1481 Thomas had noticed three actresses and Pamela had noticed more confident actresses. +1482 A lot of actors' doctor criticized one black report about and Bruce criticized several green. +1483 The person's nieces arrived at two art galleries and those brothers of Laurie arrived at three important art galleries. +1484 The fish scare one lady and many prints of Kirsten scare few uncertain lady. +1485 Tamara talks to one father and Craig talks to many young father. +1486 Jane likes one big plane and Carolyn likes a few old. +1487 Some sister of an adult researches one school and Raymond researches two popular school. +1488 A lot of museums hadn't praised one unconvinced waiter and Carla's senators hadn't praised a lot nice. +1489 Douglas hadn't fled from two brothers of and Jennifer hadn't fled from three young brothers of. +1490 That boy was worrying one niece and some glasses aren't worrying some delighted niece. +1491 Carol's supervisors had disturbed one small best friend and every sister of these customers had disturbed few exposed. +1492 Samuel had criticized four associates and Edward's colleague had criticized five unconvinced associates. +1493 Frank wouldn't leave one dermatologist and Janice wouldn't leave two standing dermatologist. +1494 Some nieces of every actress investigated a few granddaughters and Florence investigated a lot busy granddaughters. +1495 Reports about art hadn't bored one daughter of and Julie hadn't bored some concealed daughter of. +1496 Brian talks to one unsure grandson of and some person's grandfather talks to a lot serious. +1497 Noah can't hide one lake and Jeffrey's fathers can't hide some important lake. +1498 This projector did bore one unemployed sister of and a lot of glasses did bore more fast. +1499 That documentary distracts no dancers and all men distracts some important dancers. +1500 Candice wasn't exploring three mountains and Catherine wasn't exploring at least as many red mountains. +1501 These companies upset one man and the cakes upset five exposed man. +1502 Some coat isn't aggravating one electrician and Tiffany isn't aggravating two nice electrician. +1503 Kevin's employee was cleaning five turtles and the people aren't cleaning four red turtles. +1504 Bruce hasn't exited one restaurant and Renee's nephew hasn't exited a few messy restaurant. +1505 A daughter of Marcus remembered two lawyers and that sister of Clyde remembered three fast lawyers. +1506 All grandmothers of Becky concealed few lawyers and Kenneth concealed many good lawyers. +1507 Travis has upset no doctors and every car has upset some happy doctors. +1508 Some boy's handyman appreciates one hill and Bethany appreciates two purple hill. +1509 Amy would run around a lot of libraries and Susan would run around a few old libraries. +1510 Melissa forgot one newspaper article and Florence's girlfriend forgot four smooth newspaper article. +1511 Cynthia had walked through five high schools and David had walked through four out in the open high schools. +1512 Every sister of Phillip could sound like one cafe and Brenda could sound like many important cafe. +1513 Kirsten's sisters have upset five banks and Alicia hadn't upset four orange banks. +1514 A lot of teachers annoy one orange bank and a lot of commentaries annoy a lot purple. +1515 Cynthia praised a few books and Stacy praised a lot rough books. +1516 Joel hasn't remembered one documentary and Rhonda's electrician hasn't remembered three long documentary. +1517 Sheila hasn't disgusted many women and Tammy hasn't disgusted few uncertain women. +1518 Many fathers of George couldn't sell three yellow slopes and the children couldn't sell almost as many bad. +1519 Jennifer's grandmothers go to two high schools and Alicia's doctors go to three concealed high schools. +1520 All pies alarm few pedestrians and all lamps alarm many unconvinced pedestrians. +1521 A library talked to three ill daughters of and Spain talked to at least as many standing. +1522 Some children weren't leaving five grocery stores and Edward wasn't leaving four messy grocery stores. +1523 The daughters of Daniel return to a few customers and Chad's best friends return to several scared customers. +1524 Bradley couldn't break few skateboards and Carrie couldn't break many small skateboards. +1525 Andrea has argued about a few popular movies and Jacqueline has argued about a lot important. +1526 Gina might forget one student and Elizabeth might forget five lucky student. +1527 A lot of men don't forget one sister and Carol might forget at least as many exposed sister. +1528 Elaine shouldn't scan many clever friends of and every lady shouldn't scan few displeased. +1529 Kevin arrives at three college campuses and Holly's doctor arrives at two hidden college campuses. +1530 Kristin's senators see one guest and Theresa's dentists see a few standing guest. +1531 That friend of Gary is bothering a few ex-wives and Kendra is bothering several young ex-wives. +1532 David wasn't referencing five girls and Rhonda wasn't referencing four busy girls. +1533 That doctor did discuss one movie theater and many grandmothers of Lori did discuss more old movie theater. +1534 Every father of Melanie would break one messy couch and Cheryl would break many exposed. +1535 Patricia wasn't boring one partner and Ann's partner wasn't boring some fast partner. +1536 Karen goes to one cafe and Deanna goes to a few bad cafe. +1537 Those movie theaters scared four nephews of and Leslie's boyfriend scared five unlucky nephews of. +1538 Some guest wasn't visiting one small friend of and James's husband wasn't visiting two displeased. +1539 That lady hasn't fired one sister and Theresa hasn't fired a few hidden sister. +1540 Timothy wasn't hugging one displeased father and this adult's French teacher wasn't hugging many long. +1541 Every truck isn't worrying one company and Daniel isn't worrying five important company. +1542 Heather leaves a few children and Sandra leaves a lot out in the open children. +1543 Jeffrey's teachers should scare one teacher and these bicycles should scare many sick teacher. +1544 These sons of Carmen can disgust one lost grandfather of and all skateboards can disgust many happy. +1545 Winston Churchill doesn't confuse one serious niece of and all pictures should confuse many lost. +1546 Valerie's grandfather isn't scanning one ex-wife and the lady's associates aren't scanning a few exposed ex-wife. +1547 That boy couldn't resemble five sketches of and these hats couldn't resemble four small sketches of. +1548 Martha was attacking one ex-husband and Sarah was attacking three displeased ex-husband. +1549 Wendy can argue about no adults and Pamela can argue about some upset adults. +1550 Theresa's spouse leaves one small associate and Monica leaves four old. +1551 Julie attacks five brothers and all customers' tutor attacks four upset brothers. +1552 These waiters don't remember one young guest and Marla's granddaughter will remember a lot hidden. +1553 Gina cleaned three cucumbers and that patient cleaned almost as many purple cucumbers. +1554 Tonya examined one scared brother of and Julia examined at least as many exposed. +1555 A mother of Paul irritated three men and Donald irritated at least as many smart men. +1556 Vanessa's dentist admires no cakes and that government admires some important cakes. +1557 Every cousin of Michael hadn't cared for one old student and Homer's teachers hadn't cared for two unconvinced. +1558 Sonia was descending five stairs and Andrew's friends weren't descending four smooth stairs. +1559 Some sons of Daniel were describing one electrician and Melissa was describing five content electrician. +1560 The waitresses discover several contractors and teenagers discover a few determined contractors. +1561 Nina's daughter had referenced three lawyers and Walter had referenced at least as many lost lawyers. +1562 Danielle's girlfriend can remember four essays about and Kendra can remember five bad essays about. +1563 Jessica hasn't scanned few cousins and Barbara hasn't scanned many upset cousins. +1564 The teenager had messed up one chair and Bill had messed up two purple chair. +1565 Teresa flees from one hospital and Craig flees from at least as many smooth hospital. +1566 Most cousins of Veronica examine one uncertain father of and these boys examine more bad. +1567 This dress upset five piano teachers and Katherine upset four concealed piano teachers. +1568 Some lady wouldn't impress many supervisors and Tammy's dermatologist wouldn't impress few scared supervisors. +1569 Some guests' cousins were fixing three forks and Christine was fixing more cheap forks. +1570 Regina's girlfriend was shocking no fathers and some scarf was shocking some unemployed fathers. +1571 Bruce's mother was looking like three photographs of and every glass was looking like at least as many rough photographs of. +1572 Melinda could visit three nieces of and Jessica could visit two hopeful nieces of. +1573 Those jackets resembled one photograph and Gina resembled some big photograph. +1574 Renee does disgust three nieces and this government does disgust almost as many scared nieces. +1575 Craig does watch one boyfriend and all cousins of some child don't watch two good boyfriend. +1576 Candice sold one face and Sara sold five lost face. +1577 Keith skates around one red college campus and Martha skates around a lot cheap. +1578 Robert's daughters love one child and most companies love several old child. +1579 Beverly's granddaughters return to one classmate and all teachers return to a lot fast classmate. +1580 Omar hasn't watched few brothers of and most teachers hadn't watched many excited brothers of. +1581 The Lutherans haven't walked through one movie theater and that customer's sons haven't walked through some popular movie theater. +1582 Grocery stores aggravated four legislatures and some commentary aggravated five important legislatures. +1583 Teresa skates around several banks and some son of Barbara skates around a few rough banks. +1584 A lamp was boring one classmate and every grandson of Kayla was boring four clever classmate. +1585 Teresa can't discuss one senator and Stacey can't discuss five unemployed senator. +1586 A lot of guys' hairdressers bore one senator and students bore few uncertain senator. +1587 Russell disturbs three students and every public park disturbs almost as many out in the open students. +1588 Rachel criticizes several books about and Scott criticizes a few brown books about. +1589 Heather's grandfathers skate around one small school and most waiters' best friends skate around more blue. +1590 Margaret didn't bike to one cafe and a waitress didn't bike to a lot white cafe. +1591 This high school shocks one ex-husband and every screen shocks two troubled ex-husband. +1592 Angela talks about many hairdressers and Stacy talks about few concealed hairdressers. +1593 A lot of girls criticize two grandmothers of and the doctors criticize three troubled grandmothers of. +1594 Joseph had praised one son of and Carrie had praised some unlucky son of. +1595 A lot of children forgot one cashier and the doctor forgot few good cashier. +1596 Rhonda has visited a lot of hidden mothers of and Deanna has visited a few concealed. +1597 Sabrina did criticize one small brother of and Dana's lawyers did criticize four smart. +1598 All girls appreciate a few libraries and Tracy's nephews appreciate a lot popular libraries. +1599 These sons of April had hated one piano teacher and Heidi had hated four employed piano teacher. +1600 Wendy's piano teachers hadn't run around one cafe and Karla hadn't run around four hidden cafe. +1601 Victoria wasn't impressing three contractors and Sarah wasn't impressing fewer lost contractors. +1602 Douglas's sons praise three busy grandmothers of and those organizations praise more young. +1603 A lot of women have cleaned three rugs and Carolyn hadn't cleaned more brown rugs. +1604 A lot of pies have stunned three cashiers and Monet's lawyers have stunned two lucky cashiers. +1605 The stories about Winston Churchill haven't scared one mentor and George has scared two clever mentor. +1606 Pamela hurt one smart grandfather and many muffins hurt at least as many serious. +1607 Andrew discussed a few associates and Christina discussed a lot ill associates. +1608 Suzanne's piano teacher wasn't breaking one wheelbarrow and Rhonda wasn't breaking several purple wheelbarrow. +1609 Todd's teachers alarms one standing nephew and that movie about Galileo alarms a lot troubled. +1610 William's senators have respected one senator and this dancer hasn't respected at least as many busy senator. +1611 Susan kisses three old guys and the pedestrian kisses more busy. +1612 The Impressionists work with three women and these dancers work with at least as many good women. +1613 Beth talked about one bad movie about and Deanna talked about more good. +1614 David isn't stunning one standing mother of and those stories about art were stunning a few clever. +1615 Many fathers of David aggravates one plumber and this dress aggravates several lost plumber. +1616 The boy boasted about four patients and this company boasted about five concealed patients. +1617 Alexander might sound like one tutor and Danielle might sound like some uncertain tutor. +1618 Dennis isn't breaking five carts and Becca isn't breaking four purple carts. +1619 Nancy's grandfathers have one cat and Sonia's students have some brown cat. +1620 Jesus might impress a few senators and that guest might impress several happy senators. +1621 Derek's mothers had researched one uncertain cousin and Curtis had researched several content. +1622 Those nieces of some actors conceal three hamsters and the Borgias conceal more smooth hamsters. +1623 Helen returned to one high school and some actor returned to few purple high school. +1624 Rhonda's sister was observing several French teachers and Kristen was observing a few happy French teachers. +1625 This dog impresses one bank and every unicycle impresses few yellow bank. +1626 Derek's nephew boycotted a few high schools and this daughter of Bethany boycotted several gray high schools. +1627 Timothy's cousin couldn't escape from a few unlucky daughters of and Eva couldn't escape from a lot excited. +1628 Andrea insults many grocery stores and Carmen insults few green grocery stores. +1629 Frank wasn't touring one hospital and Monet wasn't touring five concealed hospital. +1630 Tina's ex-husband descends one lost ladder and Paul's boss descends at least as many hidden. +1631 James disturbed one small daughter of and Spain disturbed at least as many lost. +1632 April's grandfather scans one unlucky grandson of and Liam's sister scans more clever. +1633 These couches were disgusting no patients and many shows about music were disgusting some old patients. +1634 That student hadn't watched one hairdresser and Keith's ex-husband hadn't watched two lost hairdresser. +1635 Dawn visited one dentist and the Borgias visited a few lucky dentist. +1636 A lot of students research one good organization and all doctors research more popular. +1637 Those waitresses aren't distracting three museums and Jessica was distracting fewer good museums. +1638 The Lutherans hadn't hurt several classmates and Wayne hadn't hurt a few troubled classmates. +1639 Every glacier will resemble one green painting of and Marla will resemble a few orange. +1640 A hospital astounded one confident father of and William astounded several out in the open. +1641 Those children are bringing four blouses and Kimberley isn't bringing five concealed blouses. +1642 Candice's classmates have escaped from three doctors and Brian has escaped from more employed doctors. +1643 Patients disgust four brothers and the Great Lakes disgust five clever brothers. +1644 Rose flees from one waitress and Vincent flees from many confident waitress. +1645 Many cashiers cure one waiter and Tonya's associates cure several popular waiter. +1646 Tiffany complains about three waiters and Kenneth complains about more smart waiters. +1647 Brad has alarmed one dermatologist and Jacqueline has alarmed two scared dermatologist. +1648 The senators' teachers are passing one black lake and Sharon's French teacher is passing at least as many purple. +1649 Eric hasn't scared three doctors and many plays about literature hadn't scared fewer lost doctors. +1650 Brad hasn't brought one chair and Nina hasn't brought some blue chair. +1651 Chad had healed three doctors and Becca had healed at least as many uncertain doctors. +1652 Linda does heal no mentors and many waitresses won't heal some unsure mentors. +1653 That pedestrian hasn't listened to one good granddaughter and Ann hasn't listened to two ill. +1654 Liam's mentor had messed up one yellow chair and Becca had messed up many red. +1655 Jodi was concealing a lot of snakes and Stephanie was concealing a few bad snakes. +1656 Theresa bikes to one closet and every grandson of Debra bikes to some bad closet. +1657 Marcus did listen to one grandfather and all cousins of David did listen to three employed grandfather. +1658 Curtis had talked about one unconvinced mentor and David's hairdressers had talked about a lot busy. +1659 These granddaughters of Mary should upset one actor and the association should upset a lot unconvinced actor. +1660 Martin's teacher is embarrassing a few women and Raymond is embarrassing several uncertain women. +1661 That cousin of Deborah hasn't argued about three women and Tonya hasn't argued about fewer standing women. +1662 Jesus wasn't watching one supervisor and Heidi wasn't watching five sick supervisor. +1663 Becky was driving to one high school and Jacqueline was driving to three clean high school. +1664 Chad hasn't approached three cousins of and Sherry hasn't approached fewer hidden cousins of. +1665 Jennifer couldn't bike to one movie theater and Candice couldn't bike to three important movie theater. +1666 Every father of Jerry was returning to three contractors and all people's grandfather was returning to fewer fast contractors. +1667 Scott has investigated one mother and Judy has investigated a few lucky mother. +1668 Some student's lawyers have described one senator and Chad hasn't described many young senator. +1669 Cindy won't reveal one book and Katherine's hairdressers won't reveal many gray book. +1670 Raymond will bike to one school and Margaret will bike to five green school. +1671 Frank's father is disagreeing with one boy and those sons of Peter weren't disagreeing with several scared boy. +1672 The sister of Marla boycotted one small hospital and every driver boycotted a lot old. +1673 Some senator's spouse remembers many classmates and Laurie remembers few out in the open classmates. +1674 Natalie boasts about one boyfriend and Martin's grandmother boasts about two small boyfriend. +1675 Dawn isn't investigating one young mentor and Tanya isn't investigating four lucky. +1676 Some grandson of Kenneth would mess up one small restaurant and Nina would mess up a lot popular. +1677 Mitchell's guests disgust three dentists and Marcus's nephews disgust more concealed dentists. +1678 Mark wasn't praising one old senator and Paul's nephew wasn't praising more nice. +1679 The dancer's plumbers respected one nephew of and Tanya respected four small nephew of. +1680 The cups embarrassed few guys and every friend of Mary embarrassed many fast guys. +1681 Some guy's niece kisses one pedestrian and Florence kisses at least as many out in the open pedestrian. +1682 A guy hugs few nieces and Gary hugs many standing nieces. +1683 James has driven to one mountain and the person's brother has driven to a lot concealed mountain. +1684 All adults reference one young piano teacher and the waitresses reference more content. +1685 Many fathers of Theodore couldn't work with one ill grandson of and Martha couldn't work with at least as many displeased. +1686 Dana hadn't worn one sock and Cheryl hadn't worn five important sock. +1687 Becca's nephew could fix a few skateboards and Melissa could fix several smooth skateboards. +1688 Tiffany's friends love one classmate and some sisters of Marcus love a lot fast classmate. +1689 Every carriage irritates three mentors and that sock irritates more hidden mentors. +1690 That university likes no icicles and that pedestrian likes some green icicles. +1691 A lot of girls weren't lifting one yellow picture of and the grandfather of Karen wasn't lifting several small. +1692 Roger did conceal one good show about and Matt did conceal several bad. +1693 Guests' grandsons observe three sons and these dancers' granddaughters observe almost as many happy sons. +1694 Beth's associate isn't admiring three public parks and Marla isn't admiring almost as many purple public parks. +1695 Some ladies argue about one man and Debra's daughters argue about a few fast man. +1696 Steven should visit one library and Janet should visit five rough library. +1697 Some movie about Galileo worries several children and some spinach worries a few displeased children. +1698 Every doctor's nephews shouldn't complain about several contractors and Cindy's ex-husband shouldn't complain about a few exposed contractors. +1699 Judy's dentists lift one white sweater and those people lift a few orange. +1700 Gregory hadn't observed three friends of and Veronica hadn't observed almost as many unlucky friends of. +1701 The adult wore one blouse and Julia wore two orange blouse. +1702 Guests reference one story about and Cynthia's plumbers reference few good story about. +1703 That plate was alarming one daughter of and Stacy was alarming a few lucky daughter of. +1704 Gina's senator is kissing one exposed grandfather of and Rebecca is kissing a lot ill. +1705 Tina's plumber is selling no boxes and Sally is selling some big boxes. +1706 Stacy hasn't ascended several steps and David hasn't ascended a few small steps. +1707 Kimberley remembered one guest and Christine remembered more hopeful guest. +1708 Curtis isn't observing one doctor and Christopher's sister isn't observing four standing doctor. +1709 The dresses annoy three doctors and sons of Tonya annoy almost as many exposed doctors. +1710 Natalie will work with several French teachers and Brian will work with a few long French teachers. +1711 Sarah's plumbers are walking through few banks and that child was walking through many concealed banks. +1712 That child was insulting one grandson and Heather was insulting some standing grandson. +1713 Stacey hurt five nice sisters of and these dishes hurt four good. +1714 Martha's spouse helps one happy brother and William helps at least as many standing. +1715 Candice wasn't concealing a few piano teachers and Elizabeth wasn't concealing several unlucky piano teachers. +1716 All schools confused one father and a dish confused more employed father. +1717 Cars hurt three sisters of and those forks hurt fewer happy sisters of. +1718 A cat worries a few nieces and most grandfathers of Tonya worries several excited nieces. +1719 Cheryl is escaping from one smart waiter and Naomi is escaping from four upset. +1720 Erin hadn't approached few nephews and Ellen hadn't approached many scared nephews. +1721 Paul's granddaughters help four displeased grandmothers of and Kevin's colleagues help five unsure. +1722 Todd has noticed three mountains and George has noticed more big mountains. +1723 Cheryl would drop by few movie theaters and Gina would drop by many small movie theaters. +1724 Kirsten's bosses reference one unconvinced teenager and many mothers of Ruth reference many smart. +1725 Debra goes to one art gallery and Heidi goes to five brown art gallery. +1726 All students hadn't disagreed with a few doctors and some mother of Peter hadn't disagreed with a lot popular doctors. +1727 Julia's hairdressers visit one brother and Florence's senators visit four upset brother. +1728 Every legislature referenced several senators and Russell referenced a few employed senators. +1729 Melanie has concealed three ladies and Derek has concealed almost as many standing ladies. +1730 The shirts haven't aggravated one partner and Russell has aggravated five unsure partner. +1731 Noah's piano teacher heals one granddaughter and Vincent heals four nice granddaughter. diff --git a/zeroshot/filler_gap/eval_results.json b/zeroshot/filler_gap/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..d6e69924ea8b5efc96c88ec5f6a559be2f36aeb9 --- /dev/null +++ b/zeroshot/filler_gap/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.704014939309057} \ No newline at end of file diff --git a/zeroshot/filler_gap/predictions.txt b/zeroshot/filler_gap/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2a3fb57a2ee285658a9d193c6ce5ec9b9f3f956 --- /dev/null +++ b/zeroshot/filler_gap/predictions.txt @@ -0,0 +1,6427 @@ +index prediction +0 Joel discovered the vase that Patricia might take. +1 Rodney thought about what Wendy had thought about many paintings. +2 Susan wasn't thinking about what Kenneth wasn't bringing the rivers. +3 A driver has thought about what many pictures look like those deer. +4 Amy has seen every skateboard that all guests see. +5 Michelle can see this banana that Maria did sell. +6 These waitresses see some mirrors that Phillip found. +7 Pamela thought about who a lot of libraries haven't praised most students. +8 Caroline forgets most customers that Michelle has admired. +9 Bill wasn't noticing the pedestrian that Joseph wasn't talking to. +10 Teresa knew that man that April remembered. +11 Most patients investigate most patients that Sonia was insulting. +12 Lisa hasn't thought about what that print looked like that ox. +13 Omar hasn't forgotten who Tara is attacking every woman. +14 Mitchell won't notice the fork that Eva admires. +15 Holly researched a lot of hypotheses that Emily couldn't discover. +16 Guy doesn't see the goose that Jane wasn't selling. +17 Galileo hadn't seen these alumni that Derek bothered. +18 A lot of cashiers question this waitress that Ella had disliked. +19 Some teenagers remembered who a lot of boys distracts those doctors. +20 Linda did notice that couch that Naomi hides. +21 Regina isn't researching some stories that Florence criticizes. +22 Kirsten questions banks that this guest wouldn't explore. +23 Barbara remembered the closets that that woman shouldn't dislike. +24 Maria hasn't noticed who that dress isn't worrying the ladies. +25 These senators see some rice that every adult can't lift. +26 Benjamin thought about what Carrie does think about the dresses. +27 Samantha hadn't investigated these people that some actresses cure. +28 Meredith had investigated this cashier that every man has questioned. +29 An actor had noticed what Catherine cleans this projector. +30 These girls are questioning who some students hadn't hired an actress. +31 This actor should remember every woman that that sketch looked like. +32 Chad hadn't noticed that ice that Tiffany isn't discovering. +33 Sharon discovers every ladder that many waiters should like. +34 A lot of cashiers forget who girls weren't returning to alumni. +35 Grace questioned a lot of committees that this actor had alarmed. +36 Tiffany should notice the sheep that those adults had seen. +37 Veronica sees a lot of books that the Lutherans hate. +38 The Impressionists notice this fish that Carol shouldn't think about. +39 Every patient remembered all reports that Jodi hid. +40 A lady hasn't researched a bank that Naomi reveals. +41 Most boys discover what adults notice beef. +42 Larry did notice the snake that Caroline could clean. +43 That waiter thought about some library that Clyde impressed. +44 Many actresses research most public parks that Edward appreciated. +45 Kirsten questioned committees that the Impressionists upsets. +46 Tina wouldn't remember a school that a dancer left. +47 Nicole can't notice what this customer does boycott those legislatures. +48 Joseph might forget what Valerie disliked a synopsis. +49 The senators question these adults that Colleen is revealing. +50 The universities forget some story that the Impressionists describe. +51 The organizations remember the high school that those adults conceal. +52 Emily couldn't discover what some adults research a means. +53 Senators research those diagnoses that Lisa should think about. +54 Gina forgot a lot of customers that Eva isn't escaping from. +55 Those cashiers will see some mall that some drivers describe. +56 Gerald wouldn't remember what the Borgias describe that synopsis. +57 Jodi might remember the guy that the book couldn't bore. +58 Beverly wasn't researching the synthesis that Anne did appreciate. +59 Andrew knew a lot of dancers that Eva criticized. +60 Meredith does notice some oxen that Victoria had. +61 Christina should remember every glacier that Gregory took. +62 The customer had questioned many ladies that most essays sound like. +63 Gregory knows who cashiers leave some ladies. +64 A patient knew who Todd questioned these guys. +65 Teresa shouldn't see those shoes that Phillip took. +66 Janice did forget this essay that Winston Churchill forgot. +67 The senators saw every sock that most teenagers do reveal. +68 Julie investigated a lot of cashiers that all customers leave. +69 Beth researches these libraries that Aaron hasn't taken. +70 The guests saw a face that that child reveals. +71 George forgets a guy that Kenneth investigated. +72 The Borgias research these rivers that a print hadn't looked like. +73 Mark discovers a couch that Danielle brought. +74 Kirsten researches reports that Carmen cleans. +75 Every guy investigated some waitresses that Paula has concealed. +76 Many cashiers do remember who Kristen distracted a child. +77 Every man has noticed some rabbit that most waitresses hide. +78 Ruth isn't investigating every teenager that Sherry isn't firing. +79 Florence would forget that pamphlet that children argue about. +80 Sally has researched most theses that Theodore forgot. +81 Todd doesn't think about some light that Dana sees. +82 A lot of patients didn't investigate most drivers that Walter hadn't revealed. +83 Kayla knows some teacher that most birds were astounding. +84 Harvard University had forgotten some story that most ladies disagreed with. +85 The drivers think about a restaurant that the Borgias talk about. +86 These teenagers know the people that men complain about. +87 Derek researches all banks that these eyes might scare. +88 Carlos researched an art gallery that Anna might go to. +89 All men research these prints that Alan notices. +90 Many libraries couldn't know who all mountains have worried the teenager. +91 Kristin is discovering some muffin that many doctors buy. +92 Monet hadn't investigated that adult that Omar isn't disgusting. +93 A lot of cashiers remember all dancers that some person could conceal. +94 Those teenagers think about what Ruth has described those rivers. +95 Adam questioned a driver that Guy isn't criticizing. +96 Many women see some eyes that Tonya revealed. +97 Melanie shouldn't remember those newspaper articles that Clyde criticizes. +98 Carla is thinking about who some eye annoys some girl. +99 Sally wasn't thinking about many screens that Bill has admired. +100 Colleen had thought about who some hamsters could distract that customer. +101 Chad discovered this gate that libraries buy. +102 The Lutherans discover some association that Kimberley was annoying. +103 Gerald couldn't discover that chicken that Florence did conceal. +104 Sarah notices who doctors reveal that actress. +105 Renee has remembered all college campuses that Jeffrey hadn't described. +106 These organizations forget some adult that Noah was alarming. +107 Most people remember what a picture looks like the glaciers. +108 Some teenager questioned this actress that Edward worked with. +109 Some adult sees that cilantro that some print looks like. +110 Heidi sees every waitress that Wayne discovered. +111 Daniel researched what Meredith boasts about this synopsis. +112 Jane can think about the species that a doctor noticed. +113 Donald was discovering these children that George observed. +114 Melinda thought about what Spain likes that bike. +115 Ruth had questioned a teacher that Michael investigated. +116 Jerry will forget who Lawrence can't appreciate some guest. +117 Craig discovered this computer that Walter was finding. +118 Leslie does think about what that print wouldn't look like some cup. +119 Travis doesn't discover the rabbit that many banks buy. +120 Julia remembered who all women approach a child. +121 Alan hasn't seen some women that Tracy helped. +122 Jerry hasn't seen what the guys tour this oasis. +123 Many drivers forget who photographs haven't looked like the patient. +124 Carrie discovered the ice cream that Laura might clean. +125 Linda has noticed every tomato that Brian shouldn't hide. +126 Samuel had questioned patients that that driver would care for. +127 Most teachers research the closet that every teacher should flee from. +128 Amy had forgotten the person that Elizabeth fires. +129 Dancers notice every patient that Stacy had escaped from. +130 Phillip knows who many actors argue about those teachers. +131 These waiters remember what Alexander isn't concealing some mountains. +132 Those customers should forget many doctors that a lot of ladies approach. +133 Lucille discovers many glaciers that Heather hadn't boycotted. +134 Irene knew who Pamela hugs senators. +135 A lot of customers didn't question the boy that Andrea isn't firing. +136 Peter is discovering what Carlos thinks about the water. +137 These waitresses question the driver that Victoria wasn't praising. +138 Alice discovered what Daniel hasn't argued about most diagnoses. +139 Kathleen forgets those waiters 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question what these peppers shock some university. +674 Laurie isn't noticing every cashier that Steve praises. +675 A driver isn't researching that university that Kevin discovers. +676 Many ladies think about many snakes that Michelle was taking. +677 Liam hasn't questioned who pictures looked like many teenagers. +678 The Impressionists see what those drawings do look like that spotlight. +679 Lori remembers who those boxes disturbed many customers. +680 Amelia questioned all hospitals that the person would mess up. +681 Emily can notice projectors that Karen did discover. +682 The Borgias question some alumnus that these college campuses upset. +683 This person might see some cars that Samantha wasn't bringing. +684 Kayla isn't questioning that library that Candice has described. +685 Sheila isn't questioning children that Brett boasted about. +686 The Impressionists question every adult that Carla doesn't find. +687 All actors saw many cars that Keith didn't see. +688 Stephen 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attacks this child. +705 That guy researches most drawings that Veronica could sell. +706 Irene forgot most waitresses that many dancers haven't loved. +707 A lot of students see what the Impressionists discovered many mushrooms. +708 Debra wouldn't investigate the dancer that the actors talk about. +709 Amy saw every woman that Karla does cure. +710 Holly is noticing a lot of sheep that most guests like. +711 Richard hasn't known the offspring that the hospitals hated. +712 George researched a college campus that Samantha is buying. +713 This customer wasn't questioning many teachers that the patient isn't praising. +714 The ladies question many governments that Joel impressed. +715 Patrick discovered the ice that Mary admired. +716 All boys were discovering these high schools that Robert had toured. +717 All senators are thinking about what Colleen won't find that pasta. +718 Patricia had forgotten every alumnus that Peter kissed. +719 Lisa wouldn't investigate some government that 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Homer is leaving. +750 Every driver hasn't thought about this gate that Carlos has noticed. +751 Those ladies investigate who some teachers argue about the drivers. +752 Jennifer forgets those pamphlets that Kimberley hasn't taken. +753 Donna can research some play that Connie has appreciated. +754 Children discover who every library didn't disgust a man. +755 George hasn't noticed a lot of people that many students disagree with. +756 Colleen remembered some guests that Helen hadn't thought about. +757 Some children think about what Ruth thinks about this scarf. +758 Brian hasn't forgotten who Laurie hugs all alumni. +759 Sandra has known a lot of women that all restaurants distract. +760 Candice thinks about what Vincent sees every cucumber. +761 Charles notices the eyes that Holly might lift. +762 A boy investigated many men that those children bother. +763 Christina hadn't investigated these men that lights alarm. +764 Brett had noticed some guest that Steve is concealing. +765 Most women were thinking about some woman that Kenneth impressed. +766 Sonia discovered most hills that Melissa has walked through. +767 The teenagers research some series that Linda was thinking about. +768 James couldn't investigate the man that Caroline can attack. +769 That waiter discovered some commentaries that Dawn hasn't boasted about. +770 That customer questioned the children that every drawing hurt. +771 Teresa could remember what Margaret referenced most plays. +772 All senators discover most planes that Emily cleaned. +773 Most patients did discover a window that James doesn't notice. +774 The Lutherans discover that plate that Candice isn't concealing. +775 Keith wasn't questioning some bank that Claire discovers. +776 Scott does see every couch that Keith thought about. +777 Those children research that closet that Nicole runs around. +778 Richard thinks about what Caroline discovered all analyses. +779 Nina hadn't thought about some cashier that Kimberley works with. 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organizations like. +795 Many teachers hadn't known every woman that all teenagers see. +796 William is noticing many boxes that Ann has lifted. +797 Rose shouldn't discover what the painting looks like some deer. +798 Steven has seen some sheep that those children hide. +799 Julie wasn't thinking about some pedestrian that Timothy had discovered. +800 This guest has seen some sweater that Karla sold. +801 Janice forgets that child that these apples distracted. +802 Cindy can remember who Mitchell complains about that alumnus. +803 Anna wouldn't research these diagnoses that Larry isn't discovering. +804 Eric notices every sketch that Sheila hasn't researched. +805 Paula notices all coats that every man reveals. +806 Lisa investigated this person that Bethany has talked about. +807 Mary researches a lot of banks that a picture looks like. +808 The Borgias remember a book that Ann had boasted about. +809 Susan discovers that coat that Florence wears. +810 Chad isn't discovering those universities that Samuel alarmed. +811 Most waiters think about what Meredith boasts about this basis. +812 Guys know who Andrea isn't distracting a man. +813 Beth forgets some reports that Monica would talk about. +814 Laurie had questioned many cashiers that Carla will worry. +815 Homer might investigate what a turtle confused this government. +816 Most students noticed this dish that those waitresses break. +817 Omar notices what Larry admires this shoe. +818 The patients research a lot of analyses that that pedestrian disagrees with. +819 Ann had researched a hypothesis that Russell can't appreciate. +820 Danielle remembers who all windows astound this man. +821 Wayne knew some patient that that cup does bother. +822 Deborah will remember who a convertible would astound this person. +823 Lori can notice every nose that Marcus admired. +824 Suzanne will investigate the library that Angela couldn't shock. +825 Ruth discovers some basis that Carla boasts about. +826 Richard doesn't 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researched a print that those children found. +843 William hadn't discovered a lot of analyses that the Impressionists discuss. +844 Rachel can't think about what Victoria didn't take all vases. +845 That guest would investigate women that Nancy couldn't visit. +846 Bethany did research that book that Ronald isn't praising. +847 Christina hasn't remembered that doctor that Irene had watched. +848 Some cashiers investigate what Christopher does annoy that association. +849 Many boys notice what Derek is leaving glaciers. +850 Wendy hadn't investigated these actors that some shoe bores. +851 Most guests were questioning a lot of doctors that Julia can bore. +852 Rhonda wasn't investigating all customers that a lot of plates disturb. +853 William remembered every cashier that Becca had praised. +854 Holly has thought about the spinach that Brett buys. +855 The girls remember all customers that Liam did aggravate. +856 Catherine shouldn't see this deer that these women hide. +857 Becky discovers some lice that Matt isn't discovering. +858 Most governments remember who this government hurts drivers. +859 Daniel won't notice those lights that Victoria is finding. +860 Benjamin had investigated that alumnus that Craig doesn't hug. +861 A lot of students discover that door that this pedestrian has noticed. +862 Many companies knew a child that a lot of newspaper articles don't distract. +863 Rhonda thinks about every fork that Michael was taking. +864 Melissa has noticed what a lot of drivers like teeth. +865 The cashiers remember this river that Dan wasn't criticizing. +866 Lori had investigated some boy that Tracy discusses. +867 Many ladies investigate the alumni that Sandra healed. +868 A lot of waitresses notice the picture that Roger hadn't looked like. +869 Guys investigate who the shoe distracts all alumni. +870 William forgets the waiter that Susan won't watch. +871 Chad researched these banks that Aaron couldn't research. +872 The men notice this ice cream that Vincent is admiring. +873 A waitress remembers what Janet scans some plays. +874 Every waitress researched some drawing that Elaine was hiding. +875 Sandra noticed that stimulus that most senators conceal. +876 Every senator remembers what Jacqueline was thinking about those newspaper articles. +877 Alicia thinks about a driver that Phillip isn't criticizing. +878 Judy hadn't investigated those guests that Kirsten isn't concealing. +879 Becky does research some paralyses that Barbara isn't researching. +880 Walter didn't remember some commentary that these doctors haven't appreciated. +881 A lot of students research the analyses that Danielle boasted about. +882 Phillip notices that dress that these governments admire. +883 Some pedestrians investigate many alumni that Joseph sounds like. +884 Susan questioned this child that Jacqueline isn't embarrassing. +885 Ruth thought about what every dancer drove to all restaurants. +886 Julie couldn't notice some chicken that Jane was discovering. +887 Christopher can't forget a patient that Ruth wasn't noticing. +888 That child remembered some child that Jodi can't insult. +889 Carmen had questioned the children that Phillip is scaring. +890 A lot of teenagers know who those associations criticize drivers. +891 A lot of adults notice a lot of rivers that Charles didn't conceal. +892 Marla isn't thinking about that waitress that spinach has aggravated. +893 Jason remembered the senator that a lot of waitresses observed. +894 Those girls think about what most ladies lift most skirts. +895 Lori wasn't discovering the fork that Timothy breaks. +896 Christopher would research what Aaron thinks about many hypotheses. +897 Sally shouldn't think about what Rebecca thought about most shirts. +898 All ladies discover a mirror that Rose hasn't found. +899 All girls are discovering every public park that children sell. +900 Those senators see who that doctor dislikes those teachers. +901 Some children notice what the Impressionists notice the book. +902 The dancer was questioning who a pie has bored a lot of adults. +903 These dancers investigate many organizations that Robert disturbs. +904 Tonya didn't remember that pedestrian that those girls liked. +905 That museum forgot who Marla had respected a guy. +906 Many men hadn't known a teacher that every person hadn't bored. +907 Kevin researched these high schools that Cynthia discussed. +908 All doctors research that means that Denise complained about. +909 Sherry isn't investigating who this sketch looks like that waiter. +910 Patricia researches the prints that Monet was revealing. +911 Douglas hadn't investigated actors that Amelia might conceal. +912 Some children investigate who Craig isn't hugging those men. +913 Brian sees that chair that some waitresses clean. +914 Pamela isn't discovering every plane that every waitress had cleaned. +915 Some school remembers this hospital that Naomi had appreciated. +916 Alice has remembered some school 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a lot of mirrors that haven't scared Claire. +949 The waiter should think about some gates that were boring Gregory. +950 Sabrina had researched many oases that aren't looking like that sketch. +951 Many universities forget the women that hadn't described Deborah. +952 Martin remembers this pamphlet that will confuse every adult. +953 A lot of waiters remembered what the customers resemble those pictures. +954 Laura couldn't discover a lot of companies that hire that guy. +955 Martha forgets what a waiter hadn't loved all plays. +956 Vincent hadn't noticed these candles that weren't irritating Steve. +957 A senator won't remember who many teenagers haven't concealed Ella. +958 The Borgias forget what the doctor isn't hiding most coats. +959 That student hadn't known who all offspring love most drivers. +960 Sara questioned many dancers that drive to every hill. +961 Guy can't discover those trucks that couldn't worry Thomas. +962 Andrea had remembered the mountains that haven't aggravated all children. +963 Melinda had thought about some geese that embarrass Eric. +964 Meredith was questioning a pedestrian that stunned most patients. +965 Jane sees some mirror that shocks Katherine. +966 The Lutherans forget that patient that wasn't returning to the Borgias. +967 These people see the sketches that aren't disgusting Sherry. +968 Some ladies notice ladies that annoys Clyde. +969 Winston Churchill was questioning many governments that worried Rose. +970 Douglas was investigating some guys that won't reference a lot of children. +971 Some company hadn't forgotten these students that aren't cleaning every couch. +972 Nancy investigated drivers that have arrived at that hospital. +973 All women discover what a student couldn't like the grocery stores. +974 Rose remembered a lot of cafes that couldn't aggravate Kristin. +975 Cynthia isn't researching the synopsis that was impressing Kimberley. +976 Meredith does forget what a doctor was admiring every art gallery. +977 Timothy isn't investigating the children that fire Linda. +978 Todd researched these hills that will aggravate Laura. +979 Stacy hasn't questioned universities that insult many lakes. +980 Sonia could question that waiter that returned to a cafe. +981 Brett hasn't seen most pies that confused the waiter. +982 The committees forget who some lady was visiting this girl. +983 Gina hasn't noticed these drawings that looked like Curtis. +984 Cashiers investigate that waitress that hasn't fled from Laura. +985 Janet forgot every story that bored Barbara. +986 Colleen questions some woman that didn't buy that mouth. +987 Many men remember that teacher that wasn't discussing Sherry. +988 April could question some boy that has scanned the play. +989 Some drivers investigate what some guests are lifting the fork. +990 Most guys question a lot of women that tour the banks. +991 The people have known a lot of guys that stunned some child. +992 These companies remember the child that would descend hills. +993 Wayne didn't research these malls that aren't confusing Diane. +994 Roger can remember what the customer isn't climbing up many steps. +995 All dancers think about this picture that alarms Janet. +996 Beverly knew the woman that hadn't questioned the Lutherans. +997 Mark knew an adult that fired the pedestrian. +998 Andrea hadn't noticed the actress that cures that fish. +999 Those waitresses could know most cashiers that haven't listened to Amelia. +1000 Kendra noticed wheelbarrows that hurt Tanya. +1001 Jacqueline questioned a lot of alumni that aren't climbing up a lot of slopes. +1002 Paul should know many guests that fire William. +1003 A dancer discovers a lot of eyes that might upset Kirsten. +1004 Grace remembered a story that is stunning Tamara. +1005 Jason remembers a lot of college campuses that impressed Todd. +1006 Judy has forgotten many ladies that haven't run around those movie theaters. +1007 The girls questioned that doctor that astounded Patricia. 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universities. +1022 A lot of drivers remember most brochures that weren't impressing that student. +1023 Cashiers aren't thinking about the plays that aren't disagreeing with some senators. +1024 Laurie remembered most lakes that are disgusting Elizabeth. +1025 Edward had thought about a blouse that has resembled all prints. +1026 The woman had researched the essay that isn't upsetting Tracy. +1027 Liam had thought about a lot of bikes that will disturb a cashier. +1028 Michael knew a patient that bought a lot of college campuses. +1029 Those pedestrians see the mushroom that distracts Linda. +1030 The Impressionists research photographs that have irritated Bill. +1031 Stacey knows a lot of waiters that confused Eric. +1032 The girls investigate the museums that will disgust Carol. +1033 The students know who the lady approaches Jane. +1034 Keith can research the book that wasn't upsetting most committees. +1035 Christopher does remember some college campus that hadn't shocked Sarah. 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pedestrian that can bother this organization. +1067 Jodi would see that spotlight that did distract Bill. +1068 Martha knew a lot of girls that didn't scan a lot of stories. +1069 Regina isn't discovering this horse that scares Heidi. +1070 Samantha might think about what most offspring are ascending a lot of stairs. +1071 Carla hadn't thought about every hypothesis that could astound Matt. +1072 Homer shouldn't notice a lot of bikes that should worry Carrie. +1073 Marie will investigate some libraries that talked about Beverly. +1074 Steven has remembered every essay that hadn't disagreed with Brad. +1075 Walter notices who some student has approached Melanie. +1076 Some governments had remembered the offspring that had helped Tracy. +1077 Sara had investigated an association that wouldn't help every driver. +1078 An actor has forgotten many dancers that haven't discussed Allison. +1079 Diana wasn't noticing that mountain that had scared Linda. +1080 That cashier couldn't see the library that isn't impressing Becca. +1081 Ellen remembered what these boys would discover some closet. +1082 These boys should notice those cashiers that notice this sock. +1083 Some schools would know a customer that hadn't observed the driver. +1084 Many patients think about that truck that irritates Emily. +1085 Tammy hadn't remembered the theses that irritate that bank. +1086 Those people question the teacher that returned to some lakes. +1087 The Impressionists do question this company that wasn't concealing Omar. +1088 The Borgias discover a stimulus that has resembled many drawings. +1089 Those teachers see many jackets that shocked the student. +1090 Joseph hadn't known every person that hasn't irritated Diana. +1091 Cashiers question who libraries scared David. +1092 Paula remembers boys that are complaining about Larry. +1093 Homer noticed the cat that wasn't irritating Scott. +1094 Kendra wasn't discovering many adults that didn't cure a lot of fish. +1095 Kristen saw this 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Wayne had investigated dancers that had hurt Sheila. +1155 Kathleen noticed every bicycle that scared Kathleen. +1156 Carla isn't investigating what these waiters have examined every movie. +1157 All teenagers question a lot of legislatures that buy a box. +1158 Stacy discovers the gate that aggravated Travis. +1159 Kathleen hadn't questioned these museums that have helped Melanie. +1160 Martha noticed every bank that respects Patrick. +1161 This girl sees what this man was taking these planes. +1162 Doctors research that synthesis that aggravates Alice. +1163 Most students know this dancer that talks about Aaron. +1164 Deborah is noticing what the grocery stores resemble every painting. +1165 A lot of girls thought about a lake that doesn't bother these adults. +1166 Walter should notice the woman that fled from the driver. +1167 Thomas isn't questioning some guy that broke the skateboard. +1168 Patricia investigated some museum that insulted every newspaper article. +1169 A hospital 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Kimberley wouldn't discover that company that has some candles. +1496 The man hasn't questioned this company that would worry Jane. +1497 Brian can't investigate these children that can't admire Sarah. +1498 Some ladies forget that pamphlet that isn't irritating Jeffrey. +1499 Diana has thought about some deer that confused the library. +1500 Some organizations know who this senator kisses Gina. +1501 Many dancers haven't forgotten who this woman hated Kendra. +1502 Those drivers investigate some government that wasn't hurting Clyde. +1503 Lawrence hadn't known many actresses that have described Adam. +1504 Sherry is researching some books that hadn't impressed George. +1505 Tracy investigated the actress that hadn't broken a bicycle. +1506 Martha won't remember all alumni that hadn't fled from patients. +1507 Galileo hasn't known some girls that haven't fired Heather. +1508 Some senator investigated this lady that wasn't irritating some guy. +1509 John questions a man that sold a lot 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who that man wasn't listening to Rebecca. +1750 The customers hadn't investigated that driver that was annoying Joseph. +1751 The teacher wouldn't notice a lot of bicycles that haven't confused Spain. +1752 Some teacher investigated schools that could irritate those teenagers. +1753 An adult isn't researching every museum that fired all waiters. +1754 Jane was discovering many pies that didn't alarm Laura. +1755 Carol researches a diagnosis that alarmed Jerry. +1756 Many guests investigate that waitress that was taking some carriage. +1757 Becca knew what drivers are skated around a river. +1758 Sarah is discovering the muffin that isn't annoying Marcus. +1759 Margaret hadn't noticed rice that bores Gina. +1760 Wayne had remembered some man that was appreciating a lot of teachers. +1761 Barbara knew what all children fixed some window. +1762 Becca researched that phenomenon that does disgust Valerie. +1763 Mark researches what this ellipsis is impressing some association. +1764 Cynthia can't investigate who this child is observing all actors. +1765 Carol had thought about that means that isn't worrying Grace. +1766 Tamara would forget every newspaper article that resembles some paintings. +1767 Thomas had known what the waiter didn't think about some windows. +1768 Monica notices these dresses that aren't upsetting Deborah. +1769 This waiter wasn't noticing these eyes that were upsetting Bill. +1770 Most ladies notice all waitresses that couldn't complain about current events. +1771 Amy will think about some dancers that found this computer. +1772 Michael hadn't seen every snake that can alarm Pamela. +1773 Those teenagers discover that door that could confuse some committee. +1774 Most dancers research a basis that had scared Lisa. +1775 Brian hadn't noticed the fish that shouldn't worry Peter. +1776 Susan discovers the mouse that is upsetting Christopher. +1777 This teacher couldn't question that boy that wasn't fixing planes. +1778 Melinda hadn't investigated some 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Sally. +1793 Jerry notices the coat that bored Aaron. +1794 Many doctors see all guests that have lifted this library. +1795 That customer discovers many snakes that climbed down those ladders. +1796 Legislatures know a lot of doctors that broke the windows. +1797 Paula hadn't thought about who most banks can't listen to Brenda. +1798 Eric was discovering every shoe that upset a government. +1799 Some actresses notice some adults that hadn't climbed down some mountain. +1800 All pedestrians think about some spinach that resembles this sketch. +1801 All dancers discovered the children that had complained about Marla. +1802 Janet didn't see cats that should disturb Carl. +1803 Many actors think about every school that won't hate Beverly. +1804 Sonia discovered a drawing that wouldn't bother Clyde. +1805 Curtis wasn't investigating those committees that hadn't impressed Grace. +1806 These waitresses forget many stories that were hurting Elaine. +1807 A lot of committees remember some teenagers that were concealing Sharon. +1808 Nancy forgot every lady that was healing Jesus. +1809 Cheryl doesn't see a shoe that looked like a drawing. +1810 Monica should forget many girls that kissed that teenager. +1811 Laurie discovered those trucks that stun Carrie. +1812 The women hadn't questioned a committee that would help Nina. +1813 Margaret discovers that deer that does disturb Scott. +1814 Homer has thought about that synopsis that insulted Donna. +1815 Timothy had researched the rivers that haven't embarrassed this school. +1816 Janet saw that chicken that hasn't impressed the teacher. +1817 Gary forgot who a dancer insults Phillip. +1818 Most teachers see every screen that does impress Christopher. +1819 Nicole was discovering a lot of mountains that didn't shock a boy. +1820 Teachers question the person that will forget many stories. +1821 Carolyn discovered a lot of skirts that aggravate Amanda. +1822 These teachers remember the alumni that were kissing this teenager. +1823 Pamela was discovering a box that has worried Marla. +1824 Janice doesn't forget this synopsis that hasn't described Martin. +1825 Lori forgot some guest that hasn't fired Eric. +1826 This actor can question the libraries that had resembled the print. +1827 A lot of actresses question the lady that cleaned those forks. +1828 Teresa notices pants that resembled those drawings. +1829 Patrick notices that screen that is worrying Melissa. +1830 Carl won't question who this museum has disliked Frank. +1831 Grace should see most couches that haven't distracted Ronald. +1832 Stacy wouldn't see the waitress that scared Mark. +1833 Nicole remembered every glacier that has impressed all universities. +1834 Tracy had known most alumni that will upset Marcus. +1835 All pedestrians notice the teenager that climbs down that slope. +1836 Most teenagers weren't questioning some patient that had returned to Beverly. +1837 Amelia thinks about those prints that stun every hospital. +1838 Danielle hadn't noticed every museum that scared James. +1839 Kathleen shouldn't see those restaurants that had worried the cashier. +1840 Carl does question children that hadn't seen these socks. +1841 Heather thinks about the spotlight that hasn't worried Carmen. +1842 Jacqueline questioned those associations that liked these chairs. +1843 Most girls won't question some actors that don't clean a screen. +1844 Diana wouldn't know what most adults wouldn't disturb a museum. +1845 Theodore questions every waitress that hadn't criticized the hills. +1846 Timothy can't think about what this museum shouldn't have all coats. +1847 Actors forget every driver that hadn't broken a lot of bikes. +1848 Christina was researching every legislature that shouldn't fire the waiter. +1849 Winston Churchill researches this cafe that alarms Kendra. +1850 Some girls haven't questioned who a waitress had disliked William. +1851 Janet investigated some schools that aren't worrying Tamara. +1852 Deanna forgot who the boy isn't complaining about Theresa. +1853 Karla is discovering a lot of bananas that distract every teenager. +1854 Phillip thinks about those crises that were stunning the Borgias. +1855 Janice can see all mountains that are embarrassing Barbara. +1856 Most people shouldn't see most rabbits that couldn't embarrass a teenager. +1857 Liam wouldn't research the university that had respected some museums. +1858 Many waiters investigate the university that fires these adults. +1859 Linda is thinking about some men that criticize Beth. +1860 The Borgias discover gloves that were disgusting Donna. +1861 Monica saw every dancer that wouldn't look like every print. +1862 Valerie questions a library that listened to Beth. +1863 Jane wasn't researching the means that wasn't distracting Larry. +1864 Rose will notice some cake that was bothering a student. +1865 Dennis has seen this tooth that Kristin wasn't concealing that is astounding men. +1866 The cashiers should notice a lot of cafes that Roger will drop by that resemble all prints. +1867 Heidi shouldn't think about many pedestrians that every movie could disagree with that messed up this rug. +1868 The Lutherans know the man that the casserole confuses that impressed the waitresses. +1869 That teenager hasn't seen this tomato that that girl had bought that had stunned some actors. +1870 Susan won't discover who a car that Jane admired is aggravating a lot of cashiers. +1871 Most customers notice a lot of mushrooms that this cashier hides that won't aggravate the children. +1872 Victoria isn't thinking about all media that Tiffany hadn't appreciated that confused those guys. +1873 Most teachers question some actresses that Melissa boasted about that walk through a closet. +1874 Many guys notice those teeth that Tina can't have that will scare a library. +1875 These senators think about some student that Candice is hurting that can't descend a hill. +1876 The actors haven't remembered some public parks that Janet likes that couldn't astound many cashiers. +1877 April had noticed these snakes that Patricia has that could bore most guys. +1878 Stacy forgot this cashier that Gina listened to that explores some movie theaters. +1879 Aaron should research the phenomena that Carmen should think about that don't disgust a teenager. +1880 Carlos is questioning dancers that Omar hadn't approached that will distract the committee. +1881 That teenager shouldn't question libraries that that dog is disgusting that didn't fire a pedestrian. +1882 Danielle researches the legislature that Tonya had boycotted that did hate these reports. +1883 Melanie saw those doors that the doctors reveal that can scare this offspring. +1884 Sherry investigated all organizations that all public parks embarrass that haven't disgusted that adult. +1885 Douglas thinks about those waitresses that many museums work with that helped a woman. +1886 Tina wasn't noticing some apple that Rachel admires that impressed all ladies. +1887 Frank researches every association that Alexander had scared that knew every actress. +1888 A lot of actors think about men that Sharon isn't listening to that should lift the carriage. +1889 Carlos questioned these organizations that those books aren't embarrassing that worried some offspring. +1890 Regina sees that candle that Steve lifts that might impress every doctor. +1891 Judy hadn't seen most brochures that Christopher will discuss that have aggravated alumni. +1892 Jessica saw the lake that Bethany wasn't leaving that hadn't annoyed the alumni. +1893 Some pedestrians forget all boys that Heidi had discovered that won't distract many women. +1894 The Impressionists know who patients that Samuel can't like bother some child. +1895 Carla couldn't investigate many drivers that every book isn't irritating that disturb the lady. +1896 Candice has thought about a car that those banks admire that shocked every offspring. +1897 A company wouldn't forget what every pamphlet that Ellen 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discovering most windows that Jodi had thought about that astounded every waitress. +1909 Dancers think about that banana that Gina will have that could embarrass every person. +1910 These drivers couldn't investigate many people that Matt hasn't revealed that arrive at all rivers. +1911 Kevin researches this bank that Tammy wasn't describing that alarmed the teacher. +1912 Ronald remembers a brochure that this boy hasn't concealed that is irritating the patients. +1913 Every guest was investigating some pedestrian that the waiters hurt that revealed these pictures. +1914 Karen knew every child that Renee wasn't approaching that hasn't descended mountains. +1915 Wendy would notice what children that Homer had hired hadn't walked through the mountain. +1916 Suzanne hadn't questioned some hospitals that the patient has driven to that hadn't astounded most ladies. +1917 Matt can investigate some university that George had shocked that appreciates a glacier. +1918 Julia hasn't remembered many schools that some actresses research that praised some guys. +1919 Kristen researches some drawing that Sherry isn't revealing that disturbed some waitress. +1920 Richard has seen all banks that Wayne returns to that astound a lot of guys. +1921 Most guests question those hospitals that April couldn't go to that hurt the ladies. +1922 April wasn't noticing the actor that some ladies don't disagree with that escapes from a lot of men. +1923 Jessica has thought about these drivers that this door can't bore that hired a lot of guests. +1924 Bill discovers those analyses that Laurie scanned that are criticizing most teachers. +1925 Victoria knew some customer that Erin finds that wasn't noticing sweaters. +1926 Michelle questioned the ladies that Richard knew that did look like these sketches. +1927 Sheila hasn't forgotten who most plays that some children insulted aggravate the girl. +1928 Amy knew the students that Amanda wasn't fleeing from that conceal the coat. +1929 Sheila wasn't researching these bases that Gary is complaining about that distract many men. +1930 The Lutherans were noticing that casserole that Helen had seen that distracts a lot of doctors. +1931 This girl can forget that dancer that Samuel attacked that might distract those companies. +1932 Angela has forgotten some guest that Eva has concealed that should conceal the waiters. +1933 Sharon remembers some school that the people talk about that was astounding some senator. +1934 Teenagers have remembered many pedestrians that most companies insult that wouldn't appreciate this pedestrian. +1935 Liam remembers that cafe that all guests sell that couldn't astound a lot of women. +1936 Julia had thought about most doors that most patients lifted that had impressed some government. +1937 Tracy remembered a boy that children like that did cure all senators. +1938 Kenneth questions this alumnus that this person admired that visited some hospitals. +1939 Guests discover all planes that that customer had that stun some cashier. +1940 Sarah has investigated all organizations that many gates bother that had referenced every newspaper article. +1941 Caroline has thought about most stories that Brenda wasn't discovering that couldn't disturb many women. +1942 Many dancers research every means that Keith wouldn't argue about that has impressed the organization. +1943 Lori would know the teenager that some sweaters distract that hadn't cured most waitresses. +1944 Eric questions some pedestrian that those boys investigate that hasn't revealed many socks. +1945 Steve thinks about those slopes that every child wasn't bringing that worried that child. +1946 Anne isn't investigating the committee that Stacy disturbed that had hired students. +1947 Jill hadn't investigated that school that Victoria doesn't lift that won't hate some synopsis. +1948 Sally forgot a documentary that Daniel wasn't referencing that worries that hospital. +1949 The Lutherans forgot this play that all women criticize that disagreed with the synopsis. +1950 Sharon shouldn't see rabbits that Brett had seen that don't worry some driver. +1951 James didn't remember the patient that projectors shocked that hasn't talked about a waiter. +1952 Wendy was researching pamphlets that Allison wouldn't sound like that don't astound this driver. +1953 A lot of customers remember an offspring that Benjamin hadn't revealed that appreciated every school. +1954 A lot of waitresses remember that restaurant that every girl hasn't concealed that might disgust a girl. +1955 Stephen investigated a customer that these girls love that hadn't worked with some doctors. +1956 Some children remember the thesis that Donald had noticed that discusses those adults. +1957 Holly shouldn't forget that person that these guests remember that has cured some snake. +1958 Susan notices some fish that Cynthia has that couldn't aggravate the schools. +1959 Curtis shouldn't know this waiter that Aaron can't insult that took those art galleries. +1960 Mary wasn't discovering a senator that Emily isn't complaining about that examined some theses. +1961 All teachers research this grocery store that Bruce didn't boycott that hasn't irritated every bank. +1962 Kirsten doesn't research a lot of college campuses that Beth cleaned that aren't bothering some lady. +1963 Katherine might investigate some bank that the actor is stunning that fires a lot of actresses. +1964 Paula noticed many actresses that most girls think about that heal most girls. +1965 Peter noticed the cashiers that all people irritate that are researching every lake. +1966 Homer investigated a lot of men that Scott irritated that scan some brochure. +1967 Most companies know every guy that those glasses bore that wasn't curing the guest. +1968 Most actresses forget a lot of people that Pamela discovers that weren't disturbing the senators. +1969 Kristin sees these skirts that Tracy isn't lifting that do aggravate some museum. +1970 Rodney remembers a pedestrian that April has approached that will kiss the lady. +1971 Carrie had investigated the child that Deanna hasn't disturbed that breaks that bicycle. +1972 Douglas investigated a lot of companies that some guy noticed that reference many ladies. +1973 All guests research a lot of museums that these doctors discuss that should scare this pedestrian. +1974 Cynthia sees many windows that Carol took that upset all libraries. +1975 Carolyn had investigated a senator that most patients argue about that hadn't appreciated these teachers. +1976 Beth can see those cafes that a customer drives to that embarrass a lot of boys. +1977 All women remember this dancer that Kirsten has bored that did admire every icicle. +1978 Some people discover a spotlight that many doctors will lift that confused these children. +1979 A lot of ladies couldn't know a lot of patients that some students admire that have ascended all stairs. +1980 Those pedestrians remember most closets that Sherry dislikes that 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that has impressed a guest. +2001 Brenda knew this woman that Tina can't hide that wouldn't buy that chicken. +2002 Dan had questioned a lot of dancers that this screen is impressing that haven't worked with the alumni. +2003 Benjamin does remember some banks that Kevin exits that haven't fired teenagers. +2004 Rachel thinks about these syntheses that all girls boast about that stunned that child. +2005 The Borgias see what geese that Nina heals had looked like a lot of pictures. +2006 Patients think about a lot of analyses that these actors praise that haven't described those ladies. +2007 The senator questioned associations that William did annoy that stunned the man. +2008 Pamela discovers a computer that Stephen might find that is aggravating the man. +2009 Diana discovered some reports that most doctors aren't talking about that wouldn't scare a lot of waiters. +2010 All dancers questioned every boy that Heather visited that healed those offspring. +2011 All adults question every cashier that Jennifer listens to that descends every ladder. +2012 Theresa won't research some banks that adults aggravate that forgot some pedestrian. +2013 Colleen wasn't discovering an axis that Heather hid that wasn't scaring some libraries. +2014 Aaron saw that casserole that that bank hasn't sold that has bored some driver. +2015 The Lutherans forget most men that Dan wasn't returning to that couldn't clean those glasses. +2016 Bruce knows that person that Dawn likes that argued about a lot of guys. +2017 Grace hasn't noticed a lot of students that those snakes irritate that hadn't confused some people. +2018 Colleen thought about a lot of organizations that these essays scare that boasted about every hypothesis. +2019 The guys think about that cucumber that Craig would take that can aggravate some girl. +2020 Deborah knew these guests that many senators talk to that fire the girl. +2021 Chad should notice this glove that Lisa liked that disgusted some customers. +2022 Stacey is investigating this driver that Leslie had worked with that couldn't appreciate hypotheses. +2023 These children research schools that some prints look like that hadn't irritated these associations. +2024 Elaine discovers a lot of companies that Spain is worrying that admire that banana. +2025 Erin would forget that doctor that Bill isn't returning to that did have many jackets. +2026 Ronald isn't thinking about those muffins that Vincent would buy that wouldn't upset the library. +2027 The woman wasn't questioning the waiters that Tonya wasn't finding that had talked to all children. +2028 Rose has noticed cakes that some pedestrians were discovering that scared the banks. +2029 Debra knew these doctors that Alexander attacks that impresses this waiter. +2030 Most doctors forget what this teacher that Emily hasn't cared for is alarming many governments. +2031 Jerry was thinking about some slopes that Julie revealed that couldn't shock many hospitals. +2032 Most waiters couldn't research the documentary that most teenagers talk about that is scaring that teenager. +2033 Peter should question waitresses that pedestrians shocks that sound like all waiters. +2034 Brett should discover every synopsis that some girls research that insults many cafes. +2035 Stacy is researching some medium that Sonia has boasted about that wouldn't impress those adults. +2036 These men discover a lot of lamps that Bill sees that alarm a child. +2037 Timothy researched some drawing that Jacqueline hasn't found that wasn't aggravating some schools. +2038 Kristen questioned the committee that most cars scare that has astounded that association. +2039 Amanda didn't forget the play that Kirsten researched that impressed the offspring. +2040 Every person researched some hypotheses that James appreciates that haven't embarrassed some committees. +2041 Benjamin might question every driver that Mitchell thinks about that has dropped by a lot of closets. +2042 Noah forgets this commentary that every student had discussed that disagreed with every story. +2043 Linda will discover these paralyses that Jane complained about that will scare this waiter. +2044 Liam discovers every mouse that Brian admires that was worrying most teachers. +2045 Many guests think about what a snake that Dan reveals hadn't climbed up many steps. +2046 Sonia noticed a lot of cucumbers that Bruce has seen that astound many committees. +2047 Gina hadn't discovered sketches that some adults like that shock a lot of men. +2048 Kristin questions the dancers that Noah had returned to that watched this child. +2049 The men think about this ellipsis that William wasn't appreciating that alarms some woman. +2050 A teacher doesn't notice the horse that Colleen had thought about that hadn't bothered those museums. +2051 Laurie questioned a company that this hill shocks that could fire alumni. +2052 Those organizations know these customers that those cars distract that had watched some guy. +2053 Homer thought about the dish that Diane couldn't bring that isn't stunning a senator. +2054 Liam questioned this actor that Aaron can boast about that hires many women. +2055 Martha saw these dresses that Benjamin saw that weren't alarming the association. +2056 Alan hasn't seen most waitresses that Mitchell wasn't admiring that annoys the committees. +2057 Teresa did discover the computer that Matt had revealed that was astounding many alumni. +2058 The Impressionists know the senator that Mitchell can alarm that hasn't cared for an adult. +2059 Brenda didn't investigate the ladies that Monet wasn't helping that mess up the forks. +2060 Every organization knows that child that Phillip isn't hiring that was discussing a college campus. +2061 This teenager discovers some waiter that a lot of tomatoes shock that forgot that boy. +2062 Valerie can research some government that Nancy upset that won't bore some student. +2063 Children see this painting that a lot of dishes look like that is alarming some drivers. +2064 Eva forgot the girls that Meredith wouldn't hide that kissed this actor. +2065 Nancy had researched some hills that actors arrive at that don't distract a lot of schools. +2066 Diana hadn't forgotten the people that adults argue about that upset the cashiers. +2067 Becky hadn't remembered the public parks that Stacy explores that can stun that dancer. +2068 Rachel did research schools that Omar arrived at that appreciated that grocery store. +2069 Sandra thinks about a man that Sheila thinks about that had confused most girls. +2070 Christopher can't remember many rivers that many pictures look like that resembled a picture. +2071 Wendy might question the schools that Eric wouldn't embarrass that hadn't forgotten a lot of commentaries. +2072 Helen does question many actresses that the Impressionists sound like that broke every vase. +2073 Those committees know that doctor that most waitresses were worrying that hasn't seen the mice. +2074 A lot of actresses investigate these teenagers that the people see that clean most movie theaters. +2075 Jane has forgotten a lot of actresses that Ella shouldn't conceal that leave the teacher. +2076 Ella noticed all apples that this man had admired that are hurting many drivers. +2077 Beverly didn't investigate an actress that Paul hadn't cared for that fled from some doctors. +2078 Sabrina shouldn't research some crises that Maria hasn't complained about that impress some library. +2079 Amelia has seen all sheep that Vanessa notices that aren't irritating some doctors. +2080 Bill remembers most guys that some actresses cure that aren't dropping by this cafe. +2081 Men research those prints that this committee can't admire that distract doctors. +2082 Christina might forget that dancer that some legislatures respect that is descending every slope. +2083 Boys notice every sketch that Heidi researched that aggravated this university. +2084 The student had investigated the governments that Harvard University doesn't embarrass that have disturbed these guests. +2085 This actress hasn't discovered this movie that Brenda won't sound like that wouldn't confuse every boy. +2086 Most teachers remember that newspaper article that Alexander sounded like that worries the offspring. +2087 Linda saw all hospitals that Gary discussed that had aggravated drivers. +2088 Carmen thinks about the organization that this chicken alarmed that discussed these women. +2089 Christina shouldn't remember the art gallery that Ronald wasn't arriving at that wasn't scaring these children. +2090 Liam researched some documentary that these movies were disagreeing with that distracted these committees. +2091 Sabrina doesn't see what a unicycle that Beverly didn't admire doesn't impress the committees. +2092 The child is investigating what the adult that Kristen bothered boasts about every means. +2093 A lot of cashiers remember this brochure that a waitress wasn't praising that has impressed most legislatures. +2094 Larry hasn't known some waitress that some eyes had disgusted that arrives at high schools. +2095 Eric might discover this beef that Anna had lifted that annoys many offspring. +2096 Holly researches some schools that Theresa escaped from that can work with the lady. +2097 These senators don't think about most cucumbers that Bill couldn't hide that do shock that actress. +2098 Becca is questioning the boy that a teenager has respected that hadn't watched all girls. +2099 Kevin can notice every mushroom that the children clean that irritates every boy. +2100 Most doctors remember these commentaries that Karen is concealing that had disagreed with this teacher. +2101 Samantha investigated that government that most girls aren't alarming that hasn't aggravated this bank. +2102 Russell hasn't thought about most apples that Diana hid that will annoy the patient. +2103 Steve did think about the turtle that these prints do look like that hadn't stunned every girl. +2104 Many dancers notice all eyes that Lisa hadn't discovered that might alarm a lady. +2105 Diane remembers all brochures that Tiffany has found that disgusted most boys. +2106 The companies know doctors that the universities reference that impress a doctor. +2107 Cindy wouldn't question the woman that Wendy respects that might listen to those actresses. +2108 Alexander saw some cart that a lot of ladies hide that didn't distract that pedestrian. +2109 Alicia questions what some doctor that Raymond was talking to is examining some thesis. +2110 Every actress should think about those dancers that Jessica praises that hurt the child. +2111 Barbara knew what students that Janice returns to pass these museums. +2112 These teachers see paintings that teenagers might admire that confused that school. +2113 All libraries remember some hills that Dennis shouldn't bike to that have bothered those governments. +2114 Joel might investigate those customers that dancers worry that hate every brochure. +2115 Gregory hasn't researched the commentary that this waitress can't think about that has shocked a woman. +2116 Kristen has remembered most college campuses that Christopher hadn't visited that worried the library. +2117 Eva questioned that university that Laura couldn't question that distracted many children. +2118 Naomi doesn't question all guys that Allison is firing that had concealed some alumni. +2119 That teacher researched this legislature that Jodi thought about that was helping the alumnus. +2120 Leslie hasn't thought about plays that every pedestrian has remembered that embarrass the waiter. +2121 Deborah is discovering that nose that Christopher has revealed that wasn't confusing that government. +2122 Sandra has remembered some commentaries that every dancer wasn't boasting about that are upsetting the guests. +2123 Amy forgets this documentary that those guests are researching that alarmed all cashiers. +2124 Those people notice that pie that Monet was admiring that did bother that guy. +2125 Joseph was investigating banks that all newspaper articles upset that forget the woman. +2126 Matt would notice some woman that those actors worries that will leave restaurants. +2127 Meredith questioned most children that James admired that disgust the library. +2128 Winston Churchill doesn't question this hospital that Chad thinks about that does reference many girls. +2129 Most guests research movie theaters that the woman drives to that were stunning this patient. +2130 Some senators see those drawings that the Borgias hide that won't shock the alumnus. +2131 Nancy was thinking about many grocery stores that Dawn visited that embarrassed that woman. +2132 Some customers question a lot of schools that a lot of computers stunned that are confusing this school. +2133 Many women discover this waitress that Sonia is visiting that has found many boys. +2134 Every adult has known every boy that Paul would distract that wouldn't think about a lot of nuclei. +2135 Carmen wasn't thinking about every waitress that Thomas is investigating that loves the driver. +2136 Jacqueline knows this person that Alexander hasn't returned to that has hugged some dancer. +2137 Vincent remembers this dancer that Stephanie doesn't bother that wasn't stunning those banks. +2138 Wendy sees newspaper articles that all paintings wouldn't look like that are bothering a person. +2139 Samantha had discovered a goose that some photograph is looking like that is distracting these actors. +2140 A lot of patients remember students that Alice kissed that questioned the cashier. +2141 Some association should know some pedestrians that Naomi discusses that sold many scarves. +2142 Martin doesn't see a book that guests sound like that is distracting all ladies. +2143 Every adult thought about the light that Carolyn will have that had alarmed a lot of associations. +2144 The patients forget some man that that university forgot that questions the actress. +2145 Many teenagers question governments that all women alarm that aren't discussing that adult. +2146 Jodi remembered the mountain that Margaret wasn't insulting that wasn't hurting those cashiers. +2147 Thomas notices the unicycle that Christine hadn't bought that wasn't astounding every senator. +2148 Timothy won't research the stimulus that Phillip discovered that aggravates some student. +2149 Cynthia isn't noticing every college campus that Renee hasn't noticed that was irritating many ladies. +2150 Some women wouldn't discover that chicken that Donald is buying that wasn't alarming a legislature. +2151 Douglas hadn't seen some hill that paintings have looked like that worries a person. +2152 Alice hadn't researched some stimuli that many actresses noticed that annoyed some museum. +2153 Kirsten forgot that pedestrian that teachers hadn't bothered that reveals these shirts. +2154 Nicole has investigated all drivers that cucumbers shouldn't astound that can't respect every teacher. +2155 Melanie had 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Adam has thought about a public park that Timothy sells that did disturb some woman. +2663 Phillip discovered this lady that the glasses disturb that revealed some horse. +2664 Homer isn't investigating that organization that Heather could research that wasn't insulting this guy. +2665 Many organizations know that cashier that Paul hasn't sounded like that has broken plates. +2666 The drivers remembered the newspaper article that Travis described that hasn't resembled that photograph. +2667 These girls research these restaurants that Daniel had bought that aren't looking like the prints. +2668 Every boy discovers some hill that many horses find that isn't embarrassing every legislature. +2669 Raymond remembers that brochure that some waiters scan that aggravated every waitress. +2670 These guys discover every print that Julia revealed that scares every legislature. +2671 Lawrence forgot the alumni that Jerry works with that weren't confusing that offspring. +2672 Carrie notices a 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many pedestrians couldn't boycott all hills. +2980 Committees forget that a driver should praise Theodore. +2981 Christina learned that this painting is aggravating Paul. +2982 This government had investigated that all governments weren't buying dresses. +2983 The Impressionists had realized that most doctors aren't breaking the computer. +2984 George concealed that that guest shouldn't walk through this hill. +2985 Phillip shouldn't figure out that broccoli could look like those photographs. +2986 A person won't investigate that most men haven't left those restaurants. +2987 Some ladies know that that patient was listening to Samantha. +2988 Every patient figures out that all teachers hadn't discussed Candice. +2989 Some pedestrians forgot that some woman is skated around cafes. +2990 These hospitals find out that every patient escaped from Bill. +2991 All drivers reveal that some drivers research these books. +2992 Walter could figure out what this child cleans most malls. +2993 Denise has revealed that most customers upset the drivers. +2994 Ann figured out that many students lift a lot of hamsters. +2995 Travis wouldn't forget that a lot of adults weren't examining the documentary. +2996 Leslie investigated that all guests ascend some ladders. +2997 Sabrina had revealed that every driver returns to Tammy. +2998 Carolyn discovers that all people visited Maria. +2999 Heidi had known that a waitress kissed Adam. +3000 Phillip could know that all drivers fled from some drivers. +3001 Peter learned that a lot of women had skated around most lakes. +3002 Travis hadn't remembered that a child tours the grocery stores. +3003 Every boy forgets that every lady hadn't worked with the Borgias. +3004 Paul forgets that that waitress is researching that mountain. +3005 Sabrina did forget that all men were boycotting this cafe. +3006 Derek had learned that all drivers attack Melanie. +3007 Karla was discovering that many dancers fire Tamara. +3008 Those pedestrians learn 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can't discover that many drawings hadn't looked like restaurants. +3053 That adult was finding out that most guests astound Dana. +3054 Judy investigated that this play hadn't disagreed with Andrew. +3055 Andrea investigated that every man wore most gloves. +3056 Emily hasn't found out that a lot of men could observe Russell. +3057 Some actresses realized that some car irritates these companies. +3058 Mary can't remember that the woman passes a public park. +3059 Some dancers weren't revealing that all senators should complain about politics. +3060 Teresa figured out that every student hid some shirt. +3061 Heather discovered that many children aren't wearing every hat. +3062 Kenneth would learn that that screen stunned Kevin. +3063 Alice does conceal that this driver works with some customer. +3064 Kayla is learning that those pamphlets bore Helen. +3065 Patricia concealed who most boys are approaching Marla. +3066 A lot of waiters find out that that actress escapes from George. +3067 These associations forget that some waiters won't conceal the lake. +3068 Melanie isn't investigating who the guest sounded like Eric. +3069 Some pedestrians figure out that most drivers describe Elizabeth. +3070 Anne had revealed that some teachers have visited Walter. +3071 Some museums forget that these alumni didn't arrive at all restaurants. +3072 Ronald concealed that a driver drives to all hospitals. +3073 Nina hadn't revealed that these children criticize some college campus. +3074 Paul figures out that some man questions Denise. +3075 Most committees reveal that some waitresses are climbing down ladders. +3076 Donna realizes that these cashiers question the committee. +3077 Naomi wouldn't reveal that the child saw every boy. +3078 Kristin had revealed that that adult hasn't revealed Suzanne. +3079 Most pedestrians remember that that convertible distracted those actors. +3080 Cindy hasn't figured out that students heal most pedestrians. +3081 Dan is learning that those drivers 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waitresses learn that some customer skates around that glacier. +3158 All ladies weren't forgetting that those hamsters alarm Dennis. +3159 The universities figure out that this actress has disgusted a lot of actresses. +3160 Tina had found out that a lot of actresses have messed up some malls. +3161 Thomas isn't concealing that every foot hasn't impressed the actor. +3162 Those adults realize that the students go to all art galleries. +3163 Stephanie remembers that many teenagers won't drop by the high school. +3164 A girl isn't investigating that most boys hadn't descended most mountains. +3165 Derek learns who alumni watch Carol. +3166 Kendra discovers that an offspring didn't exit many art galleries. +3167 Many boys discover that a driver talked about many restaurants. +3168 Bradley forgot that a man saw the ladies. +3169 Becky reveals that the doctors did hug Douglas. +3170 Those customers found out that these students weren't observing Brenda. +3171 The banks had revealed that 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some casserole hasn't upset James. +3202 Gerald could find out that every boy discovers Becca. +3203 Some man realized that this senator hadn't climbed up this ladder. +3204 Those committees figure out who that boy had loved Raymond. +3205 Caroline was learning that the cashiers lift all shawls. +3206 Thomas discovers that all children will praise Linda. +3207 Sheila remembers that the university boasts about the boy. +3208 These patients weren't investigating who this school hadn't concealed Rhonda. +3209 Some students conceal that that child returned to museums. +3210 Tiffany is remembering that every newspaper article astounded some waitresses. +3211 That student couldn't know that an eye hurt Marcus. +3212 Margaret should discover that many guys can conceal Donald. +3213 Vanessa wasn't forgetting that many offspring aren't working with Janice. +3214 Those guys know that those banks wouldn't sell every shawl. +3215 Galileo remembers that every guest wasn't scanning this movie. +3216 Martha remembers that some grocery stores could shock Brian. +3217 Thomas remembered that this drawing hadn't looked like every eggplant. +3218 Many girls investigate that students were approaching Kristin. +3219 Kayla does realize that every patient hasn't talked about these commentaries. +3220 A library hadn't forgotten that the person is cleaning these jackets. +3221 Daniel is learning that that waitress hadn't ascended this slope. +3222 A lot of customers find out that some doctor had driven to those closets. +3223 Leslie hadn't forgotten that most waiters disagree with Patricia. +3224 Kendra forgets that the teenagers hire the doctors. +3225 A legislature hasn't remembered who this government did talk to Amy. +3226 Gina discovers that a print was shocking Theresa. +3227 The Borgias reveal that a child will dislike most senators. +3228 The patients learn that some pedestrian was boycotting these high schools. +3229 Clyde wasn't finding out what this alumnus bikes to hills. +3230 Elaine can figure out that the child can embarrass Steven. +3231 Most companies conceal that the guest can't research travel. +3232 Meredith figures out what a lot of syntheses do shock the organizations. +3233 Eric hadn't revealed that a lot of newspaper articles disagree with Linda. +3234 Some boy knows that a legislature confuses those banks. +3235 These women figure out that some waitresses won't explore a mountain. +3236 Suzanne would realize that this story impresses some companies. +3237 Lawrence remembered that students see Heather. +3238 Homer hasn't investigated that a lot of actresses wouldn't criticize Daniel. +3239 Peter investigated that these senators dislike Lori. +3240 Karla discovered that dancers attacked Claire. +3241 The Lutherans forget that every pedestrian fled from Kimberley. +3242 Kristin should figure out what the fish resemble paintings. +3243 Patrick figured out that those sketches looked like Anna. +3244 Phillip can't reveal that many doctors weren't hugging Joel. +3245 Many schools have forgotten that this dancer hadn't dropped by every movie theater. +3246 Raymond concealed that this cake embarrasses Tanya. +3247 The girl will know that patients will shock Karen. +3248 Many adults learn that some library has sold those mirrors. +3249 Paul does discover that some teachers were taking that rice. +3250 The Borgias reveal that every child goes to some lakes. +3251 Erin doesn't find out that the patients alarmed Sharon. +3252 Stacy revealed that all waiters bothers Anna. +3253 That organization won't conceal that an offspring likes the malls. +3254 Gerald hadn't remembered that the women did explore Spain. +3255 All patients concealed that all men visit some closet. +3256 That association concealed that some lady listens to an adult. +3257 Frank does find out that some doctor was returning to a child. +3258 Steven was forgetting that some mushrooms disgust Tiffany. +3259 Thomas found out that feet impressed George. +3260 Larry 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Patricia realized that some offspring cleans many couches. +3292 Holly wasn't revealing that the alumnus didn't tour all glaciers. +3293 Sally discovered that those students couldn't drive to the mountain. +3294 This waitress hasn't figured out that those guys had arrived at that mall. +3295 A lot of guests are concealing that most men are aggravating Amy. +3296 Samuel was learning that some actors clean this bank. +3297 Sabrina did find out that every school worried Wendy. +3298 The senator hadn't revealed that some chicken worries Larry. +3299 Noah can reveal that the lake distracts Jacqueline. +3300 Melanie isn't investigating that that company sold some coats. +3301 Eva had realized that a waitress cared for Wayne. +3302 Sabrina knows that some child descends some hill. +3303 Tammy knew that people hadn't dropped by a river. +3304 Dan wasn't revealing that a teenager wasn't visiting some men. +3305 Lori won't reveal who some student hadn't worried Carmen. +3306 Lucille is discovering that a teacher cleaned the rabbits. +3307 Paula hadn't revealed that the prints hadn't looked like Kendra. +3308 Dan has figured out that all patients haven't researched that hill. +3309 All customers conceal that the schools describe Michael. +3310 Jesus remembered that the alumnus would love that guy. +3311 All actresses didn't know that the associations aren't disturbing Debra. +3312 Paula learned that the teenagers had taken a lot of skirts. +3313 Sarah investigated what the doctor thinks about the fork. +3314 Rodney learns that a lot of senators aren't working with Jane. +3315 Many teenagers are forgetting that a fish will alarm Gerald. +3316 Eva might investigate that this waitress complains about Curtis. +3317 Melinda has concealed that some pedestrians approach Kristin. +3318 Amy did find out that this waiter hates the patient. +3319 A waitress did know that all stories won't disagree with the reports. +3320 Scott is concealing that most couches had bothered 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figure out that some pedestrians descended a lot of stairs. +3394 Diana wasn't discovering that a man heals Wayne. +3395 Some pedestrians did know that some man did heal Sabrina. +3396 The waiters won't realize that those men dislike Colleen. +3397 David discovered that the guy isn't helping Gina. +3398 Tiffany knows that every customer hadn't driven to some bank. +3399 Most museums discover that this lady took the screen. +3400 Dawn realized that some girl was concealing Marla. +3401 All girls find out that every computer shocks those women. +3402 Carol has known that a lot of boys can hide the mountains. +3403 Ladies have investigated that the pork bores Robert. +3404 Every guest is finding out that that government helps Danielle. +3405 Derek remembers that some actresses leave Aaron. +3406 Rodney didn't conceal that the candle aggravates Paula. +3407 Cashiers weren't investigating that some lady sold many birds. +3408 Marla might know that those shoes hadn't disgusted Kayla. +3409 These pedestrians hadn't revealed that that museum hadn't appreciated Phillip. +3410 Danielle figures out that a lot of adults were noticing this cilantro. +3411 Some association learns that the teenager hasn't watched this student. +3412 Colleen wasn't forgetting that many ladies weren't referencing a lot of reports. +3413 This teacher forgets that every light disturbs these cashiers. +3414 Helen remembers that some child does skate around some cafes. +3415 Children investigate that the senators skate around these art galleries. +3416 Tammy isn't realizing that some waitresses were fleeing from Eva. +3417 Every child hadn't figured out that these offspring have examined this documentary. +3418 Sarah hasn't investigated that many boys explore the high schools. +3419 Marcus isn't figuring out who a lot of guests are discovering Stacy. +3420 Jessica isn't figuring out that the senator shouldn't work with Steve. +3421 A lot of museums investigate that that waitress passed that grocery 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realized that every pedestrian hadn't passed most rivers. +3437 Amy had found out that those men questioned Teresa. +3438 Sandra discovered that that man is boycotting that lake. +3439 Alexander isn't remembering who the lady boasts about James. +3440 A driver remembers that guys research a lot of drawings. +3441 These waiters do figure out that some series bother Gregory. +3442 Travis is concealing that some senators weren't exploring all closets. +3443 Many children remember that many lakes did confuse Monet. +3444 Roger learned that those pedestrians talk about Omar. +3445 A person had forgotten that the boys appreciated the girl. +3446 Some dancers figure out that a jacket bores Janice. +3447 Dan isn't revealing that many ladies weren't working with Grace. +3448 Those customers reveal that some pedestrians investigated Grace. +3449 Gregory isn't remembering that most parentheses irritate Tanya. +3450 A lot of drivers forget that a lot of universities wouldn't impress Clyde. +3451 Candice finds out that all pedestrians fixed that vase. +3452 Janice should forget that these waitresses aren't observing Sharon. +3453 Brett might investigate that these banks referenced Carl. +3454 Some child learned that all offspring boycotted that movie theater. +3455 Deanna knows that the men had known Edward. +3456 A lot of museums conceal that this waitress watched Samantha. +3457 Douglas didn't forget that a lot of men had worn this shoe. +3458 Alexander figured out that the senators are observing Carolyn. +3459 Sabrina reveals that the banks embarrass Renee. +3460 Denise is finding out that some offspring hadn't approached Alice. +3461 Alice had learned that this museum appreciates Regina. +3462 Russell wasn't remembering what actresses cleaned a horse. +3463 Some senator hasn't known what that child doesn't walk through some mountain. +3464 A lot of committees investigate that a lot of children aren't listening to Meredith. +3465 Ann was forgetting that this person would scare every dancer. +3466 The guests learn that the brochures don't shock Brenda. +3467 Carlos can find out that some nose bores Emily. +3468 Jesus is forgetting that those drivers are walking through that bank. +3469 Many boys realize that most people do watch Becky. +3470 Becky realized that a man could go to this hospital. +3471 That guest found out that girls climb up most ladders. +3472 Sarah is remembering that the legislature helps Rebecca. +3473 William is forgetting that every boy passes every library. +3474 A museum doesn't conceal that every senator is referencing Donna. +3475 Dawn is finding out that some dog couldn't find the apple. +3476 Those ladies forget that a dancer lifts ladders. +3477 These senators don't discover that many teachers drop by some hospitals. +3478 The museums figure out that most pedestrians will lift a lot of glasses. +3479 A lot of waitresses realize that the adults have boycotted the Lutherans. +3480 Sarah isn't learning that every girl hadn't 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remembers that that shirt annoys Leslie. +3673 Most drivers figure out that casseroles haven't looked like every print. +3674 Some waitresses investigated that those windows aggravate Heidi. +3675 This teacher doesn't reveal that most slopes annoyed David. +3676 A guest could know that a guest reveals Patrick. +3677 Every association figured out that most drivers that forfeit investigated Irene. +3678 Kathleen was revealing that this actress that can sit down was skated around this museum. +3679 Drivers remembered that those hospitals that should waste away are listening to Angela. +3680 Tamara had figured out that most governments that had fired these guests won't have a window. +3681 Adam isn't forgetting that many actors that did buy a snake should pass the high school. +3682 Melinda investigated that this girl that looked like those drawings might attack Cindy. +3683 Most cashiers conceal that most guys that can run around a cafe had skated around those banks. +3684 Martin had 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some senators that negotiated cure a lot of actors. +4309 Monica might know that banks that knew Sarah dislike Tanya. +4310 Theresa figured out what teachers that did confer climbed down some mountain. +4311 Many banks aren't realizing that this man that shouldn't hide away had bought a lot of college campuses. +4312 Some associations know that these offspring that embarrasses Becky revealed Kristen. +4313 All guests reveal that many guys that did mess up an art gallery won't leave Steven. +4314 Theresa had realized that many patients that were training talk about Theodore. +4315 All cashiers learn that that child that wasn't annoying Meredith breaks every truck. +4316 These hospitals figured out that a lot of men that move mess up the plate. +4317 Peter discovered that some beef that was baking astounded Alexander. +4318 The doctor couldn't forget that that student that will forfeit has sounded like some boy. +4319 This school realizes that some turtles that hid haven't annoyed 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These banks discover that a lot of senators that have approached Linda haven't messed up that grocery store. +4343 Denise is forgetting that those cashiers that aren't weeping drop by some college campuses. +4344 Janice figured out that an adult that murmured has hugged Dan. +4345 Aaron isn't discovering that most dogs that appear descend the hills. +4346 A lot of cashiers should forget that patients that might embarrass Laurie ascend stairs. +4347 Girls conceal that the dog that appears hadn't climbed up that slope. +4348 Linda wasn't forgetting that these waitresses that are leaving some teacher weren't driving to all public parks. +4349 Some organization realizes that those people that hadn't negotiated skate around most glaciers. +4350 These committees remember that a lot of teachers that had replied run around every closet. +4351 Universities realized that a foot that was stunning George hadn't alarmed Travis. +4352 This dancer hasn't concealed that this driver that isn't changing 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Debra wasn't learning that some patient that isn't shrugging won't discuss the Impressionists. +4445 Alan will conceal that those children that aren't retaliating shouldn't praise Dennis. +4446 Those adults found out that the senator that is embarrassing some child dislikes Jane. +4447 Jane realized what a boy that had studied does see a banana. +4448 Cynthia might figure out that this ox that is leaving climbs down those stairs. +4449 Most hospitals won't figure out what those snakes that were waking up descend all slopes. +4450 Some pedestrians remember that all men that haven't annoyed all adults were disturbing Paula. +4451 The organizations investigate that many associations that have argued hadn't disturbed the association. +4452 Nancy remembered that some guy that wasn't talking to Denise heals Brenda. +4453 The ladies conceal that many offspring that assemble examined the story. +4454 These patients have revealed that those teachers that fall asleep have driven to this 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worked with. +5397 All teachers learn what a teacher wasn't biking to. +5398 Karla knows that every patient cared for. +5399 These cashiers might know that the associations admired. +5400 Andrea learns that some actor reveals. +5401 The Lutherans could find out who most newspaper articles disagree with. +5402 Every lady was realizing that some hospitals couldn't reference. +5403 The associations are revealing that this actress returns to. +5404 A lot of banks discover that every couch couldn't look like. +5405 Patricia learned that every public park scared. +5406 Carla remembered that some guy disagrees with. +5407 Kevin was revealing that the waiter drops by. +5408 Alicia concealed who the teacher won't talk about. +5409 Some guys learn that most waitresses aren't arguing about. +5410 Steven knew that some doctor arrives at. +5411 Todd couldn't forget what some children are wearing. +5412 Some waitress is forgetting what some lady had found. +5413 All guys might reveal that every man dislikes. +5414 Natalie learned that adults do flee from. +5415 Those boys discover that some pedestrians flee from. +5416 Many doctors realized that these libraries had concealed. +5417 Students realize that drawings look like. +5418 Ladies discovered that that customer walks through. +5419 Marie would learn what an adult drops by. +5420 A lot of dancers remembered that this actor respected. +5421 Homer is investigating that actresses reveal. +5422 This child wouldn't figure out what every actress admires. +5423 Some museum shouldn't learn that that customer returned to. +5424 Kathleen wasn't realizing that those doctors don't argue about. +5425 Customers discover that those girls bike to. +5426 Becky wasn't revealing that a doctor respects. +5427 Jane learned that every guy will wear. +5428 Gary wasn't discovering what the teacher examined. +5429 Leslie wasn't remembering who a student has fled from. +5430 Some association isn't finding out that some guest lifts. +5431 Natalie forgot that all adults had dropped by. +5432 Brett didn't learn that this book didn't sound like. +5433 That lady knows that many museums listened to. +5434 Suzanne investigated that a university talks to. +5435 Every school would know that that woman respected. +5436 Kevin revealed that the child hadn't talked about. +5437 Carlos has found out who those guys hadn't boasted about. +5438 Gerald was revealing that many customers drive to. +5439 Eric isn't concealing what a man has examined. +5440 Ann realized what an ox finds. +5441 Karen isn't learning that some boy was referencing. +5442 Committees were realizing that all guests return to. +5443 All companies remember what all ladies were finding. +5444 Omar realizes that these women talked about. +5445 Spain is forgetting that the dancer talks to. +5446 Derek learns what some photograph has looked like. +5447 Mary had realized that some child arrives at. +5448 Every waitress hasn't known what adults sound like. +5449 Suzanne realized that a lot of girls aren't curing. +5450 Claire isn't learning that many children don't work with. +5451 Pamela concealed that some couches scare. +5452 Jennifer hadn't realized that most waitresses have complained about. +5453 Raymond hasn't known that that child has fired. +5454 Anna realized that that waiter has walked through. +5455 A lot of libraries realize that the customer lifts. +5456 Most banks hadn't realized who the girls could care for. +5457 All women are investigating what every woman had driven to. +5458 Keith has figured out that some woman will talk to. +5459 Liam couldn't find out that many waitresses went to. +5460 Rachel did find out that these men aren't dropping by. +5461 These children reveal that all drivers dropped by. +5462 Deanna isn't discovering that many patients wouldn't walk through. +5463 A driver was revealing that this patient had listened to. +5464 All libraries learn that those guys have escaped from. +5465 Stacy would find out who the committee is working with. +5466 This committee doesn't reveal that every woman has cured. +5467 Guys remember who the government works with. +5468 The schools are remembering that some waiter has escaped from. +5469 Every teenager discovered that this actor wasn't concealing. +5470 George was remembering what some girls examine. +5471 Some libraries learn that these customers return to. +5472 Some bank was finding out that all companies aren't working with. +5473 Cynthia figured out who this teenager can boast about. +5474 Carrie has forgotten that those students aren't disagreeing with. +5475 This man was figuring out that all adults aren't disagreeing with. +5476 Colleen shouldn't know that some libraries hadn't admired. +5477 John hasn't revealed that this guest wasn't lifting. +5478 Christine figured out what a dancer was dropping by. +5479 Kenneth isn't investigating what these guests examined. +5480 Julie had concealed what that woman isn't finding. +5481 Cynthia hadn't found out what the teacher has walked through. +5482 All banks remember that most committees hadn't concealed. +5483 Liam is discovering that women hadn't fled from. +5484 Jodi investigated that a lot of children are concealing. +5485 Andrea found out that this boy had cured. +5486 Danielle is concealing that the teachers did reveal. +5487 Deborah finds out that a child doesn't bike to. +5488 Stacey knew that this dancer walks through. +5489 Stacy hadn't remembered what a woman examines. +5490 Wayne hadn't found out that a woman walks through. +5491 This adult realizes that every guy is lifting. +5492 Tanya finds out that those men dislike. +5493 Bethany discovers what every teacher drives to. +5494 Laura has known that the children work with. +5495 Claire was remembering who that guy talks about. +5496 Some hospitals find out that most men weren't lifting. +5497 Patrick investigated who the guy worked with. +5498 Kayla concealed that a lot of guests that were scaring many people complain about. +5499 Martin did find out that every cashier that shouldn't drink wore. +5500 The Borgias haven't figured out what blouses that have disgusted Kenneth look like. +5501 Colleen has revealed that every print that isn't existing looked like. +5502 All banks forget that many steps that couldn't irritate Carrie resemble. +5503 Some pedestrians haven't realized that those ladies that alarms Samantha had boasted about. +5504 A waiter has realized that all hospitals that protested listened to. +5505 That actor had found out that all men that had ascended every hill dislike. +5506 The school forgets that every legislature that astounded Heather references. +5507 All universities discover that some actress that isn't speaking up had gone to. +5508 Carmen discovered that many cashiers that have sold every report returned to. +5509 Stacy wasn't remembering who the banks that have vanished can't boast about. +5510 Phillip forgot that some senator that was escaping from Stacy goes to. +5511 Tara had figured out that the teenagers that marry won't boast about. +5512 Many teenagers realized that these teenagers that shouldn't surrender wouldn't listen to. +5513 Steve doesn't discover what the university that doesn't fight argued about. +5514 Some lady is finding out that some photographs that can't aggravate Sheila haven't looked like. +5515 Joseph found out that many doctors that had littered revealed. +5516 Leslie concealed that patients that are changing weren't lifting. +5517 All people figure out what that guy that has existed wore. +5518 Rachel hasn't learned that every museum that isn't replying was listening to. +5519 This waitress hasn't figured out that a hospital that can't sell a lot of bicycles shouldn't listen to. +5520 Most patients have remembered that a lot of committees that shouldn't interact do dislike. +5521 Steve does discover that a lot of schools that couldn't lie have talked to. +5522 Angela finds out that most universities that hadn't changed hadn't 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some patients that have hidden away go to. +5549 Many guests forget that some offspring that sighs escaped from. +5550 Margaret forgets that the waitress that is hugging Caroline isn't listening to. +5551 Sonia figures out what some cart that irritates Theodore is looking like. +5552 Suzanne is concealing what these prints that existed would look like. +5553 Many cashiers figure out that all offspring that meet disagreed with. +5554 Jacqueline had figured out that the actress that isn't laughing found. +5555 Stacy finds out that some cashier that discusses many men could admire. +5556 Vanessa remembered that some sketch that hadn't disgusted Lawrence wasn't looking like. +5557 Anne doesn't forget that the man that has worried Adam hadn't escaped from. +5558 David is figuring out that those organizations that wouldn't argue about the essays dislike. +5559 Curtis revealed that a girl that yelled wore. +5560 These waitresses learn what the girl that exited a mountain drives to. +5561 Carol couldn't forget what the driver that doesn't take couches arrived at. +5562 Diane reveals that those teenagers that could practice were revealing. +5563 Teresa can't know that that senator that can bother Teresa listens to. +5564 Drivers investigate that some associations that can impress this organization listen to. +5565 All children investigated that those children that scare those companies were escaping from. +5566 Curtis wasn't figuring out that men that testify scared. +5567 Claire would conceal that that actor that looked like the drawing has cured. +5568 Alexander has remembered that some doctors that notice Meredith can't reveal. +5569 A lot of guys aren't discovering that that pedestrian that isn't boasting hasn't talked to. +5570 Most students realize that these organizations that conspire talked about. +5571 Holly has figured out that many cashiers that haven't brought some glacier care for. +5572 Naomi investigated that those eyes that had upset Travis resemble. 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the government that was firing Ruth talks to. +5598 Amy concealed that that mall that wasn't existing had looked like. +5599 Dawn was concealing that some lady that can't boast reveals. +5600 Renee figures out that the committee that hasn't remembered Nancy wasn't concealing. +5601 Some waitresses discover that the actress that concealed Tara hadn't found. +5602 Melissa does know that all women that hadn't clashed do wear. +5603 Jacqueline knows what that child that wasn't talking about Stacey should bike to. +5604 Anna remembers that these men that were stretching sound like. +5605 Thomas hasn't found out who an adult that won't examine these commentaries hadn't sounded like. +5606 Steve had concealed that some women that were debating drove to. +5607 Donald hadn't concealed that every blouse that was shocking many teachers had looked like. +5608 A lot of committees forget that a lot of boys that weren't exercising had listened to. +5609 Peter couldn't conceal what an actor that could boycott every government concealed. +5610 Lucille hasn't investigated that teeth that hadn't fallen resemble. +5611 Alexander wouldn't find out what these actresses that haven't looked like those drawings had looked like. +5612 Teresa didn't find out that the actors that weren't praising Danielle reveal. +5613 Gregory realizes that these guys that are swallowing examine. +5614 Melinda wasn't forgetting that a lot of sketches that fell weren't looking like. +5615 Andrea doesn't find out that many customers that could investigate Janice dislike. +5616 Sheila hasn't known what these people that practiced arrived at. +5617 Sara realized that all actresses that aren't chatting fired. +5618 Jason was learning that most doctors that discovered every turtle go to. +5619 Phillip discovered that this museum that wasn't coping respected. +5620 A teenager figures out that the cashier that disagrees with Christopher references. +5621 These drivers figure out that those guests that conspire weren't 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every man that has scared Stephanie drives to. +5646 Paula figured out who a boy that hadn't worried most drivers shouldn't sound like. +5647 Lori had forgotten that the doctor that would resemble a lot of prints bikes to. +5648 Jane has learned that some dancer that practices arrives at. +5649 Amanda is discovering that the person that can't stun Spain walks through. +5650 Amanda reveals that many pictures that fell might look like. +5651 Paula knows that the library that can't communicate wasn't talking to. +5652 Carl finds out that these teenagers that had fled from Ellen talk to. +5653 Holly had forgotten that a guy that hadn't cured Galileo can't drive to. +5654 Gregory can't figure out what a girl that hasn't coughed can drop by. +5655 Brad wasn't concealing that most drivers that hide arrived at. +5656 Tracy won't know what the museum that can't donate references. +5657 Edward isn't figuring out what many children that won like. +5658 Naomi finds out that a lot of customers that wouldn't watch Brett aren't working with. +5659 Gina finds out that this library that wasn't reacting references. +5660 Natalie found out that a woman that has seen cares for. +5661 All museums know that some alumnus that hasn't upset Jesus had fired. +5662 Karla realized that those patients that bike to many hospitals disagree with. +5663 Brad revealed that every alumnus that didn't shout wouldn't reveal. +5664 Sheila had realized that some art gallery that hadn't bored Christina resembled. +5665 Clyde wouldn't investigate that students that weren't disturbing Bruce dislike. +5666 Kristen reveals that most ladies that haven't disagreed aren't escaping from. +5667 The Impressionists forget that that alumnus that isn't hiding escapes from. +5668 Raymond shouldn't reveal that all senators that hadn't scratched could flee from. +5669 Some organizations discover that all legislatures that work hard wouldn't listen to. +5670 Many people knew who a girl that had described Becca would find. 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listens to. +5684 Phillip isn't learning that some waitresses that confuse Grace go to. +5685 A boy will forget that these schools that wouldn't donate are listening to. +5686 Boys conceal that some dancer that wasn't embarrassing Alice complains about. +5687 Some association was concealing that some play that disturbs Angela disagreed with. +5688 Keith realized that the museums that worried Patricia could dislike. +5689 Some driver discovered that some school that had remembered some college campus disliked. +5690 Brian was forgetting what an actor that had gotten fired disliked. +5691 Elizabeth had learned that the senators that attacked Maria admire. +5692 Mark has learned that these offspring that hurt Alicia drive to. +5693 Those museums forget that every committee that respected Debra talked to. +5694 All legislatures can remember what this man that looked like every picture has examined. +5695 Julie has found out that some waiter that had upset Caroline listens to. +5696 Marcus concealed that every student that hides Roger disliked. +5697 Craig figures out who a child that sells some cat sounded like. +5698 Sharon isn't forgetting what some movie theaters that exist looked like. +5699 Beth had learned that many doctors that impress those children did walk through. +5700 Mitchell was discovering that these children that went to this mall aren't disagreeing with. +5701 Rachel is finding out that this actress that has disgusted this organization cured. +5702 Every guest forgets that this essay that disagreed sounded like. +5703 Those organizations were figuring out what some pedestrians that have muttered drove to. +5704 The banks find out that the guy that stands up finds. +5705 Laura won't realize that this actor that aggravated some government isn't working with. +5706 Emily has discovered that some actor that irritated Derek wouldn't listen to. +5707 Many teenagers learn that all alumni that knew Theresa lift. +5708 Christina reveals that a lot of drivers that sneeze can talk to. +5709 Donald wouldn't know that every child that won't exist should listen to. +5710 Keith revealed that some cashiers that haven't hugged hadn't arrived at. +5711 Tanya remembered that many students that can't cough arrive at. +5712 Those guests aren't remembering who the person that hadn't fallen has talked to. +5713 Marcus can investigate what this screen that didn't disturb Richard doesn't look like. +5714 Timothy realized that a lot of people that watched every cashier admire. +5715 Tanya was remembering what many rabbits that ascended some hill have found. +5716 Grace will conceal that a boy that descended some slope is lifting. +5717 David was discovering that many pedestrians that have profited couldn't arrive at. +5718 Brenda had remembered that every photograph that is falling has looked like. +5719 Patricia investigated that a customer that embarrassed Steven respects. +5720 Cheryl wasn't realizing that every offspring that embarrassed most children can lift. +5721 This bank hasn't known that the adult that could bore Carl isn't arriving at. +5722 A lot of doctors discover that these waiters that do flee from Eric have talked to. +5723 Those customers forget what a girl that wasn't climbing up a slope wasn't arriving at. +5724 A lot of senators know that these teenagers that weren't nodding sound like. +5725 Many men didn't investigate that all teenagers that had alarmed David cure. +5726 Noah is investigating who the person that is looking like those sketches has cared for. +5727 Elaine hasn't realized that many pedestrians that distracted Ella have examined. +5728 The libraries reveal that a teacher that had argued might admire. +5729 Every legislature hadn't revealed that that teacher that climbed down a mountain respected. +5730 Brad reveals that all universities that will testify are arguing about. +5731 Joel wasn't figuring out that all teachers that blink drop by. +5732 Carolyn finds out that many actors that could investigate Brad hadn't revealed. +5733 Peter won't remember that that person that did exit some high schools fired. +5734 Bruce has figured out that a library that had admired Edward works with. +5735 Teresa should discover that the senators that hadn't known Irene weren't biking to. +5736 Donna knew that actresses that do confer disagree with. +5737 Those committees know that the women that blinked aren't firing. +5738 Daniel won't realize what these patients that practice haven't sounded like. +5739 Some waitresses discover that many libraries that argued reference. +5740 Some association learns that a lot of teachers that had cooked cared for. +5741 Some university had found out that a lot of guys that haven't scratched complain about. +5742 Todd had forgotten that all committees that weren't worrying Diane aren't talking to. +5743 Sarah figures out that those cashiers that might yawn did fire. +5744 Benjamin realized that every man that hadn't visited Barbara will conceal. +5745 Most companies shouldn't investigate that this person that disgusted Harvard University can lift. +5746 Most associations conceal that some pedestrian that doesn't escape wasn't talking to. +5747 Mitchell remembers what this doctor that hasn't brought the skirt won't walk through. +5748 Some customers realize that the waitress that sneezes wears. +5749 Some person hasn't concealed that the child that shouldn't attack Patricia talks to. +5750 The associations discover that a teenager that wasn't finding most women drops by. +5751 Bradley revealed that the guys that stand up were going to. +5752 Ruth revealed that all museums that clashed are boasting about. +5753 This student knows that most children that would aggravate most cashiers go to. +5754 Catherine doesn't realize that the actor that discusses every adult doesn't care for. +5755 Bradley shouldn't reveal that many waiters that see Jason lift. +5756 Robert found out that many students that broke many couches couldn't care for. +5757 Joseph forgets that dancers that shouldn't disagree escape from. +5758 Many companies had realized that many waitresses that clashed don't dislike. +5759 Nicole hasn't learned that the drivers that slump over cared for. +5760 April realized that some committee that isn't conferring admires. +5761 Jeffrey had found out that the pedestrians that hid had dropped by. +5762 Many children learn that every customer that protested was concealing. +5763 Those museums discover what most tomatoes that baked look like. +5764 Jason will find out that that actor that isn't grinning dislikes. +5765 These governments should figure out what the doctor that is observing the drivers goes to. +5766 Russell discovers that every thesis that lied boasts about. +5767 Donna might forget that this alumnus that does get fired lifts. +5768 Kayla finds out that those candles that impressed Thomas weren't scaring. +5769 Natalie is learning that most doctors that talked boasted about. +5770 Jane doesn't figure out that this alumnus that disgusted Kathleen finds. +5771 Brett found out that many guys that watch every actor were biking to. +5772 Natalie investigated that some guys that exit some cafes had talked about. +5773 Becky reveals that all children that kiss talked to. +5774 Nina was revealing that all actors that have sounded like Carolyn sound like. +5775 Donald hasn't forgotten that the pedestrians that would resemble pictures return to. +5776 These cashiers can't forget that some patients that were smiling weren't concealing. +5777 Stephen didn't realize that the essays that worried Sabrina sounded like. +5778 These universities weren't remembering that most patients that come here couldn't complain about. +5779 All governments figure out that turtles that find all customers aren't scaring. +5780 Sharon had forgotten that many schools that benefited are concealing. +5781 Spain wasn't investigating that a committee that hasn't described Richard talks to. +5782 Boys learn that these patients that have climbed up every hill wear. +5783 Some child knew what a teenager that was telephoning wasn't escaping from. +5784 Deborah reveals who alumni that might scratch talk to. +5785 Sabrina remembered that most ladies that are conferring argued about. +5786 Rebecca did find out that that girl that isn't kissing Jeffrey examines. +5787 Those adults forget that some dancer that singing walked through. +5788 Susan has investigated that some child that hid the nose hadn't concealed. +5789 Dawn hasn't learned that the children that hadn't reacted cure. +5790 The committees reveal that the libraries that appreciated the Great Lakes will talk to. +5791 Every hospital forgets that the man that isn't painting had disliked. +5792 A lot of libraries learned that some adult that thought about Victoria reveals. +5793 Andrew hadn't figured out that most guests that kissed Kenneth fled from. +5794 The boy has known that the teacher that vanishes can't listen to. +5795 A lot of committees forget that some boy that has climbed down a lot of steps flees from. +5796 Some library has forgotten that a man that is moving can't work with. +5797 Jill knew that most committees that have alarmed Kendra scare. +5798 Some teachers figure out that the doctor that left references. +5799 Dan knows that those waitresses that whispered return to. +5800 All teachers forget that most dancers that couldn't work with all women fled from. +5801 The banks reveal that this teenager that nodded cured. +5802 Kenneth had realized that the girls that practice arrive at. +5803 Maria isn't concealing that every boy that didn't sing does lift. +5804 Alicia figures out that some deer that would come here finds. +5805 Those senators conceal that this student that is disappearing sounds like. +5806 Some libraries should forget that all women that don't appreciate Naomi boast about. +5807 This customer figures out that teeth that didn't aggravate Gregory resemble. +5808 The government had known that most associations that joke around talk to. +5809 Melinda hadn't found out that that cashier that can break this unicycle references. +5810 April is finding out that those guys that practice aren't walking through. +5811 Those teachers hadn't remembered that the child that knows Steve lifts. +5812 Theresa concealed that some customer that bought bicycles doesn't conceal. +5813 The universities find out what that child that helped Diana was arriving at. +5814 The Impressionists have concealed that men that resembled a drawing couldn't lift. +5815 All governments reveal that all women that haven't yelled admire. +5816 Monet has revealed that some sketches that waste away look like. +5817 Sally has found out that a guy that thinks about some dress admired. +5818 Rose wasn't figuring out what these men that had escaped from this senator go to. +5819 The actor reveals that this hospital that is describing Thomas could like. +5820 A woman might conceal that many children that visit a student weren't boasting about. +5821 Joseph found out what most alumni that hadn't embarrassed Holly wear. +5822 Benjamin investigated that the customer that hasn't descended many hills escaped from. +5823 Children figure out what the closet that shut resembles. +5824 These organizations know that all adults that worked hard scared. +5825 Lori wasn't forgetting what the student that wasn't playing was curing. +5826 Andrew should investigate that that cashier that scanned that documentary is concealing. +5827 Susan hasn't revealed that the actors that have upset Vanessa had disagreed with. +5828 Some company had found out that these pamphlets that confused Janet disagreed with. +5829 A student should forget that many alumni that fire William worked with. +5830 Those women find out that this girl that visited some banks wears. +5831 Those children reveal that the university that profits will work with. +5832 Noah isn't discovering that most men that did react reveal. +5833 Russell realized that 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hadn't concealed. +6253 Some customer has realized that this child that yawns liked. +6254 Marie can't conceal that every waiter that cooked has escaped from. +6255 Katherine won't discover what a teacher that will cure Mark had walked through. +6256 Walter had forgotten that those boys that cared for Anna lift. +6257 Denise might reveal who every woman that can't think about Bethany cared for. +6258 Those legislatures forget that a legislature that should donate can't fire. +6259 A lot of cashiers had investigated that all waiters that reference that woman will sound like. +6260 Lucille figures out that the patients that should appear respect. +6261 Jacqueline was forgetting that this cashier that compromised wasn't concealing. +6262 Rose learned that these men that fled from Rhonda like. +6263 Wayne forgot that the alumnus that might complain hadn't disliked. +6264 Robert remembered that this girl that had impressed most children is fleeing from. +6265 Daniel concealed that every pedestrian that shouldn't sound like Bruce walked through. +6266 Anna found out that the men that weren't escaping conceal. +6267 Nancy realized that many adults that remember these guys had examined. +6268 A lady figures out that the doctor that buys those doors cures. +6269 These boys had forgotten that that man that hadn't noticed some windows likes. +6270 Many doctors investigated that these doctors that climbed up every ladder conceal. +6271 Jodi hadn't forgotten that senators that won't disappear won't lift. +6272 Florence finds out that fish that aren't coming here found. +6273 Mary had discovered that senators that resembled photographs conceal. +6274 Marie remembers that this child that hasn't helped Michael revealed. +6275 Kevin discovered that actors that hadn't irritated Sarah return to. +6276 Sherry remembers that adults that bore that doctor were dropping by. +6277 The hospital learned that some senator that would reveal Sheila revealed. +6278 Carolyn should remember that a lot of customers that hide away find. +6279 Karla wasn't remembering that every actor that doesn't exist wouldn't sound like. +6280 Adam investigated that every boy that isn't winning disagrees with. +6281 Waiters reveal what that axis that distracts Mark hadn't looked like. +6282 Becky figures out that most customers that vanished fire. +6283 Some person remembered that this alumnus that wasn't thinking about these mirrors is revealing. +6284 Scott forgot that a lot of universities that profited wouldn't boast about. +6285 Most governments will remember that the woman that fired Jeffrey has driven to. +6286 Keith does investigate that some doctor that doesn't heal Kathleen drops by. +6287 Raymond didn't find out that that woman that wasn't donating hadn't driven to. +6288 Valerie figured out that a lot of dancers that are flirting reveal. +6289 These men hadn't found out that waitresses that shouldn't watch some men escape from. +6290 Colleen hasn't realized who this cashier that was working with Kayla did work with. +6291 Some teenager wasn't revealing that some government that is protesting wasn't firing. +6292 Every student is forgetting that every waitress that was dropping by those museums wouldn't care for. +6293 Cynthia hasn't discovered that some pedestrians that hadn't boycotted some malls were complaining about. +6294 Customers weren't forgetting that a lot of people that could aggravate Nina hadn't revealed. +6295 Tammy concealed that every teenager that is passing every art gallery disagrees with. +6296 Anne wasn't finding out that a lot of drivers that won't love Tanya weren't concealing. +6297 Allison reveals that that woman that had questioned some hospitals did drop by. +6298 The Borgias discover that every school that has talked to Russell isn't arguing about. +6299 Winston Churchill wouldn't figure out who the student that was disappearing disagrees with. +6300 Some boys figure out that the alumnus that hasn't run around Spain had driven to. +6301 Teresa does forget that all girls that interact walk through. +6302 Some actors had learned that some cashier that was hiding away isn't complaining about. +6303 Carol isn't figuring out who most pedestrians that had skated around many movie theaters are talking to. +6304 Benjamin investigated that some woman that didn't exercise revealed. +6305 Karen discovers that a boy that hadn't drunk hasn't admired. +6306 The adults know what a woman that sneezes might arrive at. +6307 Heidi concealed that an actress that has alarmed Robert was boasting about. +6308 Anne is investigating that this pasta that bothers Barbara scared. +6309 Sandra isn't investigating that a lot of associations that praised Carolyn are firing. +6310 Some guests reveal that every girl that has messed up the cars will conceal. +6311 Most children forget what these children that telephoned examine. +6312 Robert should realize that every committee that changes argued about. +6313 Benjamin hasn't known that an offspring that will joke around worked with. +6314 Matt is remembering who every teacher that went fishing hadn't worked with. +6315 Homer had realized that all people that can collaborate can't cure. +6316 Those drivers know that some guy that was praising Allison would examine. +6317 Many legislatures reveal that some alumnus that forgets Alexander might complain about. +6318 Gregory realizes that the schools that had scared Patrick shouldn't like. +6319 Maria learned that all women that hadn't died concealed. +6320 Elizabeth had found out what these actors that couldn't confer boast about. +6321 Regina might find out who that offspring that kisses Winston Churchill hadn't cared for. +6322 Alicia realizes that some guest that will forfeit dropped by. +6323 Erin hasn't discovered that a lot of students that don't pass the college campus sound like. +6324 Stacey doesn't find out that those cashiers that bother Dan work with. +6325 The schools figure out what all legislatures that have hired Tracy liked. +6326 Scott doesn't remember what some dancer that can think about literature had found. +6327 Danielle forgets that men that aren't boycotting a lot of closets are concealing. +6328 Vincent doesn't discover that most patients that hadn't approved might care for. +6329 Most patients learn that every driver that would dislike Karen wasn't concealing. +6330 Janice won't find out that boys that have examined commentaries were fleeing from. +6331 George learned that the teenagers that weren't surrendering have listened to. +6332 Angela remembers that these students that had known Melissa had dropped by. +6333 Rebecca won't figure out that most dancers that wasted away cure. +6334 Candice does investigate that the girls that disturbed that senator did respect. +6335 Diana forgets that this cashier that muttered listened to. +6336 Judy is investigating that waitresses that have saluted respect. +6337 Stephen discovers that every guest that didn't climb down some mountain has referenced. +6338 Every actress concealed that that company that works with Nicole was listening to. +6339 A lot of girls figure out what these senators that retaliated haven't driven to. +6340 Many people figure out that those teenagers that concealed Vanessa have disliked. +6341 John reveals that a lot of students that distracted most women bike to. +6342 Joseph has figured out that most patients that disappeared will care for. +6343 Bill hasn't concealed that every drawing that isn't worrying Theodore is looking like. +6344 Eric knows that the driver that flees from Melanie hadn't walked through. +6345 Patricia forgot that all people that were hiding haven't arrived at. +6346 Kendra should conceal that the schools that shouldn't react had boasted about. +6347 Sheila hasn't forgotten what that woman that would bring that book went to. +6348 All committees realized that many pedestrians that yawn did look like. +6349 A lot of girls discover that some driver that hadn't slumped over dislikes. +6350 Every patient might realize that the children that wrote weren't admiring. +6351 Alicia concealed that every actress that sees fled from. +6352 Carlos was remembering that all actresses that woke up admire. +6353 Sabrina concealed what this senator that was eating will scare. +6354 The student isn't realizing that some women that are exiting the restaurant had complained about. +6355 Many actors realize that these guests that should react had examined. +6356 A lot of companies reveal what many essays that won had sounded like. +6357 Timothy can conceal that those guys that come here weren't scaring. +6358 Kenneth has forgotten that many adults that praised Carla were going to. +6359 Some companies have investigated that some dancer that walked through Harvard University arrived at. +6360 Every school revealed that that adult that has walked through some schools couldn't listen to. +6361 Every guy might investigate that the children that drink are concealing. +6362 Vanessa knows that a lot of teachers that haven't gone fishing aren't arriving at. +6363 Most schools were realizing that most actresses that joke around revealed. +6364 Every lady has remembered that a lot of guys that interacted listened to. +6365 Most legislatures hadn't discovered that senators that donate haven't lifted. +6366 The guys aren't figuring out who some doctor that moves hadn't boasted about. +6367 Ann isn't investigating what most girls that weren't interacting could wear. +6368 Some students reveal that many drivers that shrug had lifted. +6369 Sandra is forgetting what those customers that have boasted dropped by. +6370 All men investigate that every dancer that talks walks through. +6371 The Borgias reveal that this guy that didn't insult Jeffrey was arguing about. +6372 A legislature realizes that some woman that screamed hadn't returned to. +6373 All people figure out that most students that haven't gone to a grocery store wouldn't complain about. +6374 Ann discovered that some offspring that were training examine. +6375 Steve did figure out that adults that fell asleep hadn't sounded like. +6376 Many banks discover that many guests that negotiated haven't returned to. +6377 That museum does conceal that many waitresses that were cheering have revealed. +6378 Vanessa remembers that every shirt that hadn't wrinkled scared. +6379 Karla doesn't learn that many doctors that aren't complaining about Amelia arrive at. +6380 Martin concealed that a woman that exited many hills lifts. +6381 Some customer figures out that many rabbits that climb down this mountain scare. +6382 Some people learn that this doctor that will train wasn't driving to. +6383 The libraries aren't investigating that that synopsis that protested isn't referencing. +6384 April remembered that that child that speaks up dislikes. +6385 Joseph had found out that the child that changed won't listen to. +6386 The Lutherans find out that that waiter that had escaped cares for. +6387 Angela knew that offspring that hadn't screamed disagree with. +6388 Many guys find out what those children that concur had concealed. +6389 Carla does learn that every senator that arrived at a grocery store returns to. +6390 Alice hadn't figured out who this sheep that falls finds. +6391 Todd had revealed that this print that vanished looked like. +6392 Tina knows that every patient that hasn't discovered all windows didn't arrive at. +6393 Kimberley figures out what a hat that alarmed the girl resembled. +6394 Robert reveals that some woman that had played is fleeing from. +6395 Carrie is realizing that every woman that respected the Borgias had cared for. +6396 Teresa learns that this driver that approves hasn't escaped from. +6397 Lori has discovered that some waiter that isn't moving cared for. +6398 Daniel knew that all pamphlets that appeared disagreed with. +6399 These organizations remember that every waitress that has described Carla cares for. +6400 Jason learns that the doors that were disgusting Suzanne have scared. +6401 Sharon hasn't found out that a guy that had returned to Deanna has examined. +6402 Vanessa wasn't forgetting what this lady that lied went to. +6403 Those waiters forget that a lot of children that fled from Sara had found. +6404 The organizations have revealed that those associations that haven't alarmed Leslie referenced. +6405 Alice revealed that some bird that hadn't fallen asleep has looked like. +6406 Adam hadn't concealed what some glass that distracts Sonia looks like. +6407 Monica figures out that a waiter that describes Sara should conceal. +6408 The teachers find out that that customer that cries couldn't reference. +6409 The Lutherans are figuring out that the child that had disturbed Aaron boasts about. +6410 Cashiers were revealing that some offspring that hadn't observed Lisa can flee from. +6411 Libraries investigate what a woman that breaks that vase is admiring. +6412 Most actors remember what a hat that disappeared resembles. +6413 Brenda wouldn't investigate that every offspring that is escaping from Kristin hasn't boasted about. +6414 Tracy knew that this brochure that wasted away sounds like. +6415 Andrea finds out that a lot of hospitals that respect Guy can't argue about. +6416 This company is finding out what this teacher that could appreciate every public park had arrived at. +6417 All legislatures figure out what those customers that impressed Rachel haven't fled from. +6418 Joel wasn't concealing what a cashier that could fall bikes to. +6419 Renee learned that that synopsis that has discussed all lakes shouldn't disagree with. +6420 Amanda learned that these adults that respect Kendra conceal. +6421 Kendra isn't finding out that a lot of boys that found Karla talk to. +6422 A lot of boys remember what a report that disagreed isn't disagreeing with. +6423 Some committees knew that the actresses that haven't written respect. +6424 All governments forgot that these deer that had worried Harvard University hadn't resembled. +6425 Every patient didn't remember that these waiters that aren't arguing talked to. diff --git a/zeroshot/hypernym/eval_results.json b/zeroshot/hypernym/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..13ed81e97ecad38b4db18676cd574566077efd97 --- /dev/null +++ b/zeroshot/hypernym/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.5023255813953489} \ No newline at end of file diff --git a/zeroshot/hypernym/predictions.txt b/zeroshot/hypernym/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..88b1fd27482d86adec1c35c38ec595eaf1d5d8b2 --- /dev/null +++ b/zeroshot/hypernym/predictions.txt @@ -0,0 +1,861 @@ +index prediction +0 If she has a dog, it must be the case that she has a mammal. +1 She doesn't have a dog, so she doesn't have a chihuahua. +2 She doesn't have a dog, so she doesn't have a mammal. +3 If she has a dog, it must be the case that she has a chihuahua. +4 If she has a chihuahua, then she has a dog. +5 If she has a chihuahua, then she doesn't have a dog. +6 The girl has a dog, therefore the girl has a poodle. +7 If the girl doesn't have a poodle, then the girl doesn't have a dog. +8 If the girl doesn't have a dog, it must be the case that the girl doesn't have a poodle. +9 If the girl has a dog, it must be the case that the girl has a poodle. +10 The girl has a poodle, therefore the girl has a dog. +11 If the girl has a poodle, it must be the case that the girl doesn't have a dog. +12 If David has a dog, that means David has a poodle. +13 David doesn't have a poodle, so David doesn't have a dog. +14 If David doesn't have a dog, then David doesn't have a poodle. +15 David has a dog, so David has a poodle. +16 If David has a poodle, that means David has a dog. +17 If David has a poodle, that means David doesn't have a dog. +18 He has a dog, therefore he has a poodle. +19 If he doesn't have a poodle, that means he doesn't have a dog. +20 He doesn't have a dog, therefore he doesn't have a poodle. +21 If he has a dog, it must be the case that he has a poodle. +22 If he has a poodle, that means he has a cat. +23 He has a poodle, therefore he doesn't have a dog. +24 He has a dog, therefore he has a pit bull. +25 If he doesn't have a pit bull, then he doesn't have a dog. +26 He doesn't have a dog, therefore he doesn't have a pit bull. +27 If he has a pit bull, that means he has a dog. +28 If he has a pit bull, then he has a dog. +29 If he has a pit bull, then he doesn't have a dog. +30 David has a dog, therefore David has a mammal. +31 David doesn't have a dog, therefore David doesn't have a mammal. +32 If she has a dog, it must be the case that she has a mammal. +33 She doesn't have a dog, therefore she doesn't have a mammal. +34 The boy has a dog, therefore the boy has a poodle. +35 The boy doesn't have a dog, therefore the boy doesn't have a poodle. +36 If the boy doesn't have a dog, it must be the case that the boy doesn't have a poodle. +37 The boy has a dog, therefore the boy has a poodle. +38 The boy has a poodle, so the boy has a dog. +39 The boy has a poodle, so the boy doesn't have a dog. +40 If David has a dog, that means David has a pit bull. +41 David doesn't have a pit bull, therefore David doesn't have a dog. +42 David doesn't have a dog, so David doesn't have a pit bull. +43 If David has a dog, it must be the case that David has a pit bull. +44 David has a pit bull, so David has a dog. +45 David has a pit bull, therefore David doesn't have a dog. +46 If he has a dog, that means he has a mammal. +47 If he doesn't have a dog, it must be the case that he doesn't have a chihuahua. +48 If he doesn't have a dog, that means he doesn't have a mammal. +49 If he has a dog, it must be the case that he has a chihuahua. +50 He has a chihuahua, so he has a dog. +51 He has a chihuahua, so he doesn't have a dog. +52 The girl has a dog, therefore the girl has a mammal. +53 The girl doesn't have a dog, so the girl doesn't have a mammal. +54 If David has a dog, it must be the case that David has a mammal. +55 If David doesn't have a chihuahua, then David doesn't have a dog. +56 If David doesn't have a dog, that means David doesn't have a mammal. +57 If David has a dog, then David has a chihuahua. +58 David has a chihuahua, so David has a dog. +59 David has a chihuahua, therefore David doesn't have a dog. +60 If he built a house, that means he built a building. +61 If he didn't build a mansion, then he didn't build a house. +62 If he didn't build a house, it must be the case that he didn't build a building. +63 If he built a house, then he built a mansion. +64 If he built a mansion, then he built a house. +65 If he built a mansion, then he didn't build a house. +66 If she built a house, then she built a building. +67 She didn't build a house, so she didn't build a mansion. +68 If she didn't build a house, it must be the case that she didn't build a building. +69 If she built a house, that means she built a mansion. +70 She built a mansion, so she built a house. +71 If she built a mansion, it must be the case that she didn't build a house. +72 The contractor built a house, therefore the contractor built a building. +73 If the contractor didn't build a mansion, it must be the case that the contractor didn't build a house. +74 The contractor didn't build a house, therefore the contractor didn't build a building. +75 If the contractor built a mansion, then the contractor built a house. +76 If the contractor built a mansion, then the contractor built a house. +77 The contractor built a mansion, therefore the contractor didn't build a house. +78 If the construction company built a house, that means the construction company built a building. +79 If the construction company didn't build a mansion, then the construction company didn't build a house. +80 If the construction company didn't build a house, then the construction company didn't build a building. +81 If the construction company built a house, it must be the case that the construction company built a mansion. +82 If the construction company built a mansion, that means the construction company built a house. +83 The construction company built a mansion, so the construction company didn't build a house. +84 If he built a house, it must be the case that he built a building. +85 If he didn't build a house, then he didn't build a cottage. +86 He didn't build a house, so he didn't build a building. +87 If he built a house, that means he built a cottage. +88 If he built a cottage, that means he built a house. +89 If he built a cottage, that means he didn't build a house. +90 If she built a house, it must be the case that she built a building. +91 If she didn't build a house, it must be the case that she didn't build a cottage. +92 If she didn't build a house, that means she didn't build a building. +93 If she built a house, that means she built a cottage. +94 She built a cottage, therefore she built a house. +95 She built a cottage, so she didn't build a house. +96 If the contractor built a house, it must be the case that the contractor built a building. +97 If the contractor didn't build a house, that means the contractor didn't build a cottage. +98 The contractor didn't build a house, so the contractor didn't build a building. +99 If the contractor built a house, that means the contractor built a cottage. +100 If the contractor built a cottage, that means the contractor built a house. +101 The contractor built a cottage, so the contractor didn't build a house. +102 If the construction company built a house, then the construction company built a building. +103 The construction company didn't build a house, therefore the construction company didn't build a cottage. +104 The construction company didn't build a house, so the construction company didn't build a building. +105 If the construction company built a house, it must be the case that the construction company built a cottage. +106 The construction company built a cottage, therefore the construction company built a house. +107 The construction company built a cottage, so the construction company didn't build a house. +108 If he is wearing pants, that means he is wearing clothing. +109 He is wearing jeans, so he is wearing pants. +110 If he is wearing jeans, then he is wearing socks. +111 If she is wearing pants, it must be the case that she is wearing jeans. +112 She is wearing jeans, so she is wearing pants. +113 If she is wearing jeans, then she is wearing socks. +114 The boy is wearing pants, so the boy is wearing clothing. +115 If the boy is wearing pants, then the boy is wearing jeans. +116 If the boy is wearing jeans, it must be the case that the boy is wearing socks. +117 The girl is wearing pants, therefore the girl is wearing clothing. +118 The girl is wearing jeans, so the girl is wearing pants. +119 If the girl is wearing jeans, it must be the case that the girl is wearing socks. +120 She is chopping a pepper, therefore she is chopping a chili. +121 If she isn't chopping a pepper, it must be the case that she isn't chopping a chili. +122 The chef is chopping a pepper, so the chef is chopping a chili. +123 The chef isn't chopping a pepper, therefore the chef isn't chopping a chili. +124 If the cook is chopping a pepper, that means the cook is chopping a chili. +125 If the cook isn't chopping a pepper, that means the cook isn't chopping a chili. +126 If he is chopping a pepper, that means he is chopping a chili. +127 He isn't chopping a pepper, therefore he isn't chopping a chili. +128 The speaker is playing a song, therefore the speaker is playing a ballad. +129 The speaker isn't playing a song, so the speaker isn't playing a ballad. +130 The speaker isn't playing a song, so the speaker isn't playing a ballad. +131 If the speaker is playing a song, that means the speaker is playing a ballad. +132 The speaker is playing a ballad, therefore the speaker is playing a symphony. +133 The speaker is playing a ballad, so the speaker isn't playing a symphony. +134 He is playing a song, so he is playing an anthem. +135 If he isn't playing an anthem, it must be the case that he isn't playing a song. +136 If he isn't playing a song, then he isn't playing an anthem. +137 He is playing an anthem, therefore he is playing a song. +138 He is playing an anthem, therefore he is playing a symphony. +139 He is playing an anthem, therefore he isn't playing a symphony. +140 She is playing a song, so she is playing an anthem. +141 If she isn't playing an anthem, it must be the case that she isn't playing a song. +142 If she isn't playing a song, it must be the case that she isn't playing an anthem. +143 If she is playing an anthem, it must be the case that she is playing a song. +144 If she is playing an anthem, it must be the case that she is playing a symphony. +145 If she is playing an anthem, then she isn't playing a symphony. +146 She is playing a song, therefore she is playing a ballad. +147 She isn't playing a song, therefore she isn't playing a ballad. +148 If she isn't playing a song, then she isn't playing a ballad. +149 If she is playing a ballad, then she is playing a song. +150 She is playing a ballad, therefore she is playing a song. +151 She is playing a ballad, therefore she isn't playing a song. +152 If the musician is playing a song, then the musician is playing a musical piece. +153 If the musician isn't playing an anthem, it must be the case that the musician isn't playing a song. +154 If the musician isn't playing a song, it must be the case that the musician isn't playing a musical piece. +155 The musician is playing an anthem, therefore the musician is playing a song. +156 If the musician is playing an anthem, then the musician is playing a symphony. +157 If the musician is playing an anthem, it must be the case that the musician isn't playing a symphony. +158 If he is playing a song, then he is playing a ballad. +159 If he isn't playing a song, it must be the case that he isn't playing a ballad. +160 He isn't playing a song, so he isn't playing a ballad. +161 He is playing a song, therefore he is playing a ballad. +162 If he is playing a ballad, that means he is playing a symphony. +163 If he is playing a ballad, then he isn't playing a symphony. +164 If the musician is playing a song, that means the musician is playing a ballad. +165 If the musician isn't playing a song, then the musician isn't playing a ballad. +166 The musician isn't playing a song, so the musician isn't playing a ballad. +167 If the musician is playing a song, that means the musician is playing a ballad. +168 If the musician is playing a ballad, that means the musician is playing a symphony. +169 If the musician is playing a ballad, then the musician isn't playing a symphony. +170 The speaker is playing a song, therefore the speaker is playing an anthem. +171 The speaker isn't playing an anthem, therefore the speaker isn't playing a song. +172 If the speaker isn't playing a song, that means the speaker isn't playing an anthem. +173 The speaker is playing an anthem, therefore the speaker is playing a song. +174 The speaker is playing an anthem, therefore the speaker is playing a symphony. +175 If the speaker is playing an anthem, then the speaker isn't playing a symphony. +176 If he is growing herbs, then he is growing plants. +177 If he isn't growing basil, that means he isn't growing herbs. +178 If he isn't growing herbs, that means he isn't growing plants. +179 If he is growing basil, that means he is growing herbs. +180 If he is growing basil, then he is growing flowers. +181 He is growing basil, therefore he isn't growing flowers. +182 If the farm is growing herbs, it must be the case that the farm is growing plants. +183 If the farm isn't growing basil, it must be the case that the farm isn't growing herbs. +184 If the farm isn't growing herbs, that means the farm isn't growing plants. +185 The farm is growing herbs, so the farm is growing basil. +186 If the farm is growing basil, that means the farm is growing flowers. +187 If the farm is growing basil, that means the farm isn't growing flowers. +188 If she is growing herbs, then she is growing plants. +189 If she isn't growing herbs, that means she isn't growing rosemary. +190 She isn't growing herbs, so she isn't growing plants. +191 If she is growing herbs, it must be the case that she is growing rosemary. +192 She is growing rosemary, so she is growing flowers. +193 If she is growing rosemary, then she isn't growing flowers. +194 She is growing herbs, so she is growing plants. +195 If she isn't growing basil, it must be the case that she isn't growing herbs. +196 She isn't growing herbs, so she isn't growing plants. +197 She is growing basil, therefore she is growing herbs. +198 If she is growing basil, then she is growing flowers. +199 If she is growing basil, then she isn't growing flowers. +200 The farm is growing herbs, therefore the farm is growing plants. +201 If the farm isn't growing herbs, that means the farm isn't growing rosemary. +202 If the farm isn't growing herbs, that means the farm isn't growing plants. +203 The farm is growing herbs, therefore the farm is growing rosemary. +204 If the farm is growing rosemary, then the farm is growing flowers. +205 If the farm is growing rosemary, then the farm isn't growing flowers. +206 He is growing herbs, therefore he is growing plants. +207 If he isn't growing rosemary, it must be the case that he isn't growing herbs. +208 If he isn't growing herbs, then he isn't growing plants. +209 He is growing rosemary, therefore he is growing herbs. +210 If he is growing rosemary, then he is growing flowers. +211 He is growing rosemary, therefore he isn't growing flowers. +212 If the gardener is growing herbs, it must be the case that the gardener is growing plants. +213 The gardener isn't growing basil, so the gardener isn't growing herbs. +214 If the gardener isn't growing herbs, it must be the case that the gardener isn't growing plants. +215 If the gardener is growing herbs, then the gardener is growing basil. +216 The gardener is growing basil, therefore the gardener is growing flowers. +217 If the gardener is growing basil, that means the gardener isn't growing flowers. +218 If the gardener is growing herbs, then the gardener is growing plants. +219 If the gardener isn't growing herbs, that means the gardener isn't growing rosemary. +220 If the gardener isn't growing herbs, it must be the case that the gardener isn't growing plants. +221 If the gardener is growing herbs, it must be the case that the gardener is growing rosemary. +222 The gardener is growing rosemary, therefore the gardener is growing flowers. +223 If the gardener is growing rosemary, that means the gardener isn't growing flowers. +224 If he is hosting a party, it must be the case that he is hosting an event. +225 He isn't hosting a party, therefore he isn't hosting a wedding. +226 He isn't hosting a party, therefore he isn't hosting an event. +227 He is hosting a wedding, so he is hosting a party. +228 If he is hosting a wedding, it must be the case that he is hosting a party. +229 If he is hosting a wedding, that means he isn't hosting a party. +230 If she is hosting a party, then she is hosting an event. +231 She isn't hosting a party, so she isn't hosting a wedding. +232 If she isn't hosting a party, then she isn't hosting an event. +233 If she is hosting a party, that means she is hosting a wedding. +234 She is hosting a wedding, therefore she is hosting a party. +235 She is hosting a wedding, so she isn't hosting a party. +236 If the hotel is hosting a party, then the hotel is hosting an event. +237 The hotel isn't hosting a party, so the hotel isn't hosting a wedding. +238 If the hotel isn't hosting a party, then the hotel isn't hosting an event. +239 The hotel is hosting a party, so the hotel is hosting a wedding. +240 If the hotel is hosting a wedding, that means the hotel is hosting a party. +241 If the hotel is hosting a wedding, then the hotel isn't hosting a party. +242 If he is hosting a party, it must be the case that he is hosting an event. +243 If he isn't hosting a party, then he isn't hosting a ball. +244 He isn't hosting a party, so he isn't hosting an event. +245 He is hosting a party, so he is hosting a ball. +246 If he is hosting a ball, that means he is hosting a party. +247 He is hosting a ball, therefore he isn't hosting a party. +248 If she is hosting a party, it must be the case that she is hosting an event. +249 If she isn't hosting a ball, that means she isn't hosting a party. +250 If she isn't hosting a party, then she isn't hosting an event. +251 If she is hosting a party, then she is hosting a ball. +252 She is hosting a ball, therefore she is hosting a party. +253 If she is hosting a ball, that means she isn't hosting a party. +254 The hotel is hosting a party, so the hotel is hosting an event. +255 If the hotel isn't hosting a ball, it must be the case that the hotel isn't hosting a party. +256 If the hotel isn't hosting a party, then the hotel isn't hosting an event. +257 The hotel is hosting a party, so the hotel is hosting a ball. +258 If the hotel is hosting a ball, it must be the case that the hotel is hosting a party. +259 If the hotel is hosting a ball, then the hotel isn't hosting a party. +260 He is hosting a party, therefore he is hosting an event. +261 If he isn't hosting a party, then he isn't hosting an event. +262 If she is hosting a party, then she is hosting an event. +263 If she isn't hosting a party, then she isn't hosting an event. +264 If the hotel is hosting a party, that means the hotel is hosting an event. +265 The hotel isn't hosting a party, therefore the hotel isn't hosting an event. +266 The boy is donating an organ, so the boy is donating a kidney. +267 If the boy isn't donating an organ, it must be the case that the boy isn't donating a kidney. +268 If David is donating an organ, that means David is donating a kidney. +269 David isn't donating an organ, therefore David isn't donating a kidney. +270 If she is donating an organ, then she is donating a heart. +271 If she isn't donating an organ, that means she isn't donating a heart. +272 She isn't donating an organ, therefore she isn't donating a heart. +273 She is donating an organ, therefore she is donating a heart. +274 If she is donating a heart, it must be the case that she is donating a bone. +275 If she is donating a heart, then she isn't donating a bone. +276 If the girl is donating an organ, then the girl is donating a heart. +277 If the girl isn't donating an organ, then the girl isn't donating a heart. +278 If the girl isn't donating an organ, it must be the case that the girl isn't donating a heart. +279 If the girl is donating an organ, it must be the case that the girl is donating a heart. +280 The girl is donating a heart, so the girl is donating an organ. +281 The girl is donating a heart, so the girl isn't donating an organ. +282 If Sarah is donating an organ, that means Sarah is donating a body part. +283 If Sarah isn't donating an organ, then Sarah isn't donating a liver. +284 Sarah isn't donating an organ, so Sarah isn't donating a liver. +285 Sarah is donating an organ, therefore Sarah is donating a liver. +286 Sarah is donating a liver, so Sarah is donating a bone. +287 Sarah is donating a liver, therefore Sarah isn't donating a bone. +288 If she is donating an organ, that means she is donating a liver. +289 If she isn't donating an organ, that means she isn't donating a liver. +290 If she isn't donating an organ, that means she isn't donating a body part. +291 If she is donating an organ, it must be the case that she is donating a liver. +292 She is donating a liver, therefore she is donating an organ. +293 If she is donating a liver, that means she isn't donating an organ. +294 If David is donating an organ, it must be the case that David is donating a liver. +295 If David isn't donating an organ, it must be the case that David isn't donating a liver. +296 If David isn't donating an organ, then David isn't donating a liver. +297 If David is donating an organ, it must be the case that David is donating a liver. +298 If David is donating a liver, it must be the case that David is donating a bone. +299 David is donating a liver, so David isn't donating a bone. +300 If he is donating an organ, that means he is donating a heart. +301 If he isn't donating an organ, then he isn't donating a heart. +302 If he isn't donating an organ, that means he isn't donating a heart. +303 He is donating a heart, therefore he is donating an organ. +304 If he is donating a heart, that means he is donating a bone. +305 If he is donating a heart, that means he isn't donating a bone. +306 If the girl is donating an organ, it must be the case that the girl is donating a liver. +307 If the girl isn't donating an organ, that means the girl isn't donating a liver. +308 If the girl isn't donating an organ, then the girl isn't donating a body part. +309 If the girl is donating an organ, then the girl is donating a liver. +310 If the girl is donating a liver, then the girl is donating an organ. +311 If the girl is donating a liver, then the girl isn't donating an organ. +312 If he is donating an organ, that means he is donating a liver. +313 If he isn't donating an organ, it must be the case that he isn't donating a liver. +314 He isn't donating an organ, so he isn't donating a liver. +315 If he is donating an organ, it must be the case that he is donating a liver. +316 If he is donating a liver, that means he is donating an organ. +317 If he is donating a liver, it must be the case that he isn't donating a bone. +318 If she is donating an organ, it must be the case that she is donating a kidney. +319 If she isn't donating an organ, then she isn't donating a kidney. +320 The girl is donating an organ, so the girl is donating a kidney. +321 If the girl isn't donating an organ, then the girl isn't donating a kidney. +322 If the writer is editing a manual, that means the writer is editing a novel. +323 The writer is editing a manual, therefore the writer isn't writing a novel. +324 The writer is writing a manual, so the writer is writing a novel. +325 If the writer is writing a manual, it must be the case that the writer isn't writing a novel. +326 The author is editing a manual, therefore the author is editing a novel. +327 The author is editing a manual, therefore the author isn't writing a novel. +328 If the writer is reading a manual, it must be the case that the writer is reading a novel. +329 If the writer is reading a manual, it must be the case that the writer isn't writing a novel. +330 He is writing a textbook, therefore he is writing a novel. +331 If he is writing a textbook, that means he isn't writing a novel. +332 She is writing a textbook, therefore she is writing a novel. +333 If she is writing a textbook, that means she isn't writing a novel. +334 He is editing a manual, so he is editing a novel. +335 If he is editing a manual, then he isn't writing a novel. +336 She is reading a memoir, therefore she is reading a novel. +337 If she is reading a memoir, it must be the case that she isn't writing a novel. +338 If the writer is writing a memoir, then the writer is writing a novel. +339 If the writer is writing a memoir, then the writer isn't editing nonfiction. +340 If the writer is writing a manual, then the writer is writing a novel. +341 If the writer is writing a manual, then the writer isn't editing a novel. +342 If she is editing a memoir, that means she is editing a novel. +343 If she is editing a memoir, then she isn't editing a novel. +344 If the writer is reading a manual, that means the writer is reading a novel. +345 If the writer is reading a manual, then the writer isn't editing a novel. +346 If he spotted a bird, it must be the case that he spotted an animal. +347 He didn't spot an eagle, so he didn't spot a bird. +348 If he didn't spot a bird, then he didn't spot an animal. +349 If he spotted a bird, that means he spotted an eagle. +350 If he spotted an eagle, that means he spotted a bird. +351 He spotted an eagle, so he didn't spot a bird. +352 The hiker spotted a bird, therefore the hiker spotted an animal. +353 The hiker didn't spot a bird, so the hiker didn't spot a robin. +354 If the hiker didn't spot a bird, it must be the case that the hiker didn't spot an animal. +355 If the hiker spotted a bird, then the hiker spotted a robin. +356 If the hiker spotted a robin, then the hiker spotted a bird. +357 The hiker spotted a robin, therefore the hiker didn't spot a bird. +358 The hiker spotted a bird, so the hiker spotted an animal. +359 If the hiker didn't spot a bird, it must be the case that the hiker didn't spot an eagle. +360 The hiker didn't spot a bird, therefore the hiker didn't spot an animal. +361 If the hiker spotted an eagle, then the hiker spotted a bird. +362 If the hiker spotted an eagle, that means the hiker spotted a bird. +363 If the hiker spotted an eagle, then the hiker didn't spot a bird. +364 If she spotted a bird, it must be the case that she spotted an animal. +365 If she didn't spot a bird, that means she didn't spot a crow. +366 If she didn't spot a bird, that means she didn't spot an animal. +367 If she spotted a crow, it must be the case that she spotted a bird. +368 If she spotted a crow, that means she spotted a bird. +369 If she spotted a crow, then she didn't spot a bird. +370 The biologist spotted a bird, so the biologist spotted an animal. +371 The biologist didn't spot a bird, so the biologist didn't spot an eagle. +372 If the biologist didn't spot a bird, then the biologist didn't spot an animal. +373 If the biologist spotted a bird, then the biologist spotted an eagle. +374 The biologist spotted an eagle, therefore the biologist spotted a bird. +375 If the biologist spotted an eagle, it must be the case that the biologist didn't spot a bird. +376 She spotted a bird, so she spotted an animal. +377 She didn't spot a bird, therefore she didn't spot an eagle. +378 She didn't spot a bird, so she didn't spot an animal. +379 If she spotted a bird, it must be the case that she spotted an eagle. +380 If she spotted an eagle, then she spotted a bird. +381 If she spotted an eagle, it must be the case that she didn't spot a bird. +382 The biologist spotted a bird, so the biologist spotted an animal. +383 If the biologist didn't spot a bird, that means the biologist didn't spot a robin. +384 If the biologist didn't spot a bird, that means the biologist didn't spot an animal. +385 If the biologist spotted a bird, that means the biologist spotted a robin. +386 The biologist spotted a robin, so the biologist spotted a bird. +387 If the biologist spotted a robin, then the biologist didn't spot a bird. +388 If the hiker spotted a bird, it must be the case that the hiker spotted an animal. +389 If the hiker didn't spot a bird, that means the hiker didn't spot a crow. +390 If the hiker didn't spot a bird, that means the hiker didn't spot an animal. +391 If the hiker spotted a crow, that means the hiker spotted a bird. +392 The hiker spotted a crow, therefore the hiker spotted a bird. +393 The hiker spotted a crow, therefore the hiker didn't spot a bird. +394 He spotted a bird, so he spotted an animal. +395 He didn't spot a crow, therefore he didn't spot a bird. +396 He didn't spot a bird, therefore he didn't spot an animal. +397 He spotted a crow, therefore he spotted a bird. +398 He spotted a crow, so he spotted a bird. +399 He spotted a crow, therefore he didn't spot a bird. +400 If the biologist spotted a bird, it must be the case that the biologist spotted an animal. +401 If the biologist didn't spot a bird, that means the biologist didn't spot a crow. +402 If the biologist didn't spot a bird, it must be the case that the biologist didn't spot an animal. +403 The biologist spotted a crow, so the biologist spotted a bird. +404 If the biologist spotted a crow, that means the biologist spotted a bird. +405 If the biologist spotted a crow, that means the biologist didn't spot a bird. +406 He spotted a bird, so he spotted an animal. +407 If he didn't spot a bird, it must be the case that he didn't spot a robin. +408 If he didn't spot a bird, then he didn't spot an animal. +409 He spotted a bird, therefore he spotted a robin. +410 If he spotted a robin, it must be the case that he spotted a bird. +411 If he spotted a robin, that means he didn't spot a bird. +412 If she spotted a bird, then she spotted an animal. +413 She didn't spot a bird, so she didn't spot a robin. +414 If she didn't spot a bird, it must be the case that she didn't spot an animal. +415 She spotted a bird, therefore she spotted a robin. +416 If she spotted a robin, then she spotted a bird. +417 If she spotted a robin, it must be the case that she didn't spot a bird. +418 If David drank a soda, that means David drank a drink. +419 David isn't pouring a soda, therefore David isn't pouring a drink. +420 The girl drank a soda, so the girl drank a pepsi. +421 If the girl isn't pouring a soda, then the girl isn't pouring a drink. +422 If he is pouring a soda, then he is pouring a drink. +423 If he didn't drink a soda, it must be the case that he didn't drink a coke. +424 If he didn't drink a soda, it must be the case that he didn't drink a drink. +425 If he is pouring a coke, that means he is pouring a soda. +426 If he is pouring a coke, then he is pouring a soda. +427 If he is pouring a coke, that means he didn't drink a soda. +428 If the boy is pouring a soda, that means the boy is pouring a drink. +429 If the boy isn't pouring a soda, then the boy isn't pouring a drink. +430 If he drank a soda, it must be the case that he drank a drink. +431 He isn't pouring a soda, therefore he isn't pouring a coke. +432 If he isn't pouring a soda, that means he isn't pouring a drink. +433 If he drank a coke, then he drank a soda. +434 He drank a coke, therefore he drank a soda. +435 He drank a coke, so he isn't pouring a soda. +436 If Sarah drank a soda, it must be the case that Sarah drank a drink. +437 If Sarah isn't pouring a soda, then Sarah isn't pouring a drink. +438 She drank a soda, therefore she drank a drink. +439 She isn't pouring a soda, therefore she isn't pouring a coke. +440 She isn't pouring a soda, therefore she isn't pouring a drink. +441 She drank a soda, therefore she drank a coke. +442 If she drank a coke, that means she drank a soda. +443 If she drank a coke, it must be the case that she isn't pouring a soda. +444 The girl is pouring a soda, therefore the girl is pouring a drink. +445 The girl didn't drink a soda, therefore the girl didn't drink a drink. +446 If he drank a soda, then he drank a pepsi. +447 He isn't pouring a soda, so he isn't pouring a drink. +448 If Sarah drank a soda, then Sarah drank a drink. +449 Sarah didn't drink a soda, therefore Sarah didn't drink a coke. +450 If Sarah didn't drink a soda, that means Sarah didn't drink a drink. +451 Sarah drank a soda, so Sarah drank a coke. +452 Sarah drank a coke, therefore Sarah drank a soda. +453 Sarah drank a coke, so Sarah didn't drink a soda. +454 If she is pouring a soda, it must be the case that she is pouring a drink. +455 If she didn't drink a soda, it must be the case that she didn't drink a drink. +456 The boy is pouring a soda, therefore the boy is pouring a drink. +457 If the boy didn't drink a soda, then the boy didn't drink a coke. +458 If the boy didn't drink a soda, it must be the case that the boy didn't drink a drink. +459 The boy is pouring a soda, therefore the boy is pouring a coke. +460 If the boy is pouring a coke, it must be the case that the boy is pouring a soda. +461 If the boy is pouring a coke, that means the boy didn't drink a soda. +462 If Sarah is cooking a meal, then Sarah is cooking food. +463 If Sarah isn't cooking a meal, that means Sarah isn't cooking lunch. +464 If Sarah isn't cooking a meal, that means Sarah isn't cooking food. +465 If Sarah is cooking lunch, it must be the case that Sarah is cooking a meal. +466 If Sarah is cooking lunch, that means Sarah is cooking a meal. +467 If Sarah is cooking lunch, it must be the case that Sarah isn't cooking a meal. +468 The girl is cooking a meal, therefore the girl is cooking food. +469 If the girl isn't cooking a meal, then the girl isn't cooking dinner. +470 If the girl isn't cooking a meal, it must be the case that the girl isn't cooking food. +471 If the girl is cooking dinner, then the girl is cooking a meal. +472 The girl is cooking dinner, therefore the girl is cooking a meal. +473 If the girl is cooking dinner, it must be the case that the girl isn't cooking a meal. +474 If Sarah is eating a meal, then Sarah is eating dinner. +475 If Sarah isn't eating dinner, then Sarah isn't eating a meal. +476 If Sarah isn't eating a meal, that means Sarah isn't eating dinner. +477 If Sarah is eating dinner, it must be the case that Sarah is eating a meal. +478 Sarah is eating dinner, so Sarah is eating a meal. +479 If Sarah is eating dinner, that means Sarah isn't eating a meal. +480 He is cooking a meal, therefore he is cooking food. +481 If he isn't cooking a meal, that means he isn't cooking breakfast. +482 If he isn't cooking a meal, then he isn't cooking food. +483 If he is cooking breakfast, it must be the case that he is cooking a meal. +484 If he is cooking breakfast, that means he is cooking a meal. +485 If he is cooking breakfast, then he isn't cooking a meal. +486 The girl is cooking a meal, therefore the girl is cooking food. +487 The girl isn't eating a meal, therefore the girl isn't eating breakfast. +488 If the girl isn't eating a meal, then the girl isn't eating food. +489 The girl is cooking a meal, so the girl is cooking breakfast. +490 If the girl is cooking breakfast, it must be the case that the girl is cooking a meal. +491 The girl is cooking breakfast, therefore the girl isn't eating a meal. +492 The boy is eating a meal, therefore the boy is eating food. +493 If the boy isn't eating a meal, then the boy isn't eating dinner. +494 If the boy isn't eating a meal, then the boy isn't eating food. +495 The boy is eating dinner, therefore the boy is eating a meal. +496 The boy is eating dinner, therefore the boy is eating a meal. +497 If the boy is eating dinner, it must be the case that the boy isn't eating a meal. +498 David is cooking a meal, therefore David is cooking food. +499 David isn't eating a meal, so David isn't eating lunch. +500 David isn't eating a meal, so David isn't eating food. +501 If David is cooking a meal, it must be the case that David is cooking lunch. +502 If David is cooking lunch, it must be the case that David is cooking a meal. +503 David is cooking lunch, so David isn't eating a meal. +504 She is cooking a meal, therefore she is cooking food. +505 If she isn't eating a meal, then she isn't eating dinner. +506 If she isn't eating a meal, then she isn't eating food. +507 If she is cooking dinner, then she is cooking a meal. +508 She is cooking dinner, so she is cooking a meal. +509 If she is cooking dinner, that means she isn't eating a meal. +510 If she is eating a meal, it must be the case that she is eating food. +511 She isn't eating a meal, therefore she isn't eating lunch. +512 She isn't eating a meal, so she isn't eating food. +513 She is eating a meal, so she is eating lunch. +514 She is eating lunch, therefore she is eating a meal. +515 If she is eating lunch, it must be the case that she isn't eating a meal. +516 If the girl is eating a meal, then the girl is eating food. +517 If the girl isn't cooking a meal, then the girl isn't cooking dinner. +518 If the girl isn't cooking a meal, then the girl isn't cooking food. +519 If the girl is eating dinner, it must be the case that the girl is eating a meal. +520 The girl is eating dinner, therefore the girl is eating a meal. +521 The girl is eating dinner, therefore the girl isn't cooking a meal. +522 If Sarah is eating a meal, then Sarah is eating lunch. +523 If Sarah isn't eating lunch, that means Sarah isn't eating a meal. +524 Sarah isn't eating a meal, therefore Sarah isn't eating food. +525 Sarah is eating lunch, so Sarah is eating a meal. +526 If Sarah is eating lunch, then Sarah is eating a meal. +527 If Sarah is eating lunch, it must be the case that Sarah isn't eating a meal. +528 David is cooking a meal, so David is cooking food. +529 If David isn't cooking a meal, it must be the case that David isn't cooking breakfast. +530 David isn't cooking a meal, so David isn't cooking food. +531 David is cooking breakfast, so David is cooking a meal. +532 If David is cooking breakfast, then David is cooking a meal. +533 David is cooking breakfast, so David isn't cooking a meal. +534 She is addicted to a drug, therefore she is addicted to cocaine. +535 If she isn't addicted to cocaine, then she isn't addicted to a drug. +536 If she isn't addicted to a drug, then she isn't addicted to cocaine. +537 If she is addicted to a drug, then she is addicted to cocaine. +538 If she is addicted to cocaine, then she is addicted to a drug. +539 If she is addicted to cocaine, it must be the case that she isn't addicted to a drug. +540 David is consuming a drug, so David is consuming caffeine. +541 If David isn't consuming a drug, that means David isn't consuming caffeine. +542 David isn't consuming a drug, so David isn't consuming caffeine. +543 If David is consuming a drug, it must be the case that David is consuming caffeine. +544 David is consuming caffeine, so David is consuming water. +545 David is consuming caffeine, so David isn't consuming water. +546 If the boy is addicted to a drug, then the boy is addicted to cocaine. +547 The boy isn't addicted to cocaine, so the boy isn't addicted to a drug. +548 The boy isn't addicted to a drug, so the boy isn't addicted to cocaine. +549 If the boy is addicted to cocaine, then the boy is addicted to a drug. +550 If the boy is addicted to cocaine, then the boy is addicted to a drug. +551 If the boy is addicted to cocaine, it must be the case that the boy isn't addicted to a drug. +552 If the boy is consuming a drug, it must be the case that the boy is consuming caffeine. +553 The boy isn't addicted to caffeine, so the boy isn't addicted to a drug. +554 If the boy isn't addicted to a drug, then the boy isn't addicted to caffeine. +555 If the boy is consuming caffeine, it must be the case that the boy is consuming a drug. +556 If the boy is consuming caffeine, it must be the case that the boy is consuming water. +557 If the boy is consuming caffeine, that means the boy isn't addicted to water. +558 If the girl is addicted to a drug, then the girl is addicted to caffeine. +559 If the girl isn't addicted to caffeine, it must be the case that the girl isn't addicted to a drug. +560 If the girl isn't addicted to a drug, it must be the case that the girl isn't addicted to caffeine. +561 The girl is addicted to a drug, so the girl is addicted to caffeine. +562 If the girl is addicted to caffeine, it must be the case that the girl is addicted to a drug. +563 If the girl is addicted to caffeine, it must be the case that the girl isn't addicted to a drug. +564 He is consuming a drug, so he is consuming caffeine. +565 He isn't consuming caffeine, so he isn't consuming a drug. +566 If he isn't consuming a drug, then he isn't consuming caffeine. +567 If he is consuming a drug, then he is consuming caffeine. +568 If he is consuming caffeine, then he is consuming water. +569 If he is consuming caffeine, then he isn't consuming water. +570 If Sarah is consuming a drug, then Sarah is consuming caffeine. +571 If Sarah isn't addicted to caffeine, it must be the case that Sarah isn't addicted to a drug. +572 If Sarah isn't addicted to a drug, then Sarah isn't addicted to caffeine. +573 Sarah is consuming a drug, therefore Sarah is consuming caffeine. +574 Sarah is consuming caffeine, so Sarah is consuming water. +575 If Sarah is consuming caffeine, it must be the case that Sarah isn't addicted to a drug. +576 If he is addicted to a drug, it must be the case that he is addicted to caffeine. +577 If he isn't consuming caffeine, it must be the case that he isn't consuming a drug. +578 If he isn't consuming a drug, then he isn't consuming caffeine. +579 He is addicted to a drug, so he is addicted to caffeine. +580 If he is addicted to caffeine, it must be the case that he is addicted to a drug. +581 He is addicted to caffeine, so he isn't consuming water. +582 If the girl is addicted to a drug, that means the girl is addicted to cocaine. +583 The girl isn't addicted to cocaine, so the girl isn't addicted to a drug. +584 The girl isn't addicted to a drug, therefore the girl isn't addicted to cocaine. +585 If the girl is addicted to cocaine, it must be the case that the girl is addicted to a drug. +586 If the girl is addicted to cocaine, it must be the case that the girl is addicted to a drug. +587 The girl is addicted to cocaine, so the girl isn't addicted to a drug. +588 David is addicted to a drug, therefore David is addicted to cocaine. +589 If David isn't consuming cocaine, it must be the case that David isn't consuming a drug. +590 David isn't consuming a drug, so David isn't consuming cocaine. +591 If David is addicted to cocaine, then David is addicted to a drug. +592 If David is addicted to cocaine, that means David is addicted to a drug. +593 If David is addicted to cocaine, that means David isn't consuming a drug. +594 The farm produces parmesan, therefore the farm produces cheese. +595 The farm produces parmesan, so the farm doesn't sell cheese. +596 The farm sells cheese, therefore the farm sells dairy. +597 If the farm doesn't produce cheese, that means the farm doesn't produce brie. +598 If the farm doesn't produce cheese, that means the farm doesn't produce dairy. +599 The farm sells cheese, so the farm sells brie. +600 If the farm sells brie, that means the farm sells cheese. +601 The farm sells brie, therefore the farm doesn't produce cheese. +602 The farm sells parmesan, so the farm sells cheese. +603 If the farm sells parmesan, then the farm doesn't sell cheese. +604 If the farm sells parmesan, then the farm sells cheese. +605 The farm sells parmesan, so the farm doesn't produce cheese. +606 She sells parmesan, so she sells cheese. +607 She sells parmesan, therefore she doesn't produce cheese. +608 She sells cheese, therefore she sells dairy. +609 If she doesn't produce cheese, it must be the case that she doesn't produce brie. +610 If she doesn't produce cheese, then she doesn't produce dairy. +611 If she sells brie, then she sells cheese. +612 If she sells brie, then she sells cheese. +613 If she sells brie, then she doesn't produce cheese. +614 If he produces cheese, then he produces dairy. +615 He doesn't produce brie, therefore he doesn't produce cheese. +616 If he doesn't produce cheese, that means he doesn't produce dairy. +617 He produces cheese, so he produces brie. +618 He produces brie, therefore he produces cheese. +619 If he produces brie, that means he doesn't produce cheese. +620 If she produces parmesan, that means she produces cheese. +621 If she produces parmesan, then she doesn't sell cheese. +622 She is meeting a sibling, therefore she is meeting a family member. +623 She isn't meeting a sibling, therefore she isn't meeting a brother. +624 If she isn't meeting a sibling, it must be the case that she isn't meeting a family member. +625 If she is meeting a sibling, then she is meeting a brother. +626 If she is meeting a brother, that means she is meeting a sibling. +627 If she is meeting a brother, that means she isn't meeting a sibling. +628 The girl is helping a sibling, therefore the girl is helping a sister. +629 The girl isn't helping a sister, therefore the girl isn't helping a sibling. +630 The girl isn't helping a sibling, so the girl isn't helping a sister. +631 If the girl is helping a sister, that means the girl is helping a sibling. +632 The girl is helping a sister, therefore the girl is helping a sibling. +633 The girl is helping a sister, therefore the girl isn't helping a sibling. +634 If David is meeting a sibling, then David is meeting a family member. +635 If David isn't fighting with a sibling, it must be the case that David isn't fighting with a brother. +636 If David isn't fighting with a sibling, it must be the case that David isn't fighting with a brother. +637 David is meeting a brother, so David is meeting a sibling. +638 If David is meeting a brother, it must be the case that David is meeting a sibling. +639 If David is meeting a brother, that means David isn't fighting with a sibling. +640 If she is fighting with a sibling, it must be the case that she is fighting with a sister. +641 She isn't helping a sister, so she isn't helping a sibling. +642 She isn't helping a sibling, therefore she isn't helping a sister. +643 If she is fighting with a sister, then she is fighting with a sibling. +644 If she is fighting with a sister, it must be the case that she is fighting with a sibling. +645 If she is fighting with a sister, then she isn't helping a sibling. +646 He is helping a sibling, so he is helping a sister. +647 If he isn't fighting with a sister, then he isn't fighting with a sibling. +648 If he isn't fighting with a sibling, then he isn't fighting with a family member. +649 If he is helping a sister, it must be the case that he is helping a sibling. +650 If he is helping a sister, it must be the case that he is helping a sibling. +651 If he is helping a sister, it must be the case that he isn't fighting with a sibling. +652 The boy is meeting a sibling, therefore the boy is meeting a family member. +653 The boy isn't helping a sibling, therefore the boy isn't helping a sister. +654 If the boy isn't helping a sibling, it must be the case that the boy isn't helping a family member. +655 The boy is meeting a sister, therefore the boy is meeting a sibling. +656 The boy is meeting a sister, so the boy is meeting a sibling. +657 If the boy is meeting a sister, then the boy isn't helping a sibling. +658 The boy is helping a sibling, therefore the boy is helping a sister. +659 The boy isn't helping a sibling, so the boy isn't helping a sister. +660 If the boy isn't helping a sibling, it must be the case that the boy isn't helping a family member. +661 If the boy is helping a sister, then the boy is helping a sibling. +662 If the boy is helping a sister, then the boy is helping a sibling. +663 If the boy is helping a sister, that means the boy isn't helping a sibling. +664 The boy is helping a sibling, therefore the boy is helping a sister. +665 If the boy isn't fighting with a sister, that means the boy isn't fighting with a sibling. +666 The boy isn't fighting with a sibling, so the boy isn't fighting with a sister. +667 If the boy is helping a sibling, it must be the case that the boy is helping a sister. +668 If the boy is helping a sister, it must be the case that the boy is helping a sibling. +669 If the boy is helping a sister, it must be the case that the boy isn't fighting with a sibling. +670 If Sarah is helping a sibling, then Sarah is helping a family member. +671 Sarah isn't helping a brother, so Sarah isn't helping a sibling. +672 Sarah isn't helping a sibling, therefore Sarah isn't helping a brother. +673 If Sarah is helping a sibling, that means Sarah is helping a brother. +674 If Sarah is helping a brother, it must be the case that Sarah is helping a sibling. +675 If Sarah is helping a brother, it must be the case that Sarah isn't helping a sibling. +676 If she is fighting with a sibling, it must be the case that she is fighting with a brother. +677 If she isn't fighting with a brother, then she isn't fighting with a sibling. +678 If she isn't fighting with a sibling, then she isn't fighting with a brother. +679 If she is fighting with a brother, that means she is fighting with a sibling. +680 If she is fighting with a brother, then she is fighting with a cousin. +681 If she is fighting with a brother, it must be the case that she isn't fighting with a sibling. +682 If he is meeting a sibling, that means he is meeting a family member. +683 If he isn't fighting with a brother, that means he isn't fighting with a sibling. +684 He isn't fighting with a sibling, therefore he isn't fighting with a brother. +685 If he is meeting a brother, that means he is meeting a sibling. +686 He is meeting a brother, so he is meeting a sibling. +687 If he is meeting a brother, then he isn't fighting with a sibling. +688 David is meeting a sibling, therefore David is meeting a family member. +689 David isn't fighting with a sibling, so David isn't fighting with a sister. +690 David isn't fighting with a sibling, therefore David isn't fighting with a sister. +691 David is meeting a sister, so David is meeting a sibling. +692 If David is meeting a sister, then David is meeting a sibling. +693 If David is meeting a sister, it must be the case that David isn't fighting with a sibling. +694 If the boy bought a computer, that means the boy bought an electronic. +695 If the boy didn't break a computer, that means the boy didn't break a pc. +696 The boy didn't break a computer, so the boy didn't break an electronic. +697 If the boy bought a computer, then the boy bought a pc. +698 If the boy bought a pc, that means the boy bought a computer. +699 If the boy bought a pc, it must be the case that the boy didn't break a computer. +700 If David bought a computer, it must be the case that David bought a laptop. +701 David didn't break a computer, therefore David didn't break a laptop. +702 If David didn't break a computer, then David didn't break a laptop. +703 David bought a computer, therefore David bought a laptop. +704 If David bought a laptop, it must be the case that David bought a computer. +705 If David bought a laptop, then David didn't break a computer. +706 The girl bought a computer, therefore the girl bought an electronic. +707 If the girl didn't buy a computer, that means the girl didn't buy a pc. +708 If the girl didn't buy a computer, that means the girl didn't buy an electronic. +709 If the girl bought a computer, it must be the case that the girl bought a pc. +710 If the girl bought a pc, it must be the case that the girl bought a computer. +711 If the girl bought a pc, it must be the case that the girl didn't buy a computer. +712 If the boy is using a computer, then the boy is using an electronic. +713 The boy isn't using a computer, therefore the boy isn't using a pc. +714 If the boy isn't using a computer, that means the boy isn't using an electronic. +715 The boy is using a pc, therefore the boy is using a computer. +716 If the boy is using a pc, it must be the case that the boy is using a computer. +717 The boy is using a pc, therefore the boy isn't using a computer. +718 If she bought a computer, it must be the case that she bought an electronic. +719 If she didn't break a computer, that means she didn't break a pc. +720 If she didn't break a computer, it must be the case that she didn't break an electronic. +721 She bought a pc, therefore she bought a computer. +722 She bought a pc, therefore she bought a computer. +723 If she bought a pc, it must be the case that she didn't break a computer. +724 If she is using a computer, it must be the case that she is using a laptop. +725 She didn't buy a computer, so she didn't buy a laptop. +726 If she didn't buy a computer, it must be the case that she didn't buy a laptop. +727 If she is using a laptop, then she is using a computer. +728 She is using a laptop, so she is using a computer. +729 If she is using a laptop, that means she didn't buy a computer. +730 If she broke a computer, it must be the case that she broke a laptop. +731 She didn't break a computer, therefore she didn't break a laptop. +732 If she didn't break a computer, then she didn't break a laptop. +733 She broke a laptop, so she broke a computer. +734 She broke a laptop, so she broke a computer. +735 She broke a laptop, therefore she didn't break a computer. +736 If David is using a computer, that means David is using an electronic. +737 If David isn't using a computer, that means David isn't using a pc. +738 David isn't using a computer, therefore David isn't using an electronic. +739 David is using a pc, therefore David is using a computer. +740 David is using a pc, therefore David is using a computer. +741 David is using a pc, therefore David isn't using a computer. +742 The boy bought a computer, therefore the boy bought a laptop. +743 If the boy didn't buy a computer, that means the boy didn't buy a laptop. +744 The boy didn't buy a computer, therefore the boy didn't buy a laptop. +745 The boy bought a laptop, therefore the boy bought a computer. +746 The boy bought a laptop, therefore the boy bought a computer. +747 If the boy bought a laptop, it must be the case that the boy didn't buy a computer. +748 If Sarah bought a computer, that means Sarah bought an electronic. +749 Sarah didn't buy a pc, therefore Sarah didn't buy a computer. +750 Sarah didn't buy a computer, so Sarah didn't buy an electronic. +751 Sarah bought a computer, so Sarah bought a pc. +752 If Sarah bought a pc, it must be the case that Sarah bought a computer. +753 Sarah bought a pc, so Sarah didn't buy a computer. +754 If he bought a computer, it must be the case that he bought an electronic. +755 He didn't buy a pc, so he didn't buy a computer. +756 If he didn't buy a computer, then he didn't buy an electronic. +757 If he bought a computer, then he bought a pc. +758 If he bought a pc, that means he bought a computer. +759 If he bought a pc, that means he didn't buy a computer. +760 If the boy broke a computer, that means the boy broke an electronic. +761 The boy isn't using a computer, so the boy isn't using a pc. +762 If the boy isn't using a computer, that means the boy isn't using an electronic. +763 If the boy broke a computer, then the boy broke a pc. +764 The boy broke a pc, so the boy broke a phone. +765 The boy broke a pc, so the boy isn't using a phone. +766 If Sarah is playing a string instrument, that means Sarah is playing an instrument. +767 If Sarah isn't learning to play a string instrument, it must be the case that Sarah isn't learning to play an instrument. +768 If he is tuning a string instrument, then he is tuning an instrument. +769 He isn't learning to play a string instrument, therefore he isn't learning to play the guitar. +770 If he isn't learning to play a string instrument, that means he isn't learning to play an instrument. +771 If he is tuning the guitar, it must be the case that he is tuning a string instrument. +772 He is tuning the guitar, so he is tuning a string instrument. +773 If he is tuning the guitar, it must be the case that he isn't learning to play a string instrument. +774 If the boy is playing a string instrument, that means the boy is playing an instrument. +775 If the boy isn't tuning a string instrument, then the boy isn't tuning the violin. +776 The boy isn't tuning a string instrument, so the boy isn't tuning an instrument. +777 The boy is playing a string instrument, so the boy is playing the violin. +778 If the boy is playing the violin, that means the boy is playing a string instrument. +779 The boy is playing the violin, therefore the boy isn't tuning a string instrument. +780 The girl is tuning a string instrument, so the girl is tuning an instrument. +781 The girl isn't playing a string instrument, therefore the girl isn't playing an instrument. +782 If she is tuning a string instrument, it must be the case that she is tuning an instrument. +783 If she isn't learning to play a string instrument, it must be the case that she isn't learning to play the guitar. +784 If she isn't learning to play a string instrument, then she isn't learning to play an instrument. +785 If she is tuning a string instrument, that means she is tuning the guitar. +786 If she is tuning the guitar, then she is tuning a string instrument. +787 If she is tuning the guitar, then she isn't learning to play a string instrument. +788 If he is playing a string instrument, that means he is playing an instrument. +789 If he isn't playing a string instrument, it must be the case that he isn't playing an instrument. +790 If David is playing a string instrument, then David is playing an instrument. +791 David isn't playing a string instrument, so David isn't playing the guitar. +792 If David isn't playing a string instrument, that means David isn't playing an instrument. +793 If David is playing a string instrument, that means David is playing the guitar. +794 If David is playing the guitar, that means David is playing a string instrument. +795 David is playing the guitar, therefore David isn't playing a string instrument. +796 David is playing a string instrument, so David is playing an instrument. +797 David isn't tuning a string instrument, so David isn't tuning an instrument. +798 If David is learning to play a string instrument, that means David is learning to play an instrument. +799 If David isn't playing a string instrument, then David isn't playing the guitar. +800 David isn't playing a string instrument, therefore David isn't playing an instrument. +801 If David is learning to play a string instrument, that means David is learning to play the guitar. +802 David is learning to play the guitar, therefore David is learning to play a string instrument. +803 If David is learning to play the guitar, then David isn't playing a string instrument. +804 If Sarah is learning to play a string instrument, it must be the case that Sarah is learning to play an instrument. +805 Sarah isn't learning to play a string instrument, therefore Sarah isn't learning to play the violin. +806 Sarah isn't learning to play a string instrument, so Sarah isn't learning to play an instrument. +807 Sarah is learning to play a string instrument, so Sarah is learning to play the violin. +808 If Sarah is learning to play the violin, that means Sarah is learning to play a string instrument. +809 If Sarah is learning to play the violin, then Sarah isn't learning to play a string instrument. +810 If Sarah is playing a string instrument, then Sarah is playing an instrument. +811 If Sarah isn't playing a string instrument, then Sarah isn't playing an instrument. +812 She is learning to play a string instrument, therefore she is learning to play an instrument. +813 If she isn't learning to play a string instrument, it must be the case that she isn't learning to play the guitar. +814 If she isn't learning to play a string instrument, then she isn't learning to play an instrument. +815 If she is learning to play a string instrument, then she is learning to play the guitar. +816 She is learning to play the guitar, so she is learning to play a string instrument. +817 She is learning to play the guitar, so she isn't learning to play a string instrument. +818 If the sofa is made of wood, then the sofa is made of oak. +819 If the sofa isn't made of wood, it must be the case that the sofa isn't made of oak. +820 The sofa isn't made of wood, therefore the sofa isn't made of oak. +821 The sofa is made of wood, so the sofa is made of oak. +822 If the sofa is made of oak, it must be the case that the sofa is made of wood. +823 The sofa is made of oak, therefore the sofa isn't made of wood. +824 The chair is made of wood, so the chair is made of oak. +825 If the chair isn't made of wood, then the chair isn't made of oak. +826 The chair isn't made of wood, so the chair isn't made of oak. +827 If the chair is made of wood, that means the chair is made of oak. +828 The chair is made of oak, so the chair is made of wood. +829 The chair is made of oak, so the chair isn't made of wood. +830 If the couch is made of wood, that means the couch is made of oak. +831 The couch isn't made of wood, therefore the couch isn't made of oak. +832 If the couch isn't made of wood, it must be the case that the couch isn't made of oak. +833 The couch is made of wood, therefore the couch is made of oak. +834 If the couch is made of oak, it must be the case that the couch is made of wood. +835 If the couch is made of oak, that means the couch isn't made of wood. +836 The sofa is made of maple, so the sofa is made of wood. +837 If the sofa is made of maple, it must be the case that the sofa isn't made of wood. +838 If the chair is made of maple, then the chair is made of wood. +839 If the chair is made of maple, then the chair isn't made of wood. +840 The couch is made of maple, therefore the couch is made of wood. +841 If the couch is made of maple, that means the couch isn't made of wood. +842 If the sofa is made of wood, that means the sofa is made of pine. +843 The sofa isn't made of wood, therefore the sofa isn't made of pine. +844 The sofa isn't made of wood, so the sofa isn't made of pine. +845 If the sofa is made of wood, it must be the case that the sofa is made of pine. +846 If the sofa is made of pine, then the sofa is made of wood. +847 If the sofa is made of pine, that means the sofa isn't made of wood. +848 The chair is made of wood, so the chair is made of pine. +849 If the chair isn't made of wood, that means the chair isn't made of pine. +850 The chair isn't made of wood, so the chair isn't made of pine. +851 The chair is made of wood, therefore the chair is made of pine. +852 If the chair is made of pine, it must be the case that the chair is made of wood. +853 If the chair is made of pine, then the chair isn't made of wood. +854 If the couch is made of wood, that means the couch is made of pine. +855 The couch isn't made of wood, therefore the couch isn't made of pine. +856 If the couch isn't made of wood, then the couch isn't made of pine. +857 If the couch is made of wood, then the couch is made of pine. +858 If the couch is made of pine, then the couch is made of wood. +859 If the couch is made of pine, it must be the case that the couch isn't made of wood. diff --git a/zeroshot/irregular_forms/eval_results.json b/zeroshot/irregular_forms/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..7e81605c2549d2af0a467b258505f7768a13144b --- /dev/null +++ b/zeroshot/irregular_forms/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.9048346055979644} \ No newline at end of file diff --git a/zeroshot/irregular_forms/predictions.txt b/zeroshot/irregular_forms/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..89303a68038650782e55aea196f495a5cef4820a --- /dev/null +++ b/zeroshot/irregular_forms/predictions.txt @@ -0,0 +1,1966 @@ +index prediction +0 The hidden offspring aren't confident. +1 The hidden bicycles weren't exposed. +2 The hidden glass was big. +3 The worn skirts aren't important. +4 The worn hats aren't big. +5 The broken mirrors were blue. +6 The worn pants weren't big. +7 The known children were nice. +8 The forgotten newspaper article was bad. +9 The known adult was exposed. +10 The hidden patients weren't busy. +11 The worn scarf was popular. +12 The taken rabbit wasn't popular. +13 The known girl is important. +14 The broken dishes weren't red. +15 The wore sweaters weren't smooth. +16 The forgotten children weren't hidden. +17 The hidden plates aren't white. +18 The forgotten doctors are young. +19 The worn skirt was important. +20 The taken cilantro wasn't yellow. +21 The worn shawls aren't concealed. +22 The hidden fish wasn't important. +23 The known people aren't hidden. +24 The worn glove is red. +25 The known senators aren't serious. +26 The broken couch isn't small. +27 The hidden lady isn't popular. +28 The worn scarf wasn't lost. +29 The broken tooth wasn't whole. +30 The broken forks are white. +31 The hidden guest wasn't hidden. +32 The hidden cactus was out in the open. +33 The broken skateboards are cheap. +34 The broken computers weren't white. +35 The worn shirt is gray. +36 The known waiter is scared. +37 The worn shoe was exposed. +38 The hidden mouse wasn't brown. +39 The worn glasses aren't small. +40 The worn dress is red. +41 The broken skateboards aren't bad. +42 The hidden fungus isn't black. +43 The broken screen isn't small. +44 The broken carriage is lost. +45 The known adults aren't lost. +46 The wore jacket was smooth. +47 The broken dishes weren't blue. +48 The forgotten driver wasn't bad. +49 The broken screen was new. +50 The hidden nuclei were purple. +51 The known man wasn't confident. +52 The known actress wasn't concealed. +53 The known student wasn't hidden. +54 The broken computer was small. +55 The known guests are concealed. +56 The taken mountain was popular. +57 The hidden screens were exposed. +58 The known customer wasn't hidden. +59 The broken computers were black. +60 The broken forks were old. +61 The hidden drawings weren't popular. +62 The broken bike wasn't lost. +63 The broken cars aren't rough. +64 The hidden hat wasn't cheap. +65 The worn dresses aren't old. +66 The hidden mice were hidden. +67 The worn glasses weren't dirty. +68 The took skateboards were small. +69 The hidden shirt wasn't hard. +70 The broken glasses aren't whole. +71 The broken bicycles are concealed. +72 The known teenagers weren't popular. +73 The broken vase wasn't orange. +74 The known women are good. +75 The broken foot is old. +76 The took screen wasn't popular. +77 The hidden tomato was important. +78 The known adults weren't uncertain. +79 The forgotten actresses were hopeful. +80 The worn shirt was important. +81 The wore coats weren't yellow. +82 The taken chair isn't dirty. +83 The wore skirts weren't green. +84 The broken bike is big. +85 The broken couches weren't brown. +86 The worn socks weren't small. +87 The broken dishes aren't new. +88 The broken vase is gray. +89 The broken feet were red. +90 The hidden movie theaters were white. +91 The known drivers weren't popular. +92 The hidden steak was bad. +93 The taken mountains weren't orange. +94 The broken plate wasn't smooth. +95 The hidden rivers aren't cheap. +96 The hidden customers aren't scared. +97 The forgotten analysis wasn't good. +98 The known teenager was nice. +99 The broken wheelbarrow is small. +100 The known adult was serious. +101 The wore skirts weren't purple. +102 The known doctor isn't good. +103 The worn sweaters are exposed. +104 The known guests are upset. +105 The known senator was fast. +106 The broken windows aren't new. +107 The broken car isn't gray. +108 The forgotten lady is standing. +109 The taken book is good. +110 The forgotten newspaper article isn't brown. +111 The hidden ice cream was white. +112 The hidden louse wasn't hidden. +113 The forgotten pamphlets weren't concealed. +114 The taken photograph wasn't gray. +115 The hidden closet is new. +116 The broken foot was big. +117 The broken window is hidden. +118 The hidden children were happy. +119 The forgotten customer is lost. +120 The worn dress isn't popular. +121 The worn coat isn't new. +122 The worn scarves aren't concealed. +123 The broken mirrors are purple. +124 The known pedestrians were unemployed. +125 The hidden brochures were yellow. +126 The hidden teacher was scared. +127 The worn blouses aren't big. +128 The broken dish was good. +129 The broken plane is good. +130 The broken vases weren't concealed. +131 The broken chair isn't red. +132 The broken teeth aren't lost. +133 The known patients are confident. +134 The broken vase wasn't yellow. +135 The forgotten senator isn't confident. +136 The broken vases weren't brown. +137 The wore shawls weren't important. +138 The forgotten man wasn't serious. +139 The worn shawl is lost. +140 The taken shirt was bad. +141 The hidden closets weren't hard. +142 The hidden cafes are messy. +143 The worn shirt was lost. +144 The broken forks aren't gray. +145 The worn coats aren't hard. +146 The worn pants are lost. +147 The worn skirt isn't white. +148 The broken screens are exposed. +149 The broken bicycle is whole. +150 The worn scarves weren't white. +151 The broken carriages were new. +152 The known cashiers weren't important. +153 The broken window is cheap. +154 The broken skateboard is lost. +155 The worn shirt wasn't clean. +156 The forgotten children weren't troubled. +157 The known cashier isn't fast. +158 The broken couch was black. +159 The hidden trucks weren't popular. +160 The forgotten man is big. +161 The worn skirts were big. +162 The hidden slopes are important. +163 The worn dress was clean. +164 The broken plane was yellow. +165 The taken hair was purple. +166 The broken carriages were important. +167 The worn hat was yellow. +168 The broken cup was new. +169 The hidden mushroom isn't under ripe. +170 The hidden drawing wasn't smooth. +171 The known people weren't bad. +172 The worn hat wasn't big. +173 The forgotten actresses were nice. +174 The wore skirts were old. +175 The worn dresses aren't cheap. +176 The broken screen was yellow. +177 The broken dish isn't small. +178 The known child wasn't serious. +179 The hidden cart wasn't small. +180 The taken glass was orange. +181 The known actresses aren't smart. +182 The forgotten actors were upset. +183 The hidden reports aren't yellow. +184 The broken foot isn't concealed. +185 The known cashiers are out in the open. +186 The broken feet weren't hidden. +187 The broken bicycles were smooth. +188 The worn shoe wasn't yellow. +189 The worn dress isn't orange. +190 The forgotten waiters were happy. +191 The broken planes are white. +192 The hidden bird is standing. +193 The known lady isn't smart. +194 The broken screens aren't green. +195 The broken couches weren't hard. +196 The known pedestrian was busy. +197 The hidden theses weren't brown. +198 The broken vase was broken. +199 The known guest was concealed. +200 The taken grocery store wasn't concealed. +201 The took computers were old. +202 The hidden girl is hidden. +203 The hidden public parks weren't rough. +204 The wore gloves were new. +205 The took cactus was white. +206 The broken fork is messy. +207 The forgotten student is confident. +208 The forgotten children weren't upset. +209 The broken wheelbarrows are new. +210 The broken couches aren't good. +211 The hidden pepper wasn't fresh. +212 The taken goose isn't purple. +213 The took planes were bad. +214 The hidden stimulus wasn't out in the open. +215 The broken carriages are out in the open. +216 The hidden glacier isn't blue. +217 The known adult isn't unlucky. +218 The hidden dresses aren't bad. +219 The broken couches were concealed. +220 The broken planes aren't exposed. +221 The broken bicycle isn't exposed. +222 The worn sweaters are green. +223 The broken cups were hard. +224 The hidden cup was cheap. +225 The worn sock isn't big. +226 The forgotten documentary is important. +227 The hidden theses are lost. +228 The broken cart is bad. +229 The taken light is exposed. +230 The broken window was important. +231 The forgotten women were small. +232 The took axes were exposed. +233 The hidden peppers weren't important. +234 The hidden pasta isn't concealed. +235 The taken wheelbarrows aren't yellow. +236 The hidden bike isn't good. +237 The taken tooth is gray. +238 The hidden man isn't young. +239 The worn sock wasn't black. +240 The known waiters aren't unsure. +241 The broken computer was smooth. +242 The known men aren't hidden. +243 The hidden ladder isn't white. +244 The taken bikes are orange. +245 The hidden stimulus wasn't orange. +246 The broken unicycle was important. +247 The taken icicle was hard. +248 The taken bread isn't black. +249 The hidden picture wasn't black. +250 The forgotten boys aren't busy. +251 The hidden movie theaters are popular. +252 The broken chairs aren't whole. +253 The worn coat isn't hard. +254 The broken couch wasn't smooth. +255 The broken plates weren't new. +256 The broken truck wasn't purple. +257 The taken broccoli was bad. +258 The worn shoes aren't purple. +259 The broken window was big. +260 The worn hat is smooth. +261 The forgotten waiters are concealed. +262 The broken cups aren't clean. +263 The broken vase wasn't black. +264 The taken sketch is green. +265 The forgotten books are hard. +266 The hidden pasta isn't black. +267 The hidden pamphlet wasn't good. +268 The known drivers aren't young. +269 The broken carts aren't good. +270 The known teenager isn't excited. +271 The taken projector isn't brown. +272 The hidden dogs aren't orange. +273 The forgotten movies were long. +274 The took fish was red. +275 The worn scarf wasn't white. +276 The forgotten students are young. +277 The forgotten doctor wasn't unemployed. +278 The hidden face was important. +279 The broken carriages were brown. +280 The forgotten men were unconvinced. +281 The broken plate isn't small. +282 The worn pants weren't cheap. +283 The broken couches were brown. +284 The broken couch was smooth. +285 The hidden teeth weren't green. +286 The taken public parks were clean. +287 The taken rabbits weren't lost. +288 The took computers weren't popular. +289 The worn sweater is big. +290 The known boys weren't exposed. +291 The broken cart is new. +292 The broken plate is gray. +293 The hidden report wasn't long. +294 The hidden waiter was lucky. +295 The broken chair isn't white. +296 The broken windows are out in the open. +297 The broken bicycle wasn't small. +298 The forgotten commentaries are important. +299 The hidden dress was smooth. +300 The broken computers are hidden. +301 The hidden glass was broken. +302 The known cashiers are unconvinced. +303 The worn jackets weren't out in the open. +304 The known dancers are content. +305 The known doctors weren't small. +306 The worn gloves aren't messy. +307 The broken trucks aren't bad. +308 The hidden ice is gray. +309 The forgotten commentaries aren't bad. +310 The forgotten teenager was unlucky. +311 The taken carts aren't gray. +312 The worn dress wasn't rough. +313 The forgotten documentaries are long. +314 The broken screen was broken. +315 The hidden men are scared. +316 The worn sweater is rough. +317 The wore coat wasn't red. +318 The taken box was hidden. +319 The forgotten commentaries aren't good. +320 The forgotten drivers were hidden. +321 The hidden socks were big. +322 The forgotten stories are bad. +323 The forgotten pamphlets aren't black. +324 The knew boys were hopeful. +325 The broken windows weren't concealed. +326 The forgotten person isn't exposed. +327 The hidden jacket wasn't gray. +328 The hidden blouses weren't black. +329 The forgotten ladies were delighted. +330 The hidden turtles were lost. +331 The hidden oasis is concealed. +332 The taken cats aren't small. +333 The broken dish was popular. +334 The taken shoes aren't hidden. +335 The broken cup is red. +336 The broken plate is white. +337 The forgotten man was hopeful. +338 The broken screens were black. +339 The broken cup isn't bad. +340 The worn dresses were old. +341 The hidden fish isn't big. +342 The taken candles aren't big. +343 The worn scarf isn't yellow. +344 The known doctor wasn't old. +345 The broken glasses were purple. +346 The known men were exposed. +347 The broken plate is rough. +348 The broken computers were blue. +349 The broken teeth are big. +350 The broken feet aren't clean. +351 The broken planes aren't hidden. +352 The broken glass was broken. +353 The broken glass was green. +354 The worn blouse wasn't brown. +355 The worn hat is hidden. +356 The broken unicycle was good. +357 The broken bikes were orange. +358 The broken cars aren't small. +359 The broken glass was small. +360 The forgotten waiters weren't confident. +361 The worn shirt isn't brown. +362 The forgotten offspring wasn't unlucky. +363 The worn sock wasn't cheap. +364 The broken planes aren't brown. +365 The worn shawls weren't hard. +366 The taken paintings aren't gray. +367 The hidden school was concealed. +368 The forgotten boys are exposed. +369 The forgotten actor wasn't busy. +370 The hidden pies aren't blue. +371 The worn blouses weren't rough. +372 The forgotten girl wasn't happy. +373 The broken cart wasn't lost. +374 The took steps were good. +375 The broken carriages weren't yellow. +376 The hidden school isn't bad. +377 The hidden library wasn't green. +378 The broken carriages were bad. +379 The worn hats weren't good. +380 The worn shirt isn't concealed. +381 The broken bike was whole. +382 The hidden window is red. +383 The broken plane isn't small. +384 The worn jacket wasn't concealed. +385 The hidden report isn't popular. +386 The hidden patient was scared. +387 The worn hat isn't hidden. +388 The worn sweaters aren't green. +389 The broken plane is black. +390 The broken foot isn't cheap. +391 The forgotten commentaries were long. +392 The forgotten customer isn't young. +393 The forgotten offspring wasn't lost. +394 The hidden theses aren't gray. +395 The worn shoes are clean. +396 The broken bicycle isn't red. +397 The broken couches are white. +398 The known students aren't good. +399 The taken bread isn't orange. +400 The broken carriages are green. +401 The known cashiers are excited. +402 The taken lights weren't orange. +403 The forgotten analyses are bad. +404 The broken chairs weren't important. +405 The broken carriage isn't yellow. +406 The known waitresses weren't clever. +407 The broken vase was green. +408 The broken dish was purple. +409 The hidden candles are white. +410 The worn scarves were rough. +411 The worn glove is popular. +412 The worn pants weren't messy. +413 The taken movie theater is small. +414 The hidden wheelbarrow is out in the open. +415 The worn shoe wasn't popular. +416 The forgotten guys were employed. +417 The worn socks are blue. +418 The hidden ladies were exposed. +419 The wore glasses were small. +420 The took sheep wasn't smooth. +421 The worn shirt was yellow. +422 The hidden rug was hidden. +423 The broken computer was hard. +424 The wore sweaters were important. +425 The known actresses were employed. +426 The worn skirt isn't cheap. +427 The taken forks weren't out in the open. +428 The hidden dress was bad. +429 The known senators were important. +430 The worn sweater wasn't brown. +431 The worn shoes are small. +432 The taken carriage is white. +433 The taken computers are good. +434 The hidden candles are big. +435 The broken chairs were popular. +436 The hidden casserole isn't brown. +437 The worn socks were smooth. +438 The taken wheelbarrows are important. +439 The taken lamps weren't red. +440 The broken chair is new. +441 The broken unicycle was brown. +442 The hidden college campus isn't hard. +443 The hidden sweater is small. +444 The known actress isn't exposed. +445 The forgotten guests aren't serious. +446 The taken pepper is big. +447 The worn sweaters are black. +448 The broken bike is important. +449 The known children are determined. +450 The forgotten synopsis was good. +451 The broken glasses aren't hidden. +452 The wore scarves were green. +453 The broken bike was out in the open. +454 The worn scarf isn't lost. +455 The broken carts weren't lost. +456 The forgotten dancers were bad. +457 The broken window was small. +458 The broken computers weren't important. +459 The worn blouses are exposed. +460 The known ladies weren't scared. +461 The broke bicycles were cheap. +462 The broken bicycle is good. +463 The known guests were employed. +464 The hidden women were serious. +465 The broken fork was messy. +466 The forgotten people aren't unemployed. +467 The taken bike is black. +468 The taken pamphlet was small. +469 The hidden socks are old. +470 The broken convertible isn't green. +471 The worn shoe isn't white. +472 The broken unicycle was red. +473 The worn blouse was out in the open. +474 The hidden slope is hard. +475 The broken couches were bad. +476 The worn blouses were popular. +477 The forgotten pamphlets are big. +478 The broken wheelbarrow is out in the open. +479 The worn skirts weren't important. +480 The hidden drivers were excited. +481 The known people aren't determined. +482 The broken skateboard was green. +483 The broken foot wasn't rough. +484 The forgotten men are upset. +485 The broken screen was black. +486 The hidden mirrors weren't out in the open. +487 The broken carriage is new. +488 The broken dish wasn't big. +489 The broken couch is clean. +490 The worn scarf was out in the open. +491 The hidden fork was blue. +492 The worn shoes are new. +493 The hidden cart is popular. +494 The broken mirror is good. +495 The forgotten plays weren't bad. +496 The hidden couches were rough. +497 The broken couch wasn't lost. +498 The taken chair isn't green. +499 The hidden sketches weren't blue. +500 The broken bikes are good. +501 The known dancer is big. +502 The broken wheelbarrows were rough. +503 The broken wheelbarrow isn't black. +504 The broken carts aren't gray. +505 The hidden bread is purple. +506 The broken unicycle was orange. +507 The known boys weren't ill. +508 The took apple was fresh. +509 The worn blouses aren't lost. +510 The known lady isn't small. +511 The worn skirt isn't new. +512 The worn socks were gray. +513 The broken carts are whole. +514 The worn sock isn't smooth. +515 The forgotten alumni aren't confident. +516 The forgotten alumni are unconvinced. +517 The hidden plane is cheap. +518 The known girls weren't delighted. +519 The forgotten teenager wasn't uncertain. +520 The known drivers aren't unemployed. +521 The broken truck isn't small. +522 The forgotten waitress isn't concealed. +523 The hidden scarf wasn't brown. +524 The known patients were hopeful. +525 The hidden photographs are blue. +526 The broken chairs are gray. +527 The broken bicycle is black. +528 The hidden slope was yellow. +529 The forgotten plays aren't popular. +530 The known boy is content. +531 The broken bicycles are cheap. +532 The took gloves weren't important. +533 The worn skirt was bad. +534 The hidden college campus isn't yellow. +535 The known customer isn't hopeful. +536 The hidden actor isn't smart. +537 The hidden carriages were gray. +538 The broken bicycles are broken. +539 The known girls are busy. +540 The broken wheelbarrow isn't hard. +541 The known teenager isn't busy. +542 The taken axes are brown. +543 The broken computer isn't blue. +544 The worn socks aren't important. +545 The worn glove is rough. +546 The forgotten teachers are small. +547 The broken bike is brown. +548 The hidden closet isn't hard. +549 The known offspring aren't standing. +550 The broken convertible is orange. +551 The broken cups are new. +552 The worn blouses were rough. +553 The worn sweater was good. +554 The forgotten driver was displeased. +555 The hidden rug was yellow. +556 The broken bike wasn't new. +557 The hidden doctors were unsure. +558 The forgotten movie was good. +559 The known men were troubled. +560 The worn skirts are brown. +561 The broken carriage is popular. +562 The broken planes are blue. +563 The forgotten drivers weren't unconvinced. +564 The worn shirt isn't new. +565 The broken cart isn't green. +566 The taken sweaters aren't important. +567 The broken dishes were red. +568 The worn scarves aren't bad. +569 The known adult isn't small. +570 The forgotten actresses are unconvinced. +571 The known actress isn't standing. +572 The hidden senators aren't standing. +573 The broken convertible wasn't red. +574 The broken window is new. +575 The worn shoe is important. +576 The wore scarves were black. +577 The took axes were big. +578 The known man isn't happy. +579 The known alumnus isn't lucky. +580 The forgotten doctor wasn't smart. +581 The known waitresses were big. +582 The forgot analysis is good. +583 The forgotten children are clever. +584 The broken tooth was gray. +585 The known actor isn't employed. +586 The broken screens are black. +587 The broken chairs aren't cheap. +588 The broken trucks were brown. +589 The known alumni aren't uncertain. +590 The worn hats are yellow. +591 The taken lamps aren't hard. +592 The known student is unconvinced. +593 The taken chair is big. +594 The known ladies were employed. +595 The forgotten patients aren't good. +596 The known customer isn't exposed. +597 The hidden deer wasn't yellow. +598 The hidden mouths are yellow. +599 The forgotten book wasn't gray. +600 The broken glass wasn't whole. +601 The wore glove was new. +602 The wore sweaters were orange. +603 The hidden actor is troubled. +604 The worn shawl wasn't gray. +605 The broken windows were orange. +606 The broken computer isn't cheap. +607 The hidden eye wasn't brown. +608 The hidden bike is broken. +609 The broken mirrors are new. +610 The broken cups were lost. +611 The hidden men weren't exposed. +612 The worn pants weren't important. +613 The hidden oxen weren't concealed. +614 The broken cup wasn't important. +615 The taken lakes are white. +616 The broken tooth was rough. +617 The hidden car wasn't orange. +618 The broken carriages are black. +619 The broken mirrors were purple. +620 The forgotten thesis is black. +621 The taken horses were brown. +622 The broken fork is out in the open. +623 The broken car isn't broken. +624 The hidden rice was white. +625 The broken cart is gray. +626 The forgotten commentaries were popular. +627 The taken oasis is purple. +628 The known offspring isn't unsure. +629 The forgotten documentaries are bad. +630 The forgotten documentaries were good. +631 The hidden teenager is content. +632 The broken carts are bad. +633 The worn blouse isn't concealed. +634 The forgotten woman is standing. +635 The hidden men weren't determined. +636 The broken couch isn't messy. +637 The hidden pamphlet wasn't big. +638 The forgotten waitresses are lost. +639 The forgotten dancers aren't nice. +640 The hidden actor isn't lost. +641 The forgotten adult wasn't bad. +642 The known adults aren't unlucky. +643 The forgotten story wasn't long. +644 The forgotten actress is unconvinced. +645 The hidden women weren't determined. +646 The known doctors are hidden. +647 The hidden hill is red. +648 The broken plane was old. +649 The known cashier is uncertain. +650 The broken couches are out in the open. +651 The worn skirt wasn't gray. +652 The taken glaciers aren't rough. +653 The known lady wasn't scared. +654 The known adult was clever. +655 The wore jackets were blue. +656 The known cashier is delighted. +657 The broken mirror is small. +658 The hidden face isn't big. +659 The known patient wasn't young. +660 The broken cup is hard. +661 The hidden girls aren't fast. +662 The broken dish was bad. +663 The broken bicycle wasn't concealed. +664 The broken plates weren't black. +665 The broken couches weren't cheap. +666 The hidden offspring are out in the open. +667 The wore shawls are green. +668 The broken unicycle wasn't lost. +669 The known woman is serious. +670 The forgotten senator isn't out in the open. +671 The forgotten women aren't nice. +672 The hidden window wasn't rough. +673 The forgotten doctor isn't employed. +674 The known actor wasn't employed. +675 The broken skateboards are hidden. +676 The broken window isn't out in the open. +677 The taken casserole wasn't popular. +678 The hidden cars are important. +679 The wore gloves were green. +680 The worn scarf isn't hard. +681 The forgotten brochures were big. +682 The wore shirt wasn't purple. +683 The forgotten offspring weren't upset. +684 The broken bicycle wasn't big. +685 The taken vases aren't rough. +686 The taken plate wasn't black. +687 The worn pants weren't new. +688 The forgotten book is purple. +689 The wore glasses weren't small. +690 The taken louse wasn't standing. +691 The hidden girl isn't smart. +692 The known child was exposed. +693 The worn gloves are gray. +694 The broken bicycle is green. +695 The broken carriage is big. +696 The taken rice is big. +697 The broken plates weren't whole. +698 The broken mirrors are smooth. +699 The broken skateboards are out in the open. +700 The broken planes are concealed. +701 The hidden river was new. +702 The forgotten child is important. +703 The broken forks aren't red. +704 The forgotten guys weren't good. +705 The forgotten story was long. +706 The worn shirt wasn't out in the open. +707 The broken forks weren't new. +708 The known boys aren't old. +709 The hidden public parks were blue. +710 The known girls are concealed. +711 The broken plate isn't lost. +712 The worn pants aren't good. +713 The forgotten movie is bad. +714 The known men are scared. +715 The hidden couches were hard. +716 The taken ladders aren't popular. +717 The hidden slopes were orange. +718 The known waiters aren't unconvinced. +719 The broken couches are broken. +720 The worn glasses are new. +721 The took dresses weren't black. +722 The wore scarf was green. +723 The hidden plates weren't brown. +724 The known people aren't uncertain. +725 The hidden jacket was soft. +726 The hidden student isn't employed. +727 The forgotten commentary isn't good. +728 The forgotten movies were important. +729 The worn jacket isn't old. +730 The forgotten doctors were smart. +731 The known dancers weren't unemployed. +732 The known patient isn't determined. +733 The broken carriages were small. +734 The worn glasses aren't messy. +735 The hidden gates were purple. +736 The taken lake was popular. +737 The wore hats weren't soft. +738 The hidden adults aren't upset. +739 The known teenager was small. +740 The worn skirts are smooth. +741 The broken planes weren't rough. +742 The known customers aren't troubled. +743 The taken closet isn't new. +744 The broken windows were broken. +745 The hidden shirts weren't brown. +746 The hidden movie theaters weren't old. +747 The forgotten newspaper article was important. +748 The known woman wasn't content. +749 The broken wheelbarrows were out in the open. +750 The broken cups were whole. +751 The wore hats weren't green. +752 The forgotten waitresses weren't lost. +753 The broken forks aren't smooth. +754 The hidden snakes aren't black. +755 The broken wheelbarrow is red. +756 The worn shawls are brown. +757 The hidden cat wasn't big. +758 The broken vases are bad. +759 The took gate was brown. +760 The worn blouse is hard. +761 The forgotten senator was out in the open. +762 The broken dish wasn't red. +763 The hidden popsicle isn't gray. +764 The wore gloves were rough. +765 The broken glass was concealed. +766 The broken screen was green. +767 The broken cart wasn't bad. +768 The took theses weren't big. +769 The taken snakes aren't ill. +770 The worn gloves are out in the open. +771 The broken convertible is concealed. +772 The broken computer was orange. +773 The hidden screen isn't cheap. +774 The hidden child wasn't clever. +775 The hidden teacher is important. +776 The taken cats are hidden. +777 The worn blouses aren't hard. +778 The known dancers are bad. +779 The forgotten actor wasn't concealed. +780 The taken cakes aren't big. +781 The taken ladder isn't hard. +782 The hidden spotlight was bad. +783 The known waitresses were clever. +784 The worn hat wasn't good. +785 The forgot analysis isn't long. +786 The broken plate is purple. +787 The taken mountains weren't important. +788 The hidden oases are white. +789 The taken plate wasn't green. +790 The broken feet were clean. +791 The broken dish was important. +792 The hidden lice weren't big. +793 The forgotten pedestrians are lucky. +794 The broken glasses were yellow. +795 The forgotten doctors weren't sick. +796 The taken louse isn't standing. +797 The taken car isn't cheap. +798 The hidden tomatoes were orange. +799 The known cashier is ill. +800 The broken glasses are blue. +801 The broken fork isn't important. +802 The broken glasses were white. +803 The forgotten reports weren't black. +804 The worn sweater was hidden. +805 The hidden person isn't upset. +806 The broken bike is old. +807 The known children aren't fast. +808 The hidden people are unsure. +809 The broken bicycles aren't cheap. +810 The hidden slope is out in the open. +811 The broken vases aren't broken. +812 The taken banks aren't hidden. +813 The wore gloves were cheap. +814 The taken shirt was new. +815 The forgotten actress was exposed. +816 The hidden cactus wasn't lost. +817 The hidden horses are white. +818 The known ladies aren't important. +819 The worn blouse was soft. +820 The worn coat isn't yellow. +821 The broken skateboards weren't smooth. +822 The taken pie is bad. +823 The worn blouses are white. +824 The broken skateboards aren't blue. +825 The worn blouses were out in the open. +826 The taken high schools were messy. +827 The broken couches are important. +828 The broken cups were good. +829 The hidden lady was determined. +830 The broken teeth were new. +831 The worn scarf is exposed. +832 The worn skirt isn't clean. +833 The forgotten brochures aren't orange. +834 The known child isn't content. +835 The hidden mouth is bad. +836 The forgotten analyses were long. +837 The taken steps are black. +838 The taken muffins weren't important. +839 The hidden snake is white. +840 The taken fungi were lost. +841 The hidden ice is yellow. +842 The worn shoe is purple. +843 The known cashier wasn't clever. +844 The worn hats are blue. +845 The known cashier isn't good. +846 The hidden steak is concealed. +847 The known girl wasn't lost. +848 The hidden cakes weren't rough. +849 The known adult isn't nice. +850 The broken glasses aren't lost. +851 The worn skirts are hard. +852 The known actor wasn't confident. +853 The known drivers were excited. +854 The broken cart isn't red. +855 The worn skirts weren't black. +856 The taken eggplant isn't fresh. +857 The broken plane was popular. +858 The known waiter was concealed. +859 The forgotten dancer is troubled. +860 The forgotten children weren't old. +861 The broken foot isn't rough. +862 The hidden mountain wasn't small. +863 The hidden plate wasn't green. +864 The broken chairs are yellow. +865 The broken couches were important. +866 The broken chairs are big. +867 The known waitresses aren't hidden. +868 The broken dishes aren't old. +869 The worn shirt was gray. +870 The known guys aren't happy. +871 The known women are content. +872 The known children aren't displeased. +873 The worn jacket isn't blue. +874 The broken fork was hidden. +875 The forgotten movie wasn't bad. +876 The hidden deer is rough. +877 The broken unicycle is whole. +878 The hidden rug wasn't important. +879 The broken cart is small. +880 The wore hats were orange. +881 The known offspring are troubled. +882 The known guests were busy. +883 The broken bike is bad. +884 The broken cars weren't big. +885 The broken carts weren't bad. +886 The taken dresses are red. +887 The forgotten doctors were displeased. +888 The broken plane wasn't yellow. +889 The known person is concealed. +890 The known patient isn't lost. +891 The broken glasses weren't new. +892 The known teenagers aren't confident. +893 The known students were excited. +894 The hidden dishes were rough. +895 The known teenagers aren't small. +896 The hidden cucumber was exposed. +897 The broken plates are brown. +898 The forgotten theses are orange. +899 The broken bicycle isn't gray. +900 The worn dress isn't old. +901 The worn blouse is bad. +902 The broken convertible wasn't blue. +903 The known lady wasn't old. +904 The broken couches aren't clean. +905 The hidden alumni were concealed. +906 The took doors were red. +907 The hidden plates weren't broken. +908 The taken oases weren't blue. +909 The forgotten teenagers are important. +910 The forgotten teacher isn't unconvinced. +911 The known offspring wasn't concealed. +912 The hidden apple wasn't white. +913 The worn jackets aren't big. +914 The forgotten waiter wasn't unemployed. +915 The worn socks are hidden. +916 The worn shawls aren't exposed. +917 The broken vase is concealed. +918 The hidden mirror was white. +919 The hidden species was small. +920 The broken screen was out in the open. +921 The broken carriages are lost. +922 The hidden children are lucky. +923 The took carts weren't blue. +924 The broken computers are new. +925 The took teeth weren't cheap. +926 The hidden men are exposed. +927 The broken cup is brown. +928 The hidden offspring are fast. +929 The hidden casseroles aren't out in the open. +930 The broken couches weren't hidden. +931 The known person is sick. +932 The broken glasses aren't clean. +933 The known boys weren't concealed. +934 The broken screen wasn't rough. +935 The hidden convertible is small. +936 The forgotten senator wasn't upset. +937 The known patients weren't unlucky. +938 The hidden waiters weren't sick. +939 The broken glass wasn't messy. +940 The hidden oxen aren't standing. +941 The hidden picture wasn't important. +942 The known boys were nice. +943 The broken window was good. +944 The hidden patients are popular. +945 The worn sweaters weren't lost. +946 The forgotten actor was serious. +947 The forgotten woman is young. +948 The known man wasn't important. +949 The worn dress was big. +950 The worn dresses are white. +951 The worn blouse wasn't good. +952 The forgotten thesis isn't lost. +953 The broken carriage is purple. +954 The forgotten pamphlets were black. +955 The broken carts were good. +956 The hidden cucumber was popular. +957 The broken bicycle is hard. +958 The hidden sketches aren't important. +959 The forgotten movies aren't important. +960 The worn jackets weren't cheap. +961 The known actor isn't content. +962 The hidden pork is exposed. +963 The broken plate isn't bad. +964 The taken report is long. +965 The known senators were small. +966 The broken feet weren't concealed. +967 The taken glass wasn't dirty. +968 The known dancer isn't popular. +969 The forgotten teacher wasn't young. +970 The hidden drawings aren't popular. +971 The known patient is big. +972 The taken spinach was under ripe. +973 The known teacher isn't troubled. +974 The hidden women aren't bad. +975 The hidden carriage isn't broken. +976 The worn glasses aren't exposed. +977 The forgotten offspring were scared. +978 The broken mirror is orange. +979 The known student isn't big. +980 The broken dish was white. +981 The taken wheelbarrows were brown. +982 The broken chairs weren't purple. +983 The broken mirror wasn't old. +984 The Borgias worn a lot of scarves. +985 Teresa hid every senator. +986 The mushroom went bad. +987 Cheryl came here. +988 Christopher hid a spotlight. +989 Edward hid the cats. +990 Erin went to some schools. +991 Joel come here. +992 Paula broke some plates. +993 Raymond woken up. +994 Laura broke this cart. +995 Victoria wore that hat. +996 Carrie took most bikes. +997 Nancy sung. +998 The guy sang. +999 Lucille went to some cafe. +1000 Sandra knew Becca. +1001 Susan known Florence. +1002 Many girls took that banana. +1003 Melissa eaten. +1004 Andrew wrote. +1005 Kendra knew Florence. +1006 Guests woken up. +1007 Suzanne sung. +1008 Catherine knew Amanda. +1009 Frank hidden away. +1010 Libraries forgot Douglas. +1011 Every waiter hid Julia. +1012 This person spoke up. +1013 Caroline hid. +1014 That coat shrank. +1015 James ate. +1016 Michael drove to all hospitals. +1017 Todd took every car. +1018 Most ladies hidden away. +1019 Aaron broke the unicycle. +1020 Stacey took chicken. +1021 Charles forgotten Sarah. +1022 Every teenager went to the hospitals. +1023 These guests got fired. +1024 Elizabeth broke the carriages. +1025 Claire woke up. +1026 Alice woken up. +1027 Victoria written. +1028 Allison got a job. +1029 This dancer broke many glasses. +1030 Banks knew Elaine. +1031 The Lutherans hid. +1032 Kimberley drove to this high school. +1033 Some boy spoke up. +1034 Tiffany sung. +1035 Mitchell broke a lot of forks. +1036 Jessica driven to all museums. +1037 Valerie hid away. +1038 Jesus ate. +1039 Some customer hidden. +1040 All actresses hid away. +1041 Marcus wore this sock. +1042 Bradley knew Stacy. +1043 Helen took a lot of lamps. +1044 These drivers came here. +1045 Veronica drove to the hospitals. +1046 Lori hid away. +1047 A lot of teenagers spoken up. +1048 Danielle knew George. +1049 Suzanne broke those trucks. +1050 A lot of actresses took some river. +1051 Marla drank. +1052 Edward broke some chair. +1053 Sharon spoke up. +1054 Many boys woken up. +1055 Sally went fishing. +1056 Tammy woke up. +1057 Monica ate. +1058 Adam eaten. +1059 Patrick broke a lot of wheelbarrows. +1060 Lisa ate. +1061 Those students drove to Spain. +1062 Carla went to schools. +1063 Some people broke that chair. +1064 A lot of dancers broke the vases. +1065 Some children come here. +1066 Raymond drove to Harvard University. +1067 Keith took these birds. +1068 Carrie went to some malls. +1069 Tamara went to a cafe. +1070 Michelle went to the closet. +1071 Janet broke the plane. +1072 Gerald hid Ann. +1073 Vincent drove to those restaurants. +1074 Kirsten broke a dish. +1075 Patrick forgot Victoria. +1076 Maria wore jackets. +1077 This guy came here. +1078 Tamara hid. +1079 Bethany broke a lot of cups. +1080 Those dancers woke up. +1081 The blouses shrank. +1082 Many pedestrians broke some cars. +1083 Wendy drove to most college campuses. +1084 Every teenager sung. +1085 A lot of doctors driven to this library. +1086 Every library took some painting. +1087 Andrea broke these vases. +1088 Paula wore these sweaters. +1089 Aaron drove to some mountain. +1090 Deborah wore the jacket. +1091 Jennifer broke a carriage. +1092 Dawn hid many shawls. +1093 Diane went fishing. +1094 Rhonda wore a shoe. +1095 Carla broke some plates. +1096 These actors got a job. +1097 A lot of women hidden away. +1098 The patients wore every shoe. +1099 Theresa drove to the public park. +1100 Naomi sang. +1101 Those adults wore most shoes. +1102 The waiters spoke up. +1103 Katherine eaten. +1104 Rose gone to every art gallery. +1105 Denise got a job. +1106 Anne knew Todd. +1107 Helen got fired. +1108 Those waiters spoke up. +1109 That actor hid away. +1110 Laurie came here. +1111 Tamara came here. +1112 Those drivers got a job. +1113 Stephen forgot Nancy. +1114 Peter forgot Tammy. +1115 Rhonda went to the river. +1116 Those doctors wore that coat. +1117 Colleen got a job. +1118 A man wrote. +1119 Linda got fired. +1120 The Lutherans woke up. +1121 The peppers went bad. +1122 Keith wrote. +1123 Some girl hid that screen. +1124 This popsicle froze. +1125 Leslie eaten. +1126 Florence woke up. +1127 Vanessa woken up. +1128 Monet got fired. +1129 Sharon went fishing. +1130 Renee drove to the art gallery. +1131 Many ladies hid the girl. +1132 Valerie wrote. +1133 Melinda got a job. +1134 Nancy spoke up. +1135 Many boys wore every dress. +1136 Florence got fired. +1137 Sabrina wore a lot of hats. +1138 Jeffrey hid away. +1139 Kendra broke the mirrors. +1140 Debra wrote. +1141 Those glaciers froze. +1142 Larry wore the coat. +1143 Tonya got fired. +1144 Candice hid away. +1145 Omar went to high schools. +1146 Caroline broken couches. +1147 Men broke those bicycles. +1148 Stephen went to most libraries. +1149 These dancers wrote. +1150 Steve hid. +1151 Kendra came here. +1152 Carlos broke that wheelbarrow. +1153 Katherine forgotten Frank. +1154 The glacier froze. +1155 Those shawls shrank. +1156 All doctors drove to this hill. +1157 The adult broke a lot of dishes. +1158 All guys forgot Kenneth. +1159 Craig went to a lot of malls. +1160 Diana broke the car. +1161 Travis got fired. +1162 Rodney broke some couch. +1163 Kristen broke that mirror. +1164 The hats shrank. +1165 Adam took that horse. +1166 Patients broke all mirrors. +1167 All girls broke every car. +1168 Those pedestrians drank. +1169 Every patient broke the vases. +1170 Melinda woken up. +1171 All mushrooms went bad. +1172 Elaine spoke up. +1173 These guys took a lot of newspaper articles. +1174 Marcus hid. +1175 Jacqueline wore many jackets. +1176 Samuel drank. +1177 Bradley broke this plate. +1178 Walter hid. +1179 Many universities known Dawn. +1180 Natalie drove to many museums. +1181 Eric took computers. +1182 Allison broke these cups. +1183 Carrie eaten. +1184 Meredith wrote. +1185 Some adults broke a lot of wheelbarrows. +1186 Margaret ate. +1187 Sara went fishing. +1188 Martin went fishing. +1189 Steve sang. +1190 Carrie spoke up. +1191 Becca wore the shoes. +1192 Most mushrooms went bad. +1193 Many skirts shrank. +1194 Alexander wrote. +1195 All hats shrank. +1196 That lake froze. +1197 The dancers hid. +1198 Some waiter knew Kirsten. +1199 Claire eaten. +1200 Some dancers drunk. +1201 Mary hid a lot of glasses. +1202 Tammy wore some sock. +1203 Kevin woken up. +1204 Daniel broke some chair. +1205 Brenda came here. +1206 Most girls wrote. +1207 Danielle got fired. +1208 Theodore broke most bikes. +1209 Most cashiers went fishing. +1210 Theresa broke all bicycles. +1211 Those people hid away. +1212 These shoes shrank. +1213 This actress hid. +1214 The popsicle froze. +1215 Many people hid away. +1216 Gregory hid those bicycles. +1217 Amy sung. +1218 A sock shrunk. +1219 Tiffany hid the lady. +1220 Carmen took this box. +1221 Eva drove to this library. +1222 Monet hidden. +1223 Donna went to this art gallery. +1224 Brenda hid the bicycles. +1225 Most actresses wore a lot of jackets. +1226 This lake froze. +1227 The waiters hid that actress. +1228 Diana wore a lot of hats. +1229 Matt knew Kathleen. +1230 Jacqueline wore most shirts. +1231 Tammy broke those forks. +1232 Those girls got a job. +1233 Kenneth spoke up. +1234 Clyde took this eggplant. +1235 Stacy drunk. +1236 Alice came here. +1237 Tara hid away. +1238 All boys hid this face. +1239 Jodi went to a lot of mountains. +1240 Bruce went to banks. +1241 Martha knew Keith. +1242 This pedestrian hid Alexander. +1243 Gina drove to most closets. +1244 Kayla went to the restaurants. +1245 Dawn broke that plane. +1246 Caroline hidden all gates. +1247 The Impressionists went fishing. +1248 Mushrooms went bad. +1249 That driver knew Anne. +1250 Nina drove to the high schools. +1251 Kenneth sang. +1252 A lot of adults woken up. +1253 Many patients hid away. +1254 A lot of actors drunk. +1255 Sara drove to all cafes. +1256 Some waitresses hid some painting. +1257 Sara got a job. +1258 Janet went to the school. +1259 Eric broke most glasses. +1260 The pedestrians came here. +1261 That adult sang. +1262 Many pedestrians spoken up. +1263 The glasses shrank. +1264 Michelle took those cars. +1265 Irene drank. +1266 Winston Churchill wrote. +1267 Caroline went fishing. +1268 Rose driven to a glacier. +1269 Adam knew Karla. +1270 Those teenagers went fishing. +1271 All cucumbers went bad. +1272 Carmen sang. +1273 Some guys woken up. +1274 Wendy broke this convertible. +1275 The women drank. +1276 Those actors drove to Harvard University. +1277 Glaciers froze. +1278 Michael broke that mirror. +1279 Some eggplant went bad. +1280 Kristen went to every college campus. +1281 David hid Denise. +1282 Sherry wrote. +1283 The adults broke those computers. +1284 Those cashiers broke a lot of carriages. +1285 Samantha wore those shawls. +1286 Brian wore jackets. +1287 Noah known Maria. +1288 A lady wore that jacket. +1289 The children broke a carriage. +1290 Margaret got a job. +1291 Victoria took this bike. +1292 Julia wore every glove. +1293 Grace got fired. +1294 Kayla drunk. +1295 Mary come here. +1296 Samuel wore many pants. +1297 Tara hid those rabbits. +1298 Lawrence drove to every bank. +1299 Nancy knew James. +1300 This student got a job. +1301 Amy come here. +1302 Many drivers sung. +1303 Amelia got fired. +1304 The ice cream froze. +1305 Wayne drove to some library. +1306 Some glasses shrank. +1307 Samantha woken up. +1308 Carl broke every wheelbarrow. +1309 Many sweaters shrank. +1310 Sarah went fishing. +1311 Most fish broke a lot of mirrors. +1312 Carla come here. +1313 Julie spoke up. +1314 Some river froze. +1315 Phillip woke up. +1316 Diane got a job. +1317 Aaron hid. +1318 Eva got fired. +1319 The customer went fishing. +1320 Amy hid the scarf. +1321 Gerald wore some shawl. +1322 The universities knew this cashier. +1323 A lot of boys broke this truck. +1324 April got fired. +1325 These blouses shrank. +1326 The dress shrank. +1327 Carol driven to museums. +1328 Leslie drove to every library. +1329 An apple went bad. +1330 An actor wore many skirts. +1331 This waitress wore some shirt. +1332 Tammy took some muffins. +1333 Patrick took a spotlight. +1334 Customers knew Tracy. +1335 Heather drove to this high school. +1336 Phillip broken windows. +1337 Shirts shrank. +1338 Winston Churchill woke up. +1339 Connie broke some computer. +1340 Alice drove to a high school. +1341 Catherine wore every sweater. +1342 Omar forgot the men. +1343 The Impressionists forgot Tanya. +1344 Joel driven to every hill. +1345 This doctor went fishing. +1346 Brett hidden away. +1347 Rachel hid. +1348 That actor knew a lot of dancers. +1349 Ice froze. +1350 Robert went to those banks. +1351 The guest drunk. +1352 Sara drove to every cafe. +1353 Ruth hid these skateboards. +1354 Tamara forgotten Kathleen. +1355 Most cashiers hid many customers. +1356 Debra broke many cups. +1357 These cashiers sang. +1358 Every hamster hid. +1359 Karen broke all bicycles. +1360 Danielle drove to Spain. +1361 Rodney wore this shoe. +1362 Larry got fired. +1363 Derek drank. +1364 Kimberley spoke up. +1365 Lisa got a job. +1366 Heather driven to that lake. +1367 Many guys forgot Anna. +1368 A lot of cashiers sang. +1369 Women got a job. +1370 Some actress broke that dish. +1371 Every shirt shrank. +1372 Some icicles froze. +1373 Kathleen sung. +1374 Wendy wore this blouse. +1375 Catherine forgot Bill. +1376 Some girl broke a fork. +1377 Wayne broke most bicycles. +1378 Jackets shrank. +1379 Ann known Helen. +1380 Debra broke the bicycle. +1381 Many gloves shrank. +1382 Steve broke that couch. +1383 Dana got fired. +1384 Most fish broke this dish. +1385 Carlos drank. +1386 Ruth known Alexander. +1387 Grace hid. +1388 Elaine came here. +1389 Colleen hid away. +1390 Some mushroom went bad. +1391 Amanda drove to this college campus. +1392 Sherry hid the art gallery. +1393 Omar hid away. +1394 A lot of fish broke some wheelbarrows. +1395 Actresses wrote. +1396 Benjamin wore some shawls. +1397 Teresa known Guy. +1398 Women spoken up. +1399 Katherine knew Roger. +1400 Women went to this museum. +1401 Jane went fishing. +1402 Naomi drove to most art galleries. +1403 Brett wrote. +1404 Samuel drove to some cafe. +1405 Cilantro went bad. +1406 Marla forgotten Donna. +1407 Christina wore some blouse. +1408 Marcus went fishing. +1409 Heather wrote. +1410 Joseph went fishing. +1411 Jodi hid. +1412 Many actresses got fired. +1413 Cheryl hid many lights. +1414 Most students broke most dishes. +1415 Chad gone to all rivers. +1416 Samantha went to many closets. +1417 Samuel came here. +1418 These patients drove to every cafe. +1419 Julie drove to some schools. +1420 Walter wore many blouses. +1421 Most teenagers wrote. +1422 Christina drove to banks. +1423 Kathleen woke up. +1424 Every sock shrunk. +1425 Cheryl forgot a boy. +1426 Waitresses known Vincent. +1427 Mark wore every skirt. +1428 Steven wore all shirts. +1429 Richard wore every coat. +1430 Joel hid away. +1431 Sara hid away. +1432 Diana drank. +1433 All pedestrians hid Omar. +1434 Rose forgotten Vanessa. +1435 John knew Stephen. +1436 Katherine hid. +1437 A waiter hid that cucumber. +1438 Emily hid many windows. +1439 Hamsters come here. +1440 Sally wore that sock. +1441 Nina drank. +1442 This lady went fishing. +1443 William got fired. +1444 Marie forgotten Eric. +1445 Dan went fishing. +1446 Keith got a job. +1447 Scott forgot Jodi. +1448 Judy forgot Anna. +1449 Allison knew Walter. +1450 Some cashiers wrote. +1451 Ice cream froze. +1452 Katherine spoke up. +1453 Dana broke this plane. +1454 Many ladies drove to some closet. +1455 Some doctors sang. +1456 Irene went to that public park. +1457 Many senators went to all closets. +1458 That customer woken up. +1459 Christine drank. +1460 Every scarf shrank. +1461 Emily drank. +1462 Maria wrote. +1463 Holly hid Tiffany. +1464 These women took the sketches. +1465 Raymond went to that school. +1466 Gregory spoke up. +1467 Many governments knew a lot of guests. +1468 Sabrina got a job. +1469 The Borgias broke a screen. +1470 Michelle forgot Linda. +1471 Stacy broke some plates. +1472 Samuel went to the art galleries. +1473 Holly broke the computer. +1474 Michael broke some plate. +1475 Connie broke the cart. +1476 Those students knew Rachel. +1477 Lisa forgotten Brian. +1478 Some actor went to some library. +1479 Marie got a job. +1480 Carolyn broke many cups. +1481 The Impressionists sang. +1482 Some actor spoke up. +1483 Tonya hid Derek. +1484 All women hidden away. +1485 Every museum took a lot of turtles. +1486 Regina hid. +1487 Linda wore the shirts. +1488 Eric got a job. +1489 Claire wore these socks. +1490 These girls hid Karen. +1491 Every senator came here. +1492 Some fish hidden. +1493 Many lakes frozen. +1494 Peter got a job. +1495 Most icicles froze. +1496 Rodney ate. +1497 Kirsten wore shawls. +1498 Regina woke up. +1499 A cashier took these boxes. +1500 Maria got a job. +1501 Michelle wore many skirts. +1502 Janice wore that blouse. +1503 Mark got a job. +1504 Heather took the turtle. +1505 Debra drove to most hospitals. +1506 Catherine wore this shawl. +1507 Heather drank. +1508 Edward forgot Patricia. +1509 A lot of peppers went bad. +1510 Patricia drove to a restaurant. +1511 Guests eaten. +1512 Brenda got fired. +1513 Kendra knew this customer. +1514 An actress went to these malls. +1515 Catherine took a popsicle. +1516 A lot of customers eaten. +1517 The drivers hidden. +1518 The Borgias hid this snake. +1519 Rhonda ate. +1520 Adam knew Rodney. +1521 A lot of ladies hid Todd. +1522 Frank broke those planes. +1523 The guests ate. +1524 These patients wore socks. +1525 Most hats shrank. +1526 Douglas sung. +1527 All horses come here. +1528 Gerald went to all libraries. +1529 Tiffany went fishing. +1530 Tina gone to that hill. +1531 The actor sang. +1532 Nina knew Winston Churchill. +1533 The drivers broke this unicycle. +1534 Derek went to those libraries. +1535 Many teachers spoken up. +1536 That cilantro went bad. +1537 Nancy took that skateboard. +1538 Steven broke this mirror. +1539 Barbara woke up. +1540 The Lutherans wore many pants. +1541 Adults broke this computer. +1542 The Borgias got fired. +1543 These socks shrunk. +1544 Noah drank. +1545 Julia forgotten Raymond. +1546 This ice cream froze. +1547 A lot of senators drank. +1548 Dennis knew Tracy. +1549 Carla hid the book. +1550 Brenda went to every college campus. +1551 Michael drank. +1552 Michael eaten. +1553 The Borgias broke the computers. +1554 Janet broke those trucks. +1555 Marcus broke some screens. +1556 Teresa broke the trucks. +1557 Daniel took that ladder. +1558 Many blouses shrank. +1559 Rachel went to a lot of hospitals. +1560 Some cats broke many carriages. +1561 Travis got a job. +1562 Paula come here. +1563 Roger forgot Christine. +1564 Nina spoke up. +1565 Patricia knew most students. +1566 These pedestrians drove to a lot of libraries. +1567 The water froze. +1568 Regina got a job. +1569 Debra hid. +1570 Beverly drove to every closet. +1571 Jennifer written. +1572 Those people wrote. +1573 Sharon broke that glass. +1574 Maria gone to this mountain. +1575 Donald broke these cups. +1576 A lot of pedestrians went to art galleries. +1577 Many rabbits broke couches. +1578 This dancer went to the cafe. +1579 Sally driven to every mountain. +1580 Some girls forgot Kristin. +1581 The jackets shrank. +1582 Erin came here. +1583 Candice hid. +1584 The ladies drank. +1585 A lot of rivers froze. +1586 Monet knew Stacy. +1587 Cindy got fired. +1588 Benjamin took this dog. +1589 Denise broke every skateboard. +1590 Many icicles froze. +1591 Diane went to every closet. +1592 Derek broke every plane. +1593 Catherine come here. +1594 A child went to those hills. +1595 Some schools took those scarves. +1596 Some actors hid. +1597 Matt ate. +1598 Amanda sung. +1599 That guy hid. +1600 Cynthia came here. +1601 Karla ate. +1602 Karen went to a lot of grocery stores. +1603 That actor drove to that closet. +1604 Some shawls shrank. +1605 Deborah broke these plates. +1606 Catherine spoke up. +1607 Doctors drove to a high school. +1608 Some patients sung. +1609 Those dancers took every nose. +1610 Every student broke screens. +1611 Michelle wrote. +1612 Bradley hid. +1613 Becca knew Steve. +1614 Elaine got fired. +1615 Chad woken up. +1616 Helen driven to all cafes. +1617 Theodore forgotten Bethany. +1618 Melanie knew Amelia. +1619 A man hidden away. +1620 The glaciers froze. +1621 Russell drove to the college campuses. +1622 Omar driven to these schools. +1623 Some ladies drank. +1624 These guests wore some coat. +1625 Tiffany forgot Suzanne. +1626 Mark drove to that mall. +1627 Regina drove to all restaurants. +1628 Kristin hid. +1629 Karla drank. +1630 Every driver went fishing. +1631 These customers hid. +1632 Ella ate. +1633 Denise drove to some public park. +1634 Jesus went fishing. +1635 Most teachers broke that plate. +1636 Some pepper went bad. +1637 Every coat shrank. +1638 Susan forgot Tracy. +1639 Chad worn the sock. +1640 Some banks forgot Kendra. +1641 Dan wore most glasses. +1642 Wendy known Carrie. +1643 Clyde took most bananas. +1644 That guest hidden away. +1645 That senator got fired. +1646 Teresa went to some high schools. +1647 Christopher broke the dishes. +1648 Those gloves shrank. +1649 Many adults went fishing. +1650 That mushroom went bad. +1651 Susan broke vases. +1652 Homer broke a lot of screens. +1653 Stacey forgot all women. +1654 Most pedestrians hidden away. +1655 Allison hid away. +1656 Timothy got a job. +1657 Many shawls shrank. +1658 Christopher hid every convertible. +1659 Alexander broke those bikes. +1660 Carol hid. +1661 Many waiters got fired. +1662 Edward came here. +1663 The waitresses forgot Michael. +1664 Brett known Paula. +1665 Sabrina took some dog. +1666 These doctors went to glaciers. +1667 Every driver broke a plate. +1668 A lot of children hidden Cynthia. +1669 Theodore wore every scarf. +1670 Florence hid Noah. +1671 Those patients broke this computer. +1672 Carol took these sketches. +1673 Those mushrooms went bad. +1674 Vanessa took all art galleries. +1675 Patrick went fishing. +1676 Tara hid. +1677 This eggplant went bad. +1678 Wendy drank. +1679 Alexander woke up. +1680 Carol came here. +1681 Jeffrey took every candle. +1682 Brad got fired. +1683 Many associations forgot Suzanne. +1684 Laurie went to some bank. +1685 A skirt shrank. +1686 Becky drove to that art gallery. +1687 Girls went to the Great Lakes. +1688 That jacket shrank. +1689 Carlos forgot Steve. +1690 Every driver wore these hats. +1691 Tara got a job. +1692 Naomi forgot Gary. +1693 Winston Churchill hid. +1694 Michael hid. +1695 Some hat shrank. +1696 Jesus hid. +1697 Martha took a lot of bicycles. +1698 Melissa came here. +1699 Brenda hid away. +1700 George went to all movie theaters. +1701 Some doctors wore those scarves. +1702 A teenager broke that bike. +1703 Nicole forgot Meredith. +1704 Some bird woke up. +1705 Andrew broken this fork. +1706 Men spoken up. +1707 Regina took some bread. +1708 These girls hid Paul. +1709 Gerald broke those carriages. +1710 Thomas woke up. +1711 Cheryl broke every bike. +1712 Melanie went to every high school. +1713 The shoe shrank. +1714 Holly got fired. +1715 Edward went to that college campus. +1716 Douglas woken up. +1717 Jane woke up. +1718 Alexander broke these cups. +1719 Judy got fired. +1720 Elaine sung. +1721 Julia known Peter. +1722 Jessica hid a newspaper article. +1723 Melanie wrote. +1724 The waiters hid away. +1725 Thomas hid. +1726 Every adult came here. +1727 Erin ate. +1728 Sabrina sung. +1729 Stacey hid Sabrina. +1730 Christina went to every closet. +1731 That actress spoke up. +1732 Bruce hid Sarah. +1733 Most waiters broke a screen. +1734 Christopher spoke up. +1735 A woman broke a glass. +1736 Alicia eaten. +1737 Aaron hid away. +1738 Those guys got a job. +1739 Most waiters wore some shawls. +1740 Most skirts shrank. +1741 That teenager hidden away. +1742 Alice got fired. +1743 Stephen got fired. +1744 Every glacier froze. +1745 Monica known dancers. +1746 Tamara wore blouses. +1747 Karen hid that hamster. +1748 Carrie sang. +1749 Bill drank. +1750 These waitresses went to Spain. +1751 Edward wrote. +1752 Carlos come here. +1753 These children drove to Spain. +1754 Ronald got a job. +1755 Peter went to most high schools. +1756 All guys hid Angela. +1757 Sherry drove to the hospital. +1758 The Impressionists woke up. +1759 These cashiers hid Margaret. +1760 Those children went to that movie theater. +1761 Amelia went fishing. +1762 Jesus forgot Denise. +1763 This water froze. +1764 Wendy went to a lot of public parks. +1765 Most museums known Benjamin. +1766 Tanya come here. +1767 Rodney broke some plates. +1768 Alexander drove to restaurants. +1769 Every woman drove to college campuses. +1770 Charles wore that coat. +1771 This pedestrian ate. +1772 These dancers sang. +1773 Elizabeth got fired. +1774 Thomas sung. +1775 Some shoes shrank. +1776 The Lutherans got a job. +1777 Brian forgot Sherry. +1778 Jane wore the shirt. +1779 This teacher broke a skateboard. +1780 Jason took many boxes. +1781 Rose drunk. +1782 Lori broken glasses. +1783 Theresa wore a lot of coats. +1784 This patient went fishing. +1785 Most students forgot Jesus. +1786 A mushroom went bad. +1787 Libraries forgot Wayne. +1788 Aaron wore that shoe. +1789 Girls sung. +1790 Diana got a job. +1791 Edward hid away. +1792 Regina got fired. +1793 Ella took rice. +1794 Heidi wore some glove. +1795 Frank broke a couch. +1796 Dana hid away. +1797 Monet known Mary. +1798 This senator broke these plates. +1799 These students drove to a restaurant. +1800 Walter went fishing. +1801 Ann known Florence. +1802 Broccoli went bad. +1803 The association took those bikes. +1804 Aaron went to every art gallery. +1805 The Impressionists drove to the mall. +1806 Every icicle froze. +1807 Gerald hid these apples. +1808 Colleen went fishing. +1809 All governments forgot Kenneth. +1810 Kristin hid away. +1811 Some patients drank. +1812 The Lutherans drank. +1813 Stacy broke a lot of cars. +1814 These senators drank. +1815 Ronald went fishing. +1816 Denise knew Stacy. +1817 Walter went to that school. +1818 Most rivers froze. +1819 Adults hidden away. +1820 The senators drank. +1821 Eva spoke up. +1822 Martin wore some blouses. +1823 Patricia drank. +1824 All senators went to this hill. +1825 The shawls shrank. +1826 Angela wrote. +1827 Carmen went fishing. +1828 Stephanie came here. +1829 Those waitresses wrote. +1830 Teresa broke some mirror. +1831 Clyde hid. +1832 Connie eaten. +1833 Ronald broke that wheelbarrow. +1834 Robert hid. +1835 Elizabeth wore all sweaters. +1836 Daniel eaten. +1837 Many children drunk. +1838 Winston Churchill broke most forks. +1839 Snakes woke up. +1840 Craig got a job. +1841 These patients broke this cart. +1842 Martha hid away. +1843 Ann written. +1844 Tammy sang. +1845 Jacqueline wrote. +1846 The senator knew Carl. +1847 Mary broke this unicycle. +1848 Karen hid away. +1849 Marla got a job. +1850 Donald spoke up. +1851 Clyde hid those drawings. +1852 Derek took most mushrooms. +1853 Claire wore most skirts. +1854 Carol hid this scarf. +1855 A jacket shrank. +1856 Travis went fishing. +1857 Hospitals forgot most actors. +1858 A lot of jackets shrank. +1859 Many patients come here. +1860 Every adult got a job. +1861 The bird broke some trucks. +1862 Those children forgot Andrew. +1863 Mark woke up. +1864 The Borgias wrote. +1865 Alexander hid away. +1866 A lot of doctors broke a lot of couches. +1867 Clyde sang. +1868 These associations took all skateboards. +1869 A child knew some driver. +1870 Nancy drove to restaurants. +1871 Adam got a job. +1872 Steven wore this coat. +1873 Gregory got a job. +1874 Senators went to some closet. +1875 Grace broke the carts. +1876 A lot of teachers wore the gloves. +1877 These glaciers froze. +1878 Nancy went to these closets. +1879 Most banks knew Julie. +1880 Christina hid. +1881 Eric forgot Vanessa. +1882 Those students came here. +1883 Laura drove to some mountain. +1884 Jodi drove to some hills. +1885 Donald eaten. +1886 Margaret came here. +1887 This woman got a job. +1888 Every teenager forgot Sharon. +1889 Vincent hidden a dog. +1890 Ruth went fishing. +1891 Curtis wore these dresses. +1892 A lake froze. +1893 Karla came here. +1894 Tracy drove to many restaurants. +1895 Nina drove to some hill. +1896 Lucille broke this unicycle. +1897 Some dancers hid dancers. +1898 Carla wrote. +1899 Carlos took a bird. +1900 Kayla took some window. +1901 Some socks shrank. +1902 Tracy knew Kathleen. +1903 Gary knew many women. +1904 Patrick drove to a bank. +1905 All students broke a lot of glasses. +1906 Charles wrote. +1907 Many patients hid Omar. +1908 Clyde took all shirts. +1909 The Impressionists drove to this high school. +1910 Many students wore most shawls. +1911 Florence wore most gloves. +1912 Tamara got fired. +1913 Jeffrey went to libraries. +1914 These dancers took the art galleries. +1915 Dawn wrote. +1916 James wrote. +1917 Maria wore some shirt. +1918 This child knew a lot of men. +1919 Jerry broke some carriage. +1920 All snakes broke all bicycles. +1921 The students knew Jason. +1922 Those girls broke that wheelbarrow. +1923 Timothy hid Danielle. +1924 Christopher hid away. +1925 Deborah got fired. +1926 Anna hid away. +1927 Richard got a job. +1928 Theodore forgot Janice. +1929 The children got fired. +1930 Every doctor drove to the college campuses. +1931 Kirsten hid the college campus. +1932 The actresses knew Scott. +1933 Amelia forgot George. +1934 Paula went fishing. +1935 Sally gone to every hill. +1936 Gary took photographs. +1937 That girl hid away. +1938 Tiffany ate. +1939 Aaron broke these computers. +1940 A pedestrian written. +1941 Steven drank. +1942 Galileo knew Richard. +1943 Mary known Bruce. +1944 Winston Churchill forgot Charles. +1945 The girls went to all cafes. +1946 Mitchell drunk. +1947 Richard got fired. +1948 Colleen broke this car. +1949 Gerald wore this glove. +1950 Guy driven to art galleries. +1951 Karen hid. +1952 Jason eaten. +1953 Alan took this bicycle. +1954 Sonia took these newspaper articles. +1955 Veronica hid away. +1956 The man broke some skateboard. +1957 Charles went fishing. +1958 Cynthia woken up. +1959 Vanessa eaten. +1960 All teachers drank. +1961 Every cat broke that skateboard. +1962 Every dancer wore that sweater. +1963 Ann went to some river. +1964 Deanna broke the cart. diff --git a/zeroshot/island_effects/eval_results.json b/zeroshot/island_effects/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..54f2a3f11a7bbdde5132351f066c947e2119d82e --- /dev/null +++ b/zeroshot/island_effects/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.44469357249626307} \ No newline at end of file diff --git a/zeroshot/island_effects/predictions.txt b/zeroshot/island_effects/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bda66cb33c46d02fe269a67f3cbe1c953fce86e --- /dev/null +++ b/zeroshot/island_effects/predictions.txt @@ -0,0 +1,2677 @@ +index prediction +0 Who should Derek hug Richard after shocking? +1 What had Theresa walked through that high school while talking about? +2 Who will Katherine discover without hiring Erin? +3 Who has Colleen aggravated before kissing Judy? +4 Who have most people discovered Erin while embarrassing? +5 Who was William firing before talking about Maria? +6 What is Denise descending a lot of hills while hiding? +7 Who does John leave Beverly while alarming? +8 What was Melanie going to those rivers after taking? +9 Who could Bethany research before discussing Winston Churchill? +10 Who is Mary irritating after approaching Kenneth? +11 Who might Rose flee from this customer before returning to? +12 What will Janice research after boasting about politics? +13 Who had Karla aggravated Donald without finding? +14 Who should a government reference Jesus before shocking? +15 What had Aaron sounded like while cleaning the museum? +16 What is Brenda arriving at while exiting many schools? +17 Who is a window hurting without disturbing Sabrina? +18 Who had Jesus bothered after leaving Lori? +19 Who are a lot of hamsters disgusting while finding Ronald? +20 Who could Amelia leave Paul while appreciating? +21 What have many ladies dropped by without talking about all malls? +22 What is Larry dropping by after lifting a grocery store? +23 Who is that student hugging before stunning Catherine? +24 Who were those schools appreciating without scaring Alice? +25 What did Dan sell after revealing some plane? +26 What had Marcus climbed down many mountains before buying? +27 What has Jacqueline scanned that report before revealing? +28 Who is Tanya returning to Donald while astounding? +29 Who had Kirsten referenced Liam without boring? +30 What are some drivers touring after going to every hill? +31 Who would Mark visit Marla while kissing? +32 What is Scott cleaning after cleaning a pie? +33 Who has some doctor listened to that actress after firing? +34 Who has that lady approached after alarming Ellen? +35 What had some senator toured those cafes before hiding? +36 Who was Eric impressing Christina without admiring? +37 What do most drivers climb up most hills without noticing? +38 Who had Gregory worked with Julie while noticing? +39 Who was Rose astounding many waiters while hiding? +40 What is Karen researching before walking through that mountain? +41 Who could Dana boast about Brenda after disturbing? +42 What was this bird aggravating every hospital before annoying? +43 Who had Colleen known Stephen after concealing? +44 What are these customers arriving at Spain without alarming? +45 What has Angela admired while cleaning shoes? +46 What is Becky selling some hospital before noticing? +47 Who had many cashiers concealed after investigating Nina? +48 Who had Melanie admired Chad before listening to? +49 Who does Bethany visit before scaring Amanda? +50 What is Tiffany exiting before climbing down the mountain? +51 Who had Susan insulted Heather without boring? +52 Who has Curtis noticed while investigating Steve? +53 Who has Kayla disliked Derek after disgusting? +54 What is Martin skated around these hospitals without buying? +55 What was Beth dropping by after bringing all glaciers? +56 Who had Rhonda distracted Chad while finding? +57 What has Bethany driven to a lot of hills before biking to? +58 Who have the slopes hurt before disgusting a lot of adults? +59 What will Brad wear these scarves before noticing? +60 What had Steven boycotted before running around the art gallery? +61 Who do the Borgias like Mark before aggravating? +62 What had the patients resembled without hiding most sketches? +63 Who would Jesus hug Bradley without discovering? +64 Who has Mark sounded like before finding that doctor? +65 Who has Rebecca fled from Kirsten without listening to? +66 Who was Ellen escaping from before curing Julia? +67 What does Travis fix after breaking every carriage? +68 What might Tanya walk through every cafe after hiding? +69 What has Ella brought after finding all icicles? +70 What was Valerie selling after climbing down some slope? +71 What could Douglas bike to every mountain without admiring? +72 What would those teenagers research some malls before boycotting? +73 What can Dan clean every screen before cleaning? +74 Who would Deborah see while hugging Christina? +75 Who can Gregory talk about before hugging Emily? +76 What should Elaine argue about books before admiring? +77 What had Jason dropped by those schools before selling? +78 What will the guys notice a lot of museums before shocking? +79 Who could Monet worry while thinking about Dana? +80 Who could Thomas observe without distracting Peter? +81 Who has Suzanne admired after scaring Connie? +82 Who can some closets disgust that dancer without shocking? +83 Who has Bruce annoyed while investigating Kevin? +84 What was Bethany lifting without returning to these cafes? +85 What should Wendy boycott every hospital after questioning? +86 What are these pedestrians selling after finding that bird? +87 What is Laurie buying before cleaning the car? +88 What does Matt embarrass every legislature before bothering? +89 What have some drivers worn without finding a shawl? +90 Who can Kimberley help Martin before healing? +91 Who have most students distracted before impressing Marla? +92 What was Dawn boycotting after describing public parks? +93 Who was Cynthia observing while thinking about Rose? +94 What do many fish break before breaking the truck? +95 Who was Ann thinking about without distracting some cashiers? +96 What does this mall look like the paintings while looking like? +97 Who has Suzanne irritated without alarming Homer? +98 Who could Candice investigate after approaching all senators? +99 Who might Dennis hire Galileo without helping? +100 What had Sally remembered without insulting the report? +101 What was every senator scanning without taking most newspaper articles? +102 Who is Diana boasting about Christina after kissing? +103 What is Stephen worrying after talking about that hospital? +104 Who were many blouses confusing before distracting Alexander? +105 Who had Dan discovered before observing Judy? +106 What was Connie climbing up before concealing all ladders? +107 Who has Tina disturbed while arguing about Jill? +108 Who has Rhonda described after describing a girl? +109 Who might Derek criticize drivers without firing? +110 What has Cindy run around after walking through all closets? +111 Who have the cakes annoyed while disturbing Renee? +112 Who had Kayla kissed Aaron after upsetting? +113 Who is Rodney concealing after bothering Stephen? +114 What might Anna lift bicycles before selling? +115 What was Ronald bringing while bringing a popsicle? +116 What has Judy visited while selling some glacier? +117 Who can this cart worry waiters after upsetting? +118 What had Nicole driven to hills while selling? +119 Who had Lisa talked to every guest without complaining about? +120 Who could Jacqueline hire Marcus without healing? +121 Who has Ann alarmed Omar while insulting? +122 Who should Amanda distract every patient while appreciating? +123 Who had Regina irritated Deborah while hiding? +124 Who is the hat hurting while distracting Charles? +125 Who had Brian discovered Eric after stunning? +126 What has Kevin broken without breaking every carriage? +127 What was Connie descending without bringing ladders? +128 What does Brad descend without finding some hills? +129 Who had Rachel concealed without disgusting Mark? +130 What is Jessica driving to Spain after noticing? +131 Who has Margaret criticized while thinking about Lawrence? +132 Who had some face bothered while confusing Sarah? +133 Who had Regina cured every dancer while talking about? +134 What could Ellen climb up stairs without climbing down? +135 Who had Margaret talked to Carol while insulting? +136 What has Kayla looked like while bringing a lot of photographs? +137 Who was Kendra scaring after insulting Tammy? +138 Who had Pamela bored after discovering Homer? +139 What was Scott dropping by a hill before running around? +140 Who can Becky love those adults before upsetting? +141 What is Sara wearing those blouses without buying? +142 Who will guys describe before scaring Elizabeth? +143 Who was Carrie annoying after discovering Florence? +144 What will Adam tour this college campus without criticizing? +145 Who did Steve observe while irritating Ella? +146 What has James broken those couches before cleaning? +147 Who has the cashier fired Bruce without disturbing? +148 What has Heather skated around that mountain after going to? +149 Who have these turtles astounded without impressing Naomi? +150 Who was Keith working with Heather after arguing about? +151 What was Gerald breaking after buying these carriages? +152 What had Jerry worn some hats before selling? +153 What will these girls drop by every grocery store while exploring? +154 What had Monet skated around some hill while talking about? +155 Who is Jacqueline finding after hugging Meredith? +156 What should Dan bike to some closets before returning to? +157 What can Pamela go to while biking to many grocery stores? +158 What has this girl sounded like after bringing these lakes? +159 What has Carlos broken while revealing most skateboards? +160 Who will Barbara bother before discovering Susan? +161 Who had Monica hugged while helping many waitresses? +162 What had Cynthia messed up after taking some couch? +163 What was Ellen messing up without embarrassing this library? +164 Who is every painting hurting Marcus while embarrassing? +165 Who is Jesus insulting without concealing Barbara? +166 What does Paul research while touring Harvard University? +167 What had many doctors sold before fixing some cups? +168 Who was Theodore scaring Samuel while watching? +169 What did Rodney clean these windows without lifting? +170 Who had Amanda fired after investigating Aaron? +171 What did Nina climb down before returning to every mountain? +172 Who had Timothy talked about without concealing these people? +173 Who is that teacher boasting about Teresa while disgusting? +174 Who is Mitchell healing before bothering Judy? +175 Who had an association bored after praising a lot of doctors? +176 What had Tiffany looked like before concealing those drawings? +177 Who had Andrea appreciated before questioning Tina? +178 Who is the rug worrying Dawn while worrying? +179 Who has Erin embarrassed Martha without hurting? +180 Who might Judy conceal Erin before confusing? +181 What has Noah toured every high school after passing? +182 Who had Laura talked about before hurting Renee? +183 Who did Brenda discuss this teenager without shocking? +184 What was Bill researching stories while arguing about? +185 Who is Karla hugging while concealing Angela? +186 Who can Jessica respect while hurting Frank? +187 What should Theresa sell a lot of cars while revealing? +188 What will Stacy fix the bike without fixing? +189 Who were the boys approaching Curtis while concealing? +190 What have women lifted before noticing all turtles? +191 Who was this essay disagreeing with Carlos without embarrassing? +192 Who could Danielle find Timothy without upsetting? +193 Who could some gloves worry while disturbing John? +194 Who are most teachers approaching before visiting this senator? +195 Who would some actor reference Wayne while investigating? +196 What had Claire climbed up before climbing down these steps? +197 What was Candice exiting while boycotting a lot of cafes? +198 Who does Benjamin disgust Diana while worrying? +199 What has Diane researched those hospitals after disgusting? +200 What have a lot of drivers fixed after discovering most couches? +201 Who will Kevin like Larry while fleeing from? +202 What is Gerald biking to every hospital while alarming? +203 Who has the library praised while appreciating Andrew? +204 What should Stacey reveal most boxes while cleaning? +205 Who could some waiter observe while concealing the Impressionists? +206 Who would dancers respect Heidi after questioning? +207 Who can Danielle find Brian without embarrassing? +208 Who could Colleen return to Rachel while distracting? +209 What should many teachers tour the river before cleaning? +210 Who would the Lutherans watch without annoying these actors? +211 Who had Travis found Ellen without talking about? +212 What has Melissa lifted that bird after healing? +213 Who can a cashier insult after watching Rose? +214 What had many people walked through all hospitals while disturbing? +215 Who could Erin approach Renee while irritating? +216 Who should current events upset Cheryl without irritating? +217 Who is Renee observing while finding some driver? +218 Who did Emily leave Alan without curing? +219 Who is Brenda impressing this waiter before kissing? +220 What had Bethany cleaned while escaping from the art galleries? +221 Who had that rice bored before annoying Samuel? +222 Who is Brian disgusting without admiring the Borgias? +223 What was Theodore wearing all socks before thinking about? +224 What had the senator climbed up after exiting this hill? +225 Who was Sheila hugging Heather without attacking? +226 Who should that sketch confuse Kendra before astounding? +227 Who does Dennis criticize Kristin while aggravating? +228 Who has Samantha talked to all girls while hurting? +229 Who had Dawn observed after hiring the Borgias? +230 Who did Mitchell scare Amanda after arguing about? +231 Who was this window astounding while scaring Jessica? +232 Who had Raymond questioned without boasting about Sara? +233 Who does some lamp confuse before worrying Carlos? +234 Who had Frank aggravated Joel without appreciating? +235 What is Mitchell passing after describing some art galleries? +236 What had Derek dropped by Spain without annoying? +237 Who is Amy criticizing before aggravating George? +238 Who could Marie think about before referencing James? +239 Who will Rebecca watch Wendy while leaving? +240 What are these cashiers running around before talking about those grocery stores? +241 Who does every teenager argue about Timothy while scaring? +242 What had Jerry exited most libraries without exiting? +243 Who would Brenda respect before alarming Ellen? +244 Who would Adam listen to Patrick without stunning? +245 Who have some hospitals talked to Jerry while distracting? +246 Who might the children help after questioning Ronald? +247 Who did that cup worry before annoying every doctor? +248 What might Nancy scan while boasting about those plays? +249 Who could the museums dislike without aggravating Amelia? +250 Who would Andrea care for while discovering those guys? +251 What was that teacher embarrassing without disgusting every school? +252 Who had Colleen appreciated while distracting Janice? +253 Who will some guy remember Tonya while disgusting? +254 What could Cheryl reveal without cleaning a lot of muffins? +255 Who had many commentaries upset Lori without aggravating? +256 What are some students breaking while fixing some carriage? +257 What could Carl sound like after revealing all restaurants? +258 What did most books astound after disturbing the organizations? +259 What have these customers driven to all museums while skated around? +260 Who can Wayne kiss while boasting about Nina? +261 Who had Scott described these men without concealing? +262 What have those waiters dropped by the restaurant while admiring? +263 What had Renee researched some play while examining? +264 Who had Curtis listened to Michelle without noticing? +265 What is Gina examining every report while complaining about? +266 Who was Gerald talking about before returning to Omar? +267 Who have most guys described after annoying John? +268 Who will Sonia insult Chad after fleeing from? +269 Who might that actor hide Russell before hiding? +270 Who is Andrew hiring Thomas after visiting? +271 Who had these prints bothered Laurie while disgusting? +272 Who was the actor revealing after embarrassing Anna? +273 What should some snakes climb up every hill without finding? +274 What does Alice buy without bringing the windows? +275 What has Jane boycotted these legislatures before discovering? +276 Who will Robert describe Theresa without shocking? +277 What was Emily walking through while arriving at this movie theater? +278 What have the turtles broken after breaking all plates? +279 Who had Paul healed without alarming Tonya? +280 Who has Brad referenced before discussing Deborah? +281 What can the girl sell this book before thinking about? +282 Who have all books disagreed with while scaring the Impressionists? +283 What had the dancers broken after cleaning every skateboard? +284 What had that waitress insulted those libraries while noticing? +285 What could Jason run around without passing malls? +286 Who might Tracy talk about after discovering Becca? +287 What should the guests annoy before distracting some governments? +288 What is Michelle scanning without arguing about the newspaper articles? +289 Who is this report shocking before disagreeing with Peter? +290 Who does Amy fire before observing Winston Churchill? +291 What does Galileo sell a lot of mouths without discovering? +292 Who are most books alarming before confusing Margaret? +293 Who will Nicole visit after describing Dan? +294 What will Scott remember before skated around some art gallery? +295 Who were teachers healing most people before healing? +296 Who had teenagers listened to after discovering these girls? +297 Who is that icicle disgusting a girl while shocking? +298 Who is Galileo leaving before listening to Karen? +299 What was Natalie examining after scanning commentaries? +300 Who was Jane worrying without annoying Noah? +301 Who is Sarah shocking Galileo while stunning? +302 What can Judy pass the malls after touring? +303 Who might this hospital conceal without describing Jessica? +304 Who might some adults approach William while helping? +305 Who should Deanna aggravate before embarrassing Daniel? +306 Who is Kristen firing before alarming Adam? +307 What has Jill researched while thinking about current events? +308 Who were most people revealing after admiring Curtis? +309 Who was a car scaring William while distracting? +310 Who is Colleen hugging the teenager without finding? +311 What is Ruth curing many cats before concealing? +312 Who is every newspaper article disagreeing with Kimberley after scaring? +313 Who would Ann leave without confusing Sabrina? +314 Who has Vanessa loved the actress before embarrassing? +315 Who was some government bothering while referencing those guys? +316 Who had Martin embarrassed before disturbing Nina? +317 What is William discovering without thinking about the screen? +318 What was Peter passing without noticing a lot of lakes? +319 Who might a lot of dancers see before distracting Kristin? +320 Who were the plates scaring after distracting Donna? +321 What was Debra breaking some forks after cleaning? +322 What should Raymond examine this play without talking about? +323 Who should that college campus embarrass after aggravating Theresa? +324 Who was Samuel working with some patients while disgusting? +325 What is Douglas buying these closets without admiring? +326 Who have the Impressionists listened to after criticizing Kevin? +327 What had that teacher looked like after concealing that picture? +328 Who would current events disgust Ruth before boring? +329 What can the waitress look like after discovering all pictures? +330 What was Elaine referencing before concealing the report? +331 Who had Natalie investigated while healing Martin? +332 What was Debra breaking all glasses before noticing? +333 Who would Catherine hate after visiting Julia? +334 What did doctors arrive at after leaving a lot of lakes? +335 Who does Karen notice after boasting about Caroline? +336 Who had Lisa discussed after irritating Sharon? +337 Who is Roger escaping from Monet while revealing? +338 What has Linda arrived at some hospital before noticing? +339 Who had John criticized Melissa after questioning? +340 What is Roger skated around that river while arriving at? +341 What was Kimberley cleaning that cart without finding? +342 What has Suzanne scanned commentaries while describing? +343 What had Caroline exited that lake without talking about? +344 What was Janet messing up before messing up those public parks? +345 Who had Brett described while scaring Kirsten? +346 What had Roger ascended steps while noticing? +347 Who will some cashiers hide without attacking the Impressionists? +348 Who has Rose visited most dancers without listening to? +349 Who is Sally observing this man before disgusting? +350 Who has Tammy approached Becky after working with? +351 Who have the Impressionists disagreed with before complaining about Sheila? +352 Who might Linda escape from without bothering Kathleen? +353 What is Jason noticing after thinking about all rugs? +354 Who could Valerie appreciate before aggravating Tamara? +355 What would Julia wear a dress while admiring? +356 Who might pedestrians disagree with Natalie without leaving? +357 Who was Stacey returning to Emily after embarrassing? +358 Who has Natalie fired Judy before firing? +359 What have the Borgias examined while discovering this commentary? +360 Who can a lot of legislatures work with Omar before embarrassing? +361 Who had Kayla referenced while fleeing from Samantha? +362 What have these adults passed before boycotting all schools? +363 What could Kendra descend that hill after hiding? +364 Who has that closet aggravated after confusing Steve? +365 Who has George boasted about after distracting Roger? +366 What was Meredith skated around after concealing public parks? +367 Who will all dresses embarrass this guest without distracting? +368 What has Julie lifted before going to this art gallery? +369 What were the children arriving at every cafe while finding? +370 Who can every boy hide after criticizing Stacy? +371 What did Holly return to Spain before walking through? +372 Who could Kenneth flee from Carolyn after visiting? +373 Who are most waitresses hiding before hiring Barbara? +374 What does Holly look like after cleaning some pictures? +375 What have all waiters ascended after bringing some ladders? +376 What had Jill discovered before noticing the cups? +377 Who might Carlos work with Mary while discovering? +378 Who was Beverly disgusting after appreciating some students? +379 Who did some patients praise Benjamin without listening to? +380 What should Dawn impress a lot of banks before criticizing? +381 Who had the screen scared after shocking Ann? +382 Who has Connie questioned while impressing Richard? +383 Who can all adults disagree with without concealing many senators? +384 What would Tamara discover before irritating this committee? +385 Who does Amanda find Lucille while thinking about? +386 Who would Brett watch after attacking Gina? +387 Who has Guy listened to after scaring this person? +388 Who would Ronald talk to a lot of actors after escaping from? +389 What was Lawrence healing after finding many snakes? +390 Who had Mitchell respected Travis while impressing? +391 Who would Dawn bore Allison without thinking about? +392 Who were most drivers watching while hurting Cindy? +393 What has Brian bought some plate before messing up? +394 What might Karla take without wearing every sweater? +395 What could Marcus research while walking through that public park? +396 What might Timothy lift before buying many movie theaters? +397 What has Lisa referenced before examining that essay? +398 Who are these lakes alarming after annoying Beth? +399 What was Laura breaking every vase while breaking? +400 What has Clyde toured that closet without researching? +401 What will Candice resemble without concealing the print? +402 What was Tracy scaring before boycotting the committees? +403 Who did Galileo cure Karla after healing? +404 Who is Lawrence questioning Cheryl while disgusting? +405 Who had Derek alarmed while approaching Carl? +406 What will some adults discover while astounding every committee? +407 Who were those lamps shocking after stunning Russell? +408 Who has some shoe scared after upsetting Aaron? +409 What has Ann sold after running around every high school? +410 Who could Emily talk to Amy while shocking? +411 What was Monica examining all essays before insulting? +412 Who can many people confuse after describing Regina? +413 Who is Cindy upsetting before concealing Melanie? +414 Who can Galileo return to Phillip after investigating? +415 What is a doctor cleaning some screens without revealing? +416 Who had this teacher attacked Marie before bothering? +417 What is George buying without lifting some pasta? +418 Who have the drivers hugged Bradley while revealing? +419 Who is Teresa discovering Anne while talking about? +420 Who are many cats bothering before hurting George? +421 Who had Douglas respected before aggravating Kayla? +422 Who has Sarah stunned Thomas before discovering? +423 Who would that cashier escape from while working with Carlos? +424 What does Andrea exit after arriving at many schools? +425 What has Karla sounded like after exiting these public parks? +426 Who did Claire discuss without discussing Kenneth? +427 Who has Christopher appreciated all cashiers without concealing? +428 What did Timothy have after taking apples? +429 Who does the car impress Sabrina without confusing? +430 Who could Becky disturb Grace after annoying? +431 Who is Melanie astounding after embarrassing Judy? +432 What is Gregory breaking the dish before admiring? +433 Who had Amelia noticed before worrying Andrea? +434 What would this muffin look like every print while looking like? +435 Who was Jessica astounding after referencing Margaret? +436 Who should a screen impress without disgusting Karla? +437 Who can Eva care for Lawrence while hiding? +438 Who has Sally loved those girls after admiring? +439 Who could Mitchell distract a pedestrian before leaving? +440 Who can Tara alarm Samantha after talking about? +441 Who could Kristen talk to Nina before concealing? +442 Who had Scott hugged before approaching Tracy? +443 Who had every actor cared for before healing Linda? +444 What has Benjamin messed up while exploring many cafes? +445 Who is Renee criticizing without approaching Tracy? +446 Who has every organization forgotten without stunning Brenda? +447 Who could music annoy Mitchell while shocking? +448 Who had the spinach worried after shocking Phillip? +449 What can Mitchell discuss most movies after describing? +450 Who would all schools criticize Andrea while embarrassing? +451 What was John cleaning all hospitals before disturbing? +452 Who is Cindy approaching without insulting Nina? +453 Who did Martha discover without thinking about Kenneth? +454 Who can all committees talk about a guest before upsetting? +455 Who had Kathleen described without working with some actress? +456 Who was Beth firing after insulting Marie? +457 Who did this patient flee from without discovering the Lutherans? +458 What did Douglas bring some banks without irritating? +459 Who has Debra cared for while boasting about Lisa? +460 Who does Diane talk to Galileo after hurting? +461 What is Karen cleaning this print before thinking about? +462 What was Scott driving to some malls without running around? +463 What could Craig take without escaping from all high schools? +464 What has Judy broken while concealing a window? +465 Who will a senator escape from without criticizing this student? +466 What has the guy explored the closet before dropping by? +467 What has the waitress broken before breaking a computer? +468 Who does a student embarrass before hiring Monica? +469 Who did Kevin attack before concealing Alicia? +470 Who can the man respect Janice before helping? +471 What was Alicia going to all hospitals after disgusting? +472 Who has Becky visited most drivers while confusing? +473 Who should most guests hate Dan after hiding? +474 Who is Kayla questioning while arguing about this teenager? +475 What is Tracy boycotting without selling all public parks? +476 Who had Vincent admired Gerald before attacking? +477 Who was a customer hurting after helping Brenda? +478 Who was Dennis upsetting while appreciating Lucille? +479 Who are those eyes boring after hurting Jeffrey? +480 What has Becky ascended that hill while describing? +481 Who did Rebecca see after concealing Elizabeth? +482 Who had the companies helped before criticizing James? +483 Who was Robert escaping from while praising Patricia? +484 Who was Barbara worrying without escaping from Samuel? +485 Who was Lisa disturbing Ronald without disturbing? +486 Who were many teachers complaining about while leaving Susan? +487 Who can all mushrooms shock some cashier without irritating? +488 Who could Frank observe before concealing Robert? +489 Who is Vincent firing without hurting Michelle? +490 What has that patient ascended without buying most ladders? +491 Who did Nicole cure after confusing Eric? +492 What might that customer scan every essay before criticizing? +493 Who has Craig cared for before appreciating Todd? +494 What was Veronica lifting after leaving a lot of grocery stores? +495 Who would all sketches look like Sally while disgusting? +496 What does Joseph fix most screens without finding? +497 Who do all girls talk to Amy while hugging? +498 Who should Roger care for before hiring every doctor? +499 Who had Becca cured before scaring Vanessa? +500 Who was some boy hugging the Borgias after disgusting? +501 What had Christina distracted the governments without alarming? +502 Who did some customers disagree with Melanie while questioning? +503 What have the Impressionists toured some hills without skated around? +504 What was Sara wearing while thinking about the dresses? +505 Who had Gerald questioned before investigating Beth? +506 What are most actors climbing down without climbing up most hills? +507 What would Tammy exit this river before skated around? +508 Who might Jason discuss Sherry before shocking? +509 Who had that driver discovered some patient while criticizing? +510 What does Gina run around while insulting the closet? +511 Who has the documentary distracted before annoying Theresa? +512 What has Dana dropped by the bank after running around? +513 Who has Robert seen without referencing Andrew? +514 Who might this picture shock Carrie without disgusting? +515 Who had Ellen respected without talking to Clyde? +516 What is Derek ascending every ladder without noticing? +517 Who does Sherry complain about after questioning Phillip? +518 What is Brett biking to the grocery stores without returning to? +519 Who had all steps bothered while disgusting Patricia? +520 What has Patrick cured after concealing every turtle? +521 What has Marla cleaned after wearing those shoes? +522 Who will Ronald stun while listening to Jeffrey? +523 What was Melinda revealing a mushroom without finding? +524 What are these boys noticing without finding all turtles? +525 What are men climbing up after going to many mountains? +526 Who had the Lutherans confused while disgusting that man? +527 Who might Thomas forget before healing Katherine? +528 Who was Monet talking about Monet without shocking? +529 Who was Deborah concealing while observing Ann? +530 Who should books aggravate before disagreeing with Melinda? +531 What has Ann admired while revealing a rug? +532 Who did Christina hug the Lutherans after worrying? +533 Who might Kristin visit Maria before boring? +534 Who could this hamster find Russell after annoying? +535 Who had Galileo complained about Wayne after stunning? +536 Who has Elizabeth approached Sarah before escaping from? +537 What was Steve cleaning this casserole while discovering? +538 What should these ladies look like before buying this sketch? +539 Who has this ladder worried Larry while scaring? +540 What were the women descending all steps without buying? +541 Who had that waitress insulted Veronica while worrying? +542 Who has Craig approached after revealing Gary? +543 Who had every actress hidden before scaring Jessica? +544 What had Erin passed every restaurant while skated around? +545 What was Tamara looking like without hiding every photograph? +546 What had Bill toured every mall after bringing? +547 What have the Borgias revealed after exploring some restaurants? +548 Who was Stacy returning to Todd after stunning? +549 What have these actresses looked like while bringing that painting? +550 Who was Bethany approaching every customer while bothering? +551 Who have many snakes scared after astounding Galileo? +552 Who was Katherine criticizing after referencing Veronica? +553 Who was some cafe shocking after irritating the senators? +554 What does Lori think about many schools without noticing? +555 What had some ladies thought about while buying these prints? +556 Who should a lot of glasses upset Homer without hurting? +557 Who did all pants astound many pedestrians without irritating? +558 What might every actress lift before appreciating a book? +559 Who is Ellen healing after hurting Gregory? +560 Who would this guest impress the women without approaching? +561 Who is the waitress watching Joel without discovering? +562 Who was Daniel hurting every senator while admiring? +563 Who has Jodi criticized after hiding Paula? +564 What had Ronald driven to while concealing art galleries? +565 Who would some newspaper article disagree with Stacey without stunning? +566 Who was Sandra leaving while discussing Lisa? +567 Who was every school firing before boasting about Steve? +568 Who were some pedestrians observing before describing those guests? +569 What had every sketch resembled before looking like some sketch? +570 Who was the person bothering Helen before observing? +571 Who had this book astounded without distracting Beth? +572 Who was every adult visiting while disgusting Monica? +573 What had Alan messed up while discussing this school? +574 Who should Stephanie praise the drivers after talking to? +575 What has Elizabeth arrived at after buying some river? +576 Who has Bill appreciated Richard while disturbing? +577 What was Richard touring after stunning Harvard University? +578 Who had many people healed while working with April? +579 Who has Lori hugged without visiting Keith? +580 What did that student descend after revealing these ladders? +581 Who has the waitress listened to before appreciating Edward? +582 Who did Pamela escape from Claire while working with? +583 Who has Kathleen fled from after observing Phillip? +584 Who had the teenagers talked about before concealing Rodney? +585 Who is Gerald attacking Wendy without arguing about? +586 What has Regina boycotted every glacier before finding? +587 Who has Douglas disgusted Wayne after aggravating? +588 Who had most pedestrians revealed without visiting a boy? +589 Who are a lot of museums hiring before disgusting Ann? +590 Who has Beverly cared for without attacking Steven? +591 Who is Sonia hugging after visiting Noah? +592 Who could Bethany describe Jerry after approaching? +593 Who could some essay worry while impressing Lori? +594 Who has Carol fired Steven while appreciating? +595 Who can Sherry flee from Rose while kissing? +596 What are a lot of sketches looking like while looking like reports? +597 Who has Cheryl forgotten while questioning Timothy? +598 What is Donna wearing before taking the shirts? +599 What has Irene cleaned without fleeing from the movie theater? +600 What is every waitress cleaning that restaurant while cleaning? +601 What had Florence ascended after climbing down the steps? +602 What had Tracy boycotted without skated around most cafes? +603 Who does a child hide without complaining about Dawn? +604 Who has Tiffany forgotten after criticizing that guy? +605 What had Veronica examined all essays after arguing about? +606 Who is Denise noticing Suzanne without appreciating? +607 What had that fish looked like after looking like those drawings? +608 Who had Ella fled from Kirsten while attacking? +609 What might some drivers buy before healing a lot of snakes? +610 What is Chad ascending the steps while admiring? +611 What had Caroline examined while concealing the documentaries? +612 What have a lot of drivers brought before admiring the dogs? +613 What was Kayla taking after admiring the dress? +614 Who could Allison shock Florence while embarrassing? +615 Who had Mitchell shocked before revealing Ruth? +616 What was Stacy dropping by while lifting many movie theaters? +617 Who will Raymond kiss Craig after firing? +618 Who have a lot of men attacked before referencing Patricia? +619 Who should Helen see before attacking Erin? +620 Who is Stephanie curing before observing Elaine? +621 Who was Valerie boring Victoria while boring? +622 Who has Teresa referenced all dancers while insulting? +623 What did some dancer remember most plays before insulting? +624 What had Andrew described that restaurant while exploring? +625 Who is Eric curing Larry while approaching? +626 Who does Larry conceal Kendra after curing? +627 Who is Tracy revealing after bothering Angela? +628 What had a lot of doctors messed up while arriving at schools? +629 Who can Tiffany return to Martha while talking to? +630 What had Tamara gone to most malls before lifting? +631 Who was George admiring after leaving Noah? +632 Who might Jesus work with before discovering Robert? +633 Who was Patricia kissing while concealing Edward? +634 Who has Jessica boasted about while approaching Lucille? +635 Who is Sonia concealing before annoying Katherine? +636 What will Alan bring before discussing the river? +637 Who does Tina reveal while noticing the boys? +638 Who had some people concealed after observing Caroline? +639 Who might Karla dislike Jeffrey while noticing? +640 Who is Tanya impressing while hugging Brett? +641 What could Omar go to this glacier while finding? +642 Who had Kristin cared for after escaping from these children? +643 Who had Theodore described without astounding Russell? +644 Who had Vanessa talked to these teenagers before investigating? +645 Who had Curtis listened to Jessica while disturbing? +646 What had Ronald sold without discovering the rugs? +647 What is Samantha skated around without insulting that closet? +648 Who could these cars impress Andrew without disgusting? +649 Who have the teachers investigated before revealing Guy? +650 What had Frank messed up while distracting the museum? +651 What have most banks resembled that picture before admiring? +652 Who had Pamela healed without approaching Sabrina? +653 Who had most boys escaped from Bill without boring? +654 Who has Kristin distracted while boasting about Winston Churchill? +655 Who was Connie praising a lady after stunning? +656 Who had Linda criticized Christina without irritating? +657 Who could Sandra fire Gina after listening to? +658 Who were these public parks scaring most customers while upsetting? +659 What is Sonia discovering before hiding photographs? +660 Who had a lot of students questioned before praising Connie? +661 Who is Dennis healing after boasting about Lori? +662 Who can Amy conceal without questioning many waitresses? +663 Who was Brett worrying without firing Peter? +664 What has Eric forgotten while praising every movie? +665 Who did Patricia praise without hurting the Borgias? +666 What will Sara exit before running around every school? +667 Who has every fork annoyed while bothering Anne? +668 Who has Harvard University criticized before listening to the adults? +669 Who had Caroline bothered Jerry without complaining about? +670 Who will a lot of associations work with Suzanne without insulting? +671 Who had Margaret investigated before describing Julia? +672 What is Patrick selling without selling this vase? +673 Who had Rebecca shocked while arguing about Lori? +674 Who are those guys kissing before bothering Candice? +675 What was Brad examining while concealing stories? +676 Who is Patrick boasting about Angela without complaining about? +677 What has Bethany sold glasses without messing up? +678 Who had Jeffrey criticized Angela after concealing? +679 Who had Natalie discussed before attacking Kenneth? +680 Who is Claire firing without boasting about Irene? +681 Who has Tracy left before praising Lori? +682 Who would this man discover without describing a lot of waiters? +683 Who has Laurie hired Sherry without distracting? +684 Who was James watching before disturbing some waiter? +685 What can Jennifer like without bringing some books? +686 What should Theodore explore some museums while scaring? +687 What was Kristin dropping by libraries while revealing? +688 What had Cindy driven to every river after skated around? +689 Who would Phillip fire Patricia without referencing? +690 Who has Sara questioned before revealing the adult? +691 What was Emily buying after lifting this sock? +692 Who could rugs bother Natalie after shocking? +693 Who might Gregory respect Travis without criticizing? +694 Who might every guy like before attacking Pamela? +695 Who is Diana healing before complaining about Connie? +696 Who was every rug boring before confusing Rachel? +697 Who is Clyde insulting while stunning Elaine? +698 Who were some pedestrians fleeing from before curing Sally? +699 What will Eva dislike while skated around a lot of libraries? +700 What would Andrew explore some restaurant before thinking about? +701 Who had Denise sounded like most girls after referencing? +702 What has Meredith ascended a lot of stairs while climbing up? +703 Who can all movies bother Regina without shocking? +704 Who was Tiffany praising Samuel without worrying? +705 Who had this company referenced before embarrassing Angela? +706 Who have the associations stunned while embarrassing Bill? +707 Who is Florence attacking while distracting Laura? +708 What have those guests gone to without touring some movie theaters? +709 What will Martin buy after lifting that spinach? +710 Who will Gerald help Allison without alarming? +711 Who was this screen aggravating before aggravating Leslie? +712 What had Ruth walked through without cleaning many college campuses? +713 What has Grace skated around after criticizing that glacier? +714 What had Travis arrived at many cafes after discussing? +715 Who have most ladies insulted before describing Cheryl? +716 What does Beth scan without discussing the newspaper article? +717 What does Bethany exit hills while describing? +718 What had Rebecca cleaned after bringing this fork? +719 What was Nicole discovering before finding this beef? +720 Who might Benjamin alarm before listening to Paul? +721 Who would Patricia confuse Judy while noticing? +722 What had the cashiers healed after discovering many cats? +723 What can Jerry walk through those mountains while biking to? +724 Who had those college campuses annoyed while irritating Denise? +725 What have the waitresses scanned before thinking about essays? +726 What had Sara healed before noticing some rabbits? +727 What can that patient clean after leaving some high school? +728 Who is Dana shocking after thinking about many women? +729 Who did most committees aggravate while distracting that doctor? +730 What was Scott concealing without examining those plays? +731 Who was a wheelbarrow disturbing after impressing Ann? +732 Who was Natalie disturbing a teenager before questioning? +733 What has Martha resembled some photograph before thinking about? +734 Who might Jacqueline care for after upsetting Craig? +735 Who was Steve concealing a man after astounding? +736 What had Marie bought some cake without noticing? +737 Who was Gregory discovering without appreciating Becca? +738 Who had Brett criticized before criticizing Andrew? +739 Who should Brian hire after thinking about many waitresses? +740 What has Rebecca walked through after selling most movie theaters? +741 Who had Naomi criticized before boasting about some guy? +742 Who was Diane leaving Chad before hugging? +743 Who can April help many patients without confusing? +744 Who did most cups bother without stunning a lot of guests? +745 Who has Brian boasted about without scaring Patrick? +746 What did Victoria heal dogs without healing? +747 What had Julia driven to high schools without talking about? +748 Who is Cynthia boasting about without discovering Irene? +749 Who had the driver revealed Claire after distracting? +750 Who might Heather leave without noticing Christopher? +751 Who might a dancer respect before annoying Theresa? +752 Who was Caroline referencing while talking about Sandra? +753 What is Claire going to those schools while aggravating? +754 Who was Paula leaving before embarrassing Frank? +755 Who have the patients healed while thinking about Roger? +756 What has Denise ascended before finding some slope? +757 Who was this newspaper article astounding before hurting Robert? +758 Who were all pedestrians hugging after aggravating most actors? +759 Who had Vanessa aggravated before aggravating Vincent? +760 Who has some fish astounded William before shocking? +761 Who could Denise watch Dan after insulting? +762 Who could some girl investigate without concealing Larry? +763 Who should the bread irritate before upsetting Sherry? +764 Who did Gary boast about after watching Joel? +765 Who will that screen annoy after scaring some drivers? +766 Who can Caroline annoy Laura without describing? +767 What is Paul arriving at after biking to a public park? +768 Who can James sound like before hiring Anna? +769 What can Susan insult the libraries without admiring? +770 Who is Liam curing without impressing Marcus? +771 What was Stephanie wearing while taking those coats? +772 Who was James escaping from Valerie before healing? +773 Who would the organizations dislike Jessica without hurting? +774 What is Carrie admiring without finding every mushroom? +775 What might Kirsten explore while cleaning those cafes? +776 Who was Elizabeth irritating without impressing Frank? +777 Who might this man return to after finding Jesus? +778 What was Candice buying after selling some cat? +779 What is the actor arriving at without visiting those cafes? +780 What did Michelle lift many vases before selling? +781 Who had Angela bothered while scaring the driver? +782 Who was Craig boring without alarming Anne? +783 Who was Naomi referencing while fleeing from Brenda? +784 What has a bicycle disturbed a lot of schools while irritating? +785 Who was Thomas arguing about Stephen after disgusting? +786 What had Karen found after appreciating those malls? +787 What did Bradley mess up while cleaning that college campus? +788 What is Sheila exiting all schools after talking about? +789 Who would a play embarrass Diane without aggravating? +790 What was the waitress passing all museums without stunning? +791 Who does Jesus criticize after criticizing Ellen? +792 Who had Matt remembered after shocking Jason? +793 Who have all teachers annoyed before leaving Mark? +794 Who can Jill visit while concealing Janice? +795 Who did some water bother every guy without shocking? +796 What would some guys lift most eyes before finding? +797 Who had Monet bothered after finding Lucille? +798 Who would Edward discover before upsetting Craig? +799 What had Jennifer taken some shawl without thinking about? +800 Who should a lot of actors see Larry before annoying? +801 Who was Julia disturbing many teachers without healing? +802 Who has Elaine bored without irritating Christina? +803 Who could all teenagers kiss Deborah while impressing? +804 Who might Curtis like before questioning Amanda? +805 What has April left every mountain without thinking about? +806 Who has Rodney confused without working with Larry? +807 What could the actresses flee from without finding that closet? +808 Who did Roger appreciate Chad without questioning? +809 Who does every glacier bore before irritating David? +810 Who has Tina helped before distracting Andrea? +811 What had Kayla dropped by all schools without cleaning? +812 Who can Melissa sound like before watching Donald? +813 Who is Claire investigating after irritating Carol? +814 Who had those guys disliked before praising Ronald? +815 Who has Frank impressed without embarrassing Carolyn? +816 What can Tiffany break while fixing every glass? +817 Who has Andrea observed while hugging Emily? +818 What has Jesus discovered while boasting about current events? +819 Who had Margaret hugged Christopher while hurting? +820 Who is Colleen watching Allison without approaching? +821 Who will Tamara boast about Karla without disturbing? +822 Who is Rose annoying before boasting about Gerald? +823 What was Carl breaking the plates before cleaning? +824 Who has Sonia referenced after observing Curtis? +825 Who had that doctor concealed before attacking Holly? +826 What will Kirsten lift most horses without bringing? +827 What can Judy ascend these steps without selling? +828 Who could some lake disgust Sherry before worrying? +829 Who has Naomi bothered while working with Paula? +830 What had Carolyn taken the carriages after discovering? +831 Who have some actors talked about before hiring Michael? +832 Who has Roger loved after discovering Noah? +833 Who is Barbara arguing about the women without investigating? +834 What has Laura broken a couch while fixing? +835 What had Kevin fled from most schools before annoying? +836 Who might some committees annoy without boring this senator? +837 Who should every art gallery worry Danielle without irritating? +838 Who would Monet aggravate while investigating Renee? +839 What is Aaron discovering while thinking about many rugs? +840 Who has Kevin talked about before returning to the actress? +841 What has Beverly exited most movie theaters while cleaning? +842 What are the dancers going to these banks after questioning? +843 Who would Ronald like without referencing Amelia? +844 Who is Nina hiring Rose after astounding? +845 Who can Ronald dislike without hiding Karla? +846 What is Noah walking through after climbing down this mountain? +847 Who could Jerry hate before approaching Vanessa? +848 Who might Kimberley return to most pedestrians before returning to? +849 Who had Nancy praised Phillip while hiding? +850 What has Candice disturbed before impressing these organizations? +851 What had Laurie fixed a bike while taking? +852 What had Anne concealed without taking some tomato? +853 Who will guys think about Raymond after praising? +854 Who was Denise thinking about without boasting about Tammy? +855 Who was Eric approaching without listening to Susan? +856 Who does Larry respect Jill before criticizing? +857 Who was Ronald working with Diana while attacking? +858 Who had these documentaries distracted before distracting Cynthia? +859 What does Noah sell most sweaters after wearing? +860 What are a lot of actors walking through every grocery store before leaving? +861 Who has Melinda admired before insulting many teenagers? +862 Who is a legislature astounding Nicole without appreciating? +863 Who might spinach hurt while distracting the Lutherans? +864 Who is Bill criticizing while impressing Dan? +865 What is Sharon returning to lakes while talking about? +866 Who will Karen flee from Holly after appreciating? +867 Who was Aaron insulting while thinking about Barbara? +868 Who will Jill heal Joseph while describing? +869 What will Tracy pass a lot of hospitals before describing? +870 Who has Homer hated while concealing Brad? +871 What is Christopher messing up without aggravating every library? +872 What had this girl messed up while taking the library? +873 Who had Pamela returned to Kathleen while discovering? +874 What is Ronald scanning after criticizing the movie? +875 Who are some waitresses observing after approaching Dennis? +876 Who had that teacher escaped from Irene before healing? +877 What did Sheila explore some hills without talking about? +878 What did Ellen lift that hamster before bringing? +879 What has Melissa noticed before hiding that pasta? +880 Who do the schools confuse before working with some driver? +881 Who was Jill complaining about without describing Nancy? +882 Who might Edward investigate after referencing Marla? +883 What would Sally bike to a hospital before upsetting? +884 Who are a lot of universities insulting without scaring Raymond? +885 What had that lady sold after breaking this fork? +886 Who had Mary disturbed before distracting Denise? +887 What did many pictures look like after looking like a book? +888 What has John sold while admiring a lot of dogs? +889 Who was Joel distracting Catherine without disgusting? +890 Who had Ann remembered every pedestrian after listening to? +891 Who had this waitress approached Melissa while discovering? +892 Who had all students talked about before scaring Kayla? +893 Who is Melissa scaring Carl before distracting? +894 Who had Andrew cared for while confusing Leslie? +895 Who does Heather know while referencing Guy? +896 Who might Mark respect while bothering Naomi? +897 What did Raymond skate around without describing the closet? +898 What did Liam see before running around this library? +899 What should all women examine many documentaries after thinking about? +900 Who was William describing Melissa without distracting? +901 Who were all people concealing Walter without complaining about? +902 What will Adam exit some bank after talking about? +903 What had Natalie bought that mountain before talking about? +904 What was Mark finding the dog after healing? +905 What has Adam noticed after buying some horses? +906 Who has a spotlight hurt without shocking all teachers? +907 What was every actress ascending many steps without lifting? +908 Who does Gina see while discussing Carrie? +909 Who can Kayla escape from Irene before noticing? +910 What had Michael dropped by without cleaning all public parks? +911 Who has a wheelbarrow bothered before annoying all girls? +912 What had Julie driven to every college campus without escaping from? +913 What had Dennis explored before driving to this school? +914 Who had Denise loved after boasting about Christina? +915 What have these cashiers bought without admiring the sketch? +916 What had every fork looked like after looking like most paintings? +917 What did Matt wear many pants while messing up? +918 What had Scott boycotted a government while shocking? +919 What might Natalie scan a documentary before discovering? +920 Who might Heidi care for without fleeing from Emily? +921 Who had Lucille approached Deborah while boasting about? +922 What was Kevin running around after returning to that public park? +923 Who has that print looked like before embarrassing some man? +924 Who might Colleen find a lot of people while revealing? +925 What have the boys worn the shoes without hiding? +926 What has Sarah walked through this restaurant while hiding? +927 What will Catherine clean hospitals while criticizing? +928 Who had Kristin respected Stephen after working with? +929 Who might Patrick leave without returning to Victoria? +930 Who will Melissa hug the Impressionists while talking to? +931 Who were most students leaving without shocking Jerry? +932 Who can this book scare after irritating Stephen? +933 Who can this adult respect after bothering Christina? +934 Who was some chicken worrying after embarrassing Lori? +935 Who has Jeffrey hidden while hiring Heidi? +936 Who are a lot of mushrooms disturbing while astounding Raymond? +937 Who was Steven listening to Paul while worrying? +938 Who could this child like some ladies before questioning? +939 What was Tammy ascending a lot of slopes without admiring? +940 Who has Kristin returned to Caroline without bothering? +941 Who were those birds boring while worrying these women? +942 Who had Tracy upset Anne before describing? +943 What should Eric buy this face after thinking about? +944 Who had every guest approached Marcus after shocking? +945 Who had April disturbed Tamara without revealing? +946 What was Kenneth driving to museums before thinking about? +947 What are many paintings looking like some report while looking like? +948 What will Nina climb up before selling those slopes? +949 What does Sandra criticize those restaurants without skated around? +950 Who does Naomi talk about after fleeing from Rachel? +951 Who was Kathleen returning to Tiffany while concealing? +952 Who did a lot of doctors embarrass Winston Churchill without referencing? +953 What might Ellen walk through malls without hiding? +954 Who were many schools scaring after praising Patrick? +955 Who had these ladders disturbed Margaret after disgusting? +956 Who had some dancers praised Douglas while observing? +957 Who is Kevin appreciating most drivers after observing? +958 What is Kirsten examining most stories while arguing about? +959 Who aren't most hospitals that hadn't talked about most waitresses alarming? +960 Who hasn't Cheryl attacked this sister of these dancers who shocked? +961 Who should Lawrence's lawyers who have scared some grandsons of Janice hate? +962 Who hadn't some driver who would fire Jennifer's colleague embarrassed? +963 Who haven't these fathers of Gerald who impressed Deborah's tutors loved? +964 Who couldn't Carmen observe nephews of Victoria that did conceal? +965 Who wouldn't Katherine forget all mothers of Florence who disturbed? +966 Who couldn't a movie about politics upset some teacher's piano teachers that were criticizing? +967 Who weren't these people that had hated the dancers admiring? +968 Who did Anna's dentists disturb this actor that wouldn't astound? +969 Who was Gary's lawyer who could respect Tonya approaching? +970 Who haven't Tonya's daughters questioned Edward's plumbers that had listened to? +971 What can't some actor argue about a guest who would like? +972 Who wasn't that actor's father who can't bore Samuel healing? +973 Who was Kevin approaching Andrew's ex-wife who isn't insulting? +974 Who is Janice listening to Bill's nephew that won't attack? +975 Who isn't the cousin of Craig that wasn't talking to Kathleen confusing? +976 Who hadn't these actresses' grandmothers described a brother of Renee who was upsetting? +977 Who hadn't Daniel found Steven's brothers who had seen? +978 What can't Anne admire a lot of fathers of Monet that liked? +979 What does a patient conceal Cynthia's grandmother who won't love? +980 Who couldn't this guest observe a lot of cousins of many actors that were watching? +981 Who are a lot of universities that were talking to a lot of students astounding? +982 Who might Emily's French teachers that weren't watching every senator's associates love? +983 Who can't Regina's supervisor kiss senators that should visit? +984 Who had Omar returned to some ladies that had hired? +985 Who hadn't every report sounded like Theodore's nephew that had appreciated? +986 Who was Brenda's contractor that attacked Regina hugging? +987 Who hasn't Amanda scared Holly's plumber who was curing? +988 Who isn't Diana's partner that isn't distracting Melinda finding? +989 Who aren't the mothers of Tina who hid the windows bothering? +990 Who would those schools irritate that university that appreciates? +991 Who can't Allison observe Galileo's sister that doesn't notice? +992 Who wouldn't James observe Noah's lawyer who hadn't disagreed with? +993 Who had Danielle astounded ladies who boasted about? +994 Who hasn't some son of Grace who alarmed Tanya's daughter embarrassed? +995 Who is this student praising a woman who insults? +996 Who are the customers hugging Gina's best friends that are boasting about? +997 What hasn't Jesus's associate that loved Edward thought about? +998 Who isn't the nephew of Ella that praises Mitchell curing? +999 Who is every waiter who listens to Jennifer's best friend hugging? +1000 Who should Mitchell remember that actress who will question? +1001 What has this teacher that can't find Stephanie healed? +1002 Who was Nicole talking about Gary's dentist who could embarrass? +1003 Who hadn't some friend of Melissa that has attacked Patrick talked about? +1004 Who hadn't a niece of Guy that remembers Martin worked with? +1005 Who hadn't Vincent listened to a lot of waiters that hadn't insulted? +1006 Who aren't Patrick's piano teachers hurting Aaron's grandsons that had talked about? +1007 Who wasn't Dan's boss who listened to those cashiers' daughter astounding? +1008 Who hadn't Spain hired some teacher who wasn't stunning? +1009 Who weren't some doctors disagreeing with these pedestrians that were distracting? +1010 Who won't Pamela talk about all cashiers' father that is irritating? +1011 Who has Tiffany argued about Wendy's boss that fled from? +1012 Who isn't Phillip hiring some guest that isn't hurting? +1013 Who was Beverly's sister that admired Susan's guest escaping from? +1014 Who had Allison kissed a doctor that should scare? +1015 Who hadn't this boy criticized most schools that hadn't shocked? +1016 Who hasn't William sounded like Carl's doctor that referenced? +1017 Who haven't these fathers of Julie who weren't investigating Liam's cousins watched? +1018 Who hasn't Tara concealed Karen's hairdresser that would fire? +1019 Who couldn't some senator alarm this guy that has kissed? +1020 Who has Theresa's grandmother who respects Thomas's grandmother seen? +1021 Who were some ladies discussing Tonya's colleagues who were investigating? +1022 What hadn't Eric referenced some cousins of Thomas that are arguing about? +1023 Who hasn't a granddaughter of Andrea that visits Martha astounded? +1024 Who aren't all men who investigated nephews of Lori kissing? +1025 Who hadn't every woman that concealed Irene's classmates hired? +1026 Who has Cheryl loved Beth's handyman that was hugging? +1027 Who haven't some teenagers helped these teenagers who weren't disagreeing with? +1028 Who haven't the Borgias concealed Roger's ex-girlfriends that do talk to? +1029 Who should Jerry confuse the grandfather of Daniel who had seen? +1030 Who wouldn't Sarah stun Stephen's mother who will like? +1031 Who weren't Anna's colleagues who haven't revealed Janice's supervisors boring? +1032 What does Theodore like that guy's spouse that shouldn't forget? +1033 Who doesn't Wayne work with that brother of Wayne that wasn't stunning? +1034 Who could these doctors shock some guests who talked to? +1035 Who would Julia like Stephanie's students who talked to? +1036 Who didn't Amanda reveal Linda's mentor that hasn't referenced? +1037 Who hadn't some pedestrians who disagreed with Tonya's sister scared? +1038 Who aren't all guys that can see those hospitals scaring? +1039 Who isn't this child who might work with a nephew of a lot of actors complaining about? +1040 Who won't Travis's French teacher that hasn't upset a teenager's plumber approach? +1041 Who have Rachel's bosses found all rabbits that couldn't scare? +1042 Who has the man observed every friend of Beth that disagrees with? +1043 Who wouldn't Kayla's supervisor bore Brett's contractors that couldn't boast about? +1044 Who wouldn't Linda find every boy's friends who wouldn't observe? +1045 Who doesn't Timothy worry that dancer who forgot? +1046 What shouldn't a grandson of Deborah that sounds like Sonia escape from? +1047 Who haven't the actresses stunned most pedestrians who won't leave? +1048 What wasn't Nina referencing this waitress who discovered? +1049 Who hadn't that father of Walter who sounds like Lawrence's colleagues questioned? +1050 Who isn't Laura attacking the child that couldn't upset? +1051 Who hadn't Joseph disliked Douglas's girlfriend who questioned? +1052 Who isn't Robert's dentist who has embarrassed some student's nephew healing? +1053 Who aren't Natalie's electricians that criticized Carolyn's colleagues disturbing? +1054 Who couldn't Thomas stun this brother of Janet who has worked with? +1055 Who hadn't Nancy watched some grandfather of Beverly who was aggravating? +1056 Who had Bradley bothered Valerie's teacher who helped? +1057 Who is Alicia's doctor who isn't hurting Gary listening to? +1058 Who wouldn't the Great Lakes hurt every daughter of Homer that talks about? +1059 Who is some lady that has shocked the niece of Martin stunning? +1060 Who did Valerie's nieces who hadn't alarmed Claire's father observe? +1061 Who wasn't that actress discussing Katherine's colleague who did attack? +1062 Who hadn't Christine attacked Grace's ex-wife that doesn't approach? +1063 Who could all ladies scare every museum that hadn't hurt? +1064 Who are Wayne's guests that can't argue about these essays about current events disgusting? +1065 Who isn't Craig noticing every cashier's plumber who did stun? +1066 Who had that niece of Edward admired Valerie's ex-wives that aren't hurting? +1067 Who should Jason find Marie's tutors that aggravated? +1068 Who isn't Karen's friend who couldn't confuse Elaine listening to? +1069 Who would Kayla's colleague who watched Thomas escape from? +1070 Who had Alice hated every student's guest who healed? +1071 Who hadn't William forgotten a lot of students' lawyers that investigated? +1072 Who doesn't Linda investigate Linda's ex-girlfriend who wasn't hiding? +1073 Who aren't dancers' mentors that are embarrassing Becca's grandfathers boring? +1074 Who has Stacey known Tammy's mentor that has scared? +1075 Who is that driver's French teacher that references Kendra questioning? +1076 Who don't a lot of ladies describe all nephews of Robert who were boring? +1077 Who aren't Roger's partners who respected the art galleries aggravating? +1078 Who will Karen's lawyer that approached Sally's hairdresser listen to? +1079 Who wasn't Nancy returning to Rodney's guest that worked with? +1080 Who have Sherry's daughters talked to some guests who couldn't leave? +1081 Who had Angela escaped from Amelia's ex-girlfriend that has admired? +1082 Who can that lady approach those fathers of Kayla that weren't admiring? +1083 Who weren't Tracy's guests working with this cashier's sons who might hide? +1084 Who might this grandson of Dawn who remembers the pedestrian conceal? +1085 Who had some cousins of Stephanie that forgot Anne's employee thought about? +1086 Who hadn't Jessica's plumbers who aren't hugging Sabrina's senators talked about? +1087 Who will Judy appreciate some actresses' son that wasn't attacking? +1088 Who haven't Kristin's grandsons thought about a woman's senators who did attack? +1089 Who are those customers talking about most drivers who hadn't approached? +1090 Who wasn't Cindy arguing about Teresa's dentist that admires? +1091 Who hadn't the Borgias aggravated Derek's boss that knows? +1092 Who hadn't Frank's cousin questioned Edward's grandmother who helped? +1093 Who has Cheryl hurt Jodi's ex-girlfriend that won't heal? +1094 Who are Travis's dentists that can't think about nieces of this customer leaving? +1095 Who had Sara cared for the girls' tutors who are distracting? +1096 Who haven't Claire's friends that worked with Mitchell's granddaughters hated? +1097 Who don't a lot of students discover some actresses that weren't referencing? +1098 Who does a dish annoy every mother of the Impressionists that likes? +1099 Who will a mother of Tiffany work with Melissa's employees who could disagree with? +1100 Who does Craig's boss that kissed Tamara distract? +1101 Who has this committee disturbed Florence's electrician that might disgust? +1102 What weren't those sons of David praising doctors who have discovered? +1103 Who weren't a lot of dancers who have disturbed many sons of Eva embarrassing? +1104 Who could Julia return to Patrick's supervisors that haven't hurt? +1105 Who will Martha's guest that has known Rose's employee know? +1106 What should the library criticize Beverly's teacher who is hiding? +1107 Who can't that person who appreciates Dan's colleague disgust? +1108 Who hadn't Phillip aggravated the child's ex-boyfriend that had admired? +1109 What couldn't George discover a waitress's French teachers that would find? +1110 Who didn't Tamara's grandfather that couldn't notice Heather alarm? +1111 Who hadn't Monica's mothers who shouldn't question Mary's teachers described? +1112 Who isn't Brenda escaping from Jessica's father who hasn't visited? +1113 Who might the teenager boast about Grace's teachers that have cared for? +1114 Who had Sabrina's friend that hadn't worked with Stephen's grandson worked with? +1115 What aren't the grandsons of Travis who are healing a lot of nieces of that guest concealing? +1116 Who have mothers of a driver who upset Vincent's sisters concealed? +1117 Who had Sandra's niece that had embarrassed that lady concealed? +1118 Who should Steven attack some sisters of Helen who were observing? +1119 What will Aaron's mothers visit Elaine's supervisor who escapes from? +1120 Who couldn't some fathers of Dennis who have hated this person's supervisor reference? +1121 Who might nephews of those adults that have bored George's senators flee from? +1122 Who was Chad's best friend who hasn't disturbed Michael's mentor boasting about? +1123 Who would Monet's grandmother that escaped from Diane's teacher aggravate? +1124 Who didn't Kirsten impress the brother of Danielle that hadn't kissed? +1125 Who didn't Lucille see every teenager who listens to? +1126 What is Gregory finding Martha's spouse who revealed? +1127 Who have some governments that weren't describing all malls impressed? +1128 Who can Jill's associates who liked Marie's employees reference? +1129 Who couldn't many guys who can't respect Christopher alarm? +1130 Who won't all teenagers cure many dancers that aren't returning to? +1131 Who hasn't Elaine remembered George's colleague who isn't hiring? +1132 Who couldn't Carol kiss daughters of Sonia who disliked? +1133 Who wasn't Nicole's mentor that approaches Barbara questioning? +1134 What had Jane admired Nicole's ex-girlfriends that are fleeing from? +1135 Who isn't a patient's ex-wife who is investigating the waitress returning to? +1136 Who could some teenager's classmates that aren't curing the guest's best friends discover? +1137 Who shouldn't some sister of Carl that doesn't appreciate Kirsten stun? +1138 Who wasn't some student discussing every grandfather of this actor who isn't worrying? +1139 Who haven't teachers cared for Karla's sisters who don't talk about? +1140 Who didn't this granddaughter of Pamela who isn't working with Janet's employee escape from? +1141 What isn't Renee referencing that father of Carlos who hadn't forgotten? +1142 Who wasn't every teacher who has distracted Carol's sister revealing? +1143 Who wouldn't that father of Stephen who concealed many grandsons of Monet attack? +1144 What has Charles noticed some son of Elaine that forgets? +1145 Who will that mother of Teresa who fires Sheila shock? +1146 Who will the grandmother of Dawn sound like every senator who found? +1147 Who hadn't Todd's granddaughters criticized Janice's hairdresser who has referenced? +1148 Who has this newspaper article annoyed Leslie's grandfather that could sound like? +1149 Who weren't some patients stunning a lot of cashiers that would return to? +1150 Who can Becky hide Julie's electrician that disagreed with? +1151 Who would Jerry flee from the teenagers that fired? +1152 Who are Brad's doctors that were hiring Melissa's nephews criticizing? +1153 Who hadn't the dancer argued about the actress's daughters that didn't scare? +1154 Who isn't Stephanie's boyfriend who approached Rachel admiring? +1155 Who weren't Jacqueline's colleagues annoying Bethany's cousins that are praising? +1156 Who wasn't this waitress's boss confusing Debra's grandson that hasn't revealed? +1157 Who are these waitresses hiring guests who couldn't hire? +1158 Who are most actors that hugged Denise's supervisors healing? +1159 Who isn't that person that had questioned Marla praising? +1160 Who won't Thomas discuss some dancers that are questioning? +1161 Who wasn't a student's granddaughter who had cared for Steve's colleague observing? +1162 Who didn't Steve talk to this person that was kissing? +1163 Who have adults annoyed this waitress's partners who had kissed? +1164 Who does Alicia distract the dancer that did disgust? +1165 Who haven't a lot of sons of a lot of pedestrians sounded like most teenagers that were embarrassing? +1166 What haven't most teenagers appreciated some adults who remembered? +1167 Who won't Tamara admire Jodi's students who shouldn't love? +1168 Who was a teenager that saw Carlos distracting? +1169 Who could some child's nieces work with Elaine's plumber that kisses? +1170 Who isn't that teenager that returns to every granddaughter of Jodi listening to? +1171 Who was Larry's ex-husband who hadn't hated Stephanie kissing? +1172 Who wouldn't Lori's guests who had approached Jennifer's handyman flee from? +1173 Who won't those senators' cousins who had worried Jessica's cousins disturb? +1174 Who wasn't Candice's boss that flees from April worrying? +1175 Who might Karla's plumbers respect Ann's hairdresser who can't talk to? +1176 Who weren't these banks talking to Theodore's friends that hadn't disgusted? +1177 Who can Christopher distract Keith's dermatologist who complained about? +1178 Who will Sherry worry Rose's plumbers that hadn't boasted about? +1179 Who can Judy question these dancers that do upset? +1180 What might Florence complain about this granddaughter of Kirsten that criticizes? +1181 Who hadn't Brad left these boys that are confusing? +1182 What is Marla's nephew that remembers Janice curing? +1183 Who might Sandra's electrician hire Alan's brothers who disgusted? +1184 Who were these teachers' ex-girlfriends who weren't listening to a teenager's sons worrying? +1185 Who doesn't some legislature that had irritated Natalie's son alarm? +1186 Who might Carla see James's girlfriend who aggravated? +1187 Who weren't Timothy's nephews that have discovered those forks confusing? +1188 What shouldn't Marla conceal Galileo's hairdresser who flees from? +1189 Who isn't Cindy returning to every dancer that won't shock? +1190 Who will Renee's nephew hug Leslie's niece that was disturbing? +1191 Who had Winston Churchill talked to those boys who wouldn't bother? +1192 Who hadn't Martin's plumbers who irritated that customer's associates hugged? +1193 Who would Carlos embarrass every woman that can't praise? +1194 Who isn't Brad's ex-husband that should listen to Cynthia questioning? +1195 Who wouldn't Phillip complain about most doctors' senators that had hated? +1196 Who wasn't Tiffany visiting Kathleen's spouse who was upsetting? +1197 Who hadn't the Impressionists' senators who saw Richard worried? +1198 Who aren't most drivers who were working with Candice's ex-girlfriends complaining about? +1199 Who have these sisters of Steven that haven't hated Barbara's fathers known? +1200 Who might Barbara scare that dancer who should hurt? +1201 Who can't Elizabeth's daughter that scares all daughters of Laurie question? +1202 Who is Douglas's contractor that bored Patricia describing? +1203 Who hasn't some student that hadn't impressed Sherry's piano teacher irritated? +1204 What was Florence complaining about some woman's student who hadn't healed? +1205 Who were most pedestrians criticizing many grandmothers of Katherine who wouldn't reference? +1206 Who weren't some people who did visit Renee's mentors approaching? +1207 Who has Homer listened to Lucille's ex-boyfriend that fled from? +1208 Who didn't the child discover a guest's girlfriend that had cared for? +1209 Who aren't Jerry's guests who were hiring all cashiers firing? +1210 Who did many ladies' girlfriend that wouldn't kiss those nephews of the senator investigate? +1211 Who are Larry's hairdressers that haven't hated Samuel's students impressing? +1212 Who are Joel's sisters that hadn't argued about a lot of actresses hurting? +1213 Who hadn't Leslie listened to Curtis's ex-husband who was talking about? +1214 Who are the waitresses that aren't describing many college campuses aggravating? +1215 Who weren't these children who hadn't discussed a lot of grandsons of Florence curing? +1216 Who had a waitress that hired Tanya hugged? +1217 Who doesn't Matt's son who returns to Veronica bore? +1218 Who would Laura dislike Sarah's grandfather who notices? +1219 Who is that man who wasn't discovering Rachel's lawyer aggravating? +1220 Who are most girls that have hidden the schools praising? +1221 Who might this lady that respected Tracy's colleagues leave? +1222 Who aren't these men's partners who worried Judy's nieces boring? +1223 Who hadn't a mother of Gary concealed Elizabeth's French teacher that was boasting about? +1224 Who had this government that distracted Tanya disturbed? +1225 Who does Sabrina hurt these teachers' girlfriend that isn't kissing? +1226 Who do Michelle's partners that have talked to Theodore's ex-girlfriends flee from? +1227 Who are the Impressionists disagreeing with all guests that don't scare? +1228 Who hasn't every grandson of the Borgias disgusted Mary's partner who had cured? +1229 Who wasn't the organization that was helping some brother of Keith irritating? +1230 Who can these drivers confuse Sharon's mothers who left? +1231 Who wouldn't Heather praise Renee's grandmother that hasn't disturbed? +1232 Who hasn't this waiter hurt Carol's daughter who was appreciating? +1233 Who was this man that disturbed Erin worrying? +1234 Who weren't all committees describing Jodi's supervisors who can't distract? +1235 Who hasn't every granddaughter of Pamela that cured the cashier worked with? +1236 Who hadn't many cashiers' hairdresser who hasn't hired Connie revealed? +1237 Who hasn't Jason bored every mother of Ruth that isn't working with? +1238 Who did Amelia reveal a lot of grandfathers of Bradley who are disturbing? +1239 Who aren't some drivers who aren't hurting a lot of friends of Noah admiring? +1240 Who had that waitress worked with adults' teachers that discovered? +1241 Who did Liam leave the waiter who hasn't insulted? +1242 Who can't Lisa hurt Timothy's mentor who has disgusted? +1243 Who weren't Allison's bosses that kissed most grandmothers of Carol observing? +1244 Who would this senator upset Alicia's mothers who insulted? +1245 Who do most patients confuse a lot of senators who hadn't criticized? +1246 Who was that daughter of Andrea investigating Susan's best friend that could sound like? +1247 Who don't many nieces of Ella watch some sons of David that cared for? +1248 Who hadn't Christina disgusted Sharon's father who hadn't worried? +1249 Who wasn't Bruce's lawyer who hasn't known Carl discussing? +1250 Who haven't the cashier's grandmothers boasted about Gregory's classmates that were worrying? +1251 Who weren't some guests' brothers that can't distract many pedestrians insulting? +1252 Who wasn't this man arguing about every girl that wasn't referencing? +1253 What aren't a lot of patients who wouldn't heal a lot of cashiers discovering? +1254 Who were Winston Churchill's supervisors that aren't worrying many nephews of Adam praising? +1255 Who do Jodi's cousins approach some pedestrians' grandmothers who listened to? +1256 Who hadn't Danielle talked about Mitchell's supervisor who could escape from? +1257 Who isn't Todd criticizing every granddaughter of Suzanne who wasn't noticing? +1258 Who was Adam's student that can describe John irritating? +1259 Who won't Daniel talk about a lot of teachers who had embarrassed? +1260 Who wouldn't Dawn conceal many teenagers that weren't stunning? +1261 Who does Kirsten impress that government that had described? +1262 Who has that waiter that worried Michelle attacked? +1263 Who hadn't some son of Robert cured Julie's grandfather that approached? +1264 Who aren't a lot of guests who were helping Teresa's contractors disgusting? +1265 Who aren't Gregory's mothers who hadn't insulted a lot of brothers of Claire escaping from? +1266 Who hadn't many sons of most guys annoyed those granddaughters of a child that have forgotten? +1267 Who haven't many skateboards impressed Travis's sisters who were finding? +1268 Who would Elizabeth's grandson aggravate Gregory's plumber that listens to? +1269 Who hadn't Derek admired Tiffany's son who annoyed? +1270 Who couldn't Clyde stun Jane's doctor who wasn't listening to? +1271 Who were Colleen's friends who could criticize Sherry's piano teachers hugging? +1272 Who aren't the Lutherans returning to Phillip's tutors who had irritated? +1273 Who are grandfathers of Amelia who have revealed these carts hurting? +1274 Who have the organizations discussed Meredith's contractors that were stunning? +1275 Who had Nancy observed some customer's friend that did appreciate? +1276 Who would most universities talk to the driver who annoyed? +1277 Who won't Barbara reference Margaret's mothers that are hiding? +1278 Who has Carmen's plumber that has hugged Caroline's mother praised? +1279 Who did the son of Jacqueline disgust Laura's best friends who didn't alarm? +1280 Who aren't most cousins of Donna annoying Alexander's supervisors that weren't boring? +1281 Who had Jacqueline's employees who scared this niece of the Impressionists seen? +1282 Who has that adult who irritated Rhonda left? +1283 Who wasn't every woman that discovered Todd embarrassing? +1284 Who wasn't this doctor who does upset every daughter of that woman talking about? +1285 Who haven't a lot of teachers fled from Brad's friends who shouldn't question? +1286 Who should Jacqueline care for some friend of Jason who is listening to? +1287 Who has Alexander cured Brian's ex-wife who confused? +1288 Who will every son of a patient that hadn't visited a lot of senators alarm? +1289 Who hadn't Jill hidden all grandsons of Walter who disgusted? +1290 Who does Deborah's French teacher escape from the cashier who can't listen to? +1291 Who can't newspaper articles that had disturbed Charles sound like? +1292 Who can't some girl that questioned Sonia think about? +1293 Who had Kristin found the students that were scaring? +1294 What has Thomas respected Jeffrey's contractor who was referencing? +1295 Who can Pamela reference many guests that bored? +1296 Who aren't many dancers who shouldn't help Tamara's tutors escaping from? +1297 Who was Barbara's son talking to some guy that is referencing? +1298 Who was Patricia's hairdresser who hadn't appreciated music boring? +1299 Who hasn't that brother of Timothy who hadn't impressed Natalie referenced? +1300 Who had Susan observed a lot of granddaughters of Christopher who had worried? +1301 Who were these cashiers' mothers who thought about the Lutherans talking to? +1302 What had Katherine argued about Donna's doctor who admired? +1303 Who isn't Barbara's ex-husband that talks about Heidi firing? +1304 Who could Heather investigate some woman who shouldn't worry? +1305 Who hadn't Sara revealed Rachel's father who talks to? +1306 Who aren't Mary's sisters that should alarm that adult's bosses firing? +1307 Who had most cashiers' husband that has known Alicia's handyman criticized? +1308 Who have many sisters of Roger cared for Heather's partners who watched? +1309 Who will this guest who can't hurt Carlos stun? +1310 Who weren't Christine's contractors observing Galileo's teachers that disagreed with? +1311 Who will that cashier confuse a patient who will dislike? +1312 Who has Vanessa described Erin's best friend who hadn't boasted about? +1313 Who won't Angela conceal the daughter of those dancers that is observing? +1314 Who could the guest remember some friend of Kathleen that wasn't observing? +1315 Who has Martha's grandfather who embarrassed Tanya upset? +1316 Who had Cynthia criticized these people's sons that weren't returning to? +1317 Who shouldn't Stacey's mothers that wouldn't work with Kristin investigate? +1318 Who would Peter boast about some mothers of Amy who aren't revealing? +1319 Who might Susan's teacher love a lot of grandfathers of the people who aren't watching? +1320 Who have some granddaughters of Deborah seen Gerald's sons who weren't firing? +1321 Who had Rodney's electricians who disturbed Kendra's ex-girlfriend scared? +1322 Who haven't most students returned to the girls who wouldn't listen to? +1323 Who shouldn't Dan approach the pedestrian's senator that shouldn't appreciate? +1324 Who doesn't Martin discuss Claire's doctor that referenced? +1325 What has Janice's mentor that hadn't found Tamara complained about? +1326 Who had Debra fired every guy's father that cured? +1327 Who is some customer's partner questioning Heather's grandmother who questions? +1328 Who had the customers approached Becca's associates that can't work with? +1329 Who was some association that did distract Winston Churchill astounding? +1330 Who had Nancy worked with these drivers' teachers that weren't hugging? +1331 What is Sara's hairdresser referencing the teachers' lawyer that would notice? +1332 Who wasn't Sarah stunning some brother of Laura who wasn't disturbing? +1333 What might every grandmother of Beverly that discusses Samantha hate? +1334 Who hadn't the men's piano teacher that upset Kendra listened to? +1335 Who should Galileo's cousin who discusses Charles work with? +1336 Who don't many sons of James who kissed the Impressionists fire? +1337 Who doesn't Leslie's associate who returns to Debra question? +1338 What wasn't the customers' grandfather that hasn't appreciated Valerie's niece curing? +1339 Who don't the women who could stun all women boast about? +1340 Who have most museums that haven't talked to the Lutherans disgusted? +1341 What was Brenda talking about these teenagers' mentor that wasn't insulting? +1342 Who had this actor's mother that noticed most companies fired? +1343 Who couldn't that actress attack James's niece who argued about? +1344 What doesn't Veronica's ex-girlfriend who insulted Charles flee from? +1345 Who won't Tina hire a lot of guests that found? +1346 What didn't Leslie's ex-wife that might visit Ellen's student notice? +1347 What had Carmen's plumber admired Grace's boss that hasn't referenced? +1348 Who can't a lot of people cure every lady's dermatologist that is insulting? +1349 Who can't Stacy admire Lucille's grandsons that hadn't cared for? +1350 Who aren't a lot of patients that haven't respected Noah's sisters talking to? +1351 Who haven't most actresses' partners kissed those waitresses who were shocking? +1352 Who shouldn't Douglas work with all brothers of Stacey who have hugged? +1353 What hasn't some student fled from some cousin of Jesus that flees from? +1354 Who doesn't this grandson of Dawn that cared for Ruth's doctor alarm? +1355 What couldn't Raymond appreciate Curtis's sister who should hide? +1356 What will the mothers of Alexander reveal Judy's grandmothers that haven't found? +1357 Who don't the patients notice the adult's grandsons who knew? +1358 Who couldn't Curtis's sons forget Heather's teachers who aren't leaving? +1359 Who had Emily's bosses respected Sara's senator that hasn't noticed? +1360 What weren't many men revealing most doctors who were thinking about? +1361 Who isn't this actress's girlfriend disagreeing with some movie that embarrassed? +1362 Who is Douglas's classmate appreciating Donna's doctor that can leave? +1363 Who aren't the grandmothers of Cheryl that haven't bored James's plumbers irritating? +1364 Who should Martha work with Julia's associates who forgot? +1365 Who should a brother of Christine hide Tracy's sisters who aren't curing? +1366 Who can most patients' grandsons disturb Nicole's spouse who will admire? +1367 Who don't Diane's mentors who described all men alarm? +1368 What hadn't Caroline concealed Patricia's senator who had disagreed with? +1369 Who don't many banks that hadn't criticized all men's friends upset? +1370 Who can't Curtis insult Nancy's piano teacher that hadn't disturbed? +1371 What did the niece of John heal all men that would describe? +1372 Who did Douglas's colleagues love Heather's contractor that was bothering? +1373 Who is Bethany's mentor who doesn't attack that senator attacking? +1374 Who haven't Beth's plumbers aggravated Florence's employees that can visit? +1375 Who were Elizabeth's classmates hurting Elizabeth's nephews that don't help? +1376 Who hasn't Sarah attacked that student that hadn't annoyed? +1377 What has every waiter that leaves Harvard University discussed? +1378 Who shouldn't Theodore complain about those women who will bother? +1379 Who has Melanie attacked that cousin of Bruce that isn't investigating? +1380 Who haven't these teenagers attacked the fathers of Jessica who aren't bothering? +1381 Who will Nina heal Sheila's lawyer that does upset? +1382 Who have most mothers of Marie who are observing all boys irritated? +1383 Who weren't those adults that don't complain about Roger's piano teachers referencing? +1384 Who were those teenagers thinking about all teenagers who can know? +1385 Who had this grandmother of Deanna disturbed James's husband who should fire? +1386 Who will that man observe that actress that appreciated? +1387 Who were many sisters of Tracy kissing Pamela's grandsons that were talking about? +1388 Who has Tara's handyman who admires Naomi's lawyer bothered? +1389 Who hasn't some lady who hired Curtis's friend fled from? +1390 Who hadn't the students who had alarmed Marie alarmed? +1391 Who have Russell's partners who didn't kiss some man's plumbers upset? +1392 Who didn't Galileo hire Catherine's electrician that respects? +1393 What were cashiers that had praised many nieces of Michael escaping from? +1394 Who aren't some drivers who don't love Diana's best friends investigating? +1395 Who is a mother of Renee who hasn't upset Ruth appreciating? +1396 Who had a grandmother of Craig fired cashiers' grandmothers who haven't alarmed? +1397 Who does that nephew of women that visited Robert bore? +1398 Who couldn't Scott know Wendy's friend that wasn't irritating? +1399 Who haven't the hospitals concealed Helen's nieces who don't criticize? +1400 Who are those waiters who did listen to Sarah's fathers criticizing? +1401 Who can many doctors' supervisor that hadn't known all grandsons of Erin care for? +1402 Who was a grandmother of a pedestrian that isn't noticing Brian healing? +1403 Who hadn't Samantha listened to Grace's cousins who impressed? +1404 Who hasn't Ellen referenced an adult who would investigate? +1405 Who hadn't Carlos observed Melanie's nephews who are hiding? +1406 Who won't Denise bore some adult that should hire? +1407 Who had Meredith argued about Patricia's daughters that approached? +1408 What will Stephen return to all nephews of that doctor who aren't concealing? +1409 What wouldn't Tina flee from the man's tutor that describes? +1410 Who could Naomi's hairdressers disagree with Alexander's ex-husband that didn't hire? +1411 Who hadn't Pamela complained about Carmen's friend who talked to? +1412 What weren't women who do love Travis's piano teachers arguing about? +1413 Who have many waitresses' ex-wives who had left Tanya's friends impressed? +1414 Who does Wayne watch Danielle's mother who visits? +1415 Who did Carol work with every cashier who escaped from? +1416 What aren't these daughters of Marcus who weren't curing Clyde's grandmothers concealing? +1417 Who had Homer upset the patients that hadn't talked to? +1418 Who hasn't Carmen's wife who listened to Tiffany cared for? +1419 Who was Karla listening to that cashier that was irritating? +1420 What would Linda respect that man that hadn't concealed? +1421 Who wasn't this dancer who has investigated every library arguing about? +1422 What haven't Brad's granddaughters who had seen Scott's plumbers insulted? +1423 Who are those sons of Deanna that couldn't escape from Martin's bosses hiring? +1424 Who have most adults that aren't disagreeing with all adults described? +1425 Who has Karla appreciated the guest who approached? +1426 What will Steven reference Florence's granddaughters that weren't admiring? +1427 Who didn't this child who isn't upsetting Carmen's plumber hire? +1428 Who had that teacher's teacher that references Lori disturbed? +1429 Who hasn't the grandmother of Sally seen Emily's friend who alarmed? +1430 Who isn't Tanya discussing Sandra's senator who respected? +1431 Who could Jill like Katherine's employees that won't hire? +1432 What won't Sandra like many children that remembered? +1433 Who isn't Douglas confusing Ann's ex-girlfriend who won't irritate? +1434 What won't this child who hadn't disliked Victoria's friends conceal? +1435 Who hasn't Jerry worried that cashier who was upsetting? +1436 Who isn't Diane's best friend who did bother Roger talking to? +1437 What hadn't Scott forgotten some grandfathers of Bill that aren't complaining about? +1438 Who hadn't Carol respected Samuel's associate who listens to? +1439 Who wasn't a niece of Raymond who disturbed Marcus's friend confusing? +1440 Who wasn't that waiter who remembers Tracy firing? +1441 What will a lot of grandfathers of Claire that haven't remembered a lot of cousins of Sabrina love? +1442 Who would Clyde irritate all nephews of Holly that fired? +1443 Who was a student that might shock Wendy's colleague boasting about? +1444 Who won't Donald impress an organization that hated? +1445 Who isn't the grandson of Sherry who hurt Monica listening to? +1446 Who could the hospital that doesn't help Frank bother? +1447 What has Elaine criticized a child that hadn't left? +1448 Who hasn't a teacher cared for Joseph's student that saw? +1449 Who weren't most fathers of Galileo that aren't escaping from Anna's daughters boring? +1450 Who hasn't that teenager who hadn't discussed that documentary about Galileo alarmed? +1451 What wouldn't Colleen's ex-girlfriend forget the guest that hadn't talked about? +1452 Who should Ronald hurt those children who are kissing? +1453 Who doesn't the legislature like Joel's hairdresser that scares? +1454 Who have most teenagers who are discussing a lot of drivers distracted? +1455 Who hadn't Suzanne distracted Jill's handyman that praised? +1456 Who did Susan aggravate all patients who investigated? +1457 Who hasn't Sarah argued about a driver who can't bother? +1458 Who can't Ann's niece who had boasted about the friend of Kathleen discuss? +1459 Who could this actor aggravate every actor that doesn't notice? +1460 What hasn't this waitress appreciated Margaret's ex-wife that complained about? +1461 Who will Sharon's piano teacher find every student's lawyer who cared for? +1462 Who hadn't Michael's dermatologist that notices Michael revealed? +1463 Who would a teacher discuss the children that fled from? +1464 Who can every woman's boss scare these students that weren't distracting? +1465 Who haven't a lot of sisters of Gerald sounded like those documentaries about Jesus that weren't distracting? +1466 Who don't grandfathers of Gary that have disgusted Robert's supervisors impress? +1467 Who wasn't Cheryl visiting that senator who wouldn't respect? +1468 Who isn't the girl's ex-boyfriend that stunned Paula hiring? +1469 Who was Beth concealing Sharon's electrician that questions? +1470 What might Jesus see Kristin's brothers that shouldn't describe? +1471 Who could those sons of Phillip who did reveal the steak stun? +1472 Who has Catherine's mentor bothered Carmen's ex-boyfriend that hid? +1473 Who will Nina approach some patients who admired? +1474 Who are the grandmothers of Sharon that are confusing William's mothers referencing? +1475 Who could Brenda discover a lot of friends of Jesus that shouldn't care for? +1476 Who had Debra attacked every pedestrian that discovers? +1477 What might Denise's mentor love the senator that disliked? +1478 Who wasn't Rebecca's handyman that disliked Carrie approaching? +1479 Who weren't Sheila's guests that embarrassed Florence's fathers impressing? +1480 Who should a woman admire some grandson of Cynthia who had healed? +1481 Who would Tina leave some woman who didn't confuse? +1482 Who has Phillip left most ladies' ex-husband that could investigate? +1483 Who aren't many teenagers boasting about Gina's daughters that won't worry? +1484 Who don't cashiers notice those actors who were worrying? +1485 Who couldn't Marla astound some actress who stunned? +1486 What had a father of Jerry disliked Kristen's granddaughter that does discuss? +1487 Who isn't Tanya's colleague that concealed Donna irritating? +1488 Who might Charles hug Gregory's partner that questioned? +1489 Who will Bethany dislike Eva's students that hadn't admired? +1490 Who hasn't this library that references Heidi admired? +1491 What won't Guy reference these people who were concealing? +1492 Who hasn't Janet's partner who had disgusted Kayla talked about? +1493 Who isn't Joseph's French teacher that bored that cousin of Kimberley approaching? +1494 Who are many people kissing most waitresses that are helping? +1495 Who has Beverly respected Tracy's handyman that hugs? +1496 Who was Stephen's tutor concealing Carol's associate that watched? +1497 Who were these nephews of Kristin attacking Lawrence's ex-wives that scared? +1498 Who didn't Amy love Anna's doctors who are watching? +1499 Who would the nephew of Ellen shock Renee's lawyers who did work with? +1500 What are many adults visiting Cheryl's bosses who haven't returned to? +1501 What have Matt's French teachers escaped from Erin's ex-girlfriends who haven't discovered? +1502 Who would Phillip argue about these actors that worked with? +1503 Who couldn't Samuel fire those brothers of William who discussed? +1504 What won't Charles's niece leave Mary's niece who is referencing? +1505 Who had Christine's tutor that wouldn't know most grandsons of most doctors remembered? +1506 Who have Kristin's tutors who appreciated Carrie's senators appreciated? +1507 Who weren't most nieces of Janet escaping from Peter's contractors that weren't kissing? +1508 Who won't Janet's father notice Joel's grandmothers who cared for? +1509 Who has this student's girlfriend disagreed with Eric's contractor that hugs? +1510 Who won't Laura like Katherine's cousin who hasn't talked about? +1511 Who should Jane's colleague irritate these dancers who worked with? +1512 Who are Melissa's piano teachers that have disagreed with Sharon's ex-girlfriends escaping from? +1513 Who hasn't a committee impressed that waiter who has embarrassed? +1514 Who were Dan's ex-wives talking to grandfathers of Janice that weren't stunning? +1515 Who have Alexander's French teachers who alarmed cashiers' tutors hidden? +1516 Who hasn't that bird embarrassed Joel's best friend that hasn't noticed? +1517 Who hasn't Todd sounded like Anne's doctor that hires? +1518 Who hadn't some guy listened to men's dermatologist that is firing? +1519 Who might Mitchell care for Homer's grandsons that have investigated? +1520 Who had Bruce's supervisors distracted all fathers of a boy that do disturb? +1521 Who would Roger approach some customer who observes? +1522 Who was Caroline hurting Jodi's ex-boyfriend who astounded? +1523 Who wasn't the guest who approaches Karla helping? +1524 Who had Daniel stunned every lady's piano teachers who shouldn't flee from? +1525 Who hasn't Ellen's mentor who bothered Joel's ex-husband found? +1526 Who can't Scott find many men who should shock? +1527 Who is Sally escaping from every friend of Julie that wouldn't fire? +1528 Who were all students boasting about all granddaughters of David that criticized? +1529 Who haven't a lot of pedestrians sounded like documentaries that hadn't hurt? +1530 Who would most drivers' boss irritate most libraries that haven't argued about? +1531 Who shouldn't all vases bore every sister of Frank that noticed? +1532 Who had Donald complained about Gregory's hairdressers that have investigated? +1533 Who had Thomas's senators who haven't respected Liam insulted? +1534 Who weren't the girls that helped Keith's plumbers astounding? +1535 Who can Maria worry the guy that wasn't criticizing? +1536 Who hadn't Suzanne watched Stacey's doctors that are boasting about? +1537 Who don't these doctors who will dislike Rhonda's sons approach? +1538 Who should most ladies' guest who hadn't investigated Jodi's ex-wives investigate? +1539 Who was Rachel arguing about Jill's friend that is bothering? +1540 Who had Heidi talked to those actors who weren't concealing? +1541 Who hadn't Andrew hidden that pedestrian that respects? +1542 Who had these mothers of Karla loved an actress who hadn't hurt? +1543 Who haven't a lot of mothers of Naomi returned to Galileo's sisters who were confusing? +1544 Who are Lisa's ex-wives discovering some girl's senators that shouldn't approach? +1545 Who shouldn't those sisters of Dawn aggravate most governments that hadn't worried? +1546 Who should Sarah talk about Thomas's classmates that couldn't astound? +1547 Who are Nina's students who had confused Tammy's sons referencing? +1548 Who have many shows about travel that have hurt all ladies disagreed with? +1549 What haven't those cashiers that have complained about cashiers found? +1550 Who would Marla find a bird that found? +1551 Who was Melissa's sister that discussed Eva astounding? +1552 Who wasn't Rebecca impressing Becky's niece who helped? +1553 What didn't Carrie insult some man's dentists who hadn't praised? +1554 Who would some nephews of Ruth distract that senator that would love? +1555 Who are some sisters of Florence working with David's colleagues who have complained about? +1556 Who would Carolyn's ex-husband who hasn't escaped from the Borgias attack? +1557 Who hasn't every nephew of Thomas who hadn't irritated some library worked with? +1558 What isn't Jane curing Emily's handyman who noticed? +1559 Who wouldn't Claire conceal a guest who should impress? +1560 Who had Laurie revealed the girls who couldn't admire? +1561 Who wasn't this boy criticizing Leslie's grandfather who hasn't hurt? +1562 What shouldn't Richard's senator that respects Rachel's granddaughters think about? +1563 Who wouldn't some person's colleague hire a lot of teenagers who have attacked? +1564 Who do Rebecca's daughters who liked Patricia's bosses complain about? +1565 Who hadn't some woman that saw Colleen noticed? +1566 Who haven't Tara's nephews remembered Cynthia's daughters who aren't referencing? +1567 Who wouldn't many committees fire Christine's bosses who weren't noticing? +1568 Who was that girl who scares Phillip's sister listening to? +1569 Who hadn't Marcus's nieces that impressed Dan complained about? +1570 Who weren't those friends of Matt that insulted the nephews of Sally curing? +1571 Who isn't a boy firing Galileo's best friend that hasn't embarrassed? +1572 Who weren't Anne's partners that will conceal Lawrence's classmates curing? +1573 What shouldn't these students argue about some girl that has argued about? +1574 Who was Daniel irritating Todd's dermatologist that was healing? +1575 Who did some senator hurt this child that will hide? +1576 Who has Amy's granddaughter that bored Bethany impressed? +1577 What had Samuel disliked that adult's grandfather that is boasting about? +1578 Who had most women that noticed these adults upset? +1579 Who hasn't some student that had irritated Steve's contractor aggravated? +1580 Who had Monica noticed this grandmother of Barbara that isn't talking to? +1581 Who might Carl leave Kathleen's associate that disturbed? +1582 Who has this dancer upset Renee's hairdresser that hasn't concealed? +1583 Who had Christopher aggravated Alan's doctor who hadn't disturbed? +1584 Who weren't Douglas's sisters that hadn't found the windows alarming? +1585 Who isn't some cousin of Sonia who likes Paula's friend scaring? +1586 Who hadn't Ella's electricians who concealed many waiters' doctor thought about? +1587 Who can't Omar's teachers flee from Naomi's grandsons who hadn't disgusted? +1588 Who aren't the Impressionists attacking Suzanne's mentors who bored? +1589 Who are most teenagers listening to some people who weren't kissing? +1590 Who should Robert disturb Julie's hairdressers who were fleeing from? +1591 Who hadn't Catherine concealed Meredith's senators that were disgusting? +1592 Who did Becca's classmate that forgot every movie about the Lutherans irritate? +1593 Who has some son of James that didn't leave Keith disturbed? +1594 Who hadn't Ronald admired Sheila's dentist that hasn't hired? +1595 Who do some teachers argue about Edward's friends that astounded? +1596 Who will Bruce return to Rachel's piano teacher that has left? +1597 What should a sister of Renee that shouldn't escape from the girl's mother disagree with? +1598 Who won't Jennifer hate Stacy's supervisor who is hiding? +1599 Who shouldn't Alice annoy every teacher that isn't annoying? +1600 Who hadn't some grandmothers of many actresses upset a senator who complained about? +1601 Who has that waitress who admired Dana's grandfather listened to? +1602 Who couldn't Jeffrey's girlfriend find every waitress that cared for? +1603 Who haven't Caroline's grandmothers cured Cheryl's associates that aren't talking to? +1604 What should Laurie's supervisors return to Angela's supervisors that are hiding? +1605 Who hasn't the student watched the daughter of the teacher that was returning to? +1606 Who haven't all girls alarmed Cynthia's grandsons who weren't praising? +1607 What isn't Janice's ex-boyfriend who doesn't argue about Carrie appreciating? +1608 What are these nieces of Denise returning to many nephews of a senator that hadn't appreciated? +1609 Who hadn't Veronica irritated Cheryl's piano teachers who could dislike? +1610 What won't some actress discover Heather's senators that weren't curing? +1611 Who shouldn't Tina investigate Tracy's supervisor that can't irritate? +1612 What had Amy respected a lot of cousins of Derek who do respect? +1613 Who doesn't every driver boast about Lawrence's contractor who can embarrass? +1614 Who is Matt listening to Margaret's dentist that will irritate? +1615 Who haven't a lot of teenagers alarmed senators who won't disturb? +1616 Who hasn't Melissa respected a child's friend that insulted? +1617 Who weren't all students questioning Brenda's mothers who are upsetting? +1618 Who don't the students complain about Mary's grandsons that sounded like? +1619 Who has Stacy observed Christine's grandfather that will disturb? +1620 Who had a brother of some girls discovered the nephew of Galileo that doesn't talk to? +1621 Who were ladies that hadn't found Kendra's colleagues upsetting? +1622 Who can't Kirsten's colleague who wasn't shocking Jessica's granddaughters insult? +1623 Who is the dancer that kisses Clyde upsetting? +1624 Who did this father of Ruth who has concealed Anna's French teachers fire? +1625 Who aren't Jerry's partners that would argue about a lot of boys visiting? +1626 Who wasn't Daniel finding an adult's dermatologist who returned to? +1627 Who weren't some nieces of the boy observing Jane's piano teachers who knew? +1628 Who can't Heidi alarm every library that listened to? +1629 Who hasn't Omar remembered every nephew of Joseph who wasn't describing? +1630 Who aren't those sons of Margaret that hadn't aggravated Donald's brothers fleeing from? +1631 Who are all daughters of Lisa that weren't impressing some teenager's nephews working with? +1632 What hadn't Lisa's grandmothers who hated all mothers of Tanya found? +1633 Who had most students who concealed Charles impressed? +1634 Who wasn't most children's teacher that fired Robert astounding? +1635 Who has that nephew of Ruth that isn't helping Sabrina's handyman disagreed with? +1636 Who hadn't Florence's employees who hadn't cured Heidi's contractors seen? +1637 What wouldn't Ann's associates that had concealed Joel's husband appreciate? +1638 Who have senators who remembered the mountains stunned? +1639 Who weren't those grandmothers of Ruth watching Donna's plumbers that are disgusting? +1640 Who didn't Samantha insult a lot of men who hadn't hugged? +1641 Who have a lot of cousins of every student disliked adults who haven't healed? +1642 Who had Tiffany admired that dancer's guest that approached? +1643 Who will Denise complain about that adult that embarrassed? +1644 Who might Jodi bore Theresa's electricians who weren't arguing about? +1645 Who did Holly complain about the son of Paul who is questioning? +1646 Who is Carolyn's hairdresser who questions April appreciating? +1647 What could Bill like Jill's best friends who visited? +1648 Who aren't many pedestrians who should help Emily's contractors discussing? +1649 Who hadn't a driver respected most teachers that praised? +1650 Who wasn't this actor fleeing from Tanya's contractor who shouldn't visit? +1651 Who doesn't a dancer listen to that sister of Winston Churchill who described? +1652 Who should Gerald's grandmothers work with most waitresses that aren't talking to? +1653 Who was Aaron's nephew that fires a guest escaping from? +1654 Who will Marcus's supervisor insult Tanya's grandsons who are concealing? +1655 Who does Andrea aggravate Gerald's spouse that praised? +1656 Who should Catherine fire a man's contractors who admired? +1657 What wasn't this child who shouldn't sound like Lucille finding? +1658 Who is Karen's girlfriend who disturbed every driver disgusting? +1659 Who would most grandmothers of Russell bother a pedestrian who shouldn't admire? +1660 Who can Candice describe all actors who fled from? +1661 Who was that nephew of Diana that didn't work with Elaine visiting? +1662 Who didn't those hospitals reference the child's cousin that upset? +1663 Who wasn't Elizabeth boasting about these guests' partner who hasn't attacked? +1664 Who will Sherry's grandmother who hid Samuel's grandmother worry? +1665 Who had Nina's lawyer that won't appreciate every waiter bored? +1666 Who hadn't Margaret's sister who helped Naomi annoyed? +1667 Who will Angela's piano teacher that helped Christopher reveal? +1668 Who can't Janice's senator like Anne's partners who didn't astound? +1669 Who aren't many libraries irritating Nicole's ex-wives who won't hide? +1670 Who aren't Becky's mentors concealing Sharon's hairdressers who had discussed? +1671 What had Pamela thought about Carlos's lawyer who reveals? +1672 What aren't those doctors noticing Clyde's classmates who were escaping from? +1673 Who had Linda watched a lot of drivers' teacher that did notice? +1674 Who aren't Deborah's sisters disagreeing with Theresa's contractors that couldn't aggravate? +1675 Who do most waiters criticize that guy's best friends who were praising? +1676 Who have these doctors who hadn't disturbed Roger's lawyers irritated? +1677 Who will Renee leave April's fathers that didn't disgust? +1678 Who couldn't Carlos listen to all waitresses' doctors who aren't scaring? +1679 Who are the Lutherans helping a lot of waiters that hadn't bothered? +1680 Who aren't most doctors' ex-wives that liked some blouses disturbing? +1681 Who have photographs upset these grandfathers of some doctor that aren't revealing? +1682 Who have some senator's partners that criticized nieces of teachers argued about? +1683 Who weren't Kathleen's French teachers who are curing Lucille's grandsons observing? +1684 What can Bruce argue about a lot of people's grandfathers that will discuss? +1685 Who isn't a daughter of Jane that shouldn't disturb Sarah investigating? +1686 Who shouldn't those students who couldn't know Jill's bosses remember? +1687 Who has Andrea talked to Rose's French teacher that could aggravate? +1688 Who were some girls who bored Janet's grandsons questioning? +1689 Who aren't Scott's contractors that had confused April's fathers impressing? +1690 Who do those customers return to Nancy's grandfathers that were hiring? +1691 Who can't some woman who discovered Thomas's best friends complain about? +1692 Who isn't Julia's boyfriend that sees the bread disgusting? +1693 Who could Laurie discover John's boss who hugs? +1694 Who hadn't these teenagers concealed the actress that would stun? +1695 Who aren't the friends of Teresa who hadn't boasted about Sandra's sisters shocking? +1696 Who had these pedestrians attacked these adults that disagreed with? +1697 Who were most cashiers who investigated George's tutors leaving? +1698 Who haven't those schools talked to Dan's friends that weren't alarming? +1699 Who shouldn't those sons of Candice who aggravated those girls hug? +1700 Who wouldn't most sisters of Beth that would astound Rachel's tutors talk to? +1701 Who has Holly's brother that loves this man hurt? +1702 Who couldn't Christopher dislike Andrea's cousin that cares for? +1703 Who hadn't Dennis's supervisors distracted Jill's electrician that references? +1704 Who couldn't every guest fire that daughter of Claire that is annoying? +1705 Who had most guys thought about that nephew of Linda that observes? +1706 Who weren't Deanna's sisters who were questioning every patient's brothers hiding? +1707 Who isn't the customer that didn't work with Meredith disturbing? +1708 Who has Diane's mentor that bothered Tamara confused? +1709 Who weren't many fathers of Dan who confused these cousins of Jesus arguing about? +1710 Who will Omar's nieces flee from Heidi's contractors who haven't shocked? +1711 What has William escaped from that daughter of those cashiers that healed? +1712 What haven't these actresses that are visiting Beverly's employees fled from? +1713 Who hadn't Anne's friends that talked to Gary astounded? +1714 Who has that guest's associate that wasn't impressing Charles aggravated? +1715 Who weren't many doctors confusing Kristen's doctors who can worry? +1716 Who aren't Alexander's partners that were listening to many teenagers hugging? +1717 Who is that teacher who can praise some adult annoying? +1718 Who weren't most nephews of Eric that were discovering a lot of adults hugging? +1719 Who haven't those waitresses who described reports about Harvard University bored? +1720 What don't this actor's ex-girlfriends love those guys that referenced? +1721 Who has the teenager that will find Kirsten annoyed? +1722 Who are those waiters' employees who are referencing many documentaries about Galileo irritating? +1723 What doesn't Ronald reveal Richard's guest who thought about? +1724 Who was every teacher that disliked Beth watching? +1725 Who have the students that will heal all patients confused? +1726 What has a mother of Anna healed every man that has healed? +1727 Who hadn't the nephew of every customer who reveals Renee's grandsons listened to? +1728 Who hasn't Candice disturbed every bank that couldn't worry? +1729 Who aren't these mothers of Todd who did observe those doctors' dentists alarming? +1730 Who weren't the girls firing many guests who don't annoy? +1731 Who has Derek cured Julia's dentist that hadn't noticed? +1732 Who was a sister of Cheryl that describes Kristen shocking? +1733 Who doesn't Jerry's partner who hadn't alarmed Theresa escape from? +1734 Who aren't most guests that complained about women investigating? +1735 Who couldn't Kristin impress these cashiers that aren't working with? +1736 Who hasn't Matt astounded some waiter who forgets? +1737 What wouldn't Ronald visit many people that can't discuss? +1738 Who hadn't Leslie sounded like reports about current events that stunned? +1739 Who hasn't Colleen loved John's girlfriend that healed? +1740 Who wasn't Roger complaining about Samantha's senator who won't fire? +1741 What won't the doctors dislike Joel's hairdresser that didn't describe? +1742 Who hadn't Jerry disgusted that person who was describing? +1743 Who was Maria's spouse who wouldn't praise Becca hiding? +1744 Who have most fathers of Jill who haven't found some waiters' best friends worked with? +1745 Who haven't a lot of waitresses who can't forget Cynthia's French teachers shocked? +1746 Who had Roger approached Alexander's friend who had investigated? +1747 Who doesn't every grandson of Allison that can alarm Russell care for? +1748 Who are Gregory's brothers working with James's granddaughters who can't complain about? +1749 Who weren't the dancers who might heal Grace's nephews leaving? +1750 Who had Charles talked to this sister of Stephen that isn't finding? +1751 Who couldn't Joel worry all customers that liked? +1752 What has Elizabeth praised every lady who had sounded like? +1753 What is Sally's friend who references some doctor noticing? +1754 Who would these teachers disagree with Carmen's sister that worked with? +1755 Who has Marie's ex-boyfriend that disagrees with Rose's electrician worried? +1756 Who were Donna's teachers that did shock Dan's French teachers curing? +1757 Who might Jacqueline see Donna's husband who isn't attacking? +1758 Who hasn't Jacqueline noticed Keith's nephew that wasn't hiding? +1759 Who isn't Nina's sister observing Michelle's student that wasn't attacking? +1760 Who isn't Janice's boss who has distracted Heather's partner disagreeing with? +1761 Who should Margaret's girlfriend respect Amy's guest that didn't conceal? +1762 Who weren't some drivers visiting Janice's bosses that can investigate? +1763 What shouldn't Alan see that grandmother of Amy who has admired? +1764 Who will many brothers of Vanessa that had revealed Amelia's plumbers watch? +1765 Who might Gina's brother who isn't disturbing Vincent's ex-boyfriend see? +1766 Who weren't Marcus's sisters arguing about Nina's doctors who are revealing? +1767 Who didn't Monica watch those waitresses that had alarmed? +1768 What hadn't mothers of Jane that had discovered a nephew of Maria concealed? +1769 What would a lot of brothers of Brenda that won't flee from George's ex-girlfriend disagree with? +1770 What wasn't Benjamin criticizing Holly's grandmother who did love? +1771 Who was Brian watching some woman that notices? +1772 Who couldn't Cynthia attack all pedestrians that had insulted? +1773 Who will Jessica conceal those patients that didn't disturb? +1774 Who haven't some teachers sounded like Bradley's associates that are embarrassing? +1775 Who was some girl's sister that hasn't insulted Jill astounding? +1776 Who will Melanie annoy most sisters of Heather who hadn't disliked? +1777 Who was the adult that hasn't attacked Jill concealing? +1778 Who wasn't Melissa healing some grandson of every dancer that has cared for? +1779 Who can't David's dentist that hasn't talked about Margaret's handyman reveal? +1780 Who has Phillip's handyman who listens to Kathleen alarmed? +1781 Who has Andrew worried that committee that hasn't concealed? +1782 Who hadn't Bill stunned most teachers that are bothering? +1783 Who have a lot of schools worked with Rhonda's lawyers who criticized? +1784 Who hadn't Sharon healed Daniel's electrician who observed? +1785 Who haven't Kathleen's ex-wives who would think about all art galleries scared? +1786 Who are the boys that impressed Robert's partners arguing about? +1787 Who wasn't that actress who concealed Donald's contractor talking to? +1788 Who could this doctor that has listened to this guest's supervisors astound? +1789 Who were Florence's students that had worked with some granddaughters of Tamara escaping from? +1790 Who did every dancer hate this waiter who is leaving? +1791 Who isn't the grandfather of Erin that does kiss Aaron alarming? +1792 Who won't a girl that had alarmed Andrew's supervisor reveal? +1793 Who did Margaret notice some nieces of James who are alarming? +1794 Who couldn't every driver who cured Sandra escape from? +1795 Who don't some customers stun a lot of companies that weren't disgusting? +1796 What isn't Victoria's daughter appreciating a man who hasn't criticized? +1797 Who is Michael's ex-boyfriend who hasn't astounded Alice kissing? +1798 What had Douglas praised Mitchell's spouse who referenced? +1799 Who will Julie's lawyer that will irritate Donald's dentist work with? +1800 Who might Julie help every son of Lisa who astounded? +1801 Who is Gregory observing the friend of Diana who hadn't praised? +1802 What will Lawrence discover Stephanie's daughters who admired? +1803 Who hadn't Ronald's brother who could know Veronica's grandmother sounded like? +1804 Who hadn't April's brother that could appreciate Kendra hired? +1805 Who hasn't Claire insulted the nephew of Catherine that cares for? +1806 Who is Brad's mentor that has praised Brian scaring? +1807 Who hadn't most teenagers' girlfriend that returned to Brian's grandsons referenced? +1808 Who might all granddaughters of the senator boast about Amy's hairdressers who haven't complained about? +1809 Who weren't those sisters of Sonia working with many nephews of that doctor who weren't attacking? +1810 Who hasn't Valerie impressed this doctor who hurt? +1811 What hadn't this adult's guest who had disliked every teenager's doctor boasted about? +1812 Who weren't Alicia's contractors alarming a lot of hospitals that were scaring? +1813 Who couldn't Laurie stun Elizabeth's spouse that would describe? +1814 Who can Sharon alarm a lot of hospitals that haven't bothered? +1815 Who should this granddaughter of Jason hate that nephew of Vanessa who helps? +1816 Who has Tara hurt Valerie's electrician that criticized? +1817 Who hadn't this girl's cousin fired a lot of granddaughters of all dancers who weren't leaving? +1818 What shouldn't Thomas conceal many customers who criticized? +1819 Who was Donna's ex-wife that should hate Becca's employee confusing? +1820 Who is Craig visiting a child that isn't returning to? +1821 Who can Lawrence reference these women that would complain about? +1822 Who had some nephew of Raymond referenced all drivers that disagreed with? +1823 Who hadn't this patient who is concealing this patient disturbed? +1824 Who can't Joel's doctors irritate Meredith's girlfriend that scared? +1825 Who are Carla's dentists that hadn't disliked every girl's sisters concealing? +1826 Who didn't Leslie worry a lot of brothers of Patrick who can't hug? +1827 What had the Borgias described most teachers' handyman who has admired? +1828 What are some adults arguing about Marcus's ex-girlfriends who weren't talking about? +1829 Who have waitresses observed all guys that have scared? +1830 What could Nina's mother criticize those actors that don't dislike? +1831 Who should most patients flee from April's boss who finds? +1832 Who hadn't Florence's girlfriend who alarmed Deanna healed? +1833 What hadn't many men escaped from Carrie's boyfriend who had thought about? +1834 Who were Denise's partners distracting the dancers that should respect? +1835 Who don't some museums hurt most senators who are disgusting? +1836 Who hadn't that brother of Walter that hates Galileo revealed? +1837 Who were a lot of drivers that do hug a lot of patients visiting? +1838 Who hadn't these people that saw Julie's electricians astounded? +1839 Who hadn't Rodney's ex-boyfriend who could cure Amy hidden? +1840 Who might men know a lot of patients who couldn't annoy? +1841 Who had Kristen's tutor that didn't admire Nancy's students cared for? +1842 Who weren't Bill's partners that hid those public parks distracting? +1843 Who won't Brian's friend appreciate Ruth's girlfriend that does disagree with? +1844 Who shouldn't that government that forgot a lot of waiters astound? +1845 Who hadn't the stories about the Balkans bored a lot of fathers of Jill that are boasting about? +1846 Who have these patients cured Vincent's students who are hiring? +1847 What was Alicia approaching senators and some teachers scaring? +1848 Whose is Clyde exiting restaurants and Amelia talking about? +1849 Which teenagers had Tamara hired and Grace fired? +1850 What lights could Spain sell and Andrea discover? +1851 What paintings has Melissa looked like and Erin revealed? +1852 Which carriages has Melanie found and Diana revealed? +1853 What has William stunned offspring and Melinda escaped from? +1854 What might some socks shock alumni and Denise appreciate? +1855 Which is the dress irritating girls and the Lutherans revealing? +1856 What guys could those girls flee from and a lot of socks bore? +1857 What schools were many cars embarrassing and Daniel driving to? +1858 What could some teenager skate around cafes and a lot of committees conceal? +1859 What had Elaine escaped from lakes and Gary thought about? +1860 What is Aaron lifting mountains and that woman insulting? +1861 Which children had Phillip worried and Dawn disturbed? +1862 Whose can drivers arrive at closets and Heidi walk through? +1863 Which students has Steve hired and Monet hated? +1864 Which can Heidi dislike analyses and Melissa think about? +1865 Which libraries had those waiters lifted and Alicia explored? +1866 Which children has Valerie approached and Sonia hurt? +1867 Which does Leslie kiss students and Kendra visit? +1868 Which did Veronica work with teachers and the rivers bore? +1869 What is Jodi curing adults and Karen returning to? +1870 Which might Regina work with men and that waiter cure? +1871 What had Lori descended slopes and Carlos climbed down? +1872 Which women could Emily criticize and Christine notice? +1873 Whose can Julia bring mice and Brett notice? +1874 Which teeth is Samuel fixing and Grace selling? +1875 Which girls was Tara kissing and Alice thinking about? +1876 What is Ellen shocking cashiers and the waitresses boring? +1877 Which is Angela hugging doctors and Paula admiring? +1878 Which does Theresa describe men and every coat worry? +1879 Which was Stacey curing customers and these pedestrians complaining about? +1880 What had Donna concealed guests and some lady appreciated? +1881 Which people have many universities known and Gregory praised? +1882 What is Sheila distracting waitresses and Tracy returning to? +1883 Whose might Rhonda climb up mountains and Becky explore? +1884 Which men had some museums known and some waitress listened to? +1885 What did those teenagers astound senators and Heidi criticize? +1886 Which can Rose impress students and Cindy see? +1887 What had Gerald worked with waitresses and George annoyed? +1888 Which books did Marcus hate and Craig praise? +1889 Whose can Gregory tour hospitals and Douglas irritate? +1890 Whose will Margaret wear pants and Donald lift? +1891 Which men had those cashiers visited and Jeffrey admired? +1892 Whose has Jerry toured libraries and many turtles scared? +1893 What did all actors shock men and Carrie investigate? +1894 What was Donald lifting slopes and Jacqueline selling? +1895 Which shoes had these doctors worn and the pedestrians sold? +1896 Which is Donna boring actresses and Denise alarming? +1897 What could Monet talk to actresses and some schools impress? +1898 Whose was Rachel worrying organizations and some river disturbing? +1899 What are prints confusing teachers and Amanda bothering? +1900 What were those ladies upsetting waitresses and Julie appreciating? +1901 Which had the chicken shocked boys and girls described? +1902 What should Regina fire men and Rebecca distract? +1903 Which malls has Denise cleaned and Dan brought? +1904 What had Adam returned to malls and a lot of senators disliked? +1905 What was that mirror boring waitresses and Barbara discovering? +1906 Which does Kayla lift hamsters and Amanda discover? +1907 What schools was Gary dropping by and Tina biking to? +1908 Which will Vanessa arrive at art galleries and Kimberley insult? +1909 What customers was this waiter observing and Margaret observing? +1910 Which might this girl respect public parks and Rhonda tour? +1911 What customers has Gregory forgotten and the teenager kissed? +1912 Which do all patients notice guests and the cats impress? +1913 Which stories will a teenager hate and Carl forget? +1914 What companies was music astounding and David disturbing? +1915 What has Samuel annoyed men and Cynthia observed? +1916 What children has Dan worked with and Craig insulted? +1917 Which was Noah praising boys and a child approaching? +1918 What high schools had Brenda gone to and Martha sold? +1919 What would the waitresses dislike pedestrians and Carrie praise? +1920 Whose do most doctors break windows and Melissa lift? +1921 Which teachers can Claire flee from and Andrew hide? +1922 Which schools has Frank exited and a photograph disgusted? +1923 What has Stacey appreciated students and Anne discussed? +1924 What children is a nose disgusting and Nancy escaping from? +1925 Which waitresses had Jessica insulted and Rhonda thought about? +1926 Which was Adam descending stairs and Amy climbing up? +1927 What is that lady attacking women and Marcus referencing? +1928 Whose lakes will Dan clean and Leslie reveal? +1929 Which girls had Ruth concealed and Heidi embarrassed? +1930 What could some senator boast about students and Eric cure? +1931 Which alumni should Beth listen to and the hospital hate? +1932 Whose has Carlos passed cafes and these pedestrians left? +1933 Which guests had Galileo forgotten and Kevin distracted? +1934 What apples has Gerald revealed and Angela bought? +1935 What should Nancy bring apples and Sara clean? +1936 Which was Emily driving to libraries and some guy confusing? +1937 Whose plays has an adult examined and George sounded like? +1938 Whose had most computers resembled paintings and these photographs looked like? +1939 Which movie theaters has Gregory exited and Joseph left? +1940 Whose can a guy lift skateboards and Natalie think about? +1941 What has the student distracted alumni and Diana appreciated? +1942 Whose has the lady ascended ladders and Lawrence revealed? +1943 Which reports might Katherine see and the legislature criticize? +1944 Which doctors would a guy discuss and Nicole disgust? +1945 What actors could Barbara attack and Melinda talk to? +1946 Which boxes is this sketch looking like and Regina taking? +1947 Which was a pie scaring customers and the door impressing? +1948 Which is that banana alarming guys and Teresa leaving? +1949 Whose is Tamara descending steps and Guy selling? +1950 What does every vase disturb people and every hill scare? +1951 What stories was Vincent praising and a girl appreciating? +1952 Which was Connie questioning pedestrians and Leslie distracting? +1953 Which governments were all public parks stunning and Christina aggravating? +1954 Whose is Steven discovering gates and some painting looking like? +1955 Whose did Amy wear shoes and Margaret notice? +1956 Which hills had Debra descended and James remembered? +1957 Whose has Christopher passed glaciers and Anne skated around? +1958 What are all projectors upsetting waiters and a cashier curing? +1959 Which had this driver gone to banks and Douglas skated around? +1960 What couches could Dan lift and Margaret have? +1961 Which was Catherine listening to actors and those tomatoes alarming? +1962 Whose were the Impressionists hiding carts and Martin fixing? +1963 What was Dana noticing companies and Ella scaring? +1964 Which does Timothy argue about ladies and Paul find? +1965 Which should that rug distract students and Tammy annoy? +1966 What was Steven arguing about movies and Stacey thinking about? +1967 What high schools was John exploring and Erin selling? +1968 Whose could the actress buy muffins and Maria like? +1969 Which can a company impress girls and that hair astound? +1970 What schools would some teacher clean and Jessica upset? +1971 Which was Alicia noticing oxen and Roger concealing? +1972 What people have a lot of sweaters scared and Charles observed? +1973 Which are these pants impressing cashiers and Winston Churchill investigating? +1974 Which is Jacqueline concealing guys and Sarah watching? +1975 Which grocery stores would Laurie tour and Mark describe? +1976 Which candles does Ella reveal and Theresa hide? +1977 Whose is Paula astounding committees and Wendy impressing? +1978 What has Victoria watched actresses and every cashier found? +1979 Which senators do some waitresses disturb and Jennifer reveal? +1980 What were a lot of pedestrians referencing men and Claire insulting? +1981 Whose has Craig appreciated reports and a library loved? +1982 What carts have the women broken and that person thought about? +1983 What actresses have those eyes annoyed and the steps annoyed? +1984 What would Wayne criticize malls and Nancy talk about? +1985 Which alumni have many customers sounded like and many stairs irritated? +1986 Whose had Andrew passed hills and Carlos thought about? +1987 Which stairs was Amelia ascending and this print looking like? +1988 Whose was Russell breaking plates and Joel fixing? +1989 What children had Grace cared for and those couches astounded? +1990 Which had Thomas questioned committees and Douglas worried? +1991 Which was Sally helping senators and those carts annoying? +1992 Which ladders has Dawn descended and Lori brought? +1993 Whose had Monica toured malls and Teresa fled from? +1994 Which teeth have these photographs looked like and Tracy fixed? +1995 Which will adults dislike analyses and Marla complain about? +1996 Whose was Lucille skated around oases and some children discussing? +1997 Which rabbits had Florence seen and Erin revealed? +1998 What schools was Holly boycotting and Derek touring? +1999 Whose has Deborah arrived at banks and Katherine astounded? +2000 Whose steps does Melissa descend and Carrie hide? +2001 Which oases had most children arrived at and Spain taken? +2002 What has Bruce watched drivers and Stacey boasted about? +2003 What has Brenda listened to offspring and Lisa hurt? +2004 Whose was Vanessa distracting libraries and this actress exiting? +2005 Which geese will that child admire and Tammy buy? +2006 Whose does Diane buy drawings and some pedestrians notice? +2007 Which does Lawrence reference stories and that actress research? +2008 Which could Galileo break bicycles and Barbara hide? +2009 Which cashiers has Tina hurt and Rachel irritated? +2010 What means should Amy research and Caroline research? +2011 What high schools had William seen and that pedestrian bought? +2012 What hats was Catherine hiding and Frank thinking about? +2013 Which is some man fleeing from art galleries and James boycotting? +2014 Which has Rose sounded like ladies and a lot of actors revealed? +2015 Whose did Monet praise plays and that guest hate? +2016 Which had William sounded like girls and Rhonda hired? +2017 Which was Debra upsetting women and Steven referencing? +2018 Which hills had Timothy disliked and Allison revealed? +2019 What hospitals can Curtis bother and Daniel arrive at? +2020 What has Mark hired customers and waitresses hurt? +2021 Which was Walter worrying ladies and the patient referencing? +2022 What teeth is Benjamin messing up and some teacher discovering? +2023 Which skirts has Dan worn and Cindy thought about? +2024 Which art galleries is Kristen researching and Todd messing up? +2025 Whose was a patient curing sheep and a pedestrian finding? +2026 What might Christopher cure hamsters and Meredith discover? +2027 Whose is Benjamin finding horses and Jacqueline selling? +2028 Which students had Irene investigated and some drawings looked like? +2029 Whose has Patricia exited public parks and George remembered? +2030 What will some newspaper article sound like senators and Grace praise? +2031 What might Monica bring hats and Sonia admire? +2032 Which is a senator listening to boys and every student thinking about? +2033 Which will Tara disgust committees and the mirror disgust? +2034 Whose has Jacqueline embarrassed legislatures and many hamsters stunned? +2035 Which might Thomas sound like essays and Stacy think about? +2036 What cashiers should every driver listen to and Theresa praise? +2037 Which was Tina researching glaciers and this guest concealing? +2038 Which has this senator known doctors and Karen appreciated? +2039 Which customers had Steven kissed and that actress healed? +2040 What does Craig sell hats and Valerie reveal? +2041 What can Kevin have lakes and the Lutherans exit? +2042 What hills was a teacher insulting and Carol buying? +2043 Which has Meredith listened to ladies and Rachel concealed? +2044 Which stimuli has Brian boasted about and Derek seen? +2045 What pictures would Sharon resemble and Dana notice? +2046 What teeth was Jeffrey revealing and Gregory bringing? +2047 What women had many doctors irritated and Ellen appreciated? +2048 Which children were most organizations boring and these senators disagreeing with? +2049 Which was Roger helping women and the tomato stunning? +2050 What does Candice respect adults and Douglas notice? +2051 Which were many documentaries disagreeing with patients and Donald escaping from? +2052 Which reports did Andrew forget and most boys conceal? +2053 Which was Vanessa attacking boys and Monica finding? +2054 Which carriages did Christine break and Matt buy? +2055 Which analyses will Marcus scan and Stacey discover? +2056 Whose had Adam sold trucks and Theresa concealed? +2057 Which hospitals should Martin exit and Ellen research? +2058 Which photographs should Danielle look like and George find? +2059 Whose was Eva walking through lakes and these dancers exploring? +2060 What has Pamela loved senators and these dancers observed? +2061 Which could a play disagree with children and a girl cure? +2062 What waitresses would Beverly hug and Dan notice? +2063 Whose was the actress bringing stairs and that student looking like? +2064 Which was Karen praising waitresses and Anne boring? +2065 Whose had Carol healed hamsters and many cashiers concealed? +2066 Which is every teenager thinking about cars and Becky bringing? +2067 Which children had the bicycle disgusted and Charles hated? +2068 What is Ella buying paintings and Beth revealing? +2069 What should a lot of people discuss pedestrians and Jesus watch? +2070 Which women has Adam investigated and Elaine scared? +2071 What can Travis see prints and Sandra buy? +2072 Which waiters were rabbits astounding and all bicycles aggravating? +2073 Which had Joel astounded ladies and some dogs stunned? +2074 What has Denise boasted about waitresses and Tara visited? +2075 What boys are some hospitals firing and Homer discussing? +2076 What was Regina discussing women and some spinach distracting? +2077 What banks do all cakes confuse and Jill arrive at? +2078 Which waiters have those snakes irritated and a lot of customers revealed? +2079 What does this university buy windows and Susan buy? +2080 Which have all people driven to museums and Monica criticized? +2081 What are the boys disagreeing with drivers and every woman confusing? +2082 Which was Amanda discovering dancers and the water distracting? +2083 Which children has the teenager disliked and Sally bothered? +2084 Which was Vincent concealing cucumbers and Claire noticing? +2085 Which does Chad hug people and Sherry appreciate? +2086 Which was Claire finding paintings and Winston Churchill bringing? +2087 Whose does Tamara praise reports and a guest conceal? +2088 What was some teenager buying glasses and Natalie revealing? +2089 Whose has Barbara noticed teeth and some guys discovered? +2090 Which was Kristin aggravating women and Janet embarrassing? +2091 Whose did Noah hate books and Tina think about? +2092 Which restaurants is that lady finding and Becky exiting? +2093 What could Bradley hurt students and the companies hire? +2094 Which alumni is Beth questioning and Florence helping? +2095 What dishes is Stacey selling and Curtis messing up? +2096 Whose documentaries were a lot of teachers examining and Travis talking about? +2097 Which teachers has Anne boasted about and Anne hugged? +2098 Whose lights were the Impressionists cleaning and Travis bringing? +2099 Which children had these drawings looked like and Curtis hidden? +2100 What was Bradley shocking alumni and Becca watching? +2101 What might Heather ascend slopes and Brad hide? +2102 Which had Andrew visited schools and icicles irritated? +2103 Which has Samuel shocked people and Jesus returned to? +2104 Whose is Peter skated around college campuses and Tiffany visiting? +2105 What dishes did Galileo mess up and Katherine break? +2106 Which children is Martha kissing and this mountain disgusting? +2107 What had that legislature talked to ladies and Phillip known? +2108 What can Amelia research hospitals and many actors take? +2109 Which planes will Alicia have and Kirsten reveal? +2110 What does Mitchell escape from adults and Julia appreciate? +2111 Which has Steven ascended steps and some doctor descended? +2112 What have some children known teachers and Jesus loved? +2113 Which was Karen taking horses and the customer curing? +2114 What could Scott insult adults and Douglas reference? +2115 What have the Impressionists disagreed with children and Peter worked with? +2116 Which does Deborah take cars and Janice think about? +2117 Which is Bradley working with pedestrians and Peter irritating? +2118 Which has that commentary disagreed with theses and Thomas researched? +2119 Whose has Harvard University described commentaries and Amy sounded like? +2120 What children will Cindy hurt and Jacqueline criticize? +2121 Which schools has Mary questioned and Derek discussed? +2122 What fish could many actors clean and Judy clean? +2123 Whose has Janice toured art galleries and a lot of waitresses appreciated? +2124 Which gates had Tina bought and Tanya thought about? +2125 What men was Angela listening to and Dana arguing about? +2126 What could Richard exit banks and Cheryl distract? +2127 Which essays did those men disagree with and Deanna conceal? +2128 Which trucks has Leslie taken and Kirsten revealed? +2129 What will Ella conceal drivers and that library remember? +2130 Which boys had Carrie hugged and Marcus shocked? +2131 What would some child scan books and Travis research? +2132 What has Veronica respected women and some cashier hidden? +2133 Which cats has Kevin brought and Liam brought? +2134 Whose had this person skated around lakes and Tamara researched? +2135 Which cafes have all dancers disliked and Pamela toured? +2136 What cafes has that man explored and Claire cleaned? +2137 Which icicles will Tammy discover and this man notice? +2138 What girls are some dancers disturbing and travel irritating? +2139 What are a lot of universities working with children and Tara fleeing from? +2140 Which should the legislature discuss analyses and every library hate? +2141 Which might Patricia attack doctors and Connie insult? +2142 What have many dancers sounded like drivers and Samantha seen? +2143 Which offspring is Melinda appreciating and Grace bothering? +2144 Which guys will Maria know and Nicole talk to? +2145 What drivers had Frank concealed and Janet talked about? +2146 Which screens had Cindy noticed and Gerald thought about? +2147 Which is Leslie arguing about alumni and Aaron investigating? +2148 What would Charles break skateboards and Paula see? +2149 Which reports is Martha complaining about and Anna noticing? +2150 Which museums has Amelia explored and Bill described? +2151 Which ladies have all lights upset and Renee kissed? +2152 Whose should Tiffany exit museums and Gary flee from? +2153 Which teachers has some student boasted about and Sally approached? +2154 Which commentaries had Pamela examined and Frank complained about? +2155 Which senators has the college campus alarmed and a lot of drivers disagreed with? +2156 What was Barbara astounding ladies and Wayne hurting? +2157 What is Homer concealing actresses and the cafe impressing? +2158 Which was Kimberley describing movie theaters and Kathleen lifting? +2159 Whose hills had Sabrina ascended and Theresa escaped from? +2160 Which had Christopher argued about paralyses and Monica complained about? +2161 Which is Debra arriving at lakes and some man admiring? +2162 What is Michael breaking couches and Galileo discovering? +2163 Which patients has Regina helped and Samantha revealed? +2164 What did David distract hospitals and Karla leave? +2165 What should those scarves aggravate associations and a commentary distract? +2166 Which could Eva like skirts and Robert notice? +2167 What had Candice discussed hills and Christine skated around? +2168 Which men had Kirsten found and Laurie questioned? +2169 Which has Edward fled from guys and Edward shocked? +2170 What was Edward questioning waitresses and Winston Churchill admiring? +2171 What had this commentary disagreed with dancers and some customer admired? +2172 What might Jason aggravate doctors and Donna discuss? +2173 Which museums can Wayne drop by and Joel return to? +2174 Which has Walter sounded like drivers and Sandra talked to? +2175 What can many patients disagree with drivers and Benjamin worry? +2176 What are the plays disagreeing with pamphlets and this museum concealing? +2177 What will that icicle disturb teachers and Bethany notice? +2178 Which are most fish impressing men and Chad escaping from? +2179 Whose hospitals is a carriage shocking and Homer walking through? +2180 Which might these universities confuse doctors and these students irritate? +2181 Whose should Clyde take pants and a lot of turtles find? +2182 Which had Russell known men and all closets bothered? +2183 Which were some customers working with boys and some student insulting? +2184 Which does every driver talk to boys and Carolyn appreciate? +2185 Which senators had a committee discussed and Carol watched? +2186 Which has Monica broken carts and some school concealed? +2187 Whose might Heidi forget reports and Carmen find? +2188 Which ladders has Bethany climbed down and all actors sold? +2189 What had Joel hated teenagers and ladies disagreed with? +2190 What dancers can Michael love and Adam hire? +2191 What has every child bored teachers and Alicia disgusted? +2192 What patients can Irene hate and Galileo conceal? +2193 What boys is Meredith approaching and Harvard University talking to? +2194 What eyes might Maria have and Brett clean? +2195 What was every mouth irritating waiters and Gary bothering? +2196 Which banks do many guests explore and Gregory reveal? +2197 What is Stephen describing offspring and some waiter observing? +2198 What girls will Danielle disturb and Laura conceal? +2199 What has that bird disgusted offspring and George praised? +2200 Which might those boxes bother women and the guys see? +2201 Which malls can Aaron take and that doctor return to? +2202 Whose is Susan returning to restaurants and some university taking? +2203 Which did the Impressionists alarm dancers and Larry astound? +2204 What had the hill stunned senators and Michael cured? +2205 Which does Gerald leave men and John work with? +2206 What did some restaurant impress children and Grace question? +2207 What drivers had Kimberley concealed and Eric kissed? +2208 Which girls were all eyes worrying and most actors visiting? +2209 What has some actress bought college campuses and Michael described? +2210 What drivers had Jason discussed and Bruce scared? +2211 What has this glass looked like photographs and Julie found? +2212 Which did the child kiss cashiers and this water disgust? +2213 Which men was Andrea kissing and Sally working with? +2214 Whose governments is Michelle scaring and Alexander distracting? +2215 What is Patricia lifting icicles and Sharon taking? +2216 Which has John loved dancers and some print looked like? +2217 What does Wayne remember hospitals and most children research? +2218 What women should Travis dislike and Gregory visit? +2219 Which guests had all horses found and some spotlight bothered? +2220 What is this teenager confusing girls and Richard kissing? +2221 What will Theresa insult pedestrians and Carol care for? +2222 Whose is Renee describing theses and Jill describing? +2223 Which people has every actress discussed and Stephen known? +2224 Which dogs have a lot of girls healed and Ruth brought? +2225 Which does Galileo tour hills and Teresa think about? +2226 Which can Homer shock women and Rebecca approach? +2227 What will Rose describe closets and Peter dislike? +2228 Whose did Sonia bike to museums and Florence boycott? +2229 Whose is Adam scanning reports and Carrie hiding? +2230 Which college campuses would Claire tour and Andrew admire? +2231 What has Rodney visited girls and that muffin annoyed? +2232 Which series might Marcus research and Laura boast about? +2233 What did Bill insult guys and Vanessa fire? +2234 What does Allison escape from actors and this actress praise? +2235 What cups had Barbara fixed and Candice found? +2236 What was Lucille boasting about women and actresses leaving? +2237 Whose had Mitchell thought about organizations and every dancer scared? +2238 What dancers have those plays bored and Charles worked with? +2239 Which did Carlos like chairs and every actress break? +2240 What photographs had Christine looked like and Marla cleaned? +2241 Whose is Samantha lifting glasses and Thomas breaking? +2242 What might Timothy break chairs and Karen reveal? +2243 Whose might Rhonda mess up college campuses and many organizations sell? +2244 Which actresses had the Impressionists known and this painting looked like? +2245 Which prints has Rachel looked like and Martha lifted? +2246 What should many photographs scare teenagers and these senators approach? +2247 What can Jodi arrive at libraries and James run around? +2248 What patients had Jason found and Douglas criticized? +2249 What reports did Rose hate and James find? +2250 Which is Theodore investigating associations and the rabbit annoying? +2251 Whose pamphlets is Susan concealing and Julia noticing? +2252 What is Mark fleeing from oases and Christina finding? +2253 Whose has Lawrence boasted about media and the cashier thought about? +2254 What are a lot of cashiers fleeing from hospitals and Susan questioning? +2255 What books have most prints looked like and Amelia admired? +2256 Whose could Joseph drive to grocery stores and every man return to? +2257 What was Heidi talking about men and Renee stunning? +2258 Which is every driver breaking trucks and every sketch looking like? +2259 Whose can these boys disagree with newspaper articles and Raymond conceal? +2260 What is Melissa scanning theses and every university referencing? +2261 Whose are those actors scanning commentaries and Regina discovering? +2262 What has Gerald irritated guests and Veronica hated? +2263 Which did this sweater aggravate students and Deborah sound like? +2264 What is Emily hiding stimuli and Dana discovering? +2265 What will Gerald break bicycles and Winston Churchill like? +2266 What has the museum known doctors and a lot of senators concealed? +2267 What has Rhonda left hills and Sally appreciated? +2268 Which series is the guest complaining about and Edward boasting about? +2269 What could Erin hire drivers and Theodore return to? +2270 What had Ronald hidden actresses and Tara observed? +2271 Whose had that mouth astounded hospitals and Matt passed? +2272 Whose had David escaped from high schools and Gregory revealed? +2273 What is Kimberley visiting museums and patients exiting? +2274 What restaurants has Candice disliked and Sherry walked through? +2275 Whose has Eva bothered museums and all pies embarrassed? +2276 Which bases had Cynthia discovered and Raymond discovered? +2277 Which does Jeffrey remember students and every student disgust? +2278 Which would Ellen alarm men and all teenagers criticize? +2279 What has the man described dancers and the Lutherans upset? +2280 Which has Becca returned to students and men complained about? +2281 Whose can Jacqueline look like drawings and Candice admire? +2282 What was Paul hiding horses and Lawrence thinking about? +2283 What essays might April scan and Jacqueline complain about? +2284 What had Beth sounded like patients and Veronica found? +2285 What does Noah listen to men and this story aggravate? +2286 What has Tonya seen closets and Natalie exited? +2287 Whose could Bill clean icicles and the teacher notice? +2288 What cars has April cleaned and Aaron brought? +2289 Which grocery stores is Edward touring and Larry returning to? +2290 Whose could Phillip run around art galleries and Pamela describe? +2291 What should Thomas boast about patients and Travis scare? +2292 Whose can Jennifer climb down hills and Bethany buy? +2293 What adults had most hats annoyed and a government respected? +2294 Which cashiers had Todd concealed and some doctor annoyed? +2295 Whose is Roger concealing plays and a lot of reports disagreeing with? +2296 Which did Stephanie cure boys and Melissa admire? +2297 What has Marie researched pamphlets and Roger lifted? +2298 Which students has Ruth questioned and Katherine watched? +2299 Which waitresses can these men hurt and the adult find? +2300 Which had Claire discussed glaciers and the Borgias gone to? +2301 What was Ella finding forks and Steven cleaning? +2302 Which should jackets irritate senators and most organizations help? +2303 What actors have some teachers remembered and Valerie loved? +2304 Which birds did Judy clean and Sally notice? +2305 Whose could Jacqueline mess up cups and most banks have? +2306 Which reports might Raymond clean and Stephen lift? +2307 Which stairs should Judy climb up and Deanna clean? +2308 Which movie theaters has Walter skated around and Vincent bought? +2309 Which was Monet lifting brochures and Curtis complaining about? +2310 Which is Kendra revealing grocery stores and Sheila lifting? +2311 Which was this art gallery impressing men and Martha hiding? +2312 What could a fish aggravate customers and that cashier heal? +2313 Which did Samantha cure men and Kevin praise? +2314 Which was Mary noticing actors and the child watching? +2315 Whose had George remembered museums and Matt irritated? +2316 Whose will Liam appreciate oases and Rose discover? +2317 Which customers have most jackets confused and Meredith known? +2318 What has Caroline visited drivers and this student disliked? +2319 Which is the glass distracting actresses and Kevin watching? +2320 Which men have glaciers embarrassed and Angela embarrassed? +2321 Which art galleries was that dancer messing up and Thomas touring? +2322 What reports is John lifting and Suzanne buying? +2323 What was Benjamin hugging alumni and Mary aggravating? +2324 Which waitresses has Tina left and Tammy revealed? +2325 What did Kayla heal adults and Leslie think about? +2326 What might Susan like pants and Donna conceal? +2327 Which could Leslie descend slopes and Tiffany conceal? +2328 Whose had Andrew walked through movie theaters and John remembered? +2329 What will Roger flee from hospitals and Timothy respect? +2330 Whose would Susan explore mountains and Renee describe? +2331 Which drivers had Deanna observed and Paul admired? +2332 Whose was Dennis describing pamphlets and Ann boasting about? +2333 Whose is Kayla biking to hills and Todd taking? +2334 Which could Charles argue about men and that doctor dislike? +2335 What does Samuel flee from pedestrians and Rebecca question? +2336 What had Dawn praised students and some senator hated? +2337 Which cars does Matt break and many teachers sell? +2338 Which schools should Stacey go to and Roger stun? +2339 What steps is this teacher climbing down and Gerald climbing up? +2340 What has Debra hurt actresses and that pedestrian aggravated? +2341 Which might Rhonda complain about men and Charles hire? +2342 Which could Laura question committees and Travis impress? +2343 Which was Eva approaching children and art upsetting? +2344 Which did the bank argue about commentaries and that student argue about? +2345 Whose was Ronald dropping by schools and Colleen walking through? +2346 Which is Sandra finding muffins and Spain taking? +2347 Whose reports has Sabrina examined and Tanya hidden? +2348 Which was every pepper aggravating guys and Amy annoying? +2349 What books had Theodore praised and the woman boasted about? +2350 Whose could Jill reveal theses and Omar hate? +2351 Which socks have some customers worn and Theresa bought? +2352 What had Diana fled from teenagers and this mountain distracted? +2353 Which was Barbara biking to banks and a cashier insulting? +2354 What people can Liam watch and Danielle conceal? +2355 Which is Edward talking to boys and Cheryl fleeing from? +2356 Whose photographs have some actresses looked like and Monica sold? +2357 What have the rugs irritated ladies and Valerie observed? +2358 What schools had Carl talked about and Helen thought about? +2359 What guys did Martin flee from and Rodney know? +2360 Which has Rodney climbed down slopes and the person bought? +2361 What would Sabrina work with dancers and Gregory visit? +2362 Which was literature aggravating teenagers and Bradley returning to? +2363 What planes does Laurie fix and Jodi take? +2364 Which was Andrea discussing men and Ella concealing? +2365 Which was the book disagreeing with patients and Valerie discussing? +2366 What children has Aaron known and Kendra worked with? +2367 Which was Adam admiring trucks and the cashier revealing? +2368 What slopes has Martin ascended and Frank found? +2369 What is Robert exiting restaurants and Todd buying? +2370 What had Candice astounded universities and every story alarmed? +2371 What was Nina noticing dancers and most teachers confusing? +2372 What can Tonya hate actresses and Lisa watch? +2373 What has that boy investigated teachers and Vincent helped? +2374 Which boys will Rachel discuss and some sock shock? +2375 What would most slopes bore teenagers and Galileo talk to? +2376 Whose was Robert wearing blouses and Steven thinking about? +2377 Whose did all adults heal hamsters and Kristin have? +2378 Which customers does Craig help and Adam argue about? +2379 What had some adult climbed down hills and Aaron climbed up? +2380 Which students is that window shocking and Gary stunning? +2381 What might the dancers admire slopes and Cheryl descend? +2382 Which should that boy love women and Sheila reference? +2383 Which hats did Teresa lift and Diana conceal? +2384 What would Catherine ascend slopes and pedestrians reveal? +2385 What had Connie messed up gloves and Curtis lifted? +2386 What was this window confusing men and Mary watching? +2387 Whose would Walter criticize art galleries and Becca escape from? +2388 Which apples has Linda noticed and Christina concealed? +2389 Which teenagers had many trucks irritated and Jodi visited? +2390 What have most women driven to closets and the student described? +2391 Whose was Rachel examining commentaries and Travis arguing about? +2392 What are those boys arriving at movie theaters and Jerry visiting? +2393 What was every carriage hurting teachers and Victoria listening to? +2394 Whose oases had Laurie visited and April insulted? +2395 What had Carmen hurt waitresses and Teresa worked with? +2396 Which had Sharon appreciated syntheses and Tanya complained about? +2397 Which should doctors watch women and those adults confuse? +2398 What should this shirt confuse guests and those girls care for? +2399 Which students will Meredith worry and Danielle talk about? +2400 Whose should the waitresses walk through oases and Patrick sell? +2401 What was Mary breaking wheelbarrows and that print looking like? +2402 Which patients had Heidi aggravated and Marla healed? +2403 Whose is that customer upsetting libraries and Tina passing? +2404 What feet might Aaron hide and Douglas reveal? +2405 What cashiers does some university disturb and Kayla disturb? +2406 Which pedestrians has Marie concealed and Laura remembered? +2407 Which rivers can Leslie walk through and Katherine run around? +2408 What children can many children kiss and Omar conceal? +2409 What was Benjamin exploring banks and Liam cleaning? +2410 What does Chad return to public parks and Holly arrive at? +2411 Whose has Kenneth dropped by college campuses and Wayne driven to? +2412 Whose is Marla cleaning nuclei and Derek concealing? +2413 Which has Beth fled from customers and Guy complained about? +2414 Which cashiers had Heather praised and Nancy revealed? +2415 What has Erin cured cashiers and Stacey left? +2416 What was Janice leaving men and the shawls annoying? +2417 What had Rebecca admired libraries and Kimberley confused? +2418 What forks is James concealing and every dog breaking? +2419 What would Brian sell carts and Heather reveal? +2420 What is Bill skated around museums and James discovering? +2421 What libraries had Jesus visited and Debra fled from? +2422 What can Katherine escape from boys and Harvard University help? +2423 Which students has Bruce listened to and Andrea attacked? +2424 Whose banks had Ella disgusted and George arrived at? +2425 What has every cashier explored mountains and Scott disliked? +2426 Whose was Denise complaining about paralyses and Andrea researching? +2427 Whose was Sara examining analyses and Roger scanning? +2428 Which can Tanya love dancers and Spain hire? +2429 What ladders would Debra ascend and Judy discover? +2430 Which universities had plays astounded and the hospitals impressed? +2431 What has some teacher remembered offspring and Frank discovered? +2432 Whose are these couches confusing legislatures and Emily boycotting? +2433 Which had many legislatures criticized ladies and most pedestrians observed? +2434 Which glaciers had Amanda boycotted and Sara sold? +2435 What organizations has a dress irritated and Becca noticed? +2436 What had Leslie helped waiters and a lot of drawings confused? +2437 Which was the convertible annoying people and Bruce attacking? +2438 What children should Dennis care for and Alice astound? +2439 Whose will those ladies cure rabbits and Lawrence hide? +2440 Whose did Anne wear pants and Andrea clean? +2441 What deer might Jane heal and the man bring? +2442 What is Claire fixing wheelbarrows and David admiring? +2443 Which customers had Deanna respected and some cup disturbed? +2444 Whose had Kristin thought about governments and most lights irritated? +2445 Whose had Irene disturbed hospitals and a waitress driven to? +2446 Which has Diane worked with dancers and every scarf bored? +2447 Which is a person confusing ladies and Kristin finding? +2448 Which movie theaters had every teacher passed and Mitchell noticed? +2449 Whose can those waitresses drive to movie theaters and Galileo drive to? +2450 Which does Timothy escape from girls and Beth reveal? +2451 Which does Tina notice carriages and that company like? +2452 Whose had Becca appreciated hospitals and Helen scared? +2453 What did Tammy boast about means and Anna boast about? +2454 What were a lot of cashiers disagreeing with children and Lori insulting? +2455 Which might Tina annoy boys and every drawing impress? +2456 Which public parks is Amanda passing and Timothy thinking about? +2457 Whose had Mary discovered committees and this beef irritated? +2458 Which does Grace know patients and Laurie upset? +2459 Which are the couches upsetting guests and Nina thinking about? +2460 What customers could Gary work with and some child dislike? +2461 Whose movie theaters would Sara leave and the Borgias visit? +2462 Which lakes had waiters exited and Mitchell remembered? +2463 What is Rebecca cleaning gloves and Diane revealing? +2464 What offspring was Brian hugging and Anne thinking about? +2465 What should Debra bike to cafes and Jane admire? +2466 Which species is Brett lifting and this boy revealing? +2467 What was James attacking drivers and a girl thinking about? +2468 Which has the boy listened to drivers and some cars worried? +2469 Which will Ruth shock teachers and some dancer look like? +2470 Whose was Margaret biking to banks and Rebecca messing up? +2471 Which has Renee arrived at hospitals and that shoe alarmed? +2472 What had many essays disagreed with men and Helen bored? +2473 What will this doctor approach waitresses and Carl boast about? +2474 Which is Edward returning to dancers and Becky praising? +2475 Which had Russell exited college campuses and Mary messed up? +2476 Which waitresses did Janice attack and that water distract? +2477 What can Cindy aggravate senators and Scott watch? +2478 What cups might Nina buy and Caroline admire? +2479 Whose would Debra discuss college campuses and Peter reveal? +2480 Which is Edward working with offspring and all gates impressing? +2481 Whose dishes had Anna cleaned and Donna thought about? +2482 Whose has Helen skated around oases and Kendra found? +2483 What had Sheila brought stimuli and Karla concealed? +2484 Whose plays had Jennifer forgotten and Donald thought about? +2485 Which pictures has Craig discovered and some closet looked like? +2486 What does Kendra reveal gates and that boy take? +2487 Which did Helen go to art galleries and Victoria describe? +2488 Which college campuses have some associations disliked and Sandra escaped from? +2489 Whose has Richard sounded like closets and Kristin bought? +2490 What can Winston Churchill look like prints and Carlos look like? +2491 What was Guy finding people and Amy helping? +2492 Whose would Beth clean carriages and that actress hide? +2493 Whose has Rhonda arrived at hills and Todd sounded like? +2494 Which geese did Jill bring and Janice conceal? +2495 Whose was Stephen driving to glaciers and Monica describing? +2496 Which is this hill irritating waiters and Cynthia fleeing from? +2497 What does Judy argue about dancers and many senators kiss? +2498 Which pedestrians has Tiffany alarmed and Chad listened to? +2499 Which waiters are these teachers referencing and these scarves astounding? +2500 Whose has Travis sounded like essays and Lawrence criticized? +2501 What movie theaters can Samantha visit and this adult sell? +2502 What steps has Maria descended and an actor cleaned? +2503 What has the university talked about pamphlets and Leslie sold? +2504 Which did Donald work with doctors and Samuel conceal? +2505 Whose was the actor curing snakes and Jane concealing? +2506 Whose screens does Paul conceal and Emily like? +2507 Whose was Christina healing turtles and Marie bringing? +2508 Which doctors had Charles scared and Omar watched? +2509 What offspring was Carlos disgusting and Daniel approaching? +2510 Which offspring had these waiters thought about and most hospitals boasted about? +2511 What can Connie climb down ladders and a student admire? +2512 Which was every guest driving to movie theaters and Alexander selling? +2513 Whose was every woman passing rivers and Marla hiding? +2514 What offspring are the teachers discussing and a restaurant boring? +2515 What species will Michael reveal and Tonya clean? +2516 Whose should Jane walk through high schools and Kirsten flee from? +2517 Which banks was Marcus investigating and Alicia appreciating? +2518 What does Theodore climb up stairs and this adult sell? +2519 What was Paula discussing women and some government arguing about? +2520 What should Curtis approach students and Alexander approach? +2521 What did Jesus run around mountains and the cashier bike to? +2522 What wheelbarrows could Allison break and Renee lift? +2523 What would a mall irritate senators and James flee from? +2524 Which did Amelia discover theses and Lisa look like? +2525 What has Liam kissed waiters and that mall stunned? +2526 Which have most actors disagreed with waitresses and Matt talked about? +2527 Which people will Laura listen to and Meredith embarrass? +2528 What were a lot of associations embarrassing men and Jerry talking to? +2529 Whose had Sara skated around cafes and Kayla talked about? +2530 Whose can Homer climb down slopes and ladies conceal? +2531 Whose should these students climb up stairs and Andrew descend? +2532 Which could Gina hug men and Guy conceal? +2533 Which was every boy hiding carriages and these guys breaking? +2534 Which people should Margaret reference and Timothy discuss? +2535 Which offspring might a doctor forget and Steve argue about? +2536 What was Gary hiring pedestrians and Kenneth confusing? +2537 What children is Robert listening to and Ruth alarming? +2538 Which people has Teresa astounded and Connie disliked? +2539 Whose pies has Claire hidden and Jane taken? +2540 Which children have the associations embarrassed and Natalie referenced? +2541 Which sketches can Christopher look like and Nancy take? +2542 Whose is Daniel running around oases and Christine passing? +2543 Which was Ellen appreciating brochures and Helen revealing? +2544 Which children can Alicia talk to and Matt confuse? +2545 Which men has this dancer kissed and Holly sounded like? +2546 Which was Chad climbing up slopes and Erin noticing? +2547 Which might Kendra kiss guys and those actresses alarm? +2548 What has Marla boasted about syntheses and Becca argued about? +2549 Whose coats had associations sold and Melanie thought about? +2550 What offspring can Eric investigate and these ladies appreciate? +2551 Whose has Peter found oases and Tammy sold? +2552 Which was Stephen hiding hamsters and Tiffany discovering? +2553 Which patients will Liam remember and the doctor discuss? +2554 Which would Irene care for cashiers and Veronica work with? +2555 What should Judy aggravate patients and every waitress criticize? +2556 What has Scott impressed alumni and Stephen discussed? +2557 Which might all ladies climb up slopes and Jodi descend? +2558 Whose is every actress bringing chairs and Dan finding? +2559 What children has Laura remembered and Marcus aggravated? +2560 Whose do many customers find mountains and a teacher tour? +2561 What schools had Sarah criticized and Tammy seen? +2562 What have the committees concealed men and some guys escaped from? +2563 Whose should every customer return to hospitals and some sock look like? +2564 What had Beverly sounded like ladies and many paintings looked like? +2565 Whose organizations had Spain scared and Becca worried? +2566 Which ladies did Michelle attack and Victoria upset? +2567 What associations has every school alarmed and a boy confused? +2568 What might the dancer astound governments and a convertible impress? +2569 Which closets could Monet pass and every print look like? +2570 Whose does Larry bike to oases and James appreciate? +2571 What is Kristen hugging waiters and Tiffany returning to? +2572 Which has Andrew approached pedestrians and most guys talked about? +2573 Whose has Kristen toured cafes and Charles brought? +2574 What was Laura hiring students and some pictures upsetting? +2575 Which is Tonya noticing schools and Keith revealing? +2576 Which is Allison fleeing from museums and Jason aggravating? +2577 What had Amelia worked with waitresses and these people worked with? +2578 Which was Sarah wearing scarves and Julia taking? +2579 Whose had Nicole discussed libraries and Martin gone to? +2580 Which governments have some actors stunned and Cindy stunned? +2581 Whose does Daniel reveal peppers and Pamela clean? +2582 Whose does Richard skate around museums and Laurie bike to? +2583 Which does Laurie question guests and Ronald escape from? +2584 What had Dawn fled from lakes and customers passed? +2585 What is Marie approaching teenagers and Andrea astounding? +2586 Whose had Daniel brought mouths and some adults thought about? +2587 Whose can many photographs look like screens and Brian think about? +2588 What do cafes worry doctors and Keith insult? +2589 Which customers had Vanessa approached and a sock impressed? +2590 What will Denise flee from adults and Lori argue about? +2591 What children had Richard hired and paintings irritated? +2592 What prints had Chad looked like and Kevin researched? +2593 Whose is that guest ascending hills and Adam walking through? +2594 Which is Cynthia visiting ladies and Omar arguing about? +2595 Which are most dresses shocking waitresses and Steve leaving? +2596 What children had Stacey talked about and Naomi criticized? +2597 What mountains is Jane going to and Carrie revealing? +2598 What dresses might Peter have and Cynthia see? +2599 What does Vincent run around malls and many actresses buy? +2600 What is every waitress helping girls and Pamela praising? +2601 Whose was Jennifer dropping by public parks and Helen dropping by? +2602 What doctors had Katherine embarrassed and Holly bored? +2603 What has Ruth fled from students and Bethany fired? +2604 What is Mitchell revealing students and Liam hugging? +2605 Which might every customer respect actors and Bill leave? +2606 What pictures had Veronica lifted and Leslie cleaned? +2607 What had Martin alarmed museums and Kenneth gone to? +2608 Which should Stacy conceal boys and a turtle stun? +2609 What was Donald investigating girls and Bradley hiring? +2610 Whose is Travis arriving at public parks and Ella exiting? +2611 What associations have these women aggravated and Kirsten shocked? +2612 What was Jane finding muffins and all girls taking? +2613 Which dancers can Christine know and Valerie remember? +2614 What is every art gallery distracting companies and Bethany worrying? +2615 What drivers are all guests working with and Russell scaring? +2616 Which was Martha climbing up slopes and these girls taking? +2617 What has Valerie remembered malls and Vincent discovered? +2618 Which might these teachers describe commentaries and Derek argue about? +2619 Which had Victoria gone to glaciers and Bruce talked about? +2620 What did Dennis worry drivers and Janice forget? +2621 Which teeth was Renee cleaning and Helen fixing? +2622 What was Carl hurting people and pictures looking like? +2623 Which lights had Carl taken and Eric thought about? +2624 What have the Great Lakes distracted doctors and Gregory described? +2625 Whose were these teenagers disagreeing with newspaper articles and every waitress noticing? +2626 What actors had these dancers loved and some guest left? +2627 What can Michelle hurt women and Renee aggravate? +2628 What associations has Rebecca annoyed and all teachers embarrassed? +2629 What was Heather wearing dresses and Rachel cleaning? +2630 What men had Dennis talked to and Valerie left? +2631 What did Beverly disgust cashiers and Carla listen to? +2632 Which has some customer complained about documentaries and Monica referenced? +2633 What had Angela talked about teenagers and Bruce forgotten? +2634 Which offspring had all doors disturbed and Brett insulted? +2635 Whose could Brad hide tomatoes and Renee hide? +2636 Which is Melanie biking to mountains and Amelia touring? +2637 Which dancers did Julie flee from and Amelia conceal? +2638 What would Gerald dislike guests and many actors investigate? +2639 What has Wayne returned to drivers and Danielle forgotten? +2640 What was the essay disagreeing with newspaper articles and Eva referencing? +2641 What steps is Tara descending and this patient taking? +2642 What bikes had Judy sold and Teresa broken? +2643 Whose was this pedestrian examining documentaries and Debra discovering? +2644 Which were some banks helping men and Jill irritating? +2645 What should Sabrina hate doctors and Edward praise? +2646 Which steps was Gina climbing down and Walter finding? +2647 Which newspaper articles had Rodney insulted and Steve concealed? +2648 Which was Ronald upsetting children and Monica listening to? +2649 Which is Sarah climbing down mountains and Kendra passing? +2650 What people did most guests disagree with and Marla hate? +2651 What had customers described girls and Brenda helped? +2652 What patients would Victoria approach and Beverly reference? +2653 Which people had Allison cured and that doctor cured? +2654 Which actors had Nina shocked and Christina thought about? +2655 What does Maria like ladders and Jodi discover? +2656 Which adults had Tina attacked and Kendra insulted? +2657 Which paintings have the cars looked like and Deborah thought about? +2658 What will Mary respect guests and Spain worry? +2659 What did Walter appreciate banks and Jennifer embarrass? +2660 What had Carrie referenced teenagers and Bethany concealed? +2661 Which trucks are all drawings looking like and Jeffrey taking? +2662 Which gates did this girl think about and the painting look like? +2663 What has Douglas helped students and a vase aggravated? +2664 What was Steve irritating hospitals and Alexander confusing? +2665 Whose movies could a company describe and Craig sound like? +2666 What ladders should Catherine ascend and the waitresses clean? +2667 What offspring had Holly listened to and Deanna cared for? +2668 What senators had Ellen left and Steven cured? +2669 What does Carla respect senators and Eva look like? +2670 Which customers has Mark scared and Tiffany listened to? +2671 What could every guest hurt boys and Valerie leave? +2672 Which public parks had Wayne messed up and Margaret messed up? +2673 Whose had Deanna described malls and some senator gone to? +2674 What committees is every wheelbarrow impressing and Andrea upsetting? +2675 What oases was Emily driving to and Cindy describing? diff --git a/zeroshot/npi_licensing/eval_results.json b/zeroshot/npi_licensing/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..ca2b6d423ce747dae957213daff42b51da4d1f7f --- /dev/null +++ b/zeroshot/npi_licensing/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.5781961737017917} \ No newline at end of file diff --git a/zeroshot/npi_licensing/predictions.txt b/zeroshot/npi_licensing/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..6db2e6fe2fae479e290fb3a0fbdb2e31350b502e --- /dev/null +++ b/zeroshot/npi_licensing/predictions.txt @@ -0,0 +1,6587 @@ +index prediction +0 Bruce had ever played. +1 Patrick had ever answered. +2 Some carts do ever accelerate. +3 Dawn will ever know Jeffrey. +4 Should Monica ever grin? +5 Cindy has ever anticipated most dancers to study. +6 Denise had ever doubted Galileo can't bother to train. +7 Has Jane ever worried Tonya? +8 Many customers would ever bake. +9 Mitchell should ever know Eva. +10 Could many doctors ever clash? +11 Sandra has ever planned to get a job. +12 Will the horse ever come here? +13 Did children ever cope? +14 Did Carolyn ever argue? +15 Did most dresses ever shrink? +16 Sarah might ever forget many teachers. +17 Frank might ever boast about Melanie. +18 Dennis had ever begged Joel to stretch. +19 Does Eric ever prompt Dawn to write? +20 Would Ann ever drink? +21 Did Sharon ever waste away? +22 Some screen would ever disappear. +23 Does Gary ever believe Dennis to alarm Stephanie? +24 That coat will ever warp. +25 Suzanne had ever sat down. +26 Nina has ever descended that mountain. +27 Did Jill ever die? +28 Bradley will ever mutter. +29 Many children have ever surrendered. +30 Stephen would ever hide away. +31 Steven has ever badgered every man to sound like most senators. +32 Students might ever see. +33 Some bank will ever profit. +34 Andrea would ever encourage Beth to care for Charles. +35 Brad has ever insulted Irene. +36 Vanessa could ever dislike Charles. +37 Carl had ever kissed Irene. +38 Christina will ever try to observe Dawn. +39 Did Samuel ever sneeze? +40 A lot of girls should ever benefit. +41 Some people could ever chat. +42 Colleen had ever imagined those actresses have ordered Kevin to talk. +43 Has Sharon ever climbed down a hill? +44 Catherine might ever implore Kristin's cousins to speak up. +45 A lot of teachers have ever kissed. +46 Gary has ever argued. +47 These blouses have ever shrunk. +48 Did Heather ever visit those closets? +49 Do a lot of legislatures ever profit? +50 Raymond had ever murmured. +51 Amelia has ever learned who aren't seeing. +52 Had Frank 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Paula could ever escape. +739 Had those windows ever tipped over? +740 Has this rice ever upset Nicole? +741 Did this documentary ever worry Travis? +742 Julia can ever smile. +743 Could Sally ever disturb that lady? +744 Lucille had ever wept. +745 Elaine has ever blinked. +746 These skirts had ever frayed. +747 Should Wayne ever bother to sneeze? +748 Has this man ever respected a lot of rivers? +749 Every man has ever bothered to listen to Lisa. +750 Robert had ever ascended every slope. +751 Tiffany can ever scream. +752 Beth has ever persuaded Judy to learn. +753 These bikes have ever cracked. +754 Beverly has ever healed Eric. +755 Could Stacy ever disappear? +756 Travis should ever dare some teacher to arrive at every cafe. +757 Should Jodi ever talk? +758 Martha had ever joked around. +759 Veronica can ever heal Melissa. +760 Could these drivers ever retaliate? +761 Andrea might ever discover Margaret to forget Patricia. +762 A sweater will ever fray. +763 Melissa has ever 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Anne certainly asks most companies to buy every hamster. +1145 Even Vanessa ever buys these trucks. +1146 Even Mitchell certainly climbed up the slope. +1147 Even that fork should often look like a sketch. +1148 Even every skirt would ever soak. +1149 Even many apples ever went bad. +1150 Even cashiers often cooperated. +1151 Even Daniel often thinks about some bird. +1152 Even Victoria ever fails to praise Tracy. +1153 Even Brett also complains. +1154 Even most legislatures ever urged that university to listen to a man. +1155 Even this woman ever attempts to salute. +1156 Even Brenda ever manages to forget Christopher. +1157 Even that person has often boasted about Wendy. +1158 Even Phillip should ever entice Jodi to stand up. +1159 Even Adam has really motivated Larry to murmur. +1160 Even Pamela has ever revealed who perform. +1161 Even James certainly manages to exist. +1162 Even those waitresses ever clashed. +1163 Even Daniel might ever reference Scott. +1164 Even all actors ever 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Even a screen ever darkens. +1275 Even Connie also fixed trucks. +1276 Even some cats could ever upset Patrick. +1277 Even a scarf really shrinks. +1278 Even a lot of women have ever practiced. +1279 Even Andrew should certainly attempt to think about the guest. +1280 Even these bikes ever crash. +1281 Even those teenagers often walk through a lot of restaurants. +1282 Even Kathleen had ever broken some wheelbarrow. +1283 Even this woman also induces Connie to explore this restaurant. +1284 Even Ruth ever comes here. +1285 Even senators ever doubted some ladies believe Nina to reference Gary. +1286 Even Eva has ever turned out to astound Frank. +1287 Even these committees also consider those muffins to scare Carla. +1288 Even all planes ever turned. +1289 Even this pork ever baked. +1290 Even Paul has also answered. +1291 Even Jacqueline often lies. +1292 Even a pedestrian did ever dislike Meredith. +1293 Even Erin should clearly clean all glasses. +1294 Even those pedestrians ever 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Even some scarves had ever folded. +1316 Even the library clearly retaliated. +1317 Even Melanie can ever escape. +1318 Even many tomatoes had ever ripened. +1319 Even all women ever weep. +1320 Even some adult did often nod. +1321 Even many senators also like to litter. +1322 Even Noah should ever read. +1323 Even some teachers certainly work with Jane. +1324 Even Denise has ever yearned to spin around. +1325 Even the children also shout. +1326 Even bikes also stop. +1327 Even a truck has really fallen apart. +1328 Even those patients clearly try to question Rodney. +1329 Even all men often confuses all students. +1330 Even Tara ever turns out to kiss most guys. +1331 Even Leslie could ever entreat Laura to smile. +1332 Even that ice cream often evaporates. +1333 Even the lamps ever brightened. +1334 Even all rabbits do also fall asleep. +1335 Even Noah also praised Grace. +1336 Even the guys also question Raymond. +1337 Even most waitresses would ever date. +1338 Even a doctor ever prodded Tiffany to fix this window. +1339 Even Bill ever fired Dan. +1340 Even Sheila clearly proved to notice a guy. +1341 Even Nicole ever respects that cashier. +1342 Even reports do ever astound Charles. +1343 Even Guy has ever appeared to run around the art gallery. +1344 Even the government certainly succeeds. +1345 Even Christine did ever wash. +1346 Even some associations ever met. +1347 Even Nancy will ever depart. +1348 Even Danielle could really consider Andrew to wash. +1349 Even these cashiers had ever approved. +1350 Even Carrie also investigated what don't blacken. +1351 Even the screen will clearly disappear. +1352 Even a lot of lights could ever darken. +1353 Even those guests clearly whisper. +1354 Even Sarah really studies. +1355 Even that customer had clearly answered. +1356 Even a glacier has ever vaporized. +1357 Even a lot of people often approach Meredith. +1358 Even every library had ever proposed to worry. +1359 Even Roger also hopes to care for Thomas. +1360 Even Julie often scratches. +1361 Even the ice cream ever condenses. +1362 Even all socks also tighten. +1363 Even every actress ever insults Denise. +1364 Even the Impressionists also correspond. +1365 Even these pedestrians had ever struggled. +1366 Even Carrie really sounded like Steven. +1367 Even a vase often drops. +1368 Even this unicycle might ever crash. +1369 Even Candice does ever laugh. +1370 Even some guys certainly think a lot of waitresses respect Walter. +1371 Even these shoes often unfold. +1372 Even drivers ever break this vase. +1373 Even those people often walk through a lot of grocery stores. +1374 Even schools could ever worry. +1375 Even Dennis ever coughs. +1376 Even Elaine has ever fallen asleep. +1377 Even some customers had ever hugged. +1378 Even Holly had really convinced Galileo to irritate the boy. +1379 Even a lot of teenagers ever see. +1380 Even Martin has ever protested. +1381 Even a lot of windows had ever scared Clyde. +1382 Even Sonia had ever hidden. +1383 Even Sara ever proved to nod. +1384 Even Michael clearly broke a fork. +1385 Even most shawls ever tighten. +1386 Even the Lutherans often interacted. +1387 Even Rose ever tries to stand up. +1388 Even some sock ever twirled. +1389 Even Naomi also gets a job. +1390 Even April ever plays. +1391 Even teenagers often declare Kristin to describe these children. +1392 Even James ever judged all carts to maneuver. +1393 Even many muffins ever blacken. +1394 Even all pedestrians also alarmed Vanessa. +1395 Even Gary ever looks to weep. +1396 Even many candles did really disgust a lot of guys. +1397 Even Denise has also want to question many museums. +1398 Even a convertible ever slows. +1399 Even Sherry also finds Tina to praise Samuel. +1400 Even Clyde also managed to reveal many couches. +1401 Even Eva certainly donates. +1402 Even Homer really confused David. +1403 Even the drivers have ever ordered Carlos to wash. +1404 Even every eye ever flings open. +1405 Even girls will ever look to joke around. +1406 Even a lot of customers certainly impresses Sarah. +1407 Even the pedestrian also disagrees with Marla. +1408 Even Victoria also questioned Gregory. +1409 Even Alice ever described all mountains. +1410 Even Jodi really longs to know Debra. +1411 Even Nina had also thought about who has arrived at a lot of rivers. +1412 Even that window should really open. +1413 Even these patients have ever profited. +1414 Even all doctors certainly nod. +1415 Even Brenda does really work with William. +1416 Even Irene should ever see. +1417 Even Catherine will certainly explain everything. +1418 Even those cashiers ever stretched. +1419 Even the guys also benefited. +1420 Even these shoes do ever unfold. +1421 Even Wendy had ever needed to confuse a guest. +1422 Even Stacy had ever blinked. +1423 Even Theresa had ever persuaded this driver to reference that play. +1424 Even Renee ever hired the Borgias. +1425 Even Laura ever considers those drivers to learn. +1426 Even 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Even politics really irritated Stacy. +1603 Even Cindy also passed some restaurants. +1604 Even these pedestrians also complained. +1605 Even those boxes have ever flung open. +1606 Even James also breaks some computer. +1607 Even Wendy might ever prevent Robert from describing Diana. +1608 Even Phillip ever believed these ladies to scratch. +1609 Even Winston Churchill might ever embarrass Pamela. +1610 Even Kendra has ever imagined most mushrooms had charred. +1611 Even Vanessa would ever win. +1612 Even Sara often likes some muffins. +1613 Even Vincent also declared Charles to remember Meredith. +1614 Even Regina had ever fallen. +1615 Even those adults have ever kissed. +1616 Even every shirt might ever tighten. +1617 Even James ever stunned that lady. +1618 Even every organization has ever conferred. +1619 Even Catherine had really sighed. +1620 Even Rodney ever wore the socks. +1621 Even Allison did ever eat. +1622 Even these sweaters ever frayed. +1623 Even most actors might ever happen to aggravate some organizations. +1624 Even the government had ever conferred. +1625 Even those shirts really unfold. +1626 Even those shawls ever soak. +1627 Even Allison certainly fixed all trucks. +1628 Even every guy ever pushes that guy to fall asleep. +1629 Even Russell will ever scratch. +1630 Even a lot of guests will also exit Harvard University. +1631 Even these schools certainly succeeded. +1632 Even Timothy often heals Douglas. +1633 Even Walter should also observe Charles. +1634 Even Candice does ever cough. +1635 Even some patients have ever walked through every high school. +1636 Even those teachers also flirt. +1637 Even all lights had ever darkened. +1638 Even Benjamin would often know what accelerate. +1639 Even the banks often hope to irritate Catherine. +1640 Even the legislature really plans to work hard. +1641 Even many guests ever complain. +1642 Even Lori ever gets a job. +1643 Even some waiters ever study. +1644 Even the Borgias really need to climb 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escaped. +1666 Even Grace certainly likes some movie theater. +1667 Even the plates ever shattered. +1668 Even a doctor ever researched that high school. +1669 Even Christopher had ever required Andrew to distract Edward. +1670 Even Vanessa ever screams. +1671 Even the waitresses might also admire Deborah. +1672 Even Jason did ever bake. +1673 Even a museum did certainly respond. +1674 Even Joseph ever existed. +1675 Even Carrie has ever talked to most girls. +1676 Even Douglas certainly compelled Rhonda's guest to joke around. +1677 Even all documentaries clearly scare Sally. +1678 Even Margaret had often disliked Jill. +1679 Even some guy really urges some daughters of Jerry to leave. +1680 Even Bethany will certainly walk through a mall. +1681 Even most waitresses ever train. +1682 Even a museum ever reacted. +1683 Even those men certainly pressure Rachel to explain everything. +1684 Even many museums also correspond. +1685 Even dancers ever practiced. +1686 Even Amelia often has those malls. +1687 Even Margaret would ever clean every high school. +1688 Even the convertible also drops. +1689 Even Mark had ever won. +1690 Even Marla ever had the plates. +1691 Even Joel has also fled from Brad. +1692 Even many carts also steer. +1693 Even drivers ever run around the art gallery. +1694 Even the boys ever communicate. +1695 Even some ladies ever boast. +1696 Even that pepper does often grow. +1697 Even Margaret has often practiced. +1698 Even those dishes had also vanished. +1699 Even Travis ever hunted. +1700 Even Denise ever joked around. +1701 Even most governments have ever worried. +1702 Even this dancer had ever tried to pass the Great Lakes. +1703 Even the bicycle ever rusted. +1704 Even Rebecca might really manage to get fired. +1705 Even the patient had really gone to some grocery store. +1706 Even all teenagers ever kissed. +1707 Even Russell has ever toured a grocery store. +1708 Even Jessica has often cleaned a restaurant. +1709 Even Gerald ever happened to boast. +1710 Even Kathleen has ever walked through the hill. +1711 Even Kristen really listens to a student. +1712 Even Janet has often grinned. +1713 Even some guy really irritated Thomas. +1714 Even Sheila really vanished. +1715 Even Omar ever forced this sister of Sharon to descend every mountain. +1716 Even Roger also smiles. +1717 Even Alexander has also appreciated Ella. +1718 Even that government ever embarrasses Heidi. +1719 Even Aaron really orders those grandfathers of Cheryl to lie. +1720 Even these guys certainly refuse to interact. +1721 Even Travis had ever climbed up stairs. +1722 Even Suzanne ever found Lori. +1723 Even Kendra will ever laugh. +1724 Even Gerald did also mess up an art gallery. +1725 Even some governments certainly encourage Nina to break those plates. +1726 Even a lot of essays also hurt Jessica. +1727 Even Elaine often paints. +1728 Even many guys have really refused to care for the driver. +1729 Even that committee had ever joked around. +1730 Even Vincent has ever managed to swallow. +1731 Even Sara also said these pedestrians seem to research Winston Churchill. +1732 Even boys often smile. +1733 Even Timothy really trained. +1734 Even Martin ever confused Eric. +1735 Even the pork can often confuse Leslie. +1736 Even Todd had ever disgusted Carl. +1737 Even pies can ever astound Jodi. +1738 Even most drivers ever continue to forfeit. +1739 Even every casserole also burns. +1740 Even all dancers also bake. +1741 Even Carol really commissions a doctor's grandmothers to go to the lake. +1742 Even Daniel ever compromised. +1743 Even some adults ever sound like Rebecca. +1744 Even a lot of cashiers should ever reply. +1745 Even Mitchell would also clean the couches. +1746 Even some people should really assemble. +1747 Even Tracy had really escaped. +1748 Even Dennis really exercises. +1749 Even some sweater can ever flatten. +1750 Even Nicole often continued to kiss Carrie. +1751 Even Sara ever climbs up hills. +1752 Even Beth ever thinks about most peppers. +1753 Even Candice also discovers what fall apart. +1754 Even Kendra ever answered. +1755 Even some mushrooms have ever burned. +1756 Even all waiters ever ascertain all trucks to rust. +1757 Even the drivers ever hide Monet. +1758 Even Mary had ever practiced. +1759 Even Diana does often speak up. +1760 Even Julie can often stand up. +1761 Even this truck might ever drop. +1762 Even Jennifer really scanned that book. +1763 Even April has ever listened to many cashiers. +1764 Even Alicia ever screamed. +1765 Even Jodi clearly realizes who were profiting. +1766 Even Jill really encourages some libraries to like Steven. +1767 Even a lot of scarves have really frayed. +1768 Even some child had ever scratched. +1769 Even the women also question Frank. +1770 Even some actresses have ever married. +1771 Even Donna does often like to help Janice. +1772 Even most blouses have often wrinkled. +1773 Even Ronald ever studies. +1774 Even most women ever scratch. +1775 Even Adam will ever play. +1776 Even a lot of actors ever appeared to die. +1777 Even Carlos will ever happen to die. +1778 Even every print ever bores Donald. +1779 Even ladies also fall asleep. +1780 Even legislatures ever happen to reunite. +1781 Even Donna ever departs. +1782 Even all adults often believe Vincent to annoy every organization. +1783 Even the actresses also criticize Kevin. +1784 Even David had ever existed. +1785 Even Jeffrey does often like Benjamin. +1786 Even all shawls ever flatten. +1787 Even Carl really intends to shout. +1788 Even some students also bake. +1789 Even some adult ever swallowed. +1790 Even Christina often fails to shout. +1791 Even most customers often refuse to sneeze. +1792 Even chairs ever hurt this guy. +1793 Even Thomas should also prod Cindy to visit Carrie. +1794 Even Alan had ever spurred Melanie to speak up. +1795 Even every college campus ever disappeared. +1796 Even that jacket also unfolds. +1797 Even Steven did ever ask Eva to conceal the banks. +1798 Even most girls had ever dated. +1799 Even Stacey had ever worn a dress. +1800 Even Douglas would also say girls aren't returning to many schools. +1801 Even hospitals really believed Jeffrey to drive to some banks. +1802 Even a lot of doctors also marry. +1803 Even Christine had ever sighed. +1804 Even those people really skate around Spain. +1805 Even a lot of children should often kiss. +1806 Even Karla will also move. +1807 Even this skirt would really flatten. +1808 Even many couches have ever disgusted Monet. +1809 Even Julia ever drinks. +1810 Even some senators really scream. +1811 Even a window can ever shut. +1812 Even the girls also say gloves twist. +1813 Even every waitress can ever remember Kristin. +1814 Even Veronica often messes up every cafe. +1815 Even those pants often astound Becky. +1816 Even the organizations also implore Carrie to laugh. +1817 Even Kayla should ever notice Monica. +1818 Even the drivers ever answered. +1819 Even Tracy really skates around a closet. +1820 Even mushrooms had ever charred. +1821 Even pedestrians certainly hunt. +1822 Even Chad also encourages Jennifer to bore Kevin. +1823 Even an actor also wonders who speak up. +1824 Even David has ever lifted that cafe. +1825 Even Kendra really sounded like Grace. +1826 Even Carmen has ever spun around. +1827 Even Regina ever badgered Denise to skate around all mountains. +1828 Even that screen has ever faded. +1829 Even Nancy has ever whispered. +1830 Even that person had ever cared for Tonya. +1831 Even a lot of waiters ever exit those closets. +1832 Even many waitresses also conceal who had yelled. +1833 Even April ever sat down. +1834 Even those lights also fade. +1835 Even Matt certainly forgot who fixed all dishes. +1836 Even every picture has ever wasted away. +1837 Even every waiter ever hates a lady. +1838 Even that unicycle ever rusted. +1839 Even those peppers could ever blacken. +1840 Even Angela often studied. +1841 Even the teachers did also chat. +1842 Even those senators also reunite. +1843 Even Becky often concealed Grace. +1844 Even Tammy really spurred teachers' tutor to appreciate that hospital. +1845 Even Spain also worked with those people. +1846 Even Craig had really talked about Donna. +1847 Even a lot of dancers also investigate what spurs Phillip to climb up that mountain. +1848 Even Vincent does also go fishing. +1849 Even Tanya should ever hire Jerry. +1850 Even those glasses also fold. +1851 Even Renee had ever exited every closet. +1852 Even those lights had ever brightened. +1853 Even Caroline certainly stretches. +1854 Even William also begs Beverly's teachers to reveal Kirsten. +1855 Even all windows really fling open. +1856 Even some committee has ever coped. +1857 Even Carol had ever appreciated Richard. +1858 Even Janet would also pressure Jacqueline to question the guy. +1859 Even Kirsten had ever annoyed Raymond. +1860 Even Adam really entreated Phillip to concur. +1861 Even Caroline ever prevented most shawls from boring Sabrina. +1862 Even all adults ever investigate Diane. +1863 Even Carrie had ever alarmed Ann. +1864 Even Wendy ever hoped to attack Kenneth. +1865 Even a lot of guys often communicated. +1866 Even Richard had ever driven to these hospitals. +1867 Even Nina has ever performed. +1868 Even this government might really cooperate. +1869 Even Grace had ever examined those plays. +1870 Even Sherry also brought many newspaper articles. +1871 Even Stacy had really spurred the people to whisper. +1872 Even Carolyn ever hid. +1873 Even all waitresses have ever negotiated. +1874 Even many photographs often look like Cynthia. +1875 Even Brad has also come here. +1876 Even Gregory often worried Paul. +1877 Even those mouths had ever disturbed Clyde. +1878 Even these dancers ever imagined those senators motivate Samuel to see. +1879 Even a lot of waiters can ever sit down. +1880 Even some face ever aggravates Jodi. +1881 Even all cashiers had ever cooperated. +1882 Even that sock has ever shrunk. +1883 Even most patients ever walk through the art galleries. +1884 Even Keith certainly grinned. +1885 Even these waiters ever declare Martin to work with Clyde. +1886 Even Nancy ever found Nina. +1887 Even these dancers certainly answer. +1888 Even Dennis certainly fires some women. +1889 Even this lady will certainly tempt Debra's mentors to nod. +1890 Even Tina often retaliates. +1891 Even the people have ever benefited. +1892 Even some associations also reunite. +1893 Even a lot of museums might ever reunite. +1894 Even Carol ever says Heidi falls. +1895 Even Angela also wept. +1896 Even the Borgias had ever proposed to love the woman. +1897 Even a legislature did really profit. +1898 Even that spotlight could really dim. +1899 Even Tammy ever approaches Paula. +1900 Even Anne often prevents Julie from boasting about essays. +1901 Even Anna has also found Charles. +1902 Even many museums had ever conspired. +1903 Even this association could ever confer. +1904 Even Winston Churchill has ever examined this movie. +1905 Even Todd ever studies. +1906 Even Melanie ever cheered. +1907 Even Carol also criticizes this waiter. +1908 Even Theodore ever scans essays. +1909 Even some people clearly answer. +1910 Even Lawrence certainly referenced Sally. +1911 Even Mary ever cures Alice. +1912 Even Beverly had ever admired a customer. +1913 Even the committees also need that association to fight. +1914 Even Jeffrey could ever visit those boys. +1915 Even a dancer should ever attempt to go to every mountain. +1916 Even a lot of drivers ever see Sonia. +1917 Even some banks had ever motivated Roger to salute. +1918 Even Rebecca ever cheered. +1919 Even some hospitals often continue to succeed. +1920 Even the cashiers had ever eaten. +1921 Tamara ever exited those mountains. +1922 Karen could ever boycott this association. +1923 Every committee could often sell a river. +1924 Charles had often arrived at those restaurants. +1925 Many skateboards also roll. +1926 Guy has ever listened to all customers. +1927 Mark ever struggles. +1928 Ladies also answer. +1929 A driver has ever taken many snakes. +1930 Homer ever spins around. +1931 That pepper might ever go bad. +1932 Some waiter ever praised every documentary. +1933 Janice really broke the chairs. +1934 Andrew had ever hugged Sonia. +1935 This pedestrian also expects many screens to chip. +1936 The people ever escape from Carlos. +1937 The Borgias often write. +1938 The lakes would certainly confuse the Lutherans. +1939 Valerie has certainly vanished. +1940 Some association ever urged Richard to yell. +1941 Jane did certainly require the senator to heal Brad. +1942 Noah has ever forfeited. +1943 These teenagers often eat. +1944 All horses had ever confused most children. +1945 A spotlight had ever faded. +1946 Most bananas should ever grow. +1947 Douglas could ever conceal many lakes. +1948 Julie has ever discovered Tammy to fall asleep. +1949 Some senators ever read. +1950 Pedestrians really sell every public park. +1951 This unicycle ever steers. +1952 That broccoli ever grows. +1953 Heather ever bothered to wave. +1954 Monet also references Robert. +1955 Russell has ever talked to Bruce. +1956 Christine has ever turned out to go fishing. +1957 A guest also astounded a library. +1958 Those pedestrians could ever talk about these guests. +1959 Most sketches really confuse Anna. +1960 The association had ever profited. +1961 An organization also benefited. +1962 The men might also confer. +1963 That senator clearly ascends most mountains. +1964 Craig certainly flees from Edward. +1965 The truck has ever tipped over. +1966 The bicycles really fell apart. +1967 Mary has ever scratched. +1968 The Lutherans often drink. +1969 The skirts had ever loosened. +1970 Clyde had ever baked. +1971 Homer often escapes. +1972 Candice has ever worn shirts. +1973 Gerald would ever care for Samuel. +1974 Most pedestrians ever grin. +1975 Brian might ever forfeit. +1976 Susan had certainly arrived at that library. +1977 Some universities often convince a thesis to impress Amelia. +1978 Peter ever pressures Liam to have those public parks. +1979 The shoe ever existed. +1980 A lot of glaciers ever freeze. +1981 Rodney often changes. +1982 Many ladies should also succeed. +1983 Donna could ever arrive at that hospital. +1984 Some guy ever hunted. +1985 Stephanie did ever break some chair. +1986 Denise ever irritated all companies. +1987 Some ice really condenses. +1988 Deborah did ever forget about what scorched. +1989 Gina certainly swallows. +1990 Samantha also wrote. +1991 Laurie has ever healed Raymond. +1992 Sabrina should ever wake up. +1993 Rhonda had ever watched Julie. +1994 Susan did often propose to hire Amy. +1995 Jesus has ever yelled. +1996 Catherine has also refused to swallow. +1997 Rhonda often responds. +1998 Those teachers really look to look like that cat. +1999 Those companies clearly entreat Galileo to hug Winston Churchill. +2000 The Great Lakes ever worry Beverly. +2001 Nancy should ever return to Bill. +2002 Jerry might certainly swallow. +2003 Some legislature should really clash. +2004 Some water could really evaporate. +2005 Those cashiers had ever debated. +2006 Tamara had ever concealed the senator. +2007 Brian really discovered an eye. +2008 Vincent ever looked like a painting. +2009 Some guest ever explained everything. +2010 Tamara ever forgets about who discussed Elaine. +2011 A lot of senators have ever embraced. +2012 Every skateboard ever slowed. +2013 Donald would ever urge Marie to hide. +2014 Lawrence certainly tries to sit down. +2015 Danielle should ever paint. +2016 Those boys ever want to sing. +2017 Nina ever cooperates. +2018 Homer has ever explained everything. +2019 Those glasses also twisted. +2020 Becca could ever return to Ann. +2021 Stacey ever required that school to hurt Gary. +2022 The Borgias ever profited. +2023 Tiffany ever answers. +2024 Those teachers also cough. +2025 Janice ever obligated Ronald to drink. +2026 These children did ever find Charles to hire Nicole. +2027 People ever visit Nina. +2028 Larry ever went fishing. +2029 Tiffany did ever scan that newspaper article. +2030 Most mushrooms have ever grown. +2031 Rodney did ever command every girl to tour some bank. +2032 Alan often investigated this committee. +2033 A lot of sweaters really crumpled. +2034 Ruth has ever concealed who had stood up. +2035 Dennis had ever explored a high school. +2036 Most shawls often wrinkle. +2037 All boxes often open. +2038 Some tomato did ever grow. +2039 The Lutherans might also believe Patricia to buy most ladders. +2040 Every jacket ever fell. +2041 Tanya did ever hide. +2042 All companies often hope to change. +2043 Carmen also smiles. +2044 Kathleen ever won. +2045 A lot of sweaters often appear. +2046 The Lutherans ever forget about what weren't threatening to get a job. +2047 Ruth had ever remembered what 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thought about Laura. +2095 Marcus did often tempt Carl's mothers to hire Paul. +2096 Carmen did really explain everything. +2097 Stephanie would often speak up. +2098 Amanda ever practices. +2099 Kimberley had often seemed to distract Michael. +2100 Dennis really fell. +2101 Pedestrians also spin around. +2102 Some projectors also dimmed. +2103 Jill would ever stretch. +2104 That bread will also bore Douglas. +2105 Brett ever scanned these newspaper articles. +2106 Michael does often break the window. +2107 Those hospitals also reacted. +2108 Laura really approached a lot of teenagers. +2109 The Lutherans clearly scanned the commentaries. +2110 Waitresses ever marry. +2111 Laura could really sing. +2112 Every popsicle should ever evaporate. +2113 Patrick ever talks to this guy. +2114 Kenneth can really cry. +2115 Jennifer should ever hide away. +2116 A lot of actors have ever benefited. +2117 Bicycles often crash. +2118 Theresa did ever depart. +2119 Robert should ever complain. +2120 The apples ever grew. +2121 Leslie clearly criticizes every hospital. +2122 Mitchell ever scared Jill. +2123 Roger might ever go fishing. +2124 Brad should often realize who might grin. +2125 Tonya has ever concealed Connie. +2126 Janet does really cure Michelle. +2127 Monica ever wins. +2128 A lot of teachers often listen to some actors. +2129 A lot of universities ever want Kayla to sing. +2130 Some box ever annoys Gerald. +2131 Patricia often moves. +2132 Most libraries also seem to spin around. +2133 A skateboard had ever chipped. +2134 Christopher also begs this cashier to sing. +2135 Those eyes had ever shut. +2136 Maria ever hired Heather. +2137 Omar could ever boycott the Great Lakes. +2138 These coats really twisted. +2139 Richard had certainly laughed. +2140 A lot of senators should ever yearn to love Naomi. +2141 A lot of actresses really sell the scarf. +2142 Donna also thinks about what has tipped over. +2143 Most cashiers often respect Eva. +2144 The spotlight ever dimmed. +2145 Most boys ever leave Sabrina. +2146 Most actors ever escaped from Peter. +2147 These peppers have ever ripened. +2148 Jason really fires Noah. +2149 Heidi ever drinks. +2150 A lot of actors ever irritate Bruce. +2151 Wayne ever complained. +2152 All organizations did certainly entreat these waitresses' associate to concur. +2153 The carriages often rust. +2154 The scarves ever soaked. +2155 Kevin also wears some shirt. +2156 That person has ever tried to clean the closet. +2157 Rachel has ever listened to Sandra. +2158 The vase really confuses Sarah. +2159 A gate ever closed. +2160 A cashier also swallows. +2161 Many girls would often lie. +2162 Charles does often yell. +2163 Joel also annoyed Adam. +2164 Sheila clearly pressured some analysis to change. +2165 This university would ever compete. +2166 The book often disgusted this waiter. +2167 Bill had ever known Lori. +2168 Rose ever imagines Sherry couldn't eat. +2169 Ella really hid some senators. +2170 Turtles might 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adults ever scream. +2219 Tina also expects Carrie to ascend those mountains. +2220 Raymond also manages to answer. +2221 A lot of patients ever concur. +2222 Some customers really litter. +2223 Tara would ever threaten to disappear. +2224 A lot of drivers often manage to wear all coats. +2225 Every man did ever yearn to stun some person. +2226 Those boys ever force William to disappear. +2227 The ice ever vaporized. +2228 Every shirt often flattens. +2229 That patient also works hard. +2230 Some shoes ever shrink. +2231 A lot of teachers really react. +2232 Maria had ever laughed. +2233 The girls did really look to remember Guy. +2234 An eggplant ever wastes away. +2235 Meredith also implored some cashiers' nephews to discuss most hills. +2236 The cashiers had ever listened to some lady. +2237 Every customer had ever talked. +2238 All glasses ever slip. +2239 A lot of cakes have ever burned. +2240 Veronica should ever smile. +2241 Caroline ever wakes up. +2242 A door ever opened. +2243 Most guys had ever cooperated. +2244 Adam certainly swayed Janice's ex-husband to shout. +2245 Mark ever sneezes. +2246 Vanessa ever went fishing. +2247 Some skirts ever soak. +2248 Denise did also look to practice. +2249 Travis would ever depart. +2250 Anna had ever alarmed Robert. +2251 Teresa also donated. +2252 Some men ever tempt Steven to get fired. +2253 Kathleen often baked. +2254 Spinach can ever go bad. +2255 Amy has also departed. +2256 The lamp often alarms Tonya. +2257 Daniel also litters. +2258 Sheila also reads. +2259 Most doctors often worry. +2260 Some window ever tips over. +2261 Mark also tried to skate around high schools. +2262 Christopher often pushes Tammy to complain. +2263 Carl ever persuaded Dana to boast about Michael. +2264 Those patients also wear some socks. +2265 Connie might ever yearn to wear these pants. +2266 Ella also looks to ascend mountains. +2267 Those senators ever took that mushroom. +2268 That guy clearly believed these dishes to confuse Jesus. +2269 Barbara could also notice most newspaper articles. +2270 These cashiers have certainly responded. +2271 Many pedestrians ever drive to some restaurants. +2272 Carl ever tried to exit a lot of malls. +2273 Alexander could really confuse the schools. +2274 These waitresses ever forget Paula. +2275 That legislature had ever replied. +2276 Some dresses might ever twirl. +2277 Ruth also boasts about Kirsten. +2278 Jeffrey ever falls asleep. +2279 Some lady ever grinned. +2280 Travel did also hurt every student. +2281 Nancy might really admire Jacqueline. +2282 Those people also criticize Linda. +2283 These cars had ever dropped. +2284 The Great Lakes really disgusted Cheryl. +2285 These windows ever open. +2286 Some libraries have ever moved. +2287 Raymond would also pass all high schools. +2288 Some unicycle ever cracked. +2289 Some waiters ever study. +2290 All doctors really sell most projectors. +2291 Samantha had really yelled. +2292 Bradley had often persuaded many associations to clash. +2293 Wendy really explained everything. +2294 Those organizations clearly motivate Teresa's son to fall asleep. +2295 Pamela ever left. +2296 Many mirrors ever shattered. +2297 A lot of teenagers had ever testified. +2298 Many doctors ever profit. +2299 All schools often approve. +2300 A lot of drivers ever descend every ladder. +2301 This hill also alarms Ruth. +2302 Dan ever tries to protest. +2303 A convertible ever slowed. +2304 That couch ever tips over. +2305 Many schools have also listened to Matt. +2306 Anne ever mutters. +2307 Phillip ever disliked Theodore. +2308 Stacey could ever ask Michelle to see Derek. +2309 Sabrina had ever boycotted many companies. +2310 This man had ever stood up. +2311 The museum clearly seems to pass that high school. +2312 Richard will ever want some banks to worry. +2313 Stacy ever saw. +2314 Those pictures ever looked like that hamster. +2315 Many gloves ever vanished. +2316 The Lutherans certainly discover who could hunt. +2317 Carlos has ever gotten a job. +2318 Those patients have also benefited. +2319 Many waiters ever divorce. +2320 Karen ever remembered Sara. +2321 Some window did ever shut. +2322 Libraries certainly like to confuse all companies. +2323 Linda ever sways Michelle's student to talk. +2324 Tracy might certainly train. +2325 Carlos would ever wear those shawls. +2326 Brian does also dislike Karla. +2327 Carrie also exits those malls. +2328 The senators could clearly cure Jennifer. +2329 Mary ever attacks women. +2330 Linda would ever fall asleep. +2331 Brett might also fire Sarah. +2332 Some steps ever aggravated Jessica. +2333 That actor had ever imagined Sandra sang. +2334 Susan really threatens to flee from Robert. +2335 All teachers ever prove to smile. +2336 Sharon had ever compromised. +2337 Those students certainly mutter. +2338 The actor also stretches. +2339 These actors ever chuckle. +2340 Julia certainly murmurs. +2341 Some planes also chip. +2342 Most cashiers have ever chatted. +2343 The guys would ever date. +2344 Every river ever condensed. +2345 The blouse ever flattened. +2346 Sharon will also discuss some school. +2347 That convertible can really distract Paul. +2348 A lot of associations really debate. +2349 Most customers ever reunited. +2350 Many doctors ever find a glass. +2351 Helen had ever talked. +2352 Kathleen has really nodded. +2353 That guest has ever woken up. +2354 Rachel often scratches. +2355 Natalie has ever forgotten about who has read. +2356 Valerie would really stretch. +2357 Alice clearly left James. +2358 Sharon had often praised this guest. +2359 Theresa ever doubted Ella bothers to boast. +2360 Ronald ever moves. +2361 Erin ever sighs. +2362 Erin also advised the ladies to argue about Tanya. +2363 Deanna has really persuaded Mark's colleague to help that guy. +2364 Patrick had really figured out who is whispering. +2365 Those lamps ever brighten. +2366 Most waiters often conceal Andrea. +2367 David had ever visited Lisa. +2368 Some banks can clearly cooperate. +2369 Frank had often induced Sarah to notice some beef. +2370 Gina also swallowed. +2371 Kenneth ever saluted. +2372 Every committee ever boasted about Larry. +2373 Those shawls often wrinkle. +2374 Bethany would really vanish. +2375 Christopher ever tried to mutter. +2376 All glasses often loosened. +2377 That actress can really approach Heidi. +2378 Russell often remembered Tiffany. +2379 Some teenagers would ever assemble. +2380 The glacier also melted. +2381 Sabrina has ever swallowed. +2382 Some lamp has ever brightened. +2383 Stephanie often discovers who advises Galileo to upset Jesus. +2384 Museums also discuss Leslie. +2385 Maria clearly said Kimberley telephoned. +2386 The skateboard also falls apart. +2387 Bruce could ever cure Angela. +2388 Cynthia has ever muttered. +2389 A screen has really disgusted Catherine. +2390 Those waiters have ever sounded like Christopher. +2391 Russell clearly attacks Alexander. +2392 Benjamin could certainly manage to come here. +2393 Many guys ever disagreed with Kristin. +2394 Sonia certainly needs to murmur. +2395 Helen has also sounded like a lot of customers. +2396 Susan ever boasts about Paula. +2397 Harvard University clearly succeeded. +2398 Most dancers could ever chat. +2399 Guests ever try to fall. +2400 A lot of associations ever force every grandfather of Brenda to hire some cashier. +2401 Melanie will ever consider Melissa to return to Natalie. +2402 Theresa did really get fired. +2403 Todd ever concealed every convertible. +2404 The dancers often embraced. +2405 Harvard University clearly talked about those essays. +2406 The Impressionists often fall asleep. +2407 Most eyes ever close. +2408 Wendy clearly regretted Edward shrugged. +2409 All students do certainly approve. +2410 The man often cries. +2411 Marla also wants to murmur. +2412 All pedestrians ever yell. +2413 Deborah had ever discovered Carrie. +2414 Christine ever obliged Todd to eat. +2415 Marie ever prodded Michelle to approach Christina. +2416 Every blouse ever frayed. +2417 Some unicycle ever slows. +2418 Most legislatures certainly fail to approve. +2419 Samuel has certainly sighed. +2420 Michelle does often hide these ladies. +2421 The pedestrians really see. +2422 Caroline also threatened to leave a lot of men. +2423 Stacy often departed. +2424 All museums really hope to have this couch. +2425 Valerie had ever ascertained this school to collaborate. +2426 A lot of museums will certainly correspond. +2427 Jessica had ever failed to shrug. +2428 Some doctor had ever admired Brenda. +2429 Adults could really attack Michael. +2430 Barbara has ever practiced. +2431 That hospital had ever worried. +2432 The Impressionists will also sing. +2433 Some cars can ever crash. +2434 Most organizations ever retaliated. +2435 Many ladies really disagree. +2436 Jacqueline might ever tour that lake. +2437 The senator did ever explore Spain. +2438 Some scarf also shrinks. +2439 A lot of skateboards clearly disappear. +2440 Some adults also hide Janice. +2441 The hospital often implored Cheryl to insult Nicole. +2442 Tracy also required Charles to protest. +2443 Naomi also predicts Brad to attack Sharon. +2444 Those senators ever run around many museums. +2445 Leslie often thought about who were dating. +2446 The Impressionists certainly sound like a man. +2447 Carolyn had ever lifted the icicle. +2448 Every lake does ever condense. +2449 George would also upset Danielle. +2450 Danielle certainly speaks up. +2451 The slopes really appear. +2452 Gregory might ever blink. +2453 Children ever explore Spain. +2454 Tina ever insults George. +2455 Julie has often observed this waitress. +2456 A lot of companies also talk about Erin. +2457 Catherine does often hate Omar. +2458 Many waitresses ever marry. +2459 Angela ever reveals Clyde. +2460 The Lutherans also salute. +2461 Meredith will ever cook. +2462 The Borgias also heal every person. +2463 Richard clearly insults Jason. +2464 Some pedestrians ever climb up some slopes. +2465 A light ever disappeared. +2466 Laurie had ever looked to profit. +2467 Irene also cleaned the hospital. +2468 That company should ever force this doctor to drop by those hills. +2469 Those plates do ever fall apart. +2470 Those children will ever interact. +2471 This glove has ever twirled. +2472 Tanya has ever begged Candice to examine some reports. +2473 A lot of eyes clearly upset Mary. +2474 Keith clearly pushes that girl to fall asleep. +2475 That guy does ever seem to complain about Wayne. +2476 That mushroom had ever aggravated Claire. +2477 Tanya had ever littered. +2478 The Impressionists ever badger Donna to get fired. +2479 Sheila had ever worried Marcus. +2480 Donald should also remember Bradley. +2481 The drivers have ever watched some customer. +2482 Diane had ever cooked. +2483 This glove ever frayed. +2484 The senators ever collaborate. +2485 Suzanne does really attack Alexander. +2486 Those guests also think about who shouldn't train. +2487 Many children ever collaborate. +2488 Lori should really whisper. +2489 Some dancers ever disgust Suzanne. +2490 Girls clearly clean most chairs. +2491 All senators often reply. +2492 Carrie had ever come here. +2493 These waiters can really see. +2494 Most wheelbarrows also stop. +2495 Angela clearly remembered who approach Robert. +2496 Lisa ever read. +2497 Sharon really forced all theses to disgust Omar. +2498 The Borgias can certainly profit. +2499 Every truck ever crashed. +2500 All banks had ever collaborated. +2501 Some turtles certainly exist. +2502 The muffins ever charred. +2503 Jane would really explain everything. +2504 That doctor did ever drop by every mountain. +2505 Barbara would ever remember Bruce. +2506 Every box ever flings open. +2507 Elizabeth can ever sing. +2508 Every student ever sighed. +2509 Those skirts also flattened. +2510 Stephanie has ever practiced. +2511 Most people ever practice. +2512 Jill does also discover most casseroles. +2513 Colleen had ever alarmed Stacey. +2514 Beverly clearly passed a river. +2515 These wheelbarrows had really slowed. +2516 Michael does often want those girls to reply. +2517 Mary will certainly attempt to boast. +2518 Many couches often shatter. +2519 All waitresses certainly appear to resemble a print. +2520 Julie ever surrenders. +2521 Dawn has certainly waved. +2522 Sara had ever seen boys. +2523 Every shawl ever shrinks. +2524 Brian also watched Eric. +2525 That icicle often melted. +2526 Scott had ever obliged a guest to fix some cart. +2527 Walter does ever doubt some committee won't cooperate. +2528 Every car had ever slowed. +2529 The patient also surrenders. +2530 Kathleen had ever thought Kathleen changes. +2531 Those boys ever fail to wake up. +2532 These women also confer. +2533 William has often convinced most grandsons of that cashier to arrive at glaciers. +2534 This hamster did ever impress Florence. +2535 Hospitals have also worked hard. +2536 Some closet ever shocks Martha. +2537 Diana might ever sigh. +2538 Dan ever disappears. +2539 Brenda will ever scream. +2540 Robert has ever attacked Kathleen. +2541 Gary has also explored some cafe. +2542 All actresses ever referenced Valerie. +2543 This lake should certainly melt. +2544 These committees have really worked with Sandra. +2545 These actors have ever revealed who had believed James to salute. +2546 Claire might ever threaten to lift some car. +2547 Scott has ever worked with an actress. +2548 Angela also messed up malls. +2549 Melissa also orders Anne to arrive at the art galleries. +2550 Some adults ever wish to change. +2551 Katherine would also upset Jason. +2552 Alexander had ever gotten a job. +2553 All dancers ever coped. +2554 Homer often sits down. +2555 Many teachers also date. +2556 Leslie might ever discover who hadn't pressured Candice to conceal April. +2557 That plane did also crack. +2558 April ever saw. +2559 Some convertible ever accelerated. +2560 Jacqueline ever reveals who have this box. +2561 Those people ever complain about some newspaper article. +2562 This carriage could ever chip. +2563 Most drivers also approve. +2564 Heidi ever telephones. +2565 Lisa ever littered. +2566 Emily clearly walks through every art gallery. +2567 Eric often requires Benjamin to salute. +2568 Grace also needed to upset Daniel. +2569 Tara has ever managed to criticize Theodore. +2570 Dan did really sing. +2571 Many actors might ever walk through these mountains. +2572 Every sketch also looks like Rodney. +2573 Paula can also stand up. +2574 Sarah has ever managed to annoy some governments. +2575 That museum has ever met. +2576 Larry ever sang. +2577 Teachers ever predict Harvard University to succeed. +2578 Men often cry. +2579 Theresa can certainly beg the governments to bother Barbara. +2580 Gregory clearly swayed Connie to sit down. +2581 The cakes also blackened. +2582 Colleen ever respected Keith. +2583 Nicole ever bakes. +2584 Martin clearly scanned some newspaper articles. +2585 Carrie had ever protested. +2586 A lot of libraries often retaliate. +2587 The bicycle has really distracted Douglas. +2588 Many banks also pressure Diana to wash. +2589 Phillip does ever complain. +2590 Veronica ever brought these slopes. +2591 Phillip clearly testifies. +2592 Marie ever swayed Becca's contractor to explain everything. +2593 These patients also divorced. +2594 Most guests ever pass these cafes. +2595 Tamara clearly continues to cure Stacy. +2596 Many adults often neglect to argue. +2597 The guy really begs Alicia to fix a dish. +2598 Emily does clearly visit James. +2599 Every light could ever fade. +2600 Leslie had ever figured out who had criticized Steven. +2601 Many socks certainly warp. +2602 Gerald often yawned. +2603 Edward has ever visited Larry. +2604 All men ever find out who haven't walked through that hospital. +2605 Many doctors ever confer. +2606 Most mountains should often alarm these committees. +2607 Jill would ever whisper. +2608 Teachers often hope to weep. +2609 Those doctors ever change. +2610 Daniel often hated most reports. +2611 Linda also drinks. +2612 This shoe ever flattens. +2613 Beverly has really irritated Alan. +2614 Pamela ever changes. +2615 A lot of guests ever criticize a mall. +2616 Most photographs did also look like most glasses. +2617 Noah will ever sigh. +2618 Sara ever wore a lot of sweaters. +2619 The hair also astounded Brett. +2620 Dawn had ever worn many hats. +2621 This student ever died. +2622 Wayne should clearly visit the Borgias. +2623 Anne often hugged some teenager. +2624 A bike could ever fall apart. +2625 Nancy often muttered. +2626 John has ever discovered the gates. +2627 Some government ever bores Susan. +2628 Renee often buys every pie. +2629 A lot of actors ever mutter. +2630 These dishes also exist. +2631 Bethany will also drop by those public parks. +2632 Sally can ever leave. +2633 Some customers ever buy the fish. +2634 That pedestrian will certainly investigate Ruth. +2635 Jodi has ever insulted Charles. +2636 Beth ever sees Frank. +2637 The bananas ever ripen. +2638 All adults ever testified. +2639 Helen often investigated many schools. +2640 Susan had really healed Ella. +2641 Most mirrors ever slipped. +2642 Every plate ever tips over. +2643 Every eggplant might ever rot. +2644 The museum ever coped. +2645 Debra has ever run around that public park. +2646 Ronald would ever think about Helen. +2647 Dennis also looked like that painting. +2648 Every blouse had ever folded. +2649 Aaron clearly wishes to telephone. +2650 The convertible will ever slow. +2651 A lot of waitresses ever sneeze. +2652 April will ever fail to conceal these ladders. +2653 These ladies have ever gotten a job. +2654 Tanya really cleans some mouths. +2655 Renee ever responds. +2656 A lot of boxes also disgust those children. +2657 The teacher really considered Joseph to shrug. +2658 Caroline had ever died. +2659 This waiter really sees George. +2660 Most senators ever tour schools. +2661 Tanya should ever talk. +2662 Gerald had ever read. +2663 All museums often lie. +2664 Sandra has ever yearned to escape from Cindy. +2665 That ice cream has ever evaporated. +2666 Most senators also like Nina. +2667 The woman had ever cooked. +2668 Grace also sways Marla to bake. +2669 Larry would really need to observe Deanna. +2670 Most companies ever scare a lot of guys. +2671 Those candles had ever dimmed. +2672 Michelle does ever boast. +2673 Denise often wonders what responded. +2674 These women also obliged Alexander to conceal Samuel. +2675 These pedestrians ever came here. +2676 Most adults often push Julia's granddaughters to get a job. +2677 Carolyn ever littered. +2678 Theodore ever retaliates. +2679 Regina ever thinks about every boy. +2680 Those teachers had ever wept. +2681 These reports also alarmed Anne. +2682 Most teenagers ever neglect to perform. +2683 Many waiters ever profit. +2684 Connie had ever concealed the glove. +2685 Kayla ever leaves. +2686 Theresa should really descend those steps. +2687 Most students really disagree. +2688 Guys really train. +2689 Christina had ever tried to bother Gerald. +2690 Carmen clearly pushes Alexander's guest to boycott all malls. +2691 Pamela ever exits schools. +2692 Drivers would also convince a lot of people to irritate the actresses. +2693 Janice had ever blinked. +2694 These teachers certainly dare all libraries to alarm Wendy. +2695 Irene often watches Kendra. +2696 Jessica ever hunts. +2697 This window clearly vanishes. +2698 These schools do often clash. +2699 Linda really messes up that glass. +2700 These museums should ever debate. +2701 Walter ever finds out who prefer George to go to many museums. +2702 Many dancers have ever assembled. +2703 Gina had ever stunned Larry. +2704 Edward really sat down. +2705 Doctors would also reveal a lot of books. +2706 Samuel has ever scratched. +2707 Stephen has ever learned. +2708 The Borgias certainly commission these sons of Stacey to joke around. +2709 Chairs ever embarrass Amanda. +2710 A lot of waiters ever declare Sharon to return to Eric. +2711 Julia also appears. +2712 Adam did ever attack Barbara. +2713 Christopher ever saw. +2714 Vanessa did also care for George. +2715 Erin had ever dared the children's granddaughters to hug. +2716 Susan would ever yell. +2717 Every doctor also replies. +2718 Every dress ever stretched. +2719 Beth had ever forgotten who slumped over. +2720 Eva also falls. +2721 William had ever sneezed. +2722 Debra ever argued about some person. +2723 Melanie often escapes. +2724 Some student ever helps Vincent. +2725 Christine will also drop by closets. +2726 Edward could certainly notice some library. +2727 All glaciers ever melted. +2728 Bill ever bothered to love Christopher. +2729 Jeffrey had ever murmured. +2730 These scarves ever shrank. +2731 Some children also talk. +2732 Allison should really boast about Sherry. +2733 Many pedestrians could ever cope. +2734 Those mountains often impress Thomas. +2735 Karla had ever planned to aggravate Mitchell. +2736 Cucumbers ever char. +2737 Clyde has ever cured Janice. +2738 Customers also talk to Victoria. +2739 Some skirt has ever twirled. +2740 Most cars had ever rusted. +2741 Barbara ever escaped. +2742 Nina also toured every museum. +2743 That shirt often warps. +2744 Ladies clearly fire Rachel. +2745 Stacy had ever yawned. +2746 Becky also exercises. +2747 That waiter ever disliked Noah. +2748 These pedestrians also discover that mirror. +2749 Valerie ever intended to upset Kristen. +2750 That university often replied. +2751 Paul often fixes that plate. +2752 The guy had ever hated Todd. +2753 Most cashiers often say these mushrooms blackened. +2754 These waitresses do ever sit down. +2755 Every carriage had ever rolled. +2756 This waiter clearly runs around most malls. +2757 The Impressionists would certainly worry. +2758 Monica had ever found some glacier to melt. +2759 That light really faded. +2760 Tara had ever grinned. +2761 Walter ever induced Pamela to discover Richard. +2762 Organizations should ever hope to love Jason. +2763 That fork ever chips. +2764 The rabbits would ever aggravate Bill. +2765 Those bicycles did also steer. +2766 Sarah ever said Peter wasn't playing. +2767 Some guy ever smiled. +2768 Those guys also hug Deborah. +2769 This woman certainly finds every driver. +2770 Most cucumbers could also burn. +2771 Phillip really spins around. +2772 Sara did really vanish. +2773 Many movies have ever impressed Dennis. +2774 Anna has often vanished. +2775 Jessica had ever woken up. +2776 Liam had also failed to conspire. +2777 Noah ever suffered. +2778 A lot of girls have really fallen asleep. +2779 The couch could certainly slip. +2780 Many patients often disliked pedestrians. +2781 Edward really failed to appear. +2782 Guys also scan some commentaries. +2783 A skirt also warps. +2784 Some customer can ever ascend the mountain. +2785 All skateboards ever rusted. +2786 Janet had really neglected to upset those waitresses. +2787 Bill had ever hoped to cook. +2788 Sara clearly refuses to cough. +2789 Melissa ever imagined Maria can't break every car. +2790 Jodi often scratches. +2791 Brad also worries. +2792 Lisa ever motivates every cashier to find every high school. +2793 Stephen also hides. +2794 Kristin clearly reacts. +2795 Galileo also longed to litter. +2796 Most schools should certainly cope. +2797 John has really cleaned many cups. +2798 These casseroles ever waste away. +2799 Angela also revealed Tina. +2800 Timothy often finds Carla to boycott most lakes. +2801 Steven ever wakes up. +2802 That guy does also lie. +2803 Margaret ever forgets James. +2804 Dennis ever learned. +2805 Monet has ever concealed who nods. +2806 An icicle had ever condensed. +2807 That box ever closed. +2808 Some driver ever dared Kevin to scream. +2809 Some rivers often condense. +2810 Those teenagers ever get fired. +2811 These actresses also cook. +2812 A lot of drivers often intend to visit Rodney. +2813 Connie should certainly reference Gina. +2814 Kevin would ever expect Pamela to grin. +2815 Those universities also need to aggravate Marie. +2816 Alexander also cared for a guest. +2817 Eva has ever looked like many photographs. +2818 Frank has ever trained. +2819 The Impressionists could often attack Irene. +2820 Every shirt ever unfolds. +2821 Carlos does often fail to unfold. +2822 Margaret had ever attacked all girls. +2823 Customers also embraced. +2824 Those companies clearly beg Daniel's plumber to yawn. +2825 Thomas also drops by Spain. +2826 Carrie has ever cried. +2827 Gregory has ever answered. +2828 A lot of committees often correspond. +2829 A waiter has ever cheered. +2830 Peter really hurt that cashier. +2831 This eye ever opened. +2832 Larry does often escape from Nicole. +2833 Nancy really irritated Omar. +2834 Becky also talked. +2835 Some waiters ever date. +2836 All men had ever descended many mountains. +2837 Brett has certainly nodded. +2838 The spotlight ever faded. +2839 Dawn certainly regretted Stacy tours some hill. +2840 Regina ever talks to Wendy. +2841 Martin should really long to listen to Natalie. +2842 Vanessa will also scan these commentaries. +2843 Teenagers ever tour this closet. +2844 Every hospital had ever assembled. +2845 Most companies might also order Diana to play. +2846 Chad really liked some waitress. +2847 Martha often criticized Monica. +2848 This story could also disagree. +2849 Many guys might ever flirt. +2850 Kathleen had ever moved. +2851 The art gallery clearly shocked Gary. +2852 All customers ever painted. +2853 All guys ever distracts Denise. +2854 Rose could ever threaten to approach Eric. +2855 Winston Churchill often stands up. +2856 Those pedestrians clearly hugged Stephen. +2857 The patient had really fixed the fork. +2858 Gerald has really fallen. +2859 Maria also trained. +2860 Deanna ever doubted this cilantro grew. +2861 Douglas ever practices. +2862 Samuel ever wore gloves. +2863 Alicia has ever revealed Kristen. +2864 Patricia really persuades the hospital to aggravate Monica. +2865 This shoe does often embarrass Bruce. +2866 Some hats certainly look like a lot of sketches. +2867 The people ever interact. +2868 Deborah ever joked around. +2869 Phillip will also fall. +2870 Vanessa did often turn out to visit Andrea. +2871 A lot of waiters ever negotiated. +2872 Carolyn ever commissions Ellen's piano teachers to sell these college campuses. +2873 Jessica will certainly lift a lot of peppers. +2874 Diana has ever yearned to like Pamela. +2875 Marie has also climbed up the slope. +2876 Timothy often hates the Lutherans. +2877 Tara ever donated. +2878 Only Bill would ever complain. +2879 Even Lori had ever healed Carl. +2880 Even Alexander ever looked like this drawing. +2881 Only libraries ever sway Sally to salute. +2882 Only Meredith should ever wear the shawl. +2883 Only Grace ever reveals Janice. +2884 Even Aaron ever messes up some couches. +2885 Only Elizabeth ever doubted Patricia has arrived at the hospital. +2886 Even Chad ever preferred Robert to waste away. +2887 Only the boys ever managed to watch most teenagers. +2888 Only the sweaters ever twirl. +2889 Only Susan ever tries to exercise. +2890 Only waiters ever cope. +2891 Even Kendra could ever protest. +2892 Even some dancers ever cure Debra. +2893 Only these actresses ever assembled. +2894 Only that unicycle ever crashed. +2895 Even Connie does ever learn who isn't imagining those mushrooms were growing. +2896 Only some cashiers had ever replied. +2897 Only some children ever marry. +2898 Only Kayla has ever pressed Kenneth to hate Rachel. +2899 Even Wayne could ever talk. +2900 Even Rebecca would ever observe John. +2901 Only Emily ever found Liam. +2902 Only that pedestrian will ever fail to attack Mitchell. +2903 Only Patricia had ever painted. +2904 Only Patricia ever turns out to bring a lot of plates. +2905 Only Nina ever arrives at every high school. +2906 Only Dana has ever painted. +2907 Even Andrea could ever sway Walter's French teachers to salute. +2908 Even Janice had ever bought some carriage. +2909 Even Eva ever waves. +2910 Only Helen ever shrugged. +2911 Only a plane has ever tipped over. +2912 Even those apples had ever grown. +2913 Only hospitals ever force Joseph to sing. +2914 Only these ladies ever competed. +2915 Only Alan ever proposes to laugh. +2916 Only some bicycles might ever rust. +2917 Only those cashiers had ever concealed Erin. +2918 Even those women ever wonder what isn't cracking. +2919 Even Natalie had ever induced a lot of libraries to annoy Naomi. +2920 Even Tamara ever appreciates Omar. +2921 Only some guys ever salute. +2922 Even Paula ever intends to escape from Carrie. +2923 Only Steve ever hurt Bill. +2924 Even Melissa ever liked that screen. +2925 Only Frank would ever read. +2926 Only this car ever bored Julie. +2927 Even the broccoli ever disgusted Jennifer. +2928 Even Kristen ever hoped to boast. +2929 Even Kimberley has ever helped Mark. +2930 Only Ann had ever judged Naomi to flee from a lot of cashiers. +2931 Only Nina did ever ask Katherine to learn. +2932 Only Gregory ever smiles. +2933 Only Jill ever thinks about cars. +2934 Only Mitchell ever eats. +2935 Only Candice will ever grin. +2936 Only the Impressionists ever anticipate that patient to whisper. +2937 Even the rabbit ever climbed up some mountains. +2938 Only Kayla does ever listen to Kendra. +2939 Only Homer ever fires Guy. +2940 Only Jeffrey ever worried Melinda. +2941 Only Derek had ever hidden. +2942 Only a projector had ever dimmed. +2943 Only Wendy ever neglects to question some patient. +2944 Even Teresa has ever admired Suzanne. +2945 Only Dana has ever kissed Janice. +2946 Even Tracy ever exists. +2947 Only those waitresses have ever climbed up those mountains. +2948 Even a candle ever dimmed. +2949 Even Kristin ever shouts. +2950 Even these actresses ever embrace. +2951 Even this painting ever looks like Clyde. +2952 Even Kristen did ever hug Carl. +2953 Even Richard ever nods. +2954 Only these plays ever astound Jeffrey. +2955 Only that committee ever competed. +2956 Only Elaine ever works hard. +2957 Only George ever healed Bruce. +2958 Only Dawn will ever read. +2959 Only these waiters ever entice Martha to conceal all sweaters. +2960 Even Sandra will ever require Karen's student to drop by some college campus. +2961 Only waiters have ever succeeded. +2962 Even Gregory can ever approach every person. +2963 Only the students ever find out who talked about some movie theater. +2964 Only Julie ever respects Elaine. +2965 Only that lamp has ever dimmed. +2966 Even these horses did ever waste away. +2967 Even these actresses had ever prompted Veronica's brothers to shrug. +2968 Even Victoria ever obliges all waitresses' doctors to watch some drivers. +2969 Even Carol ever argued about Brad. +2970 Only Leslie ever threatens to shrink. +2971 Only Aaron ever observed Tonya. +2972 Even Brett ever flees from Gerald. +2973 Even Mary would ever disappear. +2974 Even actresses ever ascend all ladders. +2975 Only Carlos ever chuckled. +2976 Only Amelia should ever visit Jason. +2977 Only Walter ever encouraged most organizations to answer. +2978 Even the company ever respected Irene. +2979 Only some candles ever dim. +2980 Even Jessica ever astounded Ella. +2981 Even Michael did ever shrug. +2982 Even this mushroom ever grew. +2983 Even Marie would ever scratch. +2984 Only cilantro has ever gone bad. +2985 Only some gates did ever fling open. +2986 Even Eric ever researched the Balkans. +2987 Only these women ever prevent Beverly from standing up. +2988 Only the Lutherans ever entreat Vanessa's colleagues to love these plays. +2989 Only some men ever cooperated. +2990 Only Tiffany would ever complain about the Lutherans. +2991 Even the museum might ever benefit. +2992 Even Kenneth had ever healed Carla. +2993 Only the guy ever runs around a lot of hills. +2994 Even Kimberley ever managed to train. +2995 Only Craig ever worries. +2996 Only these actors should ever boast about some pedestrians. +2997 Only Jeffrey has ever exercised. +2998 Only Bill ever spoke up. +2999 Even this dancer ever alarmed this museum. +3000 Only Anne will ever murmur. +3001 Only the mouths ever shut. +3002 Even some adults ever cured Heidi. +3003 Even those boys ever commanded Karla to distract some cashier. +3004 Only Harvard University could ever worry. +3005 Only Peter ever escaped from Diane. +3006 Only some doctors ever change. +3007 Only the drivers had ever testified. +3008 Even the mushroom ever bothers Ann. +3009 Even Susan has ever sounded like many lakes. +3010 Only some museum has ever retaliated. +3011 Only scarves can ever wrinkle. +3012 Even guests have ever ascended mountains. +3013 Only that plane ever slipped. +3014 Only Derek ever dies. +3015 Only universities ever approved. +3016 Even Deanna would ever manage to buy many scarves. +3017 Only ladies ever grinned. +3018 Even those teenagers ever explore that museum. +3019 Even a boy ever revealed the Borgias. +3020 Only the Impressionists have ever wondered who references Edward. +3021 Only Steven ever hides away. +3022 Only some icicle ever confuses Patricia. +3023 Only the Lutherans ever push Tina to describe all teachers. +3024 Only those adults ever run around most closets. +3025 Only Kathleen has ever attempted to hire Janice. +3026 Even Eric would ever respect Sherry. +3027 Only Sonia ever painted. +3028 Even Irene should ever boycott those art galleries. +3029 Only Douglas has ever loved Brad. +3030 Even Gregory could ever appreciate that art gallery. +3031 Only Russell ever spun around. +3032 Only some committees ever surrender. +3033 Even Carmen can ever listen to Eric. +3034 Even Becky ever doubts some women can question those organizations. +3035 Only those hospitals ever long to conceal Patrick. +3036 Only a lamp ever brightened. +3037 Only a coat ever wrinkled. +3038 Even icicles have ever condensed. +3039 Even Ronald has ever thought people learn. +3040 Only those art galleries ever hurt all waitresses. +3041 Even Wayne might ever sell a lot of hospitals. +3042 Even Sandra had ever realized who worked with Monica. +3043 Only Melanie ever kisses Sherry. +3044 Only Margaret ever wins. +3045 Even Jill has ever wondered who swallows. +3046 Only Scott had ever asked Colleen to aggravate Brian. +3047 Only Donald can ever litter. +3048 Even Grace might ever exit these malls. +3049 Even those pedestrians ever say Suzanne is doubting Keith was cleaning every face. +3050 Even Andrea has ever distracted Maria. +3051 Only these boys ever implore Angela to complain about Tanya. +3052 Even the book ever wastes away. +3053 Only some rivers ever evaporated. +3054 Only Stacy ever cleaned hamsters. +3055 Even the Borgias ever disagree. +3056 Only the chairs ever bother Jill. +3057 Only Brian has ever ordered Mary to practice. +3058 Only the Borgias ever retaliated. +3059 Only Peter has ever investigated Melanie. +3060 Only a pedestrian had ever looked to rust. +3061 Even Jessica has ever worried Melanie. +3062 Only the unicycle has ever rusted. +3063 Only Wendy ever went fishing. +3064 Only women ever disagree with that person. +3065 Only a woman ever regretted Tonya could imagine David is arriving at a hospital. +3066 Only Bill had ever washed. +3067 Even a waiter ever wears some shoes. +3068 Even this company has ever clashed. +3069 Only the dancers ever speak up. +3070 Only Rhonda could ever entreat Deborah to worry Andrew. +3071 Only Alicia ever urged Leslie to litter. +3072 Only some coats had ever loosened. +3073 Only Peter ever comes here. +3074 Even Barbara had ever practiced. +3075 Even the students ever appreciate Dawn. +3076 Even Carol could ever scan those commentaries. +3077 Even Valerie ever loved Guy. +3078 Only Elaine ever prompted Erin to yell. +3079 Only the Impressionists ever predict Lori to shock this association. +3080 Only the Borgias ever paint. +3081 Even Adam has ever looked like a lot of pictures. +3082 Only some bikes could ever slip. +3083 Only the Impressionists ever exist. +3084 Even Sonia can ever suffer. +3085 Only some legislatures ever find out who has continued to hide. +3086 Even tomatoes ever grow. +3087 Only some waitresses have ever climbed down those slopes. +3088 Even those people ever remember who was departing. +3089 Only that actress will ever worry Teresa. +3090 Even Curtis does ever have some turtle. +3091 Even Andrew ever scanned many stories. +3092 Only Florence did ever say the reports aren't scaring all teachers. +3093 Only Adam had ever listened to Elaine. +3094 Only Nancy had ever noticed Patrick. +3095 Only Nina ever falls asleep. +3096 Only some mirrors ever chip. +3097 Even Ann would ever push some mother of Sarah to talk about these documentaries. +3098 Only those doctors ever ascend all ladders. +3099 Even these cashiers ever research Monet. +3100 Even some adults ever cook. +3101 Even those actresses ever descend every hill. +3102 Even Melissa should ever know who prompted the actresses to play. +3103 Only Melissa can ever forget Derek. +3104 Only the boys ever escape. +3105 Only Todd ever donated. +3106 Only Jodi ever works hard. +3107 Only Amelia had ever scratched. +3108 Only Dawn could ever salute. +3109 Even Sara had ever vanished. +3110 Even that adult could ever prod some thesis to resemble that drawing. +3111 Even Brad ever boasted. +3112 Only those plates ever drop. +3113 Even Stacy would ever break most vases. +3114 Only customers ever find the lights to darken. +3115 Only Heather could ever persuade Naomi to fix the plane. +3116 Even the bread had ever worried Rebecca. +3117 Even the dancers ever scratch. +3118 Even those lakes ever melt. +3119 Even these actors would ever respect Christopher. +3120 Only Lori ever cured that dancer. +3121 Only some lake had ever frozen. +3122 Only Colleen ever nodded. +3123 Even Carla ever insulted most adults. +3124 Only Kirsten had ever approached Patrick. +3125 Even Bradley can ever lie. +3126 Only the people ever forfeited. +3127 Even some waitresses ever explore a lake. +3128 Even Eva should ever dislike John. +3129 Even Mitchell would ever climb up slopes. +3130 Only those companies will ever clash. +3131 Only Paul ever concealed Homer. +3132 Only Laurie ever hates April. +3133 Only Frank had ever hired Patrick. +3134 Even some adults ever bothers Stephanie. +3135 Even Monica ever exercises. +3136 Only these schools ever donate. +3137 Only a coat ever tightened. +3138 Even Carlos has ever prevented Bill from driving to a high school. +3139 Only Larry has ever happened to cooperate. +3140 Only Beth ever dropped by restaurants. +3141 Even that cart had ever rusted. +3142 Even Chad had ever forgotten about who explains everything. +3143 Only Ella has ever gone to a library. +3144 Only Kevin ever investigated Tonya. +3145 Only the hats can ever unfold. +3146 Only Rachel ever dislikes Rhonda. +3147 Only the Lutherans ever pass some cafes. +3148 Even an actress would ever litter. +3149 Even Susan had ever discussed Mitchell. +3150 Even Joel had ever left Matt. +3151 Only a school would ever ascertain Winston Churchill to shrug. +3152 Even those legislatures did ever communicate. +3153 Even Spain did ever reunite. +3154 Only those banks ever wish to stun Jeffrey. +3155 Only these museums ever appear to divorce. +3156 Even Melissa ever waves. +3157 Even Kendra would ever urge Tara to stun Bruce. +3158 Even Valerie had ever urged the people to bother Adam. +3159 Only David could ever talk. +3160 Only Bill will ever implore Nancy to disappear. +3161 Only women ever slump over. +3162 Even Ella had ever seemed to hurt Joel. +3163 Only Meredith would ever prove to write. +3164 Only the eyes should ever fling open. +3165 Even these men had ever assembled. +3166 Only Regina ever badgered Walter's students to bring the candles. +3167 Even Laura can ever see. +3168 Even Victoria has ever appeared. +3169 Only William had ever happened to boast. +3170 Even Aaron ever cooperates. +3171 Even Teresa ever practiced. +3172 Only Diana would ever neglect to buy this skirt. +3173 Only Colleen can ever play. +3174 Only Paul ever spurs Regina to read. +3175 Even Melissa would ever return to Clyde. +3176 Even some patient ever tries to murmur. +3177 Even Chad did ever shout. +3178 Even Lisa should ever oblige Maria to attack Kenneth. +3179 Only Dennis ever praises Carmen. +3180 Only Scott has ever figured out who haven't climbed up a mountain. +3181 Even Edward can ever declare the Impressionists to scream. +3182 Even these pants would ever warp. +3183 Only Margaret has ever obliged Omar to explain everything. +3184 Even Valerie ever impressed Kenneth. +3185 Even the Lutherans ever admire Paula. +3186 Only these guests ever predict Joseph to clean these libraries. +3187 Even Carl would ever suffer. +3188 Only Candice will ever persuade most girls to pass most museums. +3189 Only Michelle could ever find every nose. +3190 Even some dancer ever screamed. +3191 Even Sherry does ever stretch. +3192 Only the waitresses ever dare Charles to watch Tammy. +3193 Even those people ever conspire. +3194 Only Bruce will ever sigh. +3195 Only Sarah has ever shouted. +3196 Only Melinda ever argued about a lot of adults. +3197 Even the teacher does ever flee from Ella. +3198 Even the company does ever predict Gina to climb up most ladders. +3199 Only these actresses ever buy that river. +3200 Even Joseph ever approached an actor. +3201 Only the adults ever litter. +3202 Even Deborah ever existed. +3203 Even Margaret had ever hidden George. +3204 Only the Borgias ever communicate. +3205 Only Peter had ever bought a shirt. +3206 Only Carrie should ever train. +3207 Even these screens ever faded. +3208 Only this muffin ever burns. +3209 Only those doctors have ever corresponded. +3210 Even the companies ever press Sonia to scream. +3211 Only Theodore ever skates around Harvard University. +3212 Only eyes had ever flung open. +3213 Only pasta has ever blackened. +3214 Only Kevin ever approaches the Impressionists. +3215 Only some ice cream ever evaporates. +3216 Only Martin had ever trained. +3217 Only the ladies have ever noticed John. +3218 Only the Lutherans ever spin around. +3219 Even Martin had ever driven to movie theaters. +3220 Only this hospital has ever debated. +3221 Even Lawrence has ever referenced Katherine. +3222 Only Nina has ever encouraged Theresa's associates to cry. +3223 Only Tara had ever urged Samuel's French teacher to bother Laurie. +3224 Only some shoes ever fray. +3225 Only Sharon can ever forget about who did play. +3226 Only the women ever hugged. +3227 Only the governments ever help Florence. +3228 Only women ever notice a woman. +3229 Only a window ever chips. +3230 Only Amelia ever realizes what fall. +3231 Only Patrick ever exercises. +3232 Only some companies ever neglect to talk about Grace. +3233 Even Samuel ever cleaned every high school. +3234 Only some prints ever look like some glove. +3235 Even Becky ever convinces Brad to complain. +3236 Only Julia ever obligated these companies to protest. +3237 Only Samuel ever forgets about what fell. +3238 Only Catherine has ever shocked most committees. +3239 Even these dancers ever divorced. +3240 Only Keith ever seemed to heal that customer. +3241 Only Victoria ever pressed this hospital to impress Kayla. +3242 Only these doctors ever fall asleep. +3243 Only Denise ever spoke up. +3244 Even Tammy does ever cry. +3245 Only those pedestrians ever met. +3246 Even Joseph had ever swayed Denise's electrician to nod. +3247 Even Nicole ever smiles. +3248 Only some teenagers ever profit. +3249 Even Galileo ever hugs those ladies. +3250 Even Amanda could ever appear to get fired. +3251 Only Theodore ever runs around some museum. +3252 Even these associations ever criticize Marie. +3253 Only these ladies ever marry. +3254 Only Deanna has ever turned out to confer. +3255 Only Christina ever wished to talk to Dennis. +3256 Even Christina ever shrugged. +3257 Only Suzanne ever blinks. +3258 Only the Lutherans ever concur. +3259 Even Melanie had ever gone fishing. +3260 Only dogs ever fall asleep. +3261 Only Winston Churchill ever ate. +3262 Only scarves ever dry. +3263 Even Elaine could ever admire Joseph. +3264 Only some senators have ever worried. +3265 Even this bank should ever manage to love Kayla. +3266 Only Stacey ever regretted Melissa learns. +3267 Only Mitchell has ever cheered. +3268 Even the Borgias had ever divorced. +3269 Only the dancers ever care for these cashiers. +3270 Only girls ever yearn to profit. +3271 Only Colleen will ever answer. +3272 Even Beverly did ever talk to Jesus. +3273 Only Karen ever entreated Carlos's friend to work with Noah. +3274 Only some hospital has ever looked to notice Veronica. +3275 Only Diane ever judges every plane to shatter. +3276 Even those teachers ever need to wave. +3277 Only some ladies ever predict Monica to notice Kirsten. +3278 Even Becky ever urged Florence to stretch. +3279 Only Omar ever trains. +3280 Only those teenagers ever donate. +3281 Even Carrie ever escapes. +3282 Only the senators ever drop by every glacier. +3283 Only the children ever depart. +3284 Only these scarves have ever soaked. +3285 Only the eggplant ever grew. +3286 Only this steak would ever char. +3287 Even teenagers ever worried. +3288 Only the wheelbarrow ever turns. +3289 Only Julie ever wondered who had escaped. +3290 Only Frank has ever explained everything. +3291 Even that spotlight ever faded. +3292 Only the waitress ever trained. +3293 Even some doctors ever sound like Natalie. +3294 Only these organizations ever correspond. +3295 Only April ever obliged Dennis to concur. +3296 Even Maria could ever force Carol to irritate Matt. +3297 Only Andrea will ever sneeze. +3298 Only Steve ever proposes to praise Jeffrey. +3299 Only Lisa ever disturbs Kevin. +3300 Even Valerie would ever exercise. +3301 Only Suzanne could ever drop by that school. +3302 Only Paul would ever wash. +3303 Even Christopher had ever helped Lawrence. +3304 Only the couch ever tipped over. +3305 Even Stacy had ever hated Jodi. +3306 Only some government had ever negotiated. +3307 Only some cashiers ever observe Mary. +3308 Only some shawls can ever crumple. +3309 Only doctors ever profit. +3310 Only these wheelbarrows ever steer. +3311 Only Rebecca ever swallows. +3312 Even Gerald had ever performed. +3313 Even the legislatures had ever attempted to collaborate. +3314 Only Sally ever concealed the dancer. +3315 Only this organization ever testified. +3316 Only Robert ever goes fishing. +3317 Only those glasses ever stretched. +3318 Only Danielle ever appears to train. +3319 Only a library should ever fight. +3320 Only Frank has ever anticipated Tammy to litter. +3321 Only the senators ever murmured. +3322 Only Andrew has ever concealed this window. +3323 Only these companies ever hope to dislike Elaine. +3324 Only a public park could ever fall. +3325 Only some cars ever roll. +3326 Only that hospital has ever collaborated. +3327 Only the Impressionists ever scream. +3328 Even Laurie should ever attempt to complain. +3329 Even this glass had ever dropped. +3330 Only the apples ever grew. +3331 Only Cynthia ever hires Linda. +3332 Only that tomato ever bothers Alan. +3333 Even Benjamin might ever prevent Galileo from returning to Tina. +3334 Even Sabrina ever stands up. +3335 Even guests ever climb down that mountain. +3336 Only Sally has ever predicted Florence to kiss Winston Churchill. +3337 Only Donald would ever train. +3338 Only that doctor ever attacks this doctor. +3339 Only Vincent would ever stand up. +3340 Only the unicycle ever worries a driver. +3341 Only Claire ever figures out who practiced. +3342 Even Gerald ever loves Carla. +3343 Only Irene does ever aggravate this driver. +3344 Only the waitresses ever learn who trained. +3345 Only some planes ever maneuver. +3346 Only Kirsten ever cleans some bank. +3347 Only Dennis has ever failed to praise this teacher. +3348 Even Donna should ever go fishing. +3349 Only Stacy ever murmured. +3350 Even Eric had ever washed. +3351 Only some adults ever practice. +3352 Even the patient ever benefits. +3353 Only Wendy does ever sing. +3354 Even Cheryl had ever exercised. +3355 Even Stephen can ever distract Veronica. +3356 Only Gary should ever speak up. +3357 Even Tina had ever appeared to protest. +3358 Even the libraries ever hoped to disgust the teenagers. +3359 Even that mushroom had ever wasted away. +3360 Only some plates will ever exist. +3361 Only some window ever shuts. +3362 Only those carriages ever waste away. +3363 Even that river ever impresses Barbara. +3364 Only some dress should ever wrinkle. +3365 Even Carol ever falls asleep. +3366 Only Amelia ever approves. +3367 Only the ladies ever die. +3368 Only that government ever threatened to look like the bicycles. +3369 Only the libraries ever commission some governments to meet. +3370 Only a pepper had ever blackened. +3371 Even Paula might ever go fishing. +3372 Only Angela ever donates. +3373 Even Edward ever confused James. +3374 Only the Borgias ever look to come here. +3375 Only Bradley ever testifies. +3376 Only the hats ever wrinkled. +3377 Only Jodi ever encourages Benjamin to appreciate Catherine. +3378 Even Michael ever proved to fade. +3379 Even Jennifer might ever distract Steven. +3380 Only Stephen has ever come here. +3381 Only Christine ever messes up a high school. +3382 Even the paintings ever look like some jacket. +3383 Even Tracy did ever litter. +3384 Even the student had ever neglected to criticize Sarah. +3385 Even that cart did ever fall apart. +3386 Only those customers ever yawn. +3387 Even Sarah will ever dare the Impressionists to worry Natalie. +3388 Even the lakes had ever frozen. +3389 Only those actresses ever approach Brian. +3390 Even the Borgias have ever retaliated. +3391 Only these actors have ever retaliated. +3392 Only a coat has ever warped. +3393 Only Winston Churchill ever motivated a lot of pedestrians to drive to some movie theaters. +3394 Only those pedestrians have ever concealed Margaret. +3395 Only the teenagers ever buy many rugs. +3396 Only Kendra ever neglects to lift every rug. +3397 Even Guy had ever intended to cry. +3398 Even Gary had ever appeared to help Jason. +3399 Only that ice cream ever melts. +3400 Only the Lutherans ever drive to most lakes. +3401 Only Kirsten should ever hope to bake. +3402 Only the university has ever threatened to leave. +3403 Even Kristen had ever left. +3404 Only Connie ever passed the museums. +3405 Even Carlos might ever drive to this grocery store. +3406 Only Laurie ever questions every actress. +3407 Even Russell did ever force Nina to confuse all guests. +3408 Only Douglas ever performed. +3409 Even Spain might ever surrender. +3410 Only the teenagers ever drink. +3411 Only the Borgias ever wash. +3412 Only these waiters ever laugh. +3413 Only those ladies ever disturb Jodi. +3414 Only Candice ever complained. +3415 Only that cilantro ever went bad. +3416 Even Martha ever ascertained some bicycle to stop. +3417 Only Dennis ever thought about who bothered to donate. +3418 Even Kenneth might ever push this government to help that man. +3419 Only the cars can ever crack. +3420 Only these actresses ever retaliated. +3421 Even Tanya has ever helped Donna. +3422 Even Carol did ever leave Heidi. +3423 Even the spotlight ever darkens. +3424 Only Angela has ever wept. +3425 Only Catherine should ever think about that association. +3426 Only Florence has ever happened to shock Kirsten. +3427 Even these organizations ever remember Beverly. +3428 Only that gate ever opens. +3429 Only Connie had ever appeared to nod. +3430 Even Deanna might ever flee from a bank. +3431 Only that glacier ever evaporates. +3432 Even Mark ever appeared to scream. +3433 Only Eric ever motivated that lady to stun the customers. +3434 Only the convertible ever stops. +3435 Only Karla ever presses Florence to explore Spain. +3436 Only Beth ever examines stories. +3437 Only some patients ever suffer. +3438 Even the senator ever upset Colleen. +3439 Even that legislature did ever conspire. +3440 Only some committees do ever correspond. +3441 Only the Lutherans could ever chat. +3442 Only Martin should ever sing. +3443 Only Aaron has ever gone fishing. +3444 Only Keith ever benefits. +3445 Only these schools ever meet. +3446 Only a cart ever crashes. +3447 Only James ever forgets who say Deanna thinks about that projector. +3448 Only some people ever salute. +3449 Only Stacey would ever realize what were aggravating Samuel. +3450 Only the Impressionists ever played. +3451 Only Cynthia ever prodded Frank's partner to talk about waiters. +3452 Only Brenda has ever investigated Andrew. +3453 Only the actresses ever salute. +3454 Only Melanie ever worried Meredith. +3455 Even the essays ever disgust Rachel. +3456 Only Brett has ever helped Lucille. +3457 Even Chad could ever scan a lot of books. +3458 Only the Borgias ever communicated. +3459 Even Eva can ever examine that story. +3460 Only those carts ever slip. +3461 Only women have ever attempted to alarm Holly. +3462 Only the legislatures ever reference Stacy. +3463 Even Nancy could ever work with Renee. +3464 Only Patricia had ever concealed Sheila. +3465 Only the pedestrians did ever notice Curtis. +3466 Even the waiter ever tries to escape from Irene. +3467 Even that documentary ever irritates Michelle. +3468 Only Theodore has ever murmured. +3469 Only Winston Churchill ever forced every waiter to hide away. +3470 Only some hospitals ever find many senators to fight. +3471 Even Samantha ever escapes. +3472 Only Kristin had ever planned to reveal Winston Churchill. +3473 Only Danielle ever ascends a lot of hills. +3474 Even that tomato ever grows. +3475 Even Jill had ever forgotten who testifies. +3476 Even cashiers ever love Benjamin. +3477 Only some drivers have ever worked hard. +3478 Only Martha ever talked. +3479 Even Martha has ever lied. +3480 Only a blouse will ever shrink. +3481 Only Catherine ever forgot who won't scratch. +3482 Only Winston Churchill has ever grinned. +3483 Only Melissa ever mutters. +3484 Even Carmen ever praised some teenager. +3485 Only the beef ever charred. +3486 Only the glaciers ever bore that actor. +3487 Even those pedestrians ever consider Natalie to talk. +3488 Only Lucille ever longs to cry. +3489 Only the trucks could ever slip. +3490 Even this waiter ever implores Dawn's brothers to listen to Carla. +3491 Only Sherry ever hides away. +3492 Even this vase had ever chipped. +3493 Only Jessica had ever talked about Gary. +3494 Even Kayla does ever descend the stairs. +3495 Only the convertible ever slips. +3496 Only Paul had ever needed many actors to debate. +3497 Even Tanya has ever remembered who shouldn't push Kristin to telephone. +3498 Only these teenagers ever testify. +3499 Only Tracy ever answered. +3500 Even Laura might ever turn out to clash. +3501 Even Veronica ever annoyed Guy. +3502 Only Patricia ever screams. +3503 Only a pedestrian might ever reveal Caroline. +3504 Even those ladies ever found Linda. +3505 Even Lucille would ever fall asleep. +3506 Only Naomi has ever run around those art galleries. +3507 Only Deborah ever read. +3508 Only Melanie ever notices Victoria. +3509 Even Maria ever commands every synopsis to praise Becky. +3510 Only organizations ever want to take many glaciers. +3511 Only wheelbarrows have ever accelerated. +3512 Even the students have ever discovered Amanda. +3513 Only Omar ever expected Tara to boast. +3514 Only the broccoli ever ripens. +3515 Even Carol has ever known what fall. +3516 Only the banks might ever reply. +3517 Only John ever looks like photographs. +3518 Only the cucumbers ever go bad. +3519 Even Valerie ever passed this public park. +3520 Only schools could ever answer. +3521 Even this unicycle would ever crack. +3522 Only Julia ever spun around. +3523 Only these couches ever drop. +3524 Only that committee ever hurts Angela. +3525 Only Benjamin has ever remembered Rhonda. +3526 Only Stacey does ever murmur. +3527 Only these hats ever dry. +3528 Even those boys ever profit. +3529 Only the women ever donate. +3530 Only a library should ever forget what aren't shrinking. +3531 Only this sweater ever folds. +3532 Only associations ever meet. +3533 Only a patient has ever known who remembered those dancers. +3534 Only Jane ever complained about that pedestrian. +3535 Even these gloves ever shrink. +3536 Only Guy ever persuades Beth to litter. +3537 Only the customers ever depart. +3538 Only some museum ever referenced Beth. +3539 Only screens ever darken. +3540 Only these children ever disagreed. +3541 Only the lady ever figures out who would tempt those granddaughters of Kirsten to lie. +3542 Only that university has ever pushed Karen's teachers to fix these trucks. +3543 Only Carmen ever prefers some boy to see. +3544 Even Maria had ever turned out to succeed. +3545 Even Marcus should ever annoy Tamara. +3546 Only some patients ever speak up. +3547 Even Tracy had ever spoken up. +3548 Only the women ever divorce. +3549 Even Janet had ever dropped by some movie theater. +3550 Even these dancers have ever interacted. +3551 Only some icicles ever evaporated. +3552 Only organizations ever learn who has hated Todd. +3553 Even Keith ever hoped to smile. +3554 Even Erin ever studies. +3555 Even Homer has ever questioned Becky. +3556 Only waiters ever think about what close. +3557 Only Anne ever regrets a woman protested. +3558 Even Bill would ever resemble every print. +3559 Only Paul ever cures the Borgias. +3560 Only the actresses ever investigate what slowed. +3561 Only Melissa ever forfeited. +3562 Only Christopher ever played. +3563 Only some water ever melts. +3564 Only ladies ever wave. +3565 Even Cynthia had ever protested. +3566 Even the pedestrian ever sighs. +3567 Only gloves ever twisted. +3568 Even Marcus ever loves Nina. +3569 Only Carolyn has ever praised Derek. +3570 Even those adults ever figure out who sighed. +3571 Only Liam ever got a job. +3572 Even the women ever press Rebecca's son to arrive at this grocery store. +3573 Only Stacy ever badgered Martin to die. +3574 Only Christopher ever ate. +3575 Even Galileo does ever order these dancers' dentists to grin. +3576 Only a gate had ever flung open. +3577 Only some candles can ever brighten. +3578 Even Charles would ever praise Joel. +3579 Even Heidi ever sold all screens. +3580 Only this lake ever evaporates. +3581 Even the doctor ever discusses the Impressionists. +3582 Only these associations ever conferred. +3583 Only Meredith ever prompts that student to climb up that slope. +3584 Even the lamps ever hurt Lisa. +3585 Only the guests ever react. +3586 Even Joseph ever pushed Stacy's friends to explore this restaurant. +3587 Even Cheryl has ever neglected to criticize Natalie. +3588 Only David should ever criticize Derek. +3589 Only the hospitals ever plan to clash. +3590 Even Raymond has ever remembered all men. +3591 Even Claire ever ascertained Charles to smile. +3592 Only the waiters ever retaliated. +3593 Even those ladies can ever disturb Steven. +3594 Even Carl has ever doubted teachers might confer. +3595 Only forks ever dropped. +3596 Only the school ever annoyed every driver. +3597 Only guys ever clean a lot of chairs. +3598 Only Kenneth ever forfeited. +3599 Only the Impressionists ever descend the slopes. +3600 Only shawls should ever fray. +3601 Even Mary does ever concur. +3602 Only Derek has ever concealed every dish. +3603 Only Emily ever tempted Jacqueline to depart. +3604 Only Suzanne ever concealed a lot of rabbits. +3605 Only Beth ever tours that mall. +3606 Even Carmen has ever attempted to praise Carl. +3607 Only pedestrians ever cook. +3608 Even Carmen ever died. +3609 Only a mushroom ever ripened. +3610 Even that mirror ever upsets Stacy. +3611 Only some closets ever disturbed Susan. +3612 Even that ice cream ever impressed Susan. +3613 Even the men ever care for Tracy. +3614 Only these organizations ever find people to struggle. +3615 Only those ladies ever salute. +3616 Even Marcus has ever littered. +3617 Only broccoli ever ripens. +3618 Only some actors ever move. +3619 Only this association had ever assembled. +3620 Only dancers did ever marry. +3621 Even Cheryl will ever suffer. +3622 Even this movie does ever disagree. +3623 Only Guy ever argued. +3624 Only the patients ever break a carriage. +3625 Even Stephanie had ever cooked. +3626 Only Sherry has ever said all candles haven't faded. +3627 Even William ever discussed Jesus. +3628 Only Tara ever spurs Allison to slump over. +3629 Only Andrew ever sneezed. +3630 Only dancers ever conspired. +3631 Even Ruth did ever describe Homer. +3632 Even these students have ever cleaned a lot of pies. +3633 Even Margaret ever visited a lot of libraries. +3634 Even Jane has ever hunted. +3635 Only Dennis has ever predicted Robert to stand up. +3636 Only some boy ever refuses to scan the plays. +3637 Even Melissa should ever love Teresa. +3638 Even Andrea can ever protest. +3639 Even these students ever break most screens. +3640 Only Kenneth could ever read. +3641 Only guys ever sway Lori to salute. +3642 Only Joseph ever investigated who was leaving David. +3643 Only that pedestrian had ever prodded Winston Churchill to appear. +3644 Only the hospitals ever fail to retaliate. +3645 Only Marla has ever implored some actresses to bore some doctor. +3646 Only Wendy did ever bother Samantha. +3647 Even Tanya has ever confused Amelia. +3648 Even the school did ever work hard. +3649 Only teenagers ever fight. +3650 Only the ladies ever forgot who did prevent these doors from aggravating Melanie. +3651 Even Joel might ever turn out to look like most sketches. +3652 Even Carol ever required all cashiers to train. +3653 Even these children ever regret Ann healed most people. +3654 Only some waiters ever complain about Donald. +3655 Even adults ever exist. +3656 Only Ella ever begs Ruth to compromise. +3657 Even Emily did ever cry. +3658 Only gloves ever tightened. +3659 Only these organizations ever like Edward. +3660 Only the Lutherans ever cheer. +3661 Only Paul will ever question Steven. +3662 Only Dan ever looks like these sketches. +3663 Only some customers can ever date. +3664 Only guests ever walk through some rivers. +3665 Even this girl ever brought the windows. +3666 Only these customers ever hide some door. +3667 Even Dan should ever spur those theses to disturb Kimberley. +3668 Only some spinach ever goes bad. +3669 Only these doctors ever manage to hide. +3670 Only this icicle ever condenses. +3671 Only the Borgias have ever hugged. +3672 Only Stacy should ever happen to bother Gerald. +3673 Only this apple has ever burned. +3674 Only the cakes would ever char. +3675 Even Judy does ever kiss some child. +3676 Even Sabrina has ever cheered. +3677 Even a candle ever darkened. +3678 Only customers ever drive to some grocery store. +3679 Only Craig had ever happened to weep. +3680 Only a blouse ever folds. +3681 Only Gina can ever drop by the libraries. +3682 Only Sara could ever badger Colleen's hairdresser to swallow. +3683 Only Gina ever fails to shut. +3684 Even Jesus ever dislikes Nancy. +3685 Only some committees ever hope to negotiate. +3686 Only Colleen would ever shout. +3687 Only the candles ever dim. +3688 Even Steven had ever preferred these children to divorce. +3689 Even this woman should ever wish to shock Laurie. +3690 Even the casserole might ever bore these adults. +3691 Even Tamara had ever bored Victoria. +3692 Only Melissa ever noticed every drawing. +3693 Even Valerie ever wondered what hasn't conspired. +3694 Even those senators might ever flirt. +3695 Only Brian ever sounded like Marie. +3696 Even those people ever appear to meet. +3697 Only the senators ever profit. +3698 Even Pamela could ever murmur. +3699 Only the Lutherans ever kissed. +3700 Only Kristen has ever scared a guy. +3701 Even Amy would ever mutter. +3702 Only Melinda ever wants this boy to wash. +3703 Only some pedestrians ever hire Kristen. +3704 Even Gerald ever trains. +3705 Only Suzanne ever scanned some essay. +3706 Even the Borgias had ever sold every door. +3707 Only Michelle ever discovered Marcus. +3708 Even Martha had ever compromised. +3709 Even Tamara could ever depart. +3710 Even Benjamin might ever skate around that hill. +3711 Even Daniel ever broke the carriages. +3712 Even the men ever embraced. +3713 Only this sweater ever twists. +3714 Only some waiter has ever confused William. +3715 Only some women did ever kiss these women. +3716 Only the customers ever advise Marcus's senator to investigate Scott. +3717 Even Melanie could ever paint. +3718 Only Nicole ever baked. +3719 Only some men have ever divorced. +3720 Only some museums might ever have a cat. +3721 Only a carriage ever stops. +3722 Only Dennis has ever broken many dishes. +3723 Only Claire ever broke some plane. +3724 Only dishes have ever tipped over. +3725 Only these girls ever discover every icicle. +3726 Only Rodney might ever win. +3727 Only an eggplant has ever gone bad. +3728 Even the actor did ever spin around. +3729 Only those gates ever close. +3730 Even these teachers ever listen to Judy. +3731 Only those jackets ever fray. +3732 Only a gate ever alarms Jason. +3733 Only Janet ever praised Jason. +3734 Even Paula ever whispers. +3735 Even Sonia should ever hope to pass some grocery stores. +3736 Only the car could ever worry Carolyn. +3737 Only David ever remembered Denise. +3738 Only a guest ever went fishing. +3739 Only the hat has ever soaked. +3740 Only these companies have ever chatted. +3741 Only some students ever work with some drivers. +3742 Only Tina did ever help Douglas. +3743 Even Brad ever kisses Helen. +3744 Only Leslie ever happens to approach Alice. +3745 Only some shawl might ever fray. +3746 Even the carriage ever steers. +3747 Only Tina ever performed. +3748 Even Kathleen does ever talk to a lot of pedestrians. +3749 Only Naomi ever worries. +3750 Even Theodore can ever spur Bill's grandmothers to reply. +3751 Even those projectors ever dimmed. +3752 Only Kayla ever prevented those screens from appearing. +3753 Even Bethany does ever talk about a waiter. +3754 Only Harvard University has ever communicated. +3755 Even Theodore had ever longed to conceal Christine. +3756 Only Rhonda ever paints. +3757 Even legislatures ever discover a bank to cope. +3758 Even those customers had ever hoped to compromise. +3759 Only Karla ever sees Amelia. +3760 Only Bethany has ever yearned to perform. +3761 Even Caroline could ever yawn. +3762 Even that shawl would ever shrink. +3763 Only Douglas did ever sit down. +3764 Only Raymond ever plays. +3765 Only the Impressionists ever communicated. +3766 Only a library would ever need Gerald to think about many customers. +3767 Even Walter would ever litter. +3768 Only some committee does ever assemble. +3769 Even Lisa might ever swallow. +3770 Even some doctors ever believe Rachel to wave. +3771 Only the cashiers have ever kissed Tonya. +3772 Only Lawrence ever forgets that newspaper article. +3773 Only senators ever realize who muttered. +3774 Even this apple had ever ripened. +3775 Only the Lutherans ever learn. +3776 Even Katherine might ever try to go to every lake. +3777 Only the association ever annoys Omar. +3778 Only Laurie ever arrives at Spain. +3779 Only Karen did ever hunt. +3780 Only Meredith ever concealed Samuel. +3781 Even Phillip would ever speak up. +3782 Only that wheelbarrow ever bothered William. +3783 Only this unicycle could ever steer. +3784 Even the patients ever boycott a lot of associations. +3785 Even Guy ever asked Mitchell to research these lakes. +3786 Even these customers ever admire Julie. +3787 Even Kirsten has ever cleaned many cats. +3788 Only these women ever attacked Jeffrey. +3789 Even Aaron ever aggravated Kristen. +3790 Only some tomato had ever charred. +3791 Only a school could ever fail to remember Laura. +3792 Only Rhonda ever returned to Brad. +3793 Only some banana ever bakes. +3794 Only some shawls will ever shrink. +3795 Only April had ever sneezed. +3796 Only Renee ever visits Christopher. +3797 Only Liam might ever like every school. +3798 Only these students ever work with Rhonda. +3799 Only that lady ever fires Tammy. +3800 Only these dancers ever married. +3801 Even Guy has ever entreated a father of Julia to admire Julie. +3802 Only waitresses ever describe Carolyn. +3803 Only Rhonda will ever wave. +3804 Only those skateboards ever turn. +3805 Only some skirt ever dried. +3806 Even Lisa ever compels Wayne to alarm Nicole. +3807 Only cashiers have ever succeeded. +3808 Only these students ever found out who isn't prompting a waiter to yawn. +3809 Even Samuel has ever lifted eyes. +3810 Only Sandra had ever known who hasn't scanned the report. +3811 Even the dancers might ever flirt. +3812 Only that driver ever describes some patients. +3813 Even that girl ever thought about that ice. +3814 Only Amy ever won. +3815 Only the grandsons of the Impressionists who Colleen is appreciating ever encourage Phillip to stretch. +3816 That story about Monet that only Mark would research does ever worry Richard. +3817 A popsicle that only Danielle admires ever freezes. +3818 Naomi's sisters that only Martin had questioned ever seem to look like a light. +3819 Many people who only George likes ever clashed. +3820 Tammy's plumber who only a pedestrian has respected ever knows some actors. +3821 Those doctors who only Karla respects ever conceal many snakes. +3822 A cousin of Lucille that only a lot of rivers have hurt ever learned who have married. +3823 Only every government that Lawrence confused has ever respected Dana. +3824 Only cashiers' piano teachers that Judy does attack ever disturbed patients. +3825 These shirts that only paintings have looked like could ever soak. +3826 Only cashiers that broccoli was distracting had ever thought about a face. +3827 Many bicycles that only the Impressionists break ever worried the patients. +3828 Kayla's contractors that only these universities have listened to ever die. +3829 Only most grandsons of Wendy who many books disagree with did ever train. +3830 Every grandson of Adam who only many paintings should look like ever climbed up hills. +3831 Only many plays that Craig had discovered ever bothered Monet. +3832 John's teacher that only Natalie examines might ever hide away. +3833 These scarves that only Brad has have ever loosened. +3834 Only this hat that those pictures looked like ever unfolds. +3835 Martha's student that only the boy questioned ever descends most hills. +3836 Some mirrors that only Galileo discovers ever chip. +3837 Only some waiters who every guy has approached ever tried to wake up. +3838 Most ladies' supervisor who only Amy has kissed ever studies. +3839 Kendra's French teacher who only Brenda fired might ever admire Valerie. +3840 Steak that only Irene notices ever scorched. +3841 This grandson of Sarah who only all waitresses are investigating had ever discovered Walter to remember a lot of guys. +3842 Only those guys that Edward had listened to had ever brought the coat. +3843 Only the sock that Larry admires ever warped. +3844 That teenager who only that hospital has forgotten ever cleaned these art galleries. +3845 Frank's teacher that only Monet hates had ever ascended the mountains. +3846 Heather's sons that only Jerry aggravated had ever struggled. +3847 Alan's associate who only a guest remembers ever resembles a painting. +3848 Only many actresses who Melinda fled from had ever needed Vincent to litter. +3849 Only every grandmother of Connie that all waiters are boring had ever known who will regret most shirts were folding. +3850 Only these glaciers that Carl arrives at ever appear. +3851 Only the actor that Adam works with ever thinks about Grace. +3852 Most dresses that only Douglas buys can ever flatten. +3853 Only a lot of associations that Catherine questions had ever conferred. +3854 Christine's ex-boyfriend who only some teenagers work with ever obliges Eva to pass some hospital. +3855 Glasses that only Tammy was messing up can ever soak. +3856 Only some patient's tutors that that committee works with should ever hide. +3857 Elizabeth's doctor who only Homer kissed ever remembers Rachel. +3858 Only the cup that Marcus admired can ever slip. +3859 Only the woman who those guests conceal ever went to a lot of libraries. +3860 Only the grandson of Christina who most teachers hate ever weeps. +3861 This student that only Omar has talked to could ever believe Sherry to mutter. +3862 Only the carriages that Dennis has ever slip. +3863 Only those daughters of Patrick that the men find ever come here. +3864 Bill's French teacher who only that icicle stunned ever hunts. +3865 Becca's piano teacher that only Samuel aggravated ever listens to the men. +3866 Friends of Katherine who only some movie has annoyed had ever motivated David to flee from Rose. +3867 Only that grandson of Alan who Kenneth was observing ever praises most actors. +3868 Only that dress that that waiter messes up ever dried. +3869 Marcus's best friend who only George researched ever changed. +3870 Only Karen's lawyers who that senator forgot should ever ascend these mountains. +3871 Ronald's grandfather that only every doctor will leave ever wished to spin around. +3872 Only some glass that Carolyn would find ever chipped. +3873 Most schools that only some actors were passing ever insult Vanessa. +3874 This student who only Rachel left could ever spur the children to know Sherry. +3875 Only waitresses who Pamela could approach ever pressure Russell to fall asleep. +3876 Tara's teachers who only Sharon insulted ever intend to kiss the Impressionists. +3877 These computers that only some women sell had ever looked like many sketches. +3878 Only a truck that Elizabeth was selling ever chips. +3879 Only these boys' ex-girlfriends that some girl remembers might ever encourage Patricia to return to Suzanne. +3880 The teenagers who only Sheila has hidden ever refused to move. +3881 Claire's daughters that only this lady was insulting might ever go to that grocery store. +3882 Only a lot of boys that Craig was revealing ever negotiated. +3883 Only the daughter of every customer who Cynthia thinks about ever drives to this glacier. +3884 Christine's partners that only Bethany might respect could ever vanish. +3885 Only those grandmothers of Tara who Raymond visits ever fix that fork. +3886 Only the committee that these dancers bothers had ever coped. +3887 Only all friends of Nina that Thomas visited ever eat. +3888 Only that daughter of Carrie who Christina might praise can ever explain the adults wouldn't struggle. +3889 Most screens that only Brian noticed could ever brighten. +3890 Only those associations that this tomato embarrassed ever fail to take an eye. +3891 Students' best friend that only most guys listen to has ever explained a student arrived at this museum. +3892 Kirsten's granddaughters that only Sharon was finding ever attempt to play. +3893 Only a lot of drivers that William has investigated ever alarm some bank. +3894 Only a pie that Bruce lifted has ever existed. +3895 Some projector that only Kirsten lifts ever fades. +3896 Theodore's ex-wife that only Kristin would care for could ever persuade a patient to hug Lucille. +3897 Many grandmothers of Sara that only Cheryl scanned have ever learned what were shrinking. +3898 Those mouths that only Heidi admired can ever fling open. +3899 Patrick's hairdressers who only Walter had disliked might ever hug. +3900 Only Scott's dentists who April talked about ever flirt. +3901 Carlos's ex-girlfriends that only all libraries can scare might ever pass a high school. +3902 Only Carl's teachers that Christine worked with have ever brought this window. +3903 Only waiters that Rose discovered have ever required Denise's nieces to hurt many ladies. +3904 Only most pants that the Borgias liked can ever crumple. +3905 Russell's grandson who only children irritates can ever like a pepper. +3906 Only children who these teenagers had argued about should ever donate. +3907 That adult who only Bruce healed ever admires Leslie. +3908 Only the skateboards that boys notice ever chip. +3909 Only a story that all cashiers will disagree with ever alarms Timothy. +3910 Jason's grandmothers who only Frank did remember ever get fired. +3911 Only some skateboards that these students clean ever rust. +3912 Pamela's senator that only most guests worries ever insults Raymond. +3913 A plane that only Liam will find ever rusted. +3914 Natalie's spouse who only the actresses have referenced ever figures out who boycotted a lot of mountains. +3915 Most boxes that only Connie was discovering ever fling open. +3916 Claire's daughter who only Kathleen had thought about ever lied. +3917 Only some coats that Paul finds did ever fold. +3918 Jessica's friend that only April was watching ever replies. +3919 Only every dancer that Bradley reveals ever questions Larry. +3920 Ronald's doctor who only Carolyn sounded like ever declares that pasta to hurt Chad. +3921 All waitresses who only all actresses heal ever believed Rose to descend a slope. +3922 Only every cashier who Ann distracted had ever predicted Christina to suffer. +3923 Florence's nieces that only Jodi has healed had ever prompted Catherine to drive to rivers. +3924 Only the hats that Veronica admires have ever flattened. +3925 The mirrors that only David can hide had ever astounded Eric. +3926 Kimberley's teachers who only Theodore researched had ever assembled. +3927 Andrew's employees who only Raymond examines ever question Nancy. +3928 Those planes that only the student likes have ever slowed. +3929 Only horses that most pedestrians do buy have ever hurt Tina. +3930 Only these doors that Dan sold ever flung open. +3931 Some actors who only Frank did conceal ever proposed to disagree. +3932 Only the actresses that Dennis annoyed ever stretched. +3933 Only some bank that Sandra could discuss had ever prodded an adult to write. +3934 Naomi's mother who only this teenager listened to ever planned to boycott organizations. +3935 Adam's associates that only every pedestrian concealed have ever washed. +3936 Regina's students who only Scott escaped from ever press all actors to look like that print. +3937 Stephanie's brothers that only doctors disagreed with would ever communicate. +3938 Rebecca's fathers that only Angela annoyed had ever concealed an actor. +3939 Alice's cousins that only these stories alarmed ever concur. +3940 Only an actor that Tina will help did ever practice. +3941 Only every river that David concealed had ever condensed. +3942 Only this committee that all people think about did ever wish to alarm Connie. +3943 Only those socks that Judy hides had ever flattened. +3944 Gregory's father who only the pie has annoyed ever left. +3945 Only some grandmothers of Gregory that Sharon had scared can ever commission Elaine to change. +3946 Only that mouth that Larry will bring ever opened. +3947 Only a lot of senators' doctor that Donna has talked about might ever change. +3948 Andrew's hairdressers that only every boy is concealing have ever wept. +3949 Matt's daughters who only those prints were looking like ever clean the library. +3950 Only Danielle's partners that Stacey has cared for have ever surrendered. +3951 Tonya's electrician that only some actresses observed ever requires Melissa to nod. +3952 Only some girl's partner who Jill is revealing had ever researched Monet. +3953 Only the truck that Emily concealed ever crashes. +3954 Anne's plumber who only Andrea was discussing has ever healed most people. +3955 Dennis's guest that only Derek would discover ever stands up. +3956 Only Carrie's friends that Vincent has talked about can ever smile. +3957 Only those associations that James is discovering had ever anticipated a lot of guests to collaborate. +3958 Douglas's electricians who only Suzanne has watched had ever concealed who hadn't attacked some women. +3959 Daniel's partners that only Gary was criticizing had ever benefited. +3960 A committee that only Bill would disgust ever replied. +3961 Clyde's ex-boyfriend who only the print had looked like had ever discovered some spinach. +3962 Many girls who only Carol sounded like had ever performed. +3963 Donald's piano teacher that only some waitress had found will ever drop by that movie theater. +3964 Tammy's supervisor that only this picture looks like did ever suffer. +3965 All candles that only that company has taken ever hurt Raymond. +3966 Martin's plumbers who only Dawn was distracting did ever reveal who could dare every dancer to spin around. +3967 Only all drivers' associate who the Borgias admire ever scratched. +3968 Kristin's electrician that only Allison might kiss will ever continue to like every sweater. +3969 These students that only Dennis fled from have ever played. +3970 Only a lot of men that Heather watches would ever skate around some hospitals. +3971 Only waitresses that April is concealing might ever paint. +3972 Only this senator who a lot of guests conceal had ever talked. +3973 Stacey's piano teacher that only Kendra loved had ever watched Jesus. +3974 This son of Carrie that only Marie had watched ever negotiates. +3975 Only that dress that all students would discover ever unfolds. +3976 Only these cousins of Marie who that wheelbarrow alarms ever worried. +3977 The eyes that only Stephanie will lift ever resembled this sketch. +3978 Only that grandson of Theresa that most organizations are aggravating ever regretted those actresses were debating. +3979 Only a friend of Mary who Brett disagrees with ever yells. +3980 Many drivers' grandmothers who only Noah was listening to ever threaten to criticize a lake. +3981 Most schools that only Kevin could research will ever succeed. +3982 Those skirts that only Lisa could sell can ever hurt Beverly. +3983 Only Colleen's grandsons that Allison has disgusted ever investigated who has suffered. +3984 Only every shawl that Guy is concealing did ever unfold. +3985 Lori's dentists that only some rice has bored could ever discover Susan to learn. +3986 Only Andrea's friends that a jacket upsets ever communicated. +3987 Only those associations that many doors annoyed can ever talk to Elizabeth. +3988 Only that dancer that Laura is noticing would ever try to cook. +3989 Only the casseroles that Karla would hide ever char. +3990 Only a friend of the girl who Marla had criticized had ever practiced. +3991 Only the driver's father who a lot of pedestrians scan might ever argue about most books. +3992 This river that only Tamara could notice could ever evaporate. +3993 Vanessa's granddaughter that only some waiter hides might ever forget Deborah. +3994 Only some children who Deborah was embarrassing can ever fix that cup. +3995 Only all boys that Deborah had referenced had ever passed some bank. +3996 Tonya's nieces who only these prints look like ever grinned. +3997 Only this man that Homer was leaving might ever nod. +3998 Christine's sister who only Sabrina upset ever insults April. +3999 This library that only Derek is aggravating ever remembers reports. +4000 Suzanne's boss that only all drivers boast about ever intended to eat. +4001 Cynthia's colleagues that only Teresa insults ever testify. +4002 That committee that only Jennifer has distracted ever implores the man's piano teacher to explain everything. +4003 Some grandfathers of Martin that only these dancers confuse ever know many guys. +4004 A lot of women that only these children could listen to have ever woken up. +4005 Only the Borgias' piano teachers who Bruce disturbed have ever ordered Bethany to discover Sharon. +4006 Julia's senators that only many governments like ever passed every river. +4007 Only that adult's boyfriend who a public park does impress had ever painted. +4008 Only the screens that Ann did admire ever faded. +4009 Most companies that only bananas had scared ever oblige Holly's brothers to stretch. +4010 Most bicycles that only Irene found ever waste away. +4011 Only the waiter who all guests praised ever falls. +4012 Holly's grandfather who only the forks upset ever examines this commentary. +4013 Aaron's ex-girlfriend that only the couch is distracting has ever talked to Elaine. +4014 Cindy's sister that only Andrea could question would ever disgust Anne. +4015 Only every wheelbarrow that those drivers notice ever embarrasses Kimberley. +4016 Chad's supervisors that only some bananas are upsetting had ever healed Andrew. +4017 Tracy's piano teachers who only Marie was returning to ever consider an organization to argue. +4018 All cashiers that only some person did hire ever surrender. +4019 Only some box that Clyde took would ever close. +4020 Only many guests that Beverly had aggravated might ever benefit. +4021 Alice's classmate that only these men see will ever distract these senators. +4022 Only those grandmothers of Catherine who Eric leaves had ever coped. +4023 Marla's ex-wives who only Tonya fled from ever embraced. +4024 Dennis's mothers that only many drivers embarrass might ever read. +4025 Only every picture of Ella that Martha can buy ever impresses Martha. +4026 This doctor that only Amelia would listen to has ever thought about Lori. +4027 Many bicycles that only Eric was bringing do ever stop. +4028 Most mouths that only Martin likes had ever appeared. +4029 That waiter who only Tiffany has upset ever forgets Denise. +4030 Only the child who Vincent has stunned will ever train. +4031 Julie's tutors who only Monica has disliked ever paint. +4032 Only a cup that Stephanie has revealed has ever fallen apart. +4033 Christine's ex-wife that only Alexander hates had ever lifted the spinach. +4034 Only many peppers that this adult thinks about ever grow. +4035 Andrew's nieces that only some person was firing ever conceal Dana. +4036 Bruce's brothers that only most customers approach ever dislike Jessica. +4037 Curtis's piano teacher who only some company criticized ever pressed Karen to attack Carol. +4038 Only the girl who a lot of boys might worry does ever scratch. +4039 Vanessa's ex-girlfriends who only Cheryl notices ever motivate George to upset Alexander. +4040 Those ladies that only a woman has escaped from might ever exit that glacier. +4041 Only the Impressionists' sisters that Carolyn is curing ever imagined April hasn't distracted a committee. +4042 Only that actor's students who the organization had concealed ever succeeded. +4043 A lot of cashiers that only Heather has worried should ever criticize Derek. +4044 Many committees that only a coat can irritate ever failed to succeed. +4045 Only all people who this actor will attack ever declare Cindy to talk. +4046 Homer's mentor who only Renee will conceal ever played. +4047 Elizabeth's girlfriend that only that student has shocked ever screams. +4048 Samuel's brother that only Kendra did examine can ever observe these adults. +4049 Every screen that only some adults lift ever tipped over. +4050 Martin's piano teachers that only those snakes had astounded ever attempt to climb down all mountains. +4051 Only teenagers that some doctors fled from had ever chatted. +4052 Daniel's daughters that only science shocks have ever found this university to conspire. +4053 Brothers of Clyde that only a lot of banks argue about do ever chat. +4054 Only these cousins of Veronica who the Lutherans attack ever helped these women. +4055 Only a lot of granddaughters of Omar who all customers investigate ever exit a lot of libraries. +4056 Only this grandson of Regina who Meredith praised ever washes. +4057 Only every grandfather of Natalie who Carl fires can ever expect Cynthia to lift some glove. +4058 Only some guy's nieces that Ella visits could ever neglect to complain about Valerie. +4059 Only that daughter of Guy who Sharon is healing ever compels Tanya to stretch. +4060 Paula's mentor that only Grace did hide had ever visited Sabrina. +4061 Wendy's French teacher that only Dawn was irritating ever needs Kayla to climb up hills. +4062 This grandson of Patrick that only these tomatoes have alarmed ever hid away. +4063 All sons of this waitress that only Daniel would appreciate ever hope to hunt. +4064 Only every friend of senators who Bradley did talk to had ever seemed to aggravate those boys. +4065 This legislature that only the legislatures were stunning had ever boasted. +4066 Most bikes that only Stephanie fixes do ever disgust some waiter. +4067 Only the cashier that Steve disgusted ever comes here. +4068 That sock that only adults would notice ever crumpled. +4069 A fork that only Cynthia discovers ever shattered. +4070 Only that cashier's partner that most men were disagreeing with ever approves. +4071 Only Ellen's electrician who most schools loved has ever known who skates around some cafe. +4072 Amanda's student that only Angela loves ever dislikes Benjamin. +4073 Only the actresses who Jeffrey is hugging ever encourage Tara to leave this doctor. +4074 Most men's granddaughter who only Melinda was boring had ever dared Joseph to practice. +4075 Only every grandmother of Dana that the projector is embarrassing ever knows what retaliate. +4076 Only some apples that that senator thinks about ever grow. +4077 Only every hat that those children sell ever soaks. +4078 All teachers' friends who only Anne remembered can ever scare Monet. +4079 Only a lot of peppers that a photograph looks like ever grow. +4080 Only every woman who Patrick praised had ever donated. +4081 Only that boy's best friends that most people revealed ever sat down. +4082 Most pedestrians that only this school was working with ever urge Alicia's wife to attack Tamara. +4083 Only every shirt that these doctors have noticed should ever dry. +4084 Only most museums that Raymond messed up have ever wanted this newspaper article to confuse a lot of companies. +4085 Only some daughters of Julia that the person was impressing might ever approve. +4086 Only Allison's best friends who Nina cured ever boycott the Impressionists. +4087 Peter's colleague that only Alice escaped from ever fixes all cups. +4088 Debra's friend that only some actor has researched had ever seen the customer. +4089 Timothy's plumber that only some rivers were annoying ever commissions Homer to remember Jerry. +4090 Only that government that Gary can disgust had ever clashed. +4091 Laurie's supervisors that only Helen upset ever persuade some sons of many people to die. +4092 A lot of universities that only this carriage disturbs ever forgot about what weren't looking like these paintings. +4093 Lakes that only Jessica has discussed ever embarrass an adult. +4094 Carmen's daughters that only that driver did boast about ever debated. +4095 Customers that only Theresa had stunned could ever embrace. +4096 Only those drivers' dermatologist that the hamsters stun ever hopes to wake up. +4097 Chicken that only these drawings are looking like ever falls. +4098 Those gates that only Dana is revealing have ever shut. +4099 This screen that only Julia could conceal ever disappeared. +4100 Only every university that all mirrors disgusted had ever threatened to slump over. +4101 Only women who Gary was attacking ever clean the cafe. +4102 Thomas's guest that only Melanie can escape from ever goes to this movie theater. +4103 Only some pedestrian that Jill fired ever neglected to complain. +4104 Deanna's piano teachers that only Amanda has complained about could ever observe Monet. +4105 Only a lot of waitresses who the Lutherans hired could ever hide. +4106 Paul's ex-wife that only those guys should criticize ever regrets these patients had toured those closets. +4107 Only the patient that Douglas should investigate ever leaves. +4108 Only that computer that girls sell ever drops. +4109 Only some friends of these ladies who the boys question ever implored Lawrence's sister to aggravate Danielle. +4110 The mother of Laurie that only the waitresses are appreciating ever examined some movies. +4111 Patients who only the waiter was returning to should ever communicate. +4112 Only a lot of hospitals that this committee was admiring would ever help Donna. +4113 Deborah's sisters that only those dogs annoy ever researched Spain. +4114 Bruce's ex-boyfriend that only these senators should discover ever ascended a lot of slopes. +4115 Only those hats that Carl does clean did ever fall. +4116 Joseph's classmate that only Sherry listened to ever discovered who protests. +4117 Nina's associates who only Omar talks about had ever bought the dress. +4118 Derek's girlfriend that only Walter is shocking had ever talked. +4119 This teenager that only those associations criticized ever struggles. +4120 Deborah's brothers that only Brenda fires ever fall. +4121 Only many grandsons of Beth who the Impressionists think about ever studied. +4122 Only a window that the Borgias think about has ever flung open. +4123 Becky's colleague that only some cashiers worry had ever changed. +4124 Only this mirror that many actresses notice ever slips. +4125 Kayla's ex-girlfriends who only Edward is scanning ever hide away. +4126 Those grocery stores that only Aaron can discover ever stun Christine. +4127 The shows about art that only many schools should describe ever disturb the waiters. +4128 Galileo's sisters who only most banks work with ever seem to rot. +4129 Only these shoes that Kenneth is hiding ever wrinkle. +4130 Only all granddaughters of Jodi who Vincent references ever win. +4131 The library that only the adults have messed up ever ascertained Russell to criticize Pamela. +4132 Only Carrie's tutors who Kristin is discussing will ever sit down. +4133 Only some broccoli that many photographs looked like ever burns. +4134 Only all closets that most doctors brought should ever appear. +4135 Only all guys who James helped ever watch Ellen. +4136 Some pedestrian who only Bill has cared for ever prefers these children to succeed. +4137 Katherine's friend that only these associations could argue about ever played. +4138 These senators' electricians that only Carol escapes from ever wish to eat. +4139 Only many sisters of James who most schools were bothering ever scanned this documentary. +4140 Only many teenagers that Guy criticized have ever spurred many children to arrive at art galleries. +4141 Only every gate that Lawrence noticed could ever disturb Gerald. +4142 Suzanne's hairdressers who only Travis was discovering ever practiced. +4143 Only a lot of skirts that those actresses find ever soak. +4144 Only Patricia's electricians who this cashier can talk about ever change. +4145 Sheila's granddaughters that only those senators were disagreeing with ever predict some skateboards to rust. +4146 Theodore's classmates who only the teachers describe ever stand up. +4147 Only all grandfathers of Frank that that doctor aggravated ever manage to wash. +4148 Patricia's nieces who only John might hire ever scan many stories. +4149 All brothers of Carl who only Marla complained about ever looked to disappear. +4150 Grandmothers of Mary that only Grace does observe ever exercise. +4151 Clyde's dermatologist that only Joseph is talking about ever sways men to change. +4152 Only Curtis's guests that Holly forgets could ever hide away. +4153 This sock that only these girls lifted ever folds. +4154 Only every guy that Samantha is irritating ever wastes away. +4155 Only some associations that the Impressionists boycotted ever coped. +4156 Only many trucks that Nicole brought ever crack. +4157 Only a waitress who every man attacked can ever care for many students. +4158 The Impressionists' cousins that only a bank will criticize ever wear this hat. +4159 This niece of Scott that only a woman works with ever finds some car. +4160 All nephews of Aaron that only guests admire ever drive to the closet. +4161 Only some tomato that Daniel buys ever scorched. +4162 Only every grandson of Tonya who the ladder impresses ever climbs up that hill. +4163 Only a boy who that photograph looks like has ever yearned to tour some closets. +4164 This association that only this hospital aggravates had ever urged Allison to complain about Carlos. +4165 Only most pedestrians that William has appreciated did ever telephone. +4166 Those windows that only Veronica does think about have ever confused Walter. +4167 All hospitals that only Tracy could astound can ever encourage Heather's guest to disturb Harvard University. +4168 All actors who only every sketch has looked like ever benefited. +4169 Only that dancer that those boys might praise would ever explain those women remember a hospital. +4170 Only a lot of actresses that that essay had disagreed with ever collaborated. +4171 Every actress's piano teachers who only Sara can notice ever observe Amy. +4172 Only Kirsten's piano teachers that the Lutherans like ever helped Donald. +4173 Only art galleries that some ladies bike to ever stun Matt. +4174 Sabrina's lawyers that only some company had impressed have ever gone to a cafe. +4175 Some windows that only Kenneth sells ever confused the universities. +4176 Most granddaughters of Deanna who only Jesus admires had ever sneezed. +4177 Naomi's supervisors that only the people insult would ever yearn to depart. +4178 Some customers that only Paul could boast about ever yawn. +4179 Only some rivers that that school is buying ever condense. +4180 That mountain that only Kendra discussed had ever hurt Alice. +4181 Timothy's partners who only Kirsten did think about ever shock some doctor. +4182 Only some actors that these apples were embarrassing ever judge Alicia to visit the senator. +4183 Only Janice's colleagues who Bradley is astounding have ever yawned. +4184 Only some children that Sandra has bothered had ever cheered. +4185 All grandmothers of Becca that only customers hate ever bother Keith. +4186 Only Carrie's bosses who those banks are concealing had ever conferred. +4187 Only a driver who all students escape from ever turned out to shatter. +4188 Veronica's best friends that only Benjamin does alarm could ever cheer. +4189 Only some brothers of a man who universities fire have ever profited. +4190 Only some friend of Bradley that most eyes aggravate ever wears many shirts. +4191 Only some guys who the Impressionists have annoyed ever reunite. +4192 Noah's employee that only Sara discovers ever wants Christina to admire Noah. +4193 Only many associations that Pamela had worried could ever realize who hadn't boycotted a university. +4194 Some skateboards that only Carrie is admiring should ever turn. +4195 Only the child that Alexander hugs ever complains. +4196 This skateboard that only Amelia lifts ever maneuvers. +4197 Only boys' senator who Mitchell might conceal ever hides. +4198 Only that customer that Dana visits ever exercises. +4199 Some cashiers who only children disagree with ever concealed Joseph. +4200 Only the children who Beth should boast about ever fix a plate. +4201 Only the shoe that the doctors hid had ever warped. +4202 Only a lot of skirts that most associations took ever stretched. +4203 All libraries that only Kathleen might clean could ever declare Rachel to discuss all teenagers. +4204 Only the bicycle that those people took ever appeared. +4205 These guests that only Matt had cared for ever flee from Craig. +4206 That light that only Andrew saw ever dimmed. +4207 These organizations that only Ann has shocked ever implore Andrew to die. +4208 Suzanne's husband who only some guests disagreed with ever changes. +4209 A lot of nephews of Rachel that only many universities did love ever explain everything. +4210 Only Sally's tutors who the glasses distract ever ascertain a shawl to fold. +4211 This child that only Meredith cared for would ever practice. +4212 Deanna's dentist who only Emily has worried has ever moved. +4213 Only this grandmother of Steve who Homer would describe might ever fail to mess up the public park. +4214 Only this driver who Allison was helping had ever failed to yawn. +4215 Many patients' lawyer who only Gina describes ever took this snake. +4216 Only the gate that John had could ever shut. +4217 Most drivers who only Carmen had hugged did ever clash. +4218 Mark's cousins that only Jacqueline kissed ever entreat most patients to lift some cakes. +4219 Only this cart that customers fix ever disturbs James. +4220 Only Marie's girlfriend who Alexander is working with ever lies. +4221 Most guys that only Danielle has complained about will ever flirt. +4222 These teenagers' employee who only Florence remembers has ever shrugged. +4223 Every jacket that only a waiter will discover does ever unfold. +4224 That organization that only every chair is disturbing should ever conceal Carlos. +4225 Rodney's grandmother who only Holly worked with ever talks. +4226 Travis's boyfriend who only Andrea praises ever compromises. +4227 The vases that only Veronica had noticed do ever crack. +4228 Only a lot of grandmothers of some girls that Benjamin listens to ever won. +4229 Diana's grandmother who only Omar approaches has ever yawned. +4230 Only Lori's contractor who Carolyn revealed would ever sway Adam to play. +4231 Most guests that only Denise kisses ever fixed these screens. +4232 Mitchell's fathers who only Ann escaped from ever hide away. +4233 Only Steve's grandfathers who all pictures should impress ever disagreed with those dancers. +4234 Only some waiters that Jason has concealed had ever died. +4235 Only the universities that Diane did upset ever annoy Amy. +4236 Only some daughters of Phillip that most boys sound like have ever replied. +4237 Veronica's daughter that only all customers disagreed with ever learned. +4238 Beverly's classmates that only Becca had observed ever shouted. +4239 Those sweaters that only Steve will admire ever warp. +4240 Only Vanessa's dentists who most guys reveal ever conspire. +4241 Only that nephew of Homer who Candice was hiring would ever return to James. +4242 These guests who only Joel had admired would ever kiss. +4243 Florence's grandsons who only Sandra liked have ever questioned Jane. +4244 Only a lot of jackets that Ann might take ever soak. +4245 Only the skirt that Alicia reveals ever shrank. +4246 All dancers' classmate that only waiters had worked with has ever cooked. +4247 Melinda's spouse that only Travis would cure ever scans the play. +4248 Mark's brothers that only those high schools hurt ever forget who looked to exit that closet. +4249 Only some brother of every student who Becca has embarrassed ever hugs that teacher. +4250 Only every bike that Steve reveals ever crashed. +4251 Bradley's grandsons that only Homer has seen ever propose to dislike Aaron. +4252 Deanna's ex-husband who only Stephen describes ever disturbs Theresa. +4253 Emily's doctor that only most teachers question had ever referenced Laurie. +4254 Those adults who only Wayne loved should ever order Samantha to impress Mary. +4255 Only some drivers who Cynthia remembers ever explore all closets. +4256 Julia's tutor who only carts embarrass has ever want to murmur. +4257 Only the shirt that that drawing looks like has ever appeared. +4258 Judy's husband that only most drivers should care for would ever appreciate Laura. +4259 Todd's nieces that only Sheila upset do ever consider Brian to get fired. +4260 Only some lady who doctors reveal ever escaped from Donna. +4261 Only a grandfather of Patrick that many customers help could ever see. +4262 Only these men who this apple was alarming ever complain. +4263 Victoria's sisters that only dancers discover ever talk about all commentaries. +4264 Only the nieces of Katherine that this cart alarms had ever reunited. +4265 Beth's piano teacher who only Cheryl will know ever wants to hide Travis. +4266 Only those plates that Frank will discover ever shatter. +4267 Only every candle that Jerry is bringing ever dimmed. +4268 Only the socks that Harvard University buys had ever twirled. +4269 Most eyes that only Diane can notice ever shut. +4270 Vincent's sons who only Tanya has respected ever fall. +4271 All patients that only Laurie would criticize have ever communicated. +4272 That actress that only this school listened to ever moves. +4273 Susan's son who only Bethany had revealed will ever sigh. +4274 Adam's dentist that only Monica criticizes ever compels Guy to whisper. +4275 Only that dancer who the Great Lakes hurt has ever approached Ronald. +4276 Irene's grandfathers that only this story irritates could ever respond. +4277 Only many universities that Phillip was investigating ever obliged Maria to dislike Matt. +4278 Only a lot of men that the organizations hires ever admire a popsicle. +4279 Matt's ex-boyfriend who only Kayla was researching has ever commanded Valerie's best friend to break this fork. +4280 This grandson of Judy that only Sandra was boasting about had ever painted. +4281 Most plates that only Dawn sees ever drop. +4282 This waitress that only dancers criticize ever stretched. +4283 Only these children that that driver hugged ever have that bird. +4284 Only a lot of actresses that these cafes bother ever get fired. +4285 Windows that only Samuel should see had ever fallen apart. +4286 Nancy's bosses that only Maria references have ever looked like every print. +4287 Only a friend of many cashiers that these schools help did ever blink. +4288 Only those waitresses who the fish is distracting would ever get a job. +4289 Maria's mother that only most guests visit ever walks through a high school. +4290 Only the spinach that all governments have sold could ever char. +4291 Only some senator who Gerald talks to did ever alarm Jennifer. +4292 Alicia's ex-boyfriend that only current events scare ever forgets about what aren't benefiting. +4293 Only ice cream that Christina had ever melted. +4294 Most wheelbarrows that only Dennis discovers ever slip. +4295 Derek's mother who only Jessica thinks about ever respected Walter. +4296 Only every father of Nancy who the guests discover ever responds. +4297 Legislatures that only Sabrina could think about had ever planned to interact. +4298 Only some guest that that dress is annoying had ever hidden that lady. +4299 Only Julie's ex-girlfriends who Edward argued about do ever yearn to boast. +4300 Many lakes that only Beverly went to had ever frozen. +4301 Every adult that only Tanya was firing ever cooks. +4302 Only the customer that Matt watches had ever asked Rachel to walk through that closet. +4303 Only waitresses that some book aggravates ever force Craig to scratch. +4304 Most cousins of Chad that only the Borgias hug might ever respect Sharon. +4305 Only a lot of waitresses that all skateboards irritate ever preferred Renee to practice. +4306 Every grandmother of Lisa that only guests impressed ever salutes. +4307 Dan's mothers that only a lot of senators care for ever hide the pasta. +4308 Wayne's spouse who only these ladies describe ever criticized Kristen. +4309 Peter's sister that only Adam astounded ever sighs. +4310 Only Leslie's partner who Richard praises should ever bike to these museums. +4311 A lot of girls that only Victoria disagrees with ever succeed. +4312 Peter's grandmother that only Florence is bothering ever explores a lot of grocery stores. +4313 These prints of this slope that only Tiffany revealed had ever looked like all cakes. +4314 Tonya's father that only Angela researches had ever tempted Keith's ex-girlfriends to aggravate Meredith. +4315 Only those teenagers that Gregory was listening to ever protest. +4316 Only those sisters of Theodore who some organizations work with should ever buy a mouth. +4317 Anne's granddaughters who only this girl remembered did ever scratch. +4318 Only the woman that these pedestrians care for ever boasts about Phillip. +4319 Most senators that only that actor should praise ever climb up most mountains. +4320 Some teenager who only Grace watched ever requires many libraries to lie. +4321 Those mushrooms that only some child has found ever go bad. +4322 Only that committee that Deborah had upset ever fires Carl. +4323 Matt's associates that only Eric astounded ever pressed Marla's girlfriend to paint. +4324 Only some student who those actors disagreed with ever embarrassed Mitchell. +4325 Some actresses' grandfathers that only that child concealed do ever cook. +4326 Only all cousins of Steven that Monet had discussed ever see this art gallery. +4327 Every dancer that only these adults return to ever replies. +4328 Jerry's supervisor who only Gerald concealed ever works with Brian. +4329 Rodney's son who only Tanya escapes from should ever laugh. +4330 That driver who only some dancer has left had ever descended many stairs. +4331 Only a lot of people that Jill has noticed ever talk to Judy. +4332 Linda's associates who only this committee bothers ever boast. +4333 Every teenager's senators that only Sandra scanned ever insulted Paula. +4334 Only the companies that a pepper upsets ever badger the companies to correspond. +4335 The doctors that only Galileo referenced ever healed Bethany. +4336 Claire's granddaughters that only Jeffrey did fire have ever swayed Dan to litter. +4337 Only the truck that Lawrence is noticing should ever crack. +4338 Only many waitresses' ex-girlfriends who a lot of libraries criticized have ever swallowed. +4339 Most waitresses that only Tanya had complained about ever return to Jeffrey. +4340 This committee that only Debra should impress can ever convince Dennis to insult Lori. +4341 Only this child that Sarah is hugging ever escapes from those schools. +4342 Victoria's supervisors that only every girl has cured ever disagree with some guys. +4343 Only many senators that all customers confuse ever conceal the snakes. +4344 Only the socks that Heidi concealed have ever stunned Elaine. +4345 Most cashiers that only some students return to might ever ascertain men to salute. +4346 Some boys that only every doctor reveals do ever visit closets. +4347 Only some actress who this candle distracts ever induces Theresa to spin around. +4348 Ellen's granddaughters who only that child worried ever explore this bank. +4349 That grandmother of Heidi that only Carmen loved ever comes here. +4350 Daniel's contractor that only Mitchell helps ever laughed. +4351 Only those mushrooms that that painting was looking like ever go bad. +4352 Only every dress that Claire lifted ever twisted. +4353 Carmen's friend that only men love ever died. +4354 Katherine's colleague who only Nicole has forgotten has ever begged Janice to litter. +4355 Kristin's ex-wife that only an organization has boasted about ever sells some scarf. +4356 Only a lot of banks that Katherine is hiding have ever ordered Noah to skate around some grocery store. +4357 Some nieces of Janet who only Elizabeth can irritate ever disagree. +4358 Sandra's partner that only those senators irritate had ever disagreed. +4359 These friends of Beverly who only Scott was embarrassing ever thought about what irritates Gerald. +4360 Most people that only these girls were admiring ever pressure nieces of many ladies to practice. +4361 The window that only Liam was thinking about ever opens. +4362 Only some glasses that Jacqueline wears have ever stretched. +4363 Keith's classmates that only the drivers investigate ever hurts these guys. +4364 Only Christine's contractor that Natalie works with will ever intend to bike to the grocery store. +4365 The sweaters that only all teenagers will discover ever shrink. +4366 Only some sketch of a bike that most prints had looked like ever looks like the computers. +4367 An actor's sons that only a waiter discussed ever lifted a cart. +4368 These sisters of Carlos who only Alice might like did ever wear the sweater. +4369 All girls who only Dawn might see ever wish to fall asleep. +4370 Daniel's associate that only that print looked like has ever descended the mountains. +4371 Suzanne's father that only these teenagers listened to ever appeared. +4372 All teachers that only Paula can observe ever fought. +4373 Only some report that Eric will find ever worried Linda. +4374 Allison's ex-husband who only all guests will help ever reads. +4375 Only the coat that some men buy might ever annoy Amelia. +4376 Tracy's tutors that only Kevin was escaping from ever believe this glove to loosen. +4377 Denise's sons that only these waiters dislike have ever embraced. +4378 All women's colleague that only Jerry forgets has ever gone to the Great Lakes. +4379 Some cousins of Monica that only some customer reveals should ever think this shoe hasn't folded. +4380 Only these shirts that a lot of students buy ever fold. +4381 Many governments that only this cashier irritated do ever hire Martin. +4382 Gregory's guests that only the Borgias examine ever correspond. +4383 Only every cashier who Lori is watching ever shrugged. +4384 Helen's ex-wife who only Bruce could complain about ever believed Nancy to shout. +4385 Only some dress that Ronald lifts ever loosened. +4386 Only that shawl that Donna has worn ever dried. +4387 That legislature that only some movies have bothered had ever swayed Benjamin's supervisor to arrive at every hospital. +4388 Some adults that only a lot of patients are investigating ever think about who wasn't climbing up all stairs. +4389 Sandra's lawyers that only some students have approached ever bothered a man. +4390 Companies that only most libraries stun ever praise some teachers. +4391 Only those glasses that Martha was thinking about ever fold. +4392 Only many museums that some ladies escape from should ever compel Guy to hire Heather. +4393 Most cars that only Sara might see ever turn. +4394 Every art gallery that only Tiffany does flee from ever worried Denise. +4395 Only a legislature that ice cream aggravates has ever loved Amelia. +4396 Only women that Diane worried ever described a lot of guys. +4397 Paula's French teacher who only Jason will talk about ever found out who can't motivate William to impress Raymond. +4398 Only most men's senators who Winston Churchill knows have ever revealed all people. +4399 Veronica's teachers who only Charles does think about have ever helped Grace. +4400 Laurie's piano teacher that only Sarah was curing ever heals Carmen. +4401 Most closets that only Kristin dropped by have ever closed. +4402 Only pedestrians that a lot of drawings were looking like ever help Valerie. +4403 Only the granddaughters of Lisa that Diana is approaching ever hide Natalie. +4404 Many friends of Rhonda who only Erin can kiss ever hurt Natalie. +4405 Only most actors who many men were disagreeing with do ever reveal who wants rivers to alarm the adults. +4406 Only every man who Susan has visited ever exercises. +4407 Becky's father who only this man was fleeing from ever broke those bicycles. +4408 Only some beef that Phillip had thought about does ever blacken. +4409 Only this actress that these people discover had ever suffered. +4410 Holly's boss who only Kimberley discovered ever climbs up most slopes. +4411 Those jackets that only Jeffrey would discover would ever unfold. +4412 Jodi's plumbers that only Brett insulted ever consider coats to soak. +4413 Many hospitals that only Amy explores have ever continued to listen to Julie. +4414 Only a customer who Sandra can leave ever needs every fork to disturb Amy. +4415 This organization that only Jennifer does investigate ever worries some teenager. +4416 Only a lot of ladies who many actresses fled from ever sigh. +4417 Sabrina's boss that only Elizabeth approached ever reveals Ronald. +4418 Only some cousin of Benjamin that Kristin researched ever says these teachers aren't inducing Robert to telephone. +4419 Galileo's nephews that only Sarah visits ever lifted this banana. +4420 Only some guests that the painting distracted ever tour most public parks. +4421 Noah's daughters who only Stephanie revealed ever write. +4422 Only the dancer that those horses impress did ever push Carrie to find those hospitals. +4423 Only a driver who Steve is noticing ever hides Jacqueline. +4424 Only those teenagers who all pedestrians heal would ever need to sing. +4425 Guests that only some doctors argue about have ever benefited. +4426 The mouth that only April should lift ever falls. +4427 These dancers who only Christina visits ever talk to Keith. +4428 Only every waiter who Becky might complain about has ever seen these paintings. +4429 Only this patient that a lot of actresses hate will ever leave many banks. +4430 Most girls that only Tanya embarrassed had ever shouted. +4431 Only every dancer who Raymond knows should ever bake. +4432 Paula's brothers who only Stacy was returning to should ever descend many mountains. +4433 Only those girls that Steven is boasting about ever bake. +4434 That car that only this horse broke ever turned. +4435 Bruce's ex-girlfriend that only Susan has noticed had ever neglected to worry Vincent. +4436 The Borgias' sons who only Gina loves will ever marry. +4437 The couch that only an association bought ever slipped. +4438 Patients that only many schools help had ever gotten fired. +4439 All scarves that only Regina would find ever distract those boys. +4440 Only all grandmothers of Steve that Amanda is observing ever chuckle. +4441 Most computers that only all pictures look like ever fall apart. +4442 Only every boy who April loves ever regrets Samuel complains. +4443 Lisa's colleagues that only Christina talked to ever go fishing. +4444 Most nieces of Galileo that only the shawl would upset have ever laughed. +4445 Only a senator who Linda should reference has ever found out who exit that lake. +4446 Those hospitals that only the teenager has impressed ever swayed Angela's students to sigh. +4447 Only this man that Robert boasted about ever gets fired. +4448 Anna's best friend who only the patients had disagreed with had ever induced most girls' ex-husband to scream. +4449 Gary's handyman that only a lot of screens aggravated ever gets a job. +4450 Clyde's partners that only companies appreciated had ever driven to that museum. +4451 Only those teenagers who Raymond is criticizing ever whisper. +4452 Alicia's grandsons that only the senators talk to ever discover Naomi to run around this art gallery. +4453 Only many ladies that most cafes upset ever forgot Noah. +4454 This mother of Lucille that only Jessica had distracted ever induces Rose to win. +4455 Scott's nieces that only these dancers have found can ever climb up those steps. +4456 A grandson of Karla that only Donna did describe ever talks. +4457 Andrew's teacher who only Clyde is noticing ever salutes. +4458 A lot of sons of Adam who only Victoria was approaching ever failed to approve. +4459 Only this convertible that Linda was hiding ever turns. +4460 Only this mother of Maria who Pamela impressed ever sells a lot of bikes. +4461 That boy's senators that only some boys had insulted could ever weep. +4462 Only this child that Sara works with would ever bother to scan every play. +4463 Theodore's French teacher who only Cheryl could forget ever attacks Margaret. +4464 Naomi's sister who only many girls know ever answered. +4465 Only the doctor who Carolyn is praising ever drinks. +4466 Sharon's ex-boyfriend that only Mitchell approaches ever commands Dan to remember cafes. +4467 A mother of Irene who only Dana is observing ever tours a lake. +4468 James's senator who only Teresa can describe ever escapes. +4469 Leslie's lawyers who only Nicole was approaching have ever urged Tanya to respect Peter. +4470 Paul's classmates who only Gina should discuss had ever approached Beverly. +4471 A lot of customers' granddaughters that only Alan was criticizing had ever spurred Jill's associates to drink. +4472 Only every niece of Sherry that Edward is insulting ever learns. +4473 Beth's associates that only Susan did criticize ever sang. +4474 All patients that only Dana had complained about could ever work hard. +4475 Only a lot of pedestrians who those women alarms did ever remember some lady. +4476 Only that customer's tutors who that glove annoys ever assembled. +4477 Rodney's employee that only Karen has approached ever admired Becky. +4478 David's boyfriend who only Phillip had thought about ever learns. +4479 Some sock that only Regina hides has ever appeared. +4480 Only Nancy's student who a lot of dancers have disagreed with could ever salute. +4481 Only some teacher who Jodi does observe ever answers. +4482 Only those girls that Beth boasts about can ever mutter. +4483 Only a lot of grandfathers of Suzanne that Gina was researching had ever clashed. +4484 Janet's mother who only Melanie found does ever weep. +4485 The man who only Curtis was working with ever manages to practice. +4486 Only Diana's guests who Thomas confused ever debate. +4487 Phillip's brother that only some painting looked like could ever find out who isn't weeping. +4488 All patients' girlfriend that only Janice was working with would ever depart. +4489 This teenager that only Craig has concealed has ever attacked Janet. +4490 Only some light that Barbara is concealing ever wastes away. +4491 Sandra's contractor who only every student questioned ever loved Janice. +4492 Michelle's mentor who only Brian approached ever cheered. +4493 Melanie's colleagues who only Lucille had fled from have ever cooperated. +4494 Carl's mother who only Joseph was concealing will ever cure Danielle. +4495 Only the niece of Barbara that many icicles bother ever sees all cashiers. +4496 Only all rivers that Jesus remembered ever vaporized. +4497 Most shirts that only Martin discovered can ever warp. +4498 Every organization that only Lisa would confuse ever describes Andrea. +4499 Only that daughter of Joel who these waitresses approach ever had a cup. +4500 Most boxes that only Jesus should buy ever close. +4501 This father of Sonia that only Emily was working with ever reveals who had noticed Emily. +4502 Only the screen that Robert bought ever tips over. +4503 Valerie's lawyers who only a lot of boys observe ever praise Craig. +4504 Only many waiters that this child was thinking about did ever bake. +4505 Only Sheila's ex-husband that Robert was hugging ever performs. +4506 A museum that only Rebecca might go to ever struggles. +4507 Alicia's guest that only Tiffany was concealing ever failed to slow. +4508 Only every sock that most drawings looked like should ever stretch. +4509 That boy that only Sally hid has ever declared Patricia to laugh. +4510 Only this association that Linda embarrassed has ever thought about who hasn't gone fishing. +4511 Donald's students who only Anna had talked to ever train. +4512 Adam's son who only Diana researches ever smiled. +4513 Only most legislatures that Melissa has questioned ever conferred. +4514 Only the waitresses who many doctors had irritated can ever think about Suzanne. +4515 Michelle's French teacher who only Naomi kissed does ever talk to Donald. +4516 Many mirrors that only Linda was lifting ever waste away. +4517 Nicole's piano teachers who only the Borgias impress ever skated around most closets. +4518 Gregory's senator that only Tammy worked with ever notices some windows. +4519 Only that doctor who most vases will irritate ever wonders what freeze. +4520 Only some museum that Mark would pass has ever proven to reply. +4521 Benjamin's doctors who only Jodi examines will ever bake. +4522 All shirts that only those actors will think about ever unfolded. +4523 This snake that only a person has taken had ever left. +4524 Only a sweater that Judy liked ever warps. +4525 Only Valerie's friend who some waiters hate ever worries. +4526 Cynthia's student who only Alan hired did ever discover most legislatures to struggle. +4527 Only Christina's ex-wife who that mirror bothers could ever oblige Cheryl's friend to look like a picture. +4528 Some girls that only Danielle worked with ever get fired. +4529 Only every senator's boyfriend who the fish did annoy ever got fired. +4530 Most daughters of Michelle that only Sharon has fired ever oblige many governments to fire Bethany. +4531 All newspaper articles about literature that only Tara scanned have ever disagreed. +4532 Carrie's granddaughters that only Laura returns to ever cooked. +4533 The museum that only women leave might ever communicate. +4534 Only those grandsons of William that Heather had returned to ever escape from all restaurants. +4535 Catherine's colleagues that only Irene had boasted about had ever vanished. +4536 Only many girls who Melissa was firing ever advise Vanessa to play. +4537 Teresa's bosses that only Ellen would argue about ever perform. +4538 All shirts that only Todd concealed could ever warp. +4539 Ann's hairdressers who only the Impressionists complain about ever smile. +4540 Only the senator that Wayne was talking about has ever worked with some guys. +4541 Only this actor that Heather is working with ever yells. +4542 Only the actors that Christopher complains about have ever boasted. +4543 Steve's grandfathers that only Renee can describe ever skate around a mall. +4544 Only Donald's associate who Patrick has investigated has ever pressured Robert's hairdressers to cry. +4545 Vanessa's contractors that only Christina is bothering will ever forget Stephanie. +4546 Only the jacket that Kevin lifts ever dried. +4547 Only a lot of glasses that Sara hides have ever loosened. +4548 Only all shawls that Carl discovered could ever soak. +4549 Only the legislature that Natalie will research ever testified. +4550 Phillip's mentor that only every guest was referencing should ever discover the newspaper article. +4551 Only the man's spouse that some pedestrian cured might ever kiss Walter. +4552 Only this driver that Craig had sounded like should ever hide away. +4553 Many ladies who only Jennifer has distracted ever conceal Carl. +4554 A dog that only Ella is concealing ever climbed up some ladder. +4555 Only all mothers of most people who Holly can alarm ever yearn to depart. +4556 Only Janet's sister that Kevin hired should ever praise the ladies. +4557 Only James's partner who those reports alarmed should ever waste away. +4558 Only Carmen's guest who the people can disturb had ever asked Liam's doctor to bother Stacy. +4559 These guys who only Carol had criticized have ever anticipated Pamela to argue about Gregory. +4560 Only Sally's handyman who every teenager was researching has ever messed up the art gallery. +4561 Only some hat that Connie thought about ever loosened. +4562 All sweaters that only Amelia did wear ever twisted. +4563 These teachers that only Karen complained about have ever listened to Michael. +4564 A lot of carts that only Suzanne will reveal ever turn. +4565 Only all governments that Margaret was boycotting ever retaliate. +4566 Only all women's students who Curtis is praising have ever testified. +4567 Steven's grandmother who only Winston Churchill is hiring should ever seem to fold. +4568 Most boys that only the print is looking like should ever appear to hide away. +4569 Only every woman that Stephen reveals ever litters. +4570 Natalie's niece that only Lori listens to ever prevented Bethany from exploring this college campus. +4571 This cashier that only the Impressionists visit ever appeared. +4572 Michael's mothers that only many photographs look like ever praised every documentary. +4573 Wayne's associate that only Danielle disgusted ever wrote. +4574 Only some drivers that George does hire ever approve. +4575 Hats that only Monet reveals ever wrinkle. +4576 A lot of chairs that only Anne should buy ever appear. +4577 Stacy's nephews that only Regina liked ever care for Meredith. +4578 Only that hat that some organization would like ever unfolds. +4579 Only that brother of Candice that Connie discovered ever goes to that grocery store. +4580 Only that person that Melinda did hurt ever worked with Sandra. +4581 Only the boy that Gary had irritated ever messed up some high school. +4582 Most grandfathers of Connie that only this bike was shocking ever testify. +4583 Jesus's dentists that only Alexander has annoyed ever discover the Balkans. +4584 Only those grandsons of Christopher who Holly had sounded like ever blinked. +4585 Only these doctors who some blouses were boring ever badger Liam to descend the slope. +4586 Only some grandson of Joseph that a legislature has hired has ever spun around. +4587 Only every actress who most dancers attack ever testifies. +4588 The company that only Tara boycotted could ever struggle. +4589 Caroline's French teacher who only this movie theater hurts ever discovers a lot of committees to conspire. +4590 Grandfathers of Alicia who only Peter has complained about would ever exercise. +4591 Some carriage that only Ronald can fix had ever dropped. +4592 These grandfathers of Holly that only Brad remembered would ever depart. +4593 Rhonda's senators who only Sarah was discovering had ever revealed every car. +4594 This nephew of Heather who only Beverly hired has ever fired Nancy. +4595 Only some cashier who the children had scared has ever compromised. +4596 Only some grandsons of Chad that Martha dislikes have ever written. +4597 Nina's nephew that only most museums can work with ever falls asleep. +4598 Only the woman who Barbara revealed would ever require Stephen to explore the restaurants. +4599 Only the blouse that Julie discovered ever twists. +4600 All teachers' brothers who only Donna leaves had ever moved. +4601 Russell's handyman who only Alice watches ever negotiates. +4602 Alicia's partners who only many senators will cure have ever murmured. +4603 Only Samuel's dentists who the broccoli shocks ever like to speak up. +4604 Only all gates that Gerald was bringing can ever fling open. +4605 Only a customer's dentists who all students hire would ever order Nancy to bore girls. +4606 All legislatures that only some casseroles distract ever protest. +4607 Only the coats that some girls reveal ever unfold. +4608 Only the guys that Helen talks about had ever hugged. +4609 Only the grandfathers of Bethany who Jodi helped ever flee from this hospital. +4610 All photographs that only Janice is revealing ever looked like a cake. +4611 Carla's nephews that only Amanda might reference could ever kiss Monica. +4612 Becky's classmate who only Eric was attacking will ever sound like Diana. +4613 Only these customers who these newspaper articles scare should ever nod. +4614 Only every student who Brad disturbed would ever like a lot of steps. +4615 Only most senators that David does observe should ever bore the girl. +4616 Most teachers' hairdresser that only Omar has forgotten ever references Joseph. +4617 Stacey's brothers who only Michelle is concealing can ever beg Eric's electrician to break those planes. +4618 Only these adults who the pedestrian does heal ever whispered. +4619 Christine's brother who only Keith was irritating ever singing. +4620 Those actors that only Lucille helped ever skate around most libraries. +4621 Nieces of Dawn that only Vanessa had concealed would ever fire Monet. +4622 Gregory's nieces who only this actor noticed ever wonder what unfolded. +4623 Most waiters that only a doctor has cared for ever conferred. +4624 Only Alice's ex-husband who a lot of actors notice ever examines an essay. +4625 Only some coats that the women see ever stretch. +4626 This cousin of Raymond who only Sherry is embarrassing ever studies. +4627 These organizations that only every slope upsets have ever conspired. +4628 Caroline's tutor who only Danielle had concealed would ever conceal Lucille. +4629 Only Walter's electricians who those students thought about ever implored Tracy to exit Spain. +4630 Steak that only a lot of guests notice ever embarrassed Danielle. +4631 Every person's doctors who only Lori was revealing could ever prove to flee from some bank. +4632 Only many organizations that Matt had worried ever confer. +4633 Connie's bosses that only Anna was thinking about did ever observe Lisa. +4634 Only many people who students referenced do ever break the plane. +4635 Only that rice that Elaine thought about ever blackened. +4636 This son of Marla that only Carmen has distracted had ever practiced. +4637 Only that patient's dentist who that senator could think about ever practiced. +4638 Only the boys who Linda cared for had ever worked hard. +4639 A lot of actors that only an organization can reference had ever cooked. +4640 Only every school that that broccoli scares has ever required Deanna to scratch. +4641 Many students' guests who only Deborah had watched will ever fall. +4642 Only the drawings that Michael did find had ever looked like many hats. +4643 Only most friends of Janet who the doctor disagrees with ever skated around many rivers. +4644 Only a woman's cousin who Sheila listened to has ever examined every essay. +4645 Only a lot of sweaters that the Impressionists sell will ever twirl. +4646 This picture that only all actresses do think about ever impressed Tamara. +4647 That cashier that only a woman has kissed ever observed Jessica. +4648 Only some lamp that Frank is concealing has ever faded. +4649 Only women who Sabrina had fired ever figure out who was blinking. +4650 Only Anne's bosses that Chad sounded like had ever laughed. +4651 Only a cousin of Pamela who customers escape from ever testifies. +4652 Only this pepper that Margaret was discovering ever bakes. +4653 Only some daughter of Marla that Dennis had stunned had ever disagreed with Diana. +4654 Only all men that those cucumbers have shocked ever like to move. +4655 All daughters of Tracy who only all girls had appreciated ever met. +4656 All waiters' piano teacher who only the Borgias approach ever continued to fire the Borgias. +4657 Only some customer's nieces who Clyde has discussed ever communicated. +4658 Candice's sisters who only Joseph could work with ever watch Rebecca. +4659 Martin's mentors that only Deanna has respected had ever chatted. +4660 Only some pedestrian's partner that all people remember has ever saluted. +4661 Jerry's ex-wife who only Elaine has escaped from ever urged Theodore to argue. +4662 Samuel's brother who only Grace hates did ever yell. +4663 Jodi's mothers that only William had boasted about will ever divorce. +4664 A lot of sisters of Bill that only the doctors might find could ever benefit. +4665 Cilantro that only many drivers have brought ever grew. +4666 This cousin of Kristen that only Jacqueline is working with ever refuses to telephone. +4667 Every actor who only Monica has confused ever wrote. +4668 Only that grandmother of Heather who these candles had shocked ever listened to the men. +4669 Only those teenagers' sons who that committee boasts about do ever fall asleep. +4670 Only Marie's ex-wives who a waitress appreciated do ever wonder who have expected Connie to know that waitress. +4671 Every customer who only Rose does praise ever wept. +4672 Only cousins of Edward that this truck upsets ever happen to argue about Jodi. +4673 Only most computers that many pedestrians hide ever chip. +4674 Only the boy that some sketch scared has ever needed a shoe to flatten. +4675 Some pedestrians that only those doctors could conceal ever cleaned the steak. +4676 Lori's contractors that only these adults were returning to might ever flirt. +4677 Elaine's granddaughter who only Carol praised ever visited Amy. +4678 Sherry's daughters who only most teenagers hire ever wanted Sherry to talk. +4679 Dawn's ex-girlfriends who only the patients boast about ever explain many women weren't divorcing. +4680 Deborah's senator who only Tanya has escaped from has ever investigated Laura. +4681 Noah's tutors that only Catherine cares for ever appeared. +4682 Only Carlos's tutor who most adults insult ever performed. +4683 A lot of grandfathers of Tina that only Sherry escapes from ever mess up the public parks. +4684 The legislatures that only every sketch can disgust ever manage to argue about Donald. +4685 These sons of Diane that only some lamps were aggravating ever donated. +4686 Scott's dermatologist that only Linda listens to ever walked through some closet. +4687 A screen that only Tanya was fixing ever cracked. +4688 Those customers that only Kristen was noticing ever work hard. +4689 These committees that only the nose alarms ever order Ann to climb up many slopes. +4690 Nephews of Anne who only most waitresses hide will ever train. +4691 Ice that only Homer had ever evaporates. +4692 Only that adult's dermatologist who Laura forgot does ever sway Tanya to scan this commentary. +4693 Linda's French teacher who only Brett hires ever departed. +4694 These legislatures that only many teenagers scare could ever push a lot of pedestrians to argue. +4695 Only many nieces of Denise who many women appreciate ever figured out who worries. +4696 Daniel's mentor who only these wheelbarrows are upsetting ever neglected to compromise. +4697 Only that student who Stephen was hugging ever revealed who wasn't visiting Jill. +4698 April's teacher who only Timothy disliked would ever fall asleep. +4699 Only some shoe that most customers had discovered does ever wrinkle. +4700 Only the granddaughter of Vincent who Catherine had approached ever needed to disagree with Timothy. +4701 Only the customer that that documentary impresses could ever cure that woman. +4702 Only Danielle's hairdressers who this hair might hurt should ever hug Douglas. +4703 Those mothers of Clyde who only Andrew has respected ever wanted Kayla to eat. +4704 Teresa had not ever sold a movie theater. +4705 Allison has not ever taken that ice cream. +4706 Teresa would not ever ascertain Carmen to remember Jessica. +4707 Sharon might not ever argue. +4708 Those banks had not ever lied. +4709 All children do not ever struggle. +4710 Theresa can not ever shock the Impressionists. +4711 Theodore can not ever beg Sandra to stretch. +4712 Valerie does not ever manage to like some coats. +4713 Barbara has not ever boycotted that river. +4714 Sherry had not ever remembered who wasn't arriving at some closets. +4715 Those children can not ever exercise. +4716 Angela did not ever heal Becca. +4717 Julia had not ever forced Kirsten to get a job. +4718 Anna does not ever hunt. +4719 Maria might not ever reference Sara. +4720 Ice has not ever vaporized. +4721 Theresa will not ever concur. +4722 Donna had not ever tempted Sheila's boss to explore those schools. +4723 Edward did not ever attempt to appear. +4724 Jerry has not ever cleaned this report. +4725 The senator has not ever discovered who sounded like Eva. +4726 Anna had not ever escaped from Laurie. +4727 Some customers had not ever talked. +4728 Robert has not ever threatened to appear. +4729 Paul has not ever bothered to descend most mountains. +4730 The Lutherans did not ever weep. +4731 Homer has not ever disturbed those doctors. +4732 Nicole will not ever flee from that library. +4733 Grace has not ever looked like prints. +4734 Lawrence might not ever entreat Lucille to wake up. +4735 Janet has not ever want to talk to Irene. +4736 Rodney should not ever study. +4737 Rebecca will not ever donate. +4738 That cashier had not ever researched many newspaper articles. +4739 Andrea had not ever referenced Melissa. +4740 Mushrooms had not ever upset Tammy. +4741 Benjamin might not ever want Nancy to see many paintings. +4742 Many guys have probably ever stood up. +4743 Jerry does not ever induce Sabrina to climb up those steps. +4744 These mouths had not ever wasted away. +4745 Jane had not ever swallowed. +4746 A dress has not ever shrunk. +4747 Donna will not ever weep. +4748 Tonya should not ever clean this dog. +4749 Alicia has not ever sung. +4750 Joel has not ever impressed most girls. +4751 Dawn will not ever hug Liam. +4752 Gerald had not ever wept. +4753 All teenagers can not ever cope. +4754 Tamara might not ever discover Vincent. +4755 These boys would not ever communicate. +4756 Carmen will not ever insult most dancers. +4757 That apple has not ever grown. +4758 Roger had not ever judged Ann to perform. +4759 This screen will not ever bother Derek. +4760 Banks have not ever conferred. +4761 Thomas had not ever broken the computer. +4762 Kenneth would not ever eat. +4763 Every driver has not ever sat down. +4764 A lot of icicles should not ever evaporate. +4765 Samantha had not ever littered. +4766 Jessica has not ever messed up those couches. +4767 Every truck has not ever turned. +4768 Most wheelbarrows can not ever accelerate. +4769 Jennifer would not ever litter. +4770 Rebecca does not ever approach Amelia. +4771 All stories will not ever disagree with Curtis. +4772 A person had not ever swallowed. +4773 Rose has not ever neglected to climb down every mountain. +4774 A lot of senators had not ever advised Beth to nod. +4775 Kenneth had not ever passed Harvard University. +4776 Donald has not ever investigated who have retaliated. +4777 Tracy has not ever wondered who attempt to flee from Candice. +4778 Those reports have not ever disgusted Alexander. +4779 A lot of apples have not ever blackened. +4780 This pie has not ever charred. +4781 Sabrina did not ever scan those newspaper articles. +4782 Naomi had not ever cleaned this plate. +4783 Scott has not ever considered the spinach to go bad. +4784 Raymond should not ever fail to aggravate Benjamin. +4785 Those governments do not ever hurt these waitresses. +4786 Paula would not ever descend that ladder. +4787 Some legislature has not ever failed to climb down the mountain. +4788 Amy could not ever heal Julia. +4789 Linda had not ever arrived at that river. +4790 Katherine had not ever bought the car. +4791 David had not ever brought these ladders. +4792 Margaret had not ever irritated this child. +4793 David might not ever figure out what grow. +4794 Dawn will not ever alarm most schools. +4795 A customer might not ever sneeze. +4796 Mary might not ever salute. +4797 Kevin should not ever play. +4798 Heidi would not ever arrive at a high school. +4799 Those lamps will not ever darken. +4800 Paula had not ever said Ann was compromising. +4801 All actresses would not ever look like those pictures. +4802 Nicole had not ever forfeited. +4803 Tamara did not ever drive to every library. +4804 Those dresses had not ever soaked. +4805 Shoes did not ever wrinkle. +4806 Melanie had not ever cleaned those museums. +4807 Becky would not ever fire Bradley. +4808 Nicole should not ever practice. +4809 Rachel did not ever donate. +4810 Paul would not ever command Brian's sister to yell. +4811 The doctor could not ever investigate some actor. +4812 A blouse had not ever shrunk. +4813 Sarah will not ever complain. +4814 Kirsten had not ever cleaned a cup. +4815 That waitress might not ever hate this play. +4816 Martin has not ever investigated Victoria. +4817 Every sock did not ever stretch. +4818 Some vase has not ever chipped. +4819 The teachers had not ever ascertained Roger to cough. +4820 The students might not ever communicate. +4821 Galileo would not ever cheer. +4822 Those adults had not ever ascertained Guy to scratch. +4823 Rhonda will probably ever drop by all glaciers. +4824 Some hospital had not ever benefited. +4825 A lot of drivers have really ever climbed down a slope. +4826 Rodney might not ever find Martin to bring the hills. +4827 Janice has not ever painted. +4828 Helen might not ever scan some commentaries. +4829 Diane has not ever fallen asleep. +4830 Ronald might not ever want Renee to hunt. +4831 This lady can not ever swallow. +4832 Tammy had not ever coughed. +4833 Susan did not ever mutter. +4834 Actors had not ever retaliated. +4835 Jacqueline has not ever suffered. +4836 Melanie did not ever read. +4837 Every bicycle had not ever slowed. +4838 This dancer will not ever argue. +4839 A lot of trucks can not ever vanish. +4840 Karla could not ever fix many windows. +4841 Ruth can not ever say a government predicted these dancers to scream. +4842 William had not ever walked through that art gallery. +4843 Gary did not ever turn out to embrace. +4844 Some hats should not ever twist. +4845 Most companies had not ever prodded Carmen to flee from Alexander. +4846 Rhonda has not ever messed up many pants. +4847 Elaine had not ever lied. +4848 This doctor might not ever commission Sarah to respect most teenagers. +4849 Angela had not ever kissed a lot of teenagers. +4850 Ruth had not ever believed this woman to stun Alicia. +4851 Every actor has not ever chuckled. +4852 The women have not ever communicated. +4853 Rhonda should not ever forfeit. +4854 Some scarf does not ever exist. +4855 That cart had not ever turned. +4856 Every pedestrian had not ever broken that plane. +4857 Heidi did not ever boycott every public park. +4858 Cheryl had not ever known who could observe Lisa. +4859 Many waiters have not ever trained. +4860 Many socks can not ever shrink. +4861 Some guests did not ever embrace. +4862 All people had not ever learned. +4863 Every convertible might not ever steer. +4864 Kevin can not ever wave. +4865 Some broccoli did not ever bother the person. +4866 Bruce has not ever annoyed every waiter. +4867 Wendy has not ever aggravated Alan. +4868 That adult did not ever sing. +4869 Elizabeth can not ever describe a child. +4870 Martin does not ever prefer all actors to profit. +4871 Kayla had not ever judged Paula to flee from this lake. +4872 The libraries might not ever fight. +4873 Kirsten should not ever waste away. +4874 Heather will not ever admire that glacier. +4875 Patients have not ever talked. +4876 Some school could not ever compete. +4877 Carl will not ever induce some libraries to joke around. +4878 Jill can not ever prevent Marcus from messing up most public parks. +4879 All coats had not ever twirled. +4880 Children will not ever investigate Kristen. +4881 James has not ever lied. +4882 Russell should not ever whisper. +4883 Carrie might not ever plan to change. +4884 Christina had not ever yearned to skate around this movie theater. +4885 Many peppers had not ever grown. +4886 Noah did not ever investigate who left Carlos. +4887 That committee has not ever chatted. +4888 Some guests had not ever approved. +4889 Julia might not ever wave. +4890 Kayla will not ever convince a waitress to resemble this drawing. +4891 Carolyn has not ever skated around a lot of museums. +4892 Noah has not ever irritated Irene. +4893 Rose has not ever loved Brad. +4894 Ella would not ever see. +4895 Nancy could not ever cry. +4896 Suzanne can not ever manage to forfeit. +4897 Jennifer had not ever practiced. +4898 Those cashiers had not ever boycotted these committees. +4899 Steve had not ever shocked Nina. +4900 This couch has not ever chipped. +4901 Joel will not ever shock some dancers. +4902 These schools should not ever interact. +4903 A lot of children do not ever approve. +4904 Caroline might not ever praise Melinda. +4905 Karen could not ever need to work with David. +4906 A lot of tomatoes will not ever go bad. +4907 The girls might not ever fail to discover a lot of skirts. +4908 Donald can not ever expect Deborah to buy some window. +4909 A lot of senators had not ever exited some mall. +4910 Barbara has not ever sighed. +4911 Alicia has not ever wasted away. +4912 Those teachers can not ever communicate. +4913 Stephanie had not ever boasted. +4914 Sharon had not ever appeared to loosen. +4915 Allison has not ever begged Bethany's teacher to mess up a skirt. +4916 Tammy does not ever drive to the closets. +4917 Gary did not ever bike to the schools. +4918 Robert does not ever propose to chuckle. +4919 Sara can not ever conceal Beverly. +4920 Wendy should not ever leave every adult. +4921 Some pedestrian had not ever written. +4922 The company would not ever interact. +4923 Many patients have not ever continued to worry Paul. +4924 Some newspaper article has not ever disagreed. +4925 Tiffany might not ever forget about who is hunting. +4926 Becca does not ever yawn. +4927 Chad can not ever nod. +4928 That glacier had not ever frozen. +4929 Most shawls will not ever unfold. +4930 All cars had not ever fallen apart. +4931 A bird has not ever climbed down every slope. +4932 These customers had not ever hugged. +4933 Sabrina will not ever worry Tamara. +4934 Candice would not ever regret planes aren't accelerating. +4935 The cilantro might not ever grow. +4936 Marcus might not ever spur Vanessa to help Douglas. +4937 Those glasses had not ever twirled. +4938 The Borgias have not ever noticed the pork. +4939 All legislatures will not ever reveal who has left. +4940 Brenda did not ever go to some closet. +4941 The school had not ever intended to conspire. +4942 Steven has not ever described Carlos. +4943 A lot of cucumbers had not ever ripened. +4944 Most ladies will not ever like adults. +4945 Samantha might not ever respect Sally. +4946 Marla did not ever consider carts to rust. +4947 Claire did not ever resemble pictures. +4948 Some lady had not ever intended to argue. +4949 Some adult would not ever run around some bank. +4950 Joseph can probably ever buy a lot of prints. +4951 Sara will not ever disturb Holly. +4952 Keith had not ever brought all cars. +4953 Judy has not ever eaten. +4954 Jane had not ever yelled. +4955 The child had not ever saluted. +4956 James will not ever kiss Noah. +4957 Jane might not ever leave Irene. 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sock can not ever crumple. +4980 These dancers did not ever cooperate. +4981 Leslie might not ever fix a couch. +4982 Galileo did not ever urge Janet to leave. +4983 Keith will not ever scratch. +4984 Valerie has not ever answered. +4985 Alexander does not ever go fishing. +4986 Many glaciers will not ever freeze. +4987 A lot of students had not ever praised Heather. +4988 Most actresses had not ever investigated what shocks a lot of senators. +4989 Teenagers would not ever testify. +4990 Tara has not ever want to complain about John. +4991 Some shawls do not ever exist. +4992 Some banks have not ever spurred Craig to sit down. +4993 A school should not ever order Craig to cook. +4994 Some company should not ever correspond. +4995 Timothy should not ever listen to Stephen. +4996 Some people have not ever cooperated. +4997 Diana has not ever drunk. +4998 Most doctors do not ever conceal Kenneth. +4999 Elizabeth had not ever asked Alexander's ex-girlfriend to practice. +5000 The screens had not ever disgusted this guy. +5001 Karla has not ever woken up. +5002 Marla would not ever threaten to sell this rice. +5003 Some legislature has not ever retaliated. +5004 Carol should not ever protest. +5005 Patrick can not ever eat. +5006 Danielle might not ever yearn to wash. +5007 Phillip does not ever protest. +5008 Waitresses might not ever forget who are embracing. +5009 Stephanie did not ever leave Carol. +5010 Some doctors had not ever coped. +5011 Valerie will not ever sigh. +5012 Joseph has not ever planned to arrive at the river. +5013 The Lutherans might not ever say that student struggles. +5014 Jerry has not ever remembered those teenagers. +5015 Winston Churchill can not ever expect every shirt to crumple. +5016 The Lutherans had not ever met. +5017 The scarves would not ever fray. +5018 Some cars will not ever accelerate. +5019 This popsicle might not ever condense. +5020 Benjamin did not ever lie. +5021 Some planes had not ever tipped over. +5022 Valerie has not ever wished to disappear. +5023 Spain can not ever entreat Caroline to learn. +5024 Caroline did not ever say a lot of guests question some driver. +5025 Alice could not ever fire Wayne. +5026 Carmen had not ever longed to train. +5027 Janet had not ever shocked Benjamin. +5028 Colleen has not ever thought about some documentary. +5029 Edward had not ever advised the libraries to fall. +5030 Nina has not ever studied. +5031 Edward should not ever blink. +5032 Theresa does not ever disturb Robert. +5033 Raymond has not ever blinked. +5034 That pasta has not ever scorched. +5035 Kayla did not ever want every pepper to go bad. +5036 Leslie did not ever reference many newspaper articles. +5037 This lady could not ever approach Amelia. +5038 Many shirts had not ever warped. +5039 Some guest will not ever embarrass Cindy. +5040 Caroline has not ever found that woman. +5041 Jennifer might not ever remember a lot of guys. +5042 Mary has not ever murmured. +5043 Those skateboards would not ever steer. +5044 Spain had not ever competed. +5045 Those children had not ever implored that analysis to disturb most actresses. +5046 Naomi would not ever spin around. +5047 Jennifer had not ever failed to remember Patrick. +5048 Jill had not ever motivated every boy to complain. +5049 These actresses could not ever react. +5050 Brad will not ever think Jennifer shouldn't want Angela to get fired. +5051 Deborah had not ever stunned Bethany. +5052 This dancer might not ever stun Thomas. +5053 Many women do not ever interact. +5054 A picture does not ever look like Roger. +5055 Kendra has not ever visited Valerie. +5056 A lot of students had not ever joked around. +5057 Natalie had not ever cared for every cashier. +5058 Cynthia will not ever scan most newspaper articles. +5059 Museums have not ever assembled. +5060 Every river has not ever vaporized. +5061 All socks have not ever distracted Amanda. +5062 Nancy had not ever aggravated Sarah. +5063 This blouse had not ever frayed. +5064 Natalie had not ever expected most ladies to cooperate. +5065 Phillip might not ever write. +5066 A lot of glasses should not ever shrink. +5067 Teresa had not ever brought that tomato. +5068 These guests could not ever take these coats. +5069 Vanessa should not ever discover these mushrooms. +5070 Dennis had not ever aggravated some customer. +5071 Mary had not ever wished to move. +5072 These waitresses did not ever debate. +5073 Matt had not ever attacked that guest. +5074 Some chair had not ever shocked Carmen. +5075 Ellen had not ever cured Sarah. +5076 Benjamin could not ever walk through all restaurants. +5077 Tammy has not ever happened to explore some grocery store. +5078 A lot of waiters had not ever conferred. +5079 A lot of cashiers could not ever testify. +5080 Kimberley would not ever astound that lady. +5081 Michael would not ever appear. +5082 The Borgias have not ever kissed. +5083 Wayne should not ever require all schools to cope. +5084 Curtis should not ever conceal what shrink. +5085 A blouse did not ever fall. +5086 James could not ever cough. +5087 Tammy had not ever found Sabrina. +5088 Bradley had not ever pressured Sonia to telephone. +5089 Every pedestrian will not ever get fired. +5090 Curtis does not ever attempt to escape. +5091 Tammy had not ever begged Tanya to scream. +5092 Kimberley has not ever found those actresses to compete. +5093 Some dancers will not ever badger Denise to have a lot of mirrors. +5094 Samantha should not ever hide away. +5095 Gina could not ever return to Julia. +5096 Jodi had not ever climbed down that mountain. +5097 Mushrooms had not ever ripened. +5098 Elaine can not ever figure out who kisses James. +5099 A door has not ever flung open. +5100 Most bananas have not ever charred. +5101 Wendy has not ever proposed to complain about Denise. +5102 Most bicycles have not ever steered. +5103 Melissa had not ever answered. +5104 Some candles have not ever darkened. +5105 Nancy has not ever fled from some guest. 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+5126 This unicycle does not ever worry Kayla. +5127 Nina would not ever reference Caroline. +5128 These glasses had not ever twisted. +5129 George did not ever conceal what were dropping. +5130 These cucumbers had not ever ripened. +5131 Most guys would not ever embrace. +5132 Lawrence did not ever protest. +5133 Kirsten should not ever cook. +5134 Regina might not ever protest. +5135 Most schools might not ever negotiate. +5136 Most senators would not ever talk. +5137 Claire has not ever chuckled. +5138 Aaron has not ever happened to confer. +5139 Diane had not ever donated. +5140 Theresa had not ever commanded all teenagers to lift this bike. +5141 Kristin did not ever hide some lamp. +5142 The shoe will not ever shrink. +5143 Beth had not ever looked to win. +5144 A light could not ever darken. +5145 Wendy had not ever upset Deanna. +5146 Monet had not ever looked to question those schools. +5147 Those guests have not ever retaliated. +5148 All drivers had not ever lied. +5149 The cashiers have not ever conferred. +5150 Teachers do not ever chat. +5151 These hats might not ever soak. +5152 Alice had not ever forfeited. +5153 These ladies could not ever date. +5154 That eye can not ever fling open. +5155 Carlos can not ever lift the paintings. +5156 Ann had not ever come here. +5157 A dancer has not ever cheered. +5158 Amy will not ever alarm Sara. +5159 Pamela would not ever sound like Gary. +5160 Richard had not ever passed Harvard University. +5161 Ronald did not ever joke around. +5162 Bradley had not ever thought about who messed up a lot of pants. +5163 Teachers might not ever donate. +5164 A lady has not ever lifted the carts. +5165 Marcus has not ever needed all ladies to surrender. +5166 Tiffany can not ever persuade Wayne to eat. +5167 Every bank should not ever correspond. +5168 Timothy has not ever obligated that guest to spin around. +5169 Some customers have not ever stretched. +5170 Stacey has not ever found Guy to fall. +5171 That jacket does not ever dry. +5172 Carl does not ever write. +5173 Natalie will not ever help Cindy. +5174 Many doctors might not ever conspire. +5175 These ladies had not ever profited. +5176 Martin will not ever seem to clean most glasses. +5177 Stacey would not ever know Kathleen. +5178 Homer could not ever have fish. +5179 These girls did not ever know Catherine. +5180 A carriage will not ever shatter. +5181 Alexander had not ever found many carts to stun Robert. +5182 Natalie has not ever lifted every bank. +5183 Rachel would not ever urge Sandra's ex-girlfriends to describe an art gallery. +5184 Most governments will not ever testify. +5185 Kayla has not ever approached Meredith. +5186 The spinach had not ever existed. +5187 The senator will not ever sway Karen to have that convertible. +5188 Linda has not ever explained everything. +5189 Some boys could not ever conceal Lucille. +5190 Many senators might not ever flirt. +5191 Most actresses have not ever lifted a chair. +5192 The Borgias have not ever married. +5193 Linda had not ever scared Edward. +5194 A lot of jackets have not ever wrinkled. +5195 Bradley might not ever want to impress these cashiers. +5196 Bradley had not ever changed. +5197 Sharon had not ever complained about Judy. +5198 Joel has not ever cared for Carmen. +5199 Those actors have not ever competed. +5200 A lot of planes did not ever drop. +5201 Denise should not ever hire Sheila. +5202 The girl did not ever approach Marie. +5203 That glass had not ever fallen apart. +5204 This ice has not ever frozen. +5205 April had not ever protested. +5206 That dancer had not ever turned out to wave. +5207 Phillip had not ever pushed Timothy to lift mountains. +5208 An actor could not ever reference Lucille. +5209 Many hospitals will not ever wish to have that cake. +5210 Rebecca would not ever induce Omar to forget Alicia. +5211 Many schools had not ever discovered who observed Jodi. +5212 Kirsten had not ever found guests to kiss. +5213 Tanya had not ever hoped to have most dishes. +5214 A lot of waiters might not ever look like the drawings. +5215 Carmen would not ever hire the waiter. +5216 Every screen has not ever shattered. +5217 Janet would not ever laugh. +5218 Caroline had not ever kissed Jessica. +5219 Marie should not ever run around the Great Lakes. +5220 Michael had not ever expected most organizations to meet. +5221 Paula has not ever cared for Melissa. +5222 Galileo has not ever swayed most friends of a customer to disturb Marie. +5223 Donald might not ever like to conceal the Impressionists. +5224 A lot of pedestrians had not ever gone fishing. +5225 Cashiers had not ever negotiated. +5226 These guys have not ever benefited. +5227 Cilantro does not ever go bad. +5228 A lot of actors can not ever assemble. +5229 Heather has not ever hoped to kiss Phillip. +5230 The people do not ever appear. +5231 That spinach has not ever ripened. +5232 Amelia has not ever swallowed. +5233 The couches did not ever slip. +5234 Ella has not ever 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+5256 Amanda can not ever reveal who hasn't induced Wayne to scan all books. +5257 That doctor had not ever questioned Tonya. +5258 Every company has not ever swayed every adult to play. +5259 April had not ever laughed. +5260 All senators had not ever longed to argue about Bethany. +5261 That convertible has not ever dropped. +5262 Brad could not ever consider Samuel to hide. +5263 Sonia has not ever driven to some glacier. +5264 Richard does not ever ask Elizabeth's senators to drive to that lake. +5265 Homer had not ever known Kristen. +5266 Joseph has not ever scared Barbara. +5267 Laurie had not ever disliked that river. +5268 Daniel would not ever leave the Lutherans. +5269 This man would not ever complain. +5270 All carriages should not ever slip. +5271 This association will not ever reveal who fell asleep. +5272 Diana can not ever play. +5273 Laurie should not ever practice. +5274 Peter had not ever gotten a job. +5275 Carrie can not ever perform. +5276 Chad has not ever slumped over. +5277 All guests have not ever taken this window. +5278 Laura can not ever return to Brenda. +5279 Theresa would not ever bother to research the Borgias. +5280 Sharon has not ever compromised. +5281 Nancy will not ever sigh. +5282 The tomato will not ever blacken. +5283 Irene does not ever say Candice yearned to shock cashiers. +5284 Bradley had not ever ascertained Helen to scan many essays. +5285 That dress would not ever stretch. +5286 Keith would not ever remember what has chipped. +5287 Russell does not ever discuss April. +5288 Most customers have not ever disagreed with Allison. +5289 Carmen had not ever yawned. +5290 All customers have not ever succeeded. +5291 Marcus will not ever wave. +5292 A waiter does not ever leave. +5293 Carol had not ever gotten fired. +5294 Dawn does not ever have that spotlight. +5295 Katherine can not ever research every restaurant. +5296 These cars had not ever turned. +5297 Mitchell would not ever escape from pedestrians. +5298 Wayne had not ever expected Brian to forfeit. +5299 The teenagers do not ever concur. +5300 Melinda has not ever prodded Sarah to see Guy. +5301 Tamara could not ever beg this lady to salute. +5302 Martin would not ever remember Irene. +5303 Alan can not ever entreat Tracy to vanish. +5304 Margaret had not ever swallowed. +5305 Cindy had not ever baked. +5306 This skirt will not ever stretch. +5307 Those icicles had not ever frozen. +5308 Some guys had probably ever coped. +5309 Spain should not ever remember who was forgetting about who implored Rhonda to play. +5310 The shawl has not ever frayed. +5311 These organizations have not ever embarrassed most students. +5312 Elaine has not ever imagined this museum looks to walk through Harvard University. +5313 A lot of lakes had not ever vaporized. +5314 Carolyn did not ever depart. +5315 Many senators might not ever surrender. +5316 All actresses could not ever ascend a lot of ladders. +5317 The woman would not ever protest. +5318 Those snakes have not ever found Angela. +5319 Judy has not ever ascended the hill. +5320 Naomi will not ever cook. +5321 Many girls could not ever train. +5322 Alice has not ever played. +5323 A lot of shoes will not ever wrinkle. +5324 Susan has not ever wept. +5325 This ice has not ever evaporated. +5326 Ella can not ever commission Martha to climb up some ladder. +5327 These cashiers could not ever marry. +5328 That rice has not ever looked like many drawings. +5329 Julie can not ever return to a restaurant. +5330 That spinach has not ever grown. +5331 Those pants had not ever loosened. +5332 Frank might not ever climb down some hills. +5333 Lori will not ever visit that hill. +5334 Debra can not ever joke around. +5335 The couches might not ever chip. +5336 Marie does not ever scratch. +5337 A lot of universities could not ever reference Marie. +5338 The banana can not ever blacken. +5339 Hospitals would not ever work hard. +5340 Dancers would not ever cooperate. +5341 Dancers have not ever threatened to upset Monet. +5342 Many cashiers will not ever alarm Tanya. +5343 That guest has not ever disliked Vincent. +5344 Spain can not ever conceal Stacey. +5345 Icicles have not ever frozen. +5346 Every lake will not ever melt. +5347 Alicia had not ever changed. +5348 Those schools have not ever communicated. +5349 Kimberley had not ever prodded Bradley's dentists to cheer. +5350 All teenagers have not ever seen. +5351 Beth does not ever return to some mountains. +5352 Russell should not ever believe Sarah to murmur. +5353 Kirsten has not ever argued. +5354 Every banana will not ever astound Robert. +5355 Most coats should not ever dry. +5356 The Lutherans had not ever left Jeffrey. +5357 This government did not ever talk about Linda. +5358 Every guest should not ever resemble some paintings. +5359 Matt has not ever taken that public park. +5360 Alexander had not ever regretted many glasses don't fold. +5361 That popsicle has not ever evaporated. +5362 Kenneth can not ever return to Adam. +5363 Brad would not ever play. +5364 Cynthia had not ever cleaned the painting. +5365 These actors had not ever shocked Judy. +5366 Cheryl could not ever look to open. +5367 Craig had not ever convinced Tiffany to slump over. +5368 Beth had not ever gotten a job. +5369 Todd can not ever skate around some restaurant. +5370 Most legislatures had not ever figured out who performs. +5371 Winston Churchill had not ever gone fishing. +5372 Some sock can not ever wrinkle. +5373 Julie should not ever argue. +5374 Carla might not ever dare all friends of Christopher to observe Suzanne. +5375 Jessica can not ever turn out to talk. +5376 Karla will not ever press Mark to stun Julia. +5377 Janet has not ever left most waitresses. +5378 A lot of bicycles had not ever embarrassed Samuel. +5379 Thomas might not ever cook. +5380 Every school has not ever communicated. +5381 Bradley can not ever tour a lot of rivers. +5382 Tanya has not ever begged every organization to lie. +5383 This carriage could not ever accelerate. +5384 Monet had not ever performed. +5385 A bike would not ever tip over. +5386 A sweater has not ever flattened. +5387 Jason might not ever hate the student. +5388 Tanya had not ever begged Alicia to remember the libraries. +5389 Anne has not ever thought about Carol. +5390 Paula should probably ever tour that library. +5391 A lot of actresses do not ever distract most doctors. +5392 April might not ever see every print. +5393 Leslie might not ever escape from Tracy. +5394 Mary had not ever muttered. +5395 Those trucks would not ever appear. +5396 Dennis has not ever boasted about Noah. +5397 The guests did not ever compete. +5398 Carlos has not ever known Susan. +5399 That man has not ever cried. +5400 Most shawls had not ever twisted. +5401 Martha has not ever saluted. +5402 April had not ever intended to leave this mountain. +5403 Rose would not ever imagine Stephen can appear to learn. +5404 Christopher had not ever exercised. +5405 Brett would not ever criticize Eric. +5406 Michael would not ever remember who should assemble. +5407 This nose would not ever embarrass Wayne. +5408 Many bicycles might not ever tip over. +5409 Donald will not ever investigate who approach Sonia. +5410 Tracy does not ever whisper. +5411 Suzanne will not ever practice. +5412 Pamela had not ever escaped. +5413 Leslie might not ever flee from Brian. +5414 Some legislature does not ever chat. +5415 A lot of girls will not ever persuade Sharon's supervisor to hurt Holly. +5416 Michael should not ever flee from Theresa. +5417 Lucille could not ever lie. +5418 Kristen had not ever explained these banks want to like this bicycle. +5419 A lot of boys had not ever conferred. +5420 Some jacket had not ever crumpled. +5421 Jennifer can not ever protest. +5422 That lake had not ever confused associations. +5423 Most jackets had not ever loosened. +5424 Some organization has not ever wanted Rodney to know Gregory. +5425 The movie can not ever disagree. +5426 Renee will not ever turn out to rust. +5427 Steve would not ever eat. +5428 Marie has not ever longed to exist. +5429 These universities have not ever retaliated. +5430 The Impressionists do not ever break a lot of glasses. +5431 Larry would not ever clean the cucumber. +5432 Jason had probably ever seen. +5433 Many men had not ever succeeded. +5434 Some glaciers will not ever impress Travis. +5435 John has not ever argued. +5436 Mark has not ever spun around. +5437 The library had not ever conferred. +5438 Linda might not ever hide some car. +5439 Some children have not ever dated. +5440 These pedestrians had not ever conferred. +5441 Todd can not ever command Dana's ex-husband to listen to Helen. +5442 The gates should not ever open. +5443 Rachel had not ever cured Suzanne. +5444 This tomato did not ever go bad. +5445 Clyde had not ever thought about what haven't tipped over. +5446 Every bike would not ever roll. +5447 Richard had not ever screamed. +5448 Roger has not ever pressured Andrew to hunt. +5449 The Impressionists can probably ever wonder what figure out who plans to upset Charles. +5450 Nancy could not ever smile. +5451 Christopher can not ever clean the car. +5452 Rhonda should not ever arrive at those hospitals. +5453 Cynthia could not ever escape from Rachel. +5454 Erin did not ever insult Rachel. +5455 Museums do not ever profit. +5456 Science might not ever stun Kristin. +5457 Melanie has not ever healed Judy. +5458 Rebecca has not ever baked. +5459 Thomas could not ever want some cashiers to kiss. +5460 That dog will not ever leave. +5461 Lori had not ever fallen. +5462 Omar should not ever get a job. +5463 Those computers should not ever tip over. +5464 Some universities will not ever debate. +5465 Melissa would not ever compromise. +5466 Most pedestrians had not ever dated. +5467 Danielle does not ever love the boy. +5468 Derek has not ever yearned to explain everything. +5469 Russell has not ever talked about Margaret. +5470 Julia had not ever commissioned Rebecca to learn. +5471 Daniel had not ever upset the Lutherans. +5472 The Lutherans have not ever scratched. +5473 The skirt can not ever shrink. +5474 Denise has not ever regretted this unicycle is accelerating. +5475 Donald has not ever gotten fired. +5476 Most waiters had not ever approved. +5477 This driver has not ever boasted about literature. +5478 Daniel could not ever observe Stacy. +5479 The Impressionists would not ever explain Lisa hadn't wept. +5480 A hat had not ever flattened. +5481 That actress can not ever research the Balkans. +5482 Tanya had not ever anticipated Sharon to hide away. +5483 Every student had not ever forfeited. +5484 Most drivers have not ever hidden away. +5485 This organization had not ever predicted every actor to scan some documentaries. +5486 Jesus can not ever scan all movies. +5487 All students will not ever profit. +5488 A lot of skateboards did not ever slow. +5489 Ice cream did not ever stun Eric. +5490 Amanda did not ever dare those guests to weep. +5491 Wayne should not ever shrug. +5492 Richard has not ever kissed Elaine. +5493 Those cashiers do not ever embrace. +5494 George does not ever complain. +5495 All shoes had not ever flattened. +5496 A lot of guys will not ever eat. +5497 Teenagers do not ever react. +5498 Winston Churchill had not ever hidden. +5499 James would not ever reveal who hasn't sneezed. +5500 Peter might not ever realize what realize who fails to suffer. +5501 Some unicycle should not ever shatter. +5502 Carla had not ever attacked Phillip. +5503 A guy could not ever criticize Brenda. +5504 Allison can not ever shock Lucille. +5505 Raymond has not ever gone fishing. +5506 Liam has not ever bothered to impress Kristen. +5507 The dancers did not ever retaliate. +5508 Sally might not ever require every cashier to shout. +5509 Kenneth might not ever bike to these movie theaters. +5510 Martin can not ever discover Maria to read. +5511 Christopher might not ever figure out who cooked. +5512 Denise has not ever kissed Jessica. +5513 Every guy should not ever hope to salute. +5514 These rivers have not ever melted. +5515 Adam does not ever boast. +5516 Julie had not ever cleaned a plate. +5517 Ann does not ever observe the patients. +5518 These wheelbarrows do not ever roll. +5519 Every eggplant had not ever scorched. +5520 Kirsten had not ever commissioned the adult to visit Martha. +5521 The patient would not ever forget about who hadn't hunted. +5522 Homer might not ever skate around the lakes. +5523 Richard can not ever yearn to annoy Becky. +5524 Many mirrors should not ever aggravate the patients. +5525 Marcus has not ever failed to tour most museums. +5526 That cashier should not ever intend to distract Paul. +5527 The doctors do not ever think about Alan. +5528 Kimberley can not ever describe a lot of documentaries. +5529 The Impressionists should not ever respond. +5530 Many patients had not ever profited. +5531 The chicken has not ever disgusted Sherry. +5532 Guy had not ever wasted away. +5533 Some senator has not ever toured a public park. +5534 This teacher should not ever explain everything. +5535 Steven has not ever planned to salute. +5536 Janice could not ever vanish. +5537 Those planes will not ever tip over. +5538 Alexander has not ever practiced. +5539 Jessica could not ever descend these steps. +5540 Melanie could not ever impress this pedestrian. +5541 Tammy will not ever boast. +5542 Clyde does not ever command a lot of ladies to cook. +5543 Julie does not ever dislike Julie. +5544 Tiffany can not ever move. +5545 Craig has not ever slumped over. +5546 Catherine has not ever forgotten who hasn't nodded. +5547 Bradley has not ever attempted to visit Elizabeth. +5548 Carolyn had not ever attacked Paula. +5549 These vases have not ever alarmed Adam. +5550 A lot of libraries had not ever approved. +5551 Some legislature did not ever hate many doctors. +5552 Some skirts have not ever twisted. +5553 Gina had not ever stunned Todd. +5554 The Lutherans will not ever shout. +5555 Those schools might not ever fire Jerry. +5556 That guest will not ever climb down most stairs. +5557 Most legislatures have not ever benefited. +5558 Tonya could not ever reference Nina. +5559 A glass did not ever shatter. +5560 Laura had not ever dropped by a college campus. +5561 Julie has not ever irritated Dan. +5562 Amy has not ever entreated Brian to wear a sweater. +5563 Deanna had not ever healed Gerald. +5564 Wayne should not ever sound like this book. +5565 This ice would not ever freeze. +5566 Kristen had not ever pressured these ladies' contractor to yawn. +5567 A lot of jackets do not ever warp. +5568 Steven had not ever visited Edward. +5569 Most students had not ever returned to Michael. +5570 Many bananas will not ever rot. +5571 Laurie should not ever long to visit Natalie. +5572 Marie has not ever forgotten about what buy many photographs. +5573 All doctors have not ever spurred Kayla's hairdresser to talk. +5574 Linda had not ever laughed. +5575 Richard had not ever intended to murmur. +5576 Sheila has not ever argued about Kimberley. +5577 Phillip has not ever hated Florence. +5578 Melissa can not ever describe Noah. +5579 A skirt did not ever shrink. +5580 Some glasses have not ever twisted. +5581 Every jacket had not ever soaked. +5582 All associations can not ever profit. +5583 Brian does not ever train. +5584 Those pedestrians have not ever boasted about Carl. +5585 Most guests can not ever communicate. +5586 These girls would not ever marry. +5587 A glacier does not ever evaporate. +5588 All lakes had not ever melted. +5589 Many libraries did not ever negotiate. +5590 All people do not ever escape from some mountains. +5591 Eric should not ever sneeze. +5592 Ellen had not ever broken windows. +5593 Heather had not ever liked to annoy a teenager. +5594 Jeffrey has not ever talked about that waitress. +5595 The organizations have not ever ordered Anne to notice Mary. +5596 Frank could not ever prompt Melanie to respect Timothy. +5597 That cake had not ever baked. +5598 Stacey might not ever want to die. +5599 Steven will not ever talk. +5600 Laurie does not ever go to a mountain. +5601 Some doctor can not ever see some apples. +5602 The guys can not ever push Christina's dentists to astound Dan. +5603 Tiffany had not ever approached Stacey. +5604 Peter had not ever hidden away. +5605 Kayla might not ever reference Clyde. +5606 Many women could not ever kiss. +5607 A lot of lakes might not ever condense. +5608 Mary had not ever smiled. +5609 This school would not ever profit. +5610 Wayne does not ever scan every newspaper article. +5611 Dawn did not ever believe that jacket to fold. +5612 Beth had not ever protested. +5613 Jill would not ever laugh. +5614 Every actor had not ever shouted. +5615 Christine might not ever pressure Jessica to fire Omar. +5616 Laura will not ever compromise. +5617 Jacqueline had not ever examined documentaries. +5618 Amelia will not ever explain an apple goes bad. +5619 Those legislatures do not ever cooperate. +5620 Stephen will not ever see. +5621 Christina has not ever forgotten what accelerated. +5622 Those cucumbers could not ever rot. +5623 Tonya has not ever skated around these malls. +5624 The jacket had not ever twisted. +5625 Deborah can not ever scan some play. +5626 Samuel has not ever sneezed. +5627 Some guys had not ever cooked. +5628 Jennifer has not ever cheered. +5629 Allison has not ever continued to sit down. +5630 Carla has not ever seen George. +5631 Jerry might not ever wish to break a convertible. +5632 Jessica had not ever slumped over. +5633 Phillip did not ever go to the high schools. +5634 Sheila would not ever exercise. +5635 Those companies had not ever talked about James. +5636 Brenda can not ever fall asleep. +5637 Some schools will not ever look to rot. +5638 Most pedestrians have not ever flirted. +5639 Holly does not ever reference a report. +5640 The man would not ever prompt Naomi's colleagues to talk about some child. +5641 A skirt does not ever bore Rebecca. +5642 Beverly would not ever plan to ascend a lot of stairs. +5643 The Lutherans have not ever kissed. +5644 Frank would not ever fail to watch Teresa. +5645 Carrie had not ever imagined many schools force a customer's contractors to love Wendy. +5646 April would not ever spur Lawrence's students to confer. +5647 Those cashiers have not ever blinked. +5648 Most guests might not ever describe Patricia. +5649 Julia has not ever yearned to reference Jodi. +5650 A lot of guys have not ever concealed the glasses. +5651 Helen has not ever longed to train. +5652 Gary has not ever tried to lift most grocery stores. +5653 A bike did not ever stop. +5654 Donald has not ever arrived at most libraries. +5655 Some lakes have not ever melted. +5656 Women might not ever go to that hill. +5657 Homer will not ever spin around. +5658 Every coat has not ever tightened. +5659 Chad should not ever declare Amy to hate commentaries. +5660 Some window has not ever fallen apart. +5661 Sarah has not ever bored Gregory. +5662 Rachel might not ever propose to weep. +5663 Samantha has not ever kissed Dan. +5664 Cindy will not ever regret the doctors haven't conspired. +5665 The associations that had worried Cynthia have not ever planned to shock every actress. +5666 Every son of Jerry who has insulted Jerry can not ever die. +5667 Most pedestrians' tutors that have hired Alice can not ever kiss. +5668 The doctor that has referenced Diane might not ever move. +5669 Some glasses that did not disturb Craig could ever flatten. +5670 Every skirt that has disgusted Beverly had not ever unfolded. +5671 Every plane that was not aggravating this waiter could ever accelerate. +5672 Those turtles that are boring April could not ever break those couches. +5673 Every coat that did not scare Nina has ever wrinkled. +5674 A lot of men who should hate Sharon can not ever struggle. +5675 Renee's piano teachers that had referenced Cindy could not ever refuse to perform. +5676 Tonya's sons who were observing Stephanie can not ever talk about Carla. +5677 Many candles that might astound Naomi did not ever fade. +5678 Jeffrey's son that has climbed down the mountain has not ever argued. +5679 William's senators that should complain about Jodi might not ever stretch. +5680 These pedestrians that are dropping by all glaciers had not ever coughed. +5681 Tammy's bosses who have alarmed these people had not ever spun around. +5682 That daughter of Sarah that was not annoying Dana has ever needed to walk through Harvard University. +5683 The brothers of Gary that had helped Ella could not ever declare every scarf to wrinkle. +5684 Beth's cousins who have cared for Scott do not ever turn out to reference Katherine. +5685 Many grandfathers of Heather that had remembered Jessica have not ever neglected to disagree with Stacy. +5686 A lot of brothers of Anne that have loved Alan had not ever thought many organizations have discovered Heather to come here. +5687 Keith's husband that has fired a lot of customers had not ever listened to Derek. +5688 Sons of Anna who are messing up a college campus might not ever laugh. +5689 Most people that were driving to most high schools will not ever think about who messed up some mall. +5690 Kirsten's bosses who can shock Amanda did not ever investigate who isn't telephoning. +5691 Dan's best friend that had not disagreed with Sally has ever gotten fired. +5692 Some grandmothers of Danielle who did love Chad do not ever communicate. +5693 Most ladies' contractor who is concealing Melanie could not ever badger Benjamin's boyfriend to sit down. +5694 Anna's mentors that have escaped from Renee should not ever bother to sing. +5695 Some cart that was bothering these dancers had not ever crashed. +5696 Some cucumber that is disgusting Tanya could not ever char. +5697 Ann's guests that will reveal the dancers can not ever assemble. +5698 These apples that do distract those guests did not ever char. +5699 A lot of shawls that did not irritate some doctor have ever existed. +5700 These friends of Brenda that had ascended that mountain should not ever say Melissa will remember what went bad. +5701 Carrie's student who was describing Andrea could not ever smile. +5702 Those actors that had disgusted Linda will not ever try to bake. +5703 A lot of waiters who are selling the lake do not ever describe Helen. +5704 Pamela's boss that can reveal Christine can not ever advise some man's employees to heal Todd. +5705 Carol's French teacher who was not concealing all students has ever donated. +5706 All teenagers' sons that had worn these scarves can not ever discover Meredith to pass many college campuses. +5707 Some waitress who should not skate around the hill has ever swallowed. +5708 Most grandsons of Andrew that were skated around most art galleries might not ever boast. +5709 Denise's classmates who do not tour some restaurants have ever said Rodney whispers. +5710 A lot of actresses who do approach most guests do not ever bring those steps. +5711 Every adult that has upset many legislatures had not ever shrugged. +5712 The cashiers who have disgusted the Lutherans had not ever continued to read. +5713 All guests who are descending the hill will not ever boycott Spain. +5714 This bank that is appreciating art galleries would not ever discover what haven't shocked a lot of teenagers. +5715 Those scarves that have irritated Carrie did not ever twist. +5716 This convertible that should not shock Elaine had ever bored the waiter. +5717 Dana's lawyer who is not running around all banks had ever disturbed Tonya. +5718 Some students that are not revealing this picture have ever whispered. +5719 Charles's French teacher who is boycotting this restaurant has not ever climbed down this slope. +5720 Those students' associates that were not discussing most guys have ever observed Richard. +5721 This museum that should not talk to Alice had ever fought. +5722 Amy's associates who were buying a glove will not ever forget who refused to praise Renee. +5723 Most boys that were insulting Theodore would not ever worry. +5724 Aaron's grandfather who is healing the actors had not ever attempted to shock Mary. +5725 Adam's grandfathers who have worn all gloves could not ever insult Caroline. +5726 Benjamin's spouse who was finding this chair did not ever forfeit. +5727 Christina's partner who would describe Kristin should not ever complain. +5728 Irene's classmate that can not break this couch had ever swayed Irene to dislike Heather. +5729 Sara's ex-wives that were hurting Jill did not ever talk. +5730 Becky's grandfather who is praising Stacy did not ever shout. +5731 Bethany's associate that had aggravated this woman will not ever suffer. +5732 Most pedestrians who have admired these actresses do not ever stand up. +5733 Marcus's electrician that has revealed that mushroom does not ever fail to slump over. +5734 Dawn's plumber that was upsetting those schools has not ever fired Thomas. +5735 Larry's dentists that were hugging Jodi might not ever marry. +5736 Every child that was attacking Nina could not ever boast. +5737 Some organizations that did not hire these women have ever fought. +5738 That convertible that can not irritate Elaine has ever stopped. +5739 Dancers who had revealed Winston Churchill had not ever pressed Elizabeth's teachers to conspire. +5740 Every cup that has astounded Adam should not ever tip over. +5741 Many high schools that have alarmed Alicia have not ever scared Alexander. +5742 A lot of windows that will annoy Jessica have not ever opened. +5743 Alice's niece that is impressing Samantha has not ever come here. +5744 Patricia's boss that can respect Peter has not ever found Scott to discuss some high school. +5745 Elaine's plumber who has skated around the lake had not ever broken this bike. +5746 Every unicycle that had shocked Claire has not ever steered. +5747 Elaine's electricians that do not disagree with Carrie should ever entreat Vanessa to disappear. +5748 That lady's associate that might flee from Maria had not ever boasted about Peter. +5749 A grandmother of some dancer who did not reveal Grace has ever remembered what astound Galileo. +5750 These cars that were not stunning Scott have ever turned. +5751 Lucille's friend who is fixing that vase should not ever badger Paula to cry. +5752 The son of Bradley who had lifted some fish had not ever learned. +5753 That pedestrian that is fleeing from Bill should not ever hug Craig. +5754 A waitress that has walked through all schools had not ever continued to exit most museums. +5755 This patient who was annoying Debra had not ever known Steve. +5756 A granddaughter of Becca that is watching Tonya has not ever lifted many ladders. +5757 Sally's French teachers that had not seen this couch had ever worried. +5758 Winston Churchill's granddaughter who will hate guests can not ever talk. +5759 All waiters that should not disturb Jill have ever gotten a job. +5760 The Borgias' dentists who are cleaning every art gallery have not ever replied. +5761 Ladies who will not aggravate the banks had ever compelled the cousins of Alexander to bother Florence. +5762 Patrick's senators who do not like the waiter have ever flirted. +5763 That senator who is not firing all teenagers had ever gone fishing. +5764 Wendy's grandmothers that had stunned Wendy do not ever try to buy drawings. +5765 These cars that had alarmed Tina will not ever irritate Steve. +5766 A lot of men who will distract Phillip have not ever cured Julie. +5767 Jessica's associate who was not admiring that bike has ever appeared to flee from Alexander. +5768 That granddaughter of Holly who has disagreed with Larry has not ever answered. +5769 The mushroom that had not irritated Cheryl has ever scorched. +5770 Some photograph that has astounded Julia will not ever disgust Caroline. +5771 Bananas that should bother Keith do not ever stun these students. +5772 Eric's best friends that might attack Clyde should not ever study. +5773 Many ladies who would flee from Timothy did not ever know what testified. +5774 Lawrence's classmate who should not visit Leslie has ever fallen asleep. +5775 Carlos's nephew who had shocked Sara had not ever worried Douglas. +5776 That banana that did not disturb Dan had ever disturbed Paul. +5777 Gregory's tutors that should work with Beth can not ever confuse this government. +5778 Many sons of Cheryl who have broken every computer would not ever lift all mushrooms. +5779 Nicole's husband that had not worried children had ever begged Spain to appreciate that grocery store. +5780 These pants that had worried Ann have not ever dried. +5781 The sisters of Kendra that will not complain about Dawn had ever persuaded the dancer's nieces to sneeze. +5782 Susan's friends who should research schools had not ever questioned Amelia. +5783 That glass that could hurt the Borgias had not ever stunned Jacqueline. +5784 This woman who was climbing down a ladder has not ever obligated Erin to drink. +5785 A father of Adam who has not returned to Sherry had ever shouted. +5786 Sara's granddaughter who has not scared those drivers had ever baked. +5787 Barbara's student who would not take the boxes has ever swayed Liam to explain everything. +5788 These grandmothers of Theresa who can pass this lake had not ever worked hard. +5789 A screen that could not disturb Beth would ever astound Amelia. +5790 The government that is not disgusting most men had ever happened to discover this glass. +5791 An actor that is complaining about Joseph did not ever prefer this bread to burn. +5792 Gina's grandmother that has irritated Cynthia could not ever stand up. +5793 Schools that have not talked about William have ever helped Benjamin. +5794 That rice that has worried Sara will not ever char. +5795 Kimberley's hairdresser that has toured restaurants would not ever oblige some men to go to the grocery store. +5796 That association that is not impressing Helen has ever disliked John. +5797 The grandsons of an actress that have annoyed the Borgias should not ever prove to paint. +5798 Alan's cousins who can buy some ice cream had not ever cleaned a face. +5799 This government that was insulting Chad can not ever intend to correspond. +5800 A hospital that is not astounding all organizations had ever lied. +5801 A lot of movies about the Borgias that might not worry Phillip can ever embarrass all cashiers. +5802 Those students who had cared for Raymond would not ever change. +5803 Barbara's student who did listen to Gina does not ever drink. +5804 A bank that is stunning a lot of pedestrians can not ever badger Todd to sound like Bradley. +5805 Museums that were not concealing Carla had ever corresponded. +5806 A lot of people's bosses who will not criticize Michelle can ever wash. +5807 Those gates that are embarrassing Melanie might not ever worry Tara. +5808 A lot of sons of Susan that could astound Steven did not ever spin around. +5809 The lamp that could bore Walter does not ever look like those pictures. +5810 Cheryl's hairdressers who can disagree with Ellen would not ever forget about what has responded. +5811 Diana's cousin that was referencing the books has not ever believed Renee to skate around most glaciers. +5812 That actor who is impressing Raymond will not ever yell. +5813 All sons of Dawn who had not described William had ever disliked Angela. +5814 A person who is not touring many college campuses has ever whispered. +5815 Those waiters who were boring Vincent should not ever cure Amanda. +5816 A lot of waiters who are not taking the sweater could ever command Barbara's best friends to study. +5817 Amanda's wife that could drop by some museums had not ever saluted. +5818 Monica's grandfathers that had not cared for Kristen had ever needed Amy to find Frank. +5819 Derek's best friends who are admiring pedestrians might not ever advise Rebecca to paint. +5820 Many carriages that have annoyed Tonya had not ever existed. +5821 The guests that are observing Theresa had not ever boycotted some committees. +5822 All boys' husband that had not watched Tamara had ever obligated Guy to wake up. +5823 Lucille's boyfriend who will not disagree with many people had ever confused Rebecca. +5824 Beverly's sisters that are talking about Catherine do not ever meet. +5825 The coat that could not resemble a sketch has ever twirled. +5826 Those waitresses' best friends that had cleaned this cake do not ever run around the grocery store. +5827 Customers' nephews who did not walk through the hospitals had ever dated. +5828 Some wheelbarrows that had shocked these companies had not ever stopped. +5829 Those dancers that can break most dishes can not ever drive to that high school. +5830 This pedestrian who had appreciated Derek would not ever prompt Kristen to clean these cars. +5831 Every guy who can heal Rose does not ever neglect to impress Renee. +5832 Some door that is not stunning Barbara had ever closed. +5833 The cashiers' associate that does investigate Alexander does not ever cry. +5834 A lot of friends of Caroline who had not admired a picture had ever kissed. +5835 These glasses that can not scare Bill had ever twirled. +5836 Banks that were praising this actor did not ever find Alan to climb up this mountain. +5837 That convertible that can bore a waiter has not ever disappeared. +5838 Some apples that might look like every painting do not ever ripen. +5839 That mother of Christina who had helped most senators does not ever appreciate the libraries. +5840 A lady who could wear socks would not ever hide away. +5841 Ronald's supervisor who was walking through most libraries has not ever loved those actors. +5842 Maria's nephew who had irritated those governments did not ever fall. +5843 The university that was buying some bicycle did not ever entreat Daniel's guest to arrive at many hills. +5844 The student who had not returned to Jerry has ever climbed up many stairs. +5845 Sally's senator that will not know Mary should ever want Anne to donate. +5846 Many brothers of Benjamin who are not referencing Theodore had ever climbed up the mountains. +5847 A plane that had not aggravated Elizabeth had ever shattered. +5848 Ann's spouse who could not think about every snake had ever wondered who came here. +5849 Some pants that might annoy Amanda could not ever shrink. +5850 Some cup that had hurt Ann can not ever fall apart. +5851 A lot of pedestrians that were not alarming Scott had ever swallowed. +5852 Many paintings of a car that have astounded a university can not ever annoy Heather. +5853 The dress that will astound Bradley could not ever twirl. +5854 Those planes that are upsetting Jodi had not ever slipped. +5855 Sabrina's dermatologist that does not climb down many stairs has ever cleaned the gloves. +5856 Michelle's cousin that will discover Tanya could not ever resemble a painting. +5857 Carl's plumbers that were referencing William did not ever embrace. +5858 These cashiers' sisters that were passing most closets might not ever conceal Liam. +5859 Those planes that do not stun Gary had ever rusted. +5860 These guys' dermatologist who had left Noah has not ever described Alicia. +5861 Every woman that might not attack Beth has ever eaten. +5862 Most customers who are not exiting every high school had ever hugged. +5863 All glasses that had embarrassed a pedestrian had not ever warped. +5864 Those drivers who would not scare Brett had ever corresponded. +5865 Martha's ex-boyfriend who could not wear many skirts has ever nodded. +5866 Some men's ex-girlfriend who has investigated Deborah had not ever gone fishing. +5867 Tamara's nephews that have referenced Amanda had not ever practiced. +5868 Some person who did not disgust Timothy had ever revealed what fail to boast about Tracy. +5869 Tiffany's piano teachers that have approached Teresa did not ever suffer. +5870 Matt's doctor who had taken this sock had not ever slumped over. +5871 Every teenager's boyfriend that is thinking about the mountains had not ever climbed up the ladders. +5872 Suzanne's student that should flee from Susan has not ever sighed. +5873 Granddaughters of Tiffany that have boasted about Lucille had not ever shouted. +5874 Many rivers that are upsetting Judy have not ever vaporized. +5875 The girls who could not listen to Michael might ever imagine most scarves will soak. +5876 Many sons of Benjamin who will bike to the college campuses should not ever read. +5877 That pepper that had upset the Lutherans had not ever grown. +5878 Every customer who is ascending all stairs might not ever escape from Sonia. +5879 Many pedestrians who can not discover most ladders had ever regretted Carl can't hunt. +5880 All nephews of Amy who were concealing Carla would not ever wash. +5881 Mark's ex-girlfriends who had hugged Leslie will not ever discuss these guests. +5882 Brett's French teachers who are not describing Joseph had ever needed to scare Florence. +5883 Jane's students that have not climbed up every ladder have ever discovered what would impress Amelia. +5884 Banks that have helped Sheila can not ever confuse Matt. +5885 A lot of granddaughters of Deborah who were arriving at these grocery stores had not ever hurt Adam. +5886 Customers who will astound many senators would not ever play. +5887 Some guy that was concealing Gina should not ever pressure a cousin of Mark to sound like a library. +5888 Some commentary about literature that is not bothering Julia has ever disturbed this guest. +5889 Irene's ex-wives that have not concealed this hospital have ever cried. +5890 Some friend of Gerald that should not insult Monica has ever brought the report. +5891 A library that has not talked about Raymond has ever existed. +5892 Those mothers of Robert who have embarrassed all museums might not ever plan to benefit. +5893 Most boys' supervisor that was investigating Carlos had not ever proposed to waste away. +5894 Daniel's husband who is not taking these chairs had ever remembered who hadn't examined the documentaries. +5895 These mouths that are not aggravating Jesus have ever flung open. +5896 Some lake that has not aggravated Rhonda had ever melted. +5897 Every cat that had ascended a ladder had not ever embarrassed Sarah. +5898 Some skateboard that is scaring Mary had not ever accelerated. +5899 Every popsicle that has not shocked Stacey has ever melted. +5900 Lucille's partners that were touring a college campus do not ever communicate. +5901 A museum that should not alarm Stacy has ever astounded Teresa. +5902 This legislature that had not aggravated Diana had ever struggled. +5903 Danielle's husband that was skated around most college campuses had not ever pressed Diana to impress Rodney. +5904 The hamsters that had embarrassed Dennis had not ever hidden. +5905 Elizabeth's cousins that were worrying this bank should not ever want to clean some grocery store. +5906 The adult's wife that is revealing many children has not ever complained. +5907 Amanda's dermatologist that was passing Harvard University has not ever continued to read. +5908 Those men that had hired some teenager will not ever investigate Heather. +5909 The book that could not bore Denise has ever aggravated Ronald. +5910 Every eggplant that might embarrass those libraries could not ever shock the libraries. +5911 Rebecca's ex-girlfriend who has scanned this report has not ever cheered. +5912 Grace's dentist who has shocked a lot of libraries might not ever go to every cafe. +5913 Larry's associates who could bike to these college campuses could not ever wish to irritate Tanya. +5914 Every doctor that was walking through those rivers might not ever reveal who doesn't find most prints. +5915 These students that could know Kathleen had not ever bored Valerie. +5916 The mouth that was not hurting some patient has ever flung open. +5917 A waiter who was worrying Daniel has not ever visited a customer. +5918 The dress that can impress Jason can not ever hurt Regina. +5919 Some vase that was confusing Linda can not ever slip. +5920 The customers that were astounding some men might not ever testify. +5921 A lot of cousins of Stacy who have driven to all hills have not ever cheered. +5922 That adult's French teachers who had worried Vanessa have not ever revealed all gates. +5923 Those cars that can bore Thomas have not ever rolled. +5924 All sisters of Jane who are annoying the dancer can not ever surrender. +5925 Kayla's mentors that are dropping by a school will not ever describe Allison. +5926 Irene's nephews who can bike to this cafe have not ever examined some reports. +5927 These sons of those children that do not distract Tamara will ever reply. +5928 The dish that will not worry Denise had ever slipped. +5929 The girls who were not boring Sally had ever hired Sabrina. +5930 Some bank that is arguing about Frank might not ever approve. +5931 Nancy's spouse that has worn the hats has not ever concealed this jacket. +5932 Angela's best friends who were not scaring most associations have ever urged Tiffany to heal Paula. +5933 A student that is criticizing Victoria will not ever predict that guy to forfeit. +5934 The ice that had worried Ella will not ever evaporate. +5935 Many women that have researched some movie theater have not ever spurred Christina to scratch. +5936 That person who had shocked Jane does not ever regret Christopher slumped over. +5937 A waitress who would not reference Mary had ever yelled. +5938 Carmen's dentist that was insulting Regina does not ever hide those dresses. +5939 Every customer who had exited all mountains did not ever oblige Rachel to buy these wheelbarrows. +5940 Those universities that do not annoy Vanessa did ever happen to dry. +5941 These fathers of that pedestrian that have lifted all forks had not ever found out who has wondered what tips over. +5942 Dennis's senators who do not skate around most hills had ever tried to cough. +5943 Kristen's ex-wives that were not exiting that lake can ever scan all plays. +5944 This skirt that should not shock Carol might ever dry. +5945 Some pork that was not bothering Adam had ever blackened. +5946 Every sketch that can not bother Diana can ever disappear. +5947 Most museums that might embarrass Dan had not ever bothered to discuss Scott. +5948 Most ladies who will not alarm Caroline had ever blinked. +5949 A driver who has hugged Anne has not ever entreated Sherry to return to that waiter. +5950 Martha's brothers who had bored all dancers did not ever admire every woman. +5951 The girl who can not mess up these cafes has ever regretted most plates shattered. +5952 This cousin of those guys who is exploring the museum will not ever litter. +5953 All icicles that will not embarrass Cynthia have ever condensed. +5954 Margaret's electricians that are not disturbing Bill had ever intended to cheer. +5955 A scarf that has scared these people had not ever dried. +5956 Every mushroom that is confusing Wayne might not ever go bad. +5957 Samuel's grandfather who had hidden that couch will not ever wave. +5958 Some fathers of Anne that have astounded Gerald should not ever discover a lot of essays. +5959 Many guests' nephews who have not returned to the banks have ever testified. +5960 The brother of the teenagers that had embarrassed Katherine had not ever tried to study. +5961 A lot of dancers that had not complained about Christina have ever conferred. +5962 This company that did alarm Adam did not ever argue about Lori. +5963 That hospital that is discussing Diane might not ever discover Rose to respect Samuel. +5964 Carmen's grandmothers who have scared Diane had not ever studied. +5965 Sonia's electrician who is running around a lot of schools could not ever change. +5966 Steve's associates that should not observe Florence can ever compete. +5967 Every granddaughter of Melinda that has cleaned a sock does not ever flee from Bethany. +5968 Janice's dentist that was driving to most art galleries does not ever talk. +5969 These fathers of Ruth who will not irritate Larry have ever wasted away. +5970 A lady who does not reference Melanie has ever scratched. +5971 This convertible that was confusing Galileo had not ever crashed. +5972 Carrie's nephews that are approaching this guy had not ever persuaded Peter's best friends to dislike Patricia. +5973 Elaine's senator that did not respect Victoria has ever proposed to drop by every mall. +5974 The fathers of Dennis that were not finding the restaurant have ever hoped to reveal some school. +5975 Some cashiers who are biking to a lot of closets have not ever chatted. +5976 The boy who is embarrassing Cynthia might not ever grin. +5977 These prints of these actresses that would look like these sketches could not ever disappear. +5978 Kimberley's dermatologist who could bring that casserole will not ever love the driver. +5979 The sweater that was not disturbing Elaine can ever tighten. +5980 The adult's friend who would not boycott these organizations has ever hidden away. +5981 Gregory's brother who had described Stacey had not ever brought every cart. +5982 A lot of lamps that are scaring Stephanie did not ever dim. +5983 That man's senators that could worry Ella might not ever weep. +5984 That girl who has investigated Bethany had not ever criticized those adults. +5985 The cashiers who have climbed down that mountain will not ever intend to sneeze. +5986 The company that might annoy Phillip might not ever dare many offspring to vanish. +5987 A niece of Meredith who had watched that dancer should not ever argue. +5988 This government that has praised Kevin has not ever joked around. +5989 A lot of pedestrians' French teachers that are not bothering that customer had ever pressed Nicole to bring the fork. +5990 Many dresses that have not irritated Allison could ever flatten. +5991 Some report that had not hurt Julie has ever confused the actress. +5992 Heather's dentist that had broken bicycles has not ever nodded. +5993 Some father of Monica that will not watch Deanna has ever yearned to clean the art gallery. +5994 Leslie's guest that might not criticize Tonya had ever brought many screens. +5995 A lot of eyes that are impressing Beth had not ever opened. +5996 These children who were exiting every closet had not ever come here. +5997 All apples that do not stun that dancer had ever gone bad. +5998 All doctors' mentor that is not wearing most jackets has ever forced Bill to arrive at the restaurant. +5999 Heidi's husband who has known Paula does not ever require Sheila to clean some gate. +6000 Alan's father that would attack Amy did not ever hate Charles. +6001 Stephanie's ex-boyfriend who has researched the Impressionists would not ever hire Vincent. +6002 That hospital that had embarrassed Regina had not ever surrendered. +6003 The spinach that would not bore Tracy had ever charred. +6004 Colleen's doctors that do not wear some skirt would ever telephone. +6005 Every commentary about politics that has distracted those children can not ever upset the Lutherans. +6006 All actresses who could hide some couches had not ever appeared to embarrass Harvard University. +6007 A father of John who had exited a lot of schools had not ever written. +6008 Renee's partners that will not worry Phillip will ever cook. +6009 All tomatoes that will not disturb Roger had ever scorched. +6010 Victoria's mothers that have loved James did not ever answer. +6011 Some couch that is not shocking Alice would ever shatter. +6012 Mary's boss who might scare Peter does not ever criticize the high school. +6013 Ronald's electricians that will attack Walter should not ever clash. +6014 All museums that were disgusting Renee have not ever conferred. +6015 Some legislature that had embarrassed Paula has not ever ordered Douglas to ascend these stairs. +6016 A lot of shoes that have impressed Meredith had not ever warped. +6017 Every patient that is climbing up these mountains had not ever proven to wake up. +6018 Some legislature that has boasted about Richard will not ever like Elaine. +6019 The cousin of Stephanie that had researched some documentaries did not ever leave. +6020 Douglas's girlfriend that has not observed Catherine has ever loved many reports. +6021 Patricia's tutors that were not listening to the patients have ever telephoned. +6022 Those lights that are bothering a lot of senators might not ever brighten. +6023 Brad's electricians who had approached Ruth might not ever command Matt to hate Victoria. +6024 Those shoes that had not stunned Jill had ever twirled. +6025 Paula's nephews that were embarrassing the actress do not ever answer. +6026 A lot of museums that might hurt Amy would not ever lie. +6027 That waitress who has annoyed Beverly had not ever climbed up this slope. +6028 Every museum that does not scare Diana had ever retaliated. +6029 Scott's lawyers who can not like some gate had ever respected Leslie. +6030 Carol's senator who was lifting those pictures did not ever wake up. +6031 Edward's French teachers who did not skate around a school had ever departed. +6032 Some museums that can astound Carolyn had not ever swayed Valerie to grin. +6033 Every cart that was upsetting Andrew has not ever fallen apart. +6034 Carmen's wife who was not wearing shawls had ever grinned. +6035 Those mouths that should not shock teachers had ever closed. +6036 Some museum that would not sell all muffins has ever distracted Melinda. +6037 This door that is alarming the guys has not ever opened. +6038 These grandmothers of Douglas that might disgust those actresses had not ever obliged Beverly to argue about every doctor. +6039 These grandmothers of Tamara that have not examined the commentary have ever hated Clyde. +6040 Sharon's cousins that are investigating Stacy will not ever eat. +6041 These teenagers who are approaching Kathleen would not ever conceal every child. +6042 Every actor who will sound like Sarah has not ever left. +6043 A lot of bikes that could astound Donald had not ever slowed. +6044 These apples that had distracted Gerald should not ever ripen. +6045 Steven's piano teacher who had not talked about the essay has ever resembled that drawing. +6046 Frank's fathers that have insulted Allison had not ever cleaned this cafe. +6047 Vanessa's brothers who should talk about those adults might not ever hope to bring that bike. +6048 Ruth's grandmothers that have not descended ladders have ever urged Jason to drink. +6049 The customers that could walk through the Great Lakes had not ever questioned Dana. +6050 Kristen's hairdresser who has boycotted those lakes did not ever visit Eric. +6051 Karen's spouse who might not help Bethany has ever yelled. +6052 Most drivers that were noticing Samantha will not ever kiss. +6053 Chad's ex-girlfriends who will complain about this play did not ever scratch. +6054 Many grandsons of Karla who are upsetting Todd would not ever oblige Gary's teachers to kiss Theresa. +6055 The ladies that had shocked Elaine have not ever fled from Andrea. +6056 A lot of candles that will not worry some actor have ever faded. +6057 Diane's cousin that can worry Theodore does not ever forget about who is cheering. +6058 Russell's guests who can escape from Keith did not ever write. +6059 Many cucumbers that have annoyed Brenda have not ever baked. +6060 Most people that were not going to some public parks have ever surrendered. +6061 The waitresses that are not insulting the boy had ever attacked Steven. +6062 A woman who was boasting about Holly did not ever pressure an actress to astound Michael. +6063 Stephanie's cousin who should disturb Teresa will not ever induce Carla to clean some college campuses. +6064 This adult that is touring cafes has not ever vanished. +6065 Some waiters' best friends that were talking about Margaret will not ever surrender. +6066 Committees that have not alarmed Colleen have ever admired Natalie. +6067 The chair that did not disgust Veronica had ever annoyed Rachel. +6068 Scott's guest that has attacked Lawrence might not ever return to Dan. +6069 Kristin's electrician who could not reveal many rivers has ever motivated Janet to sit down. +6070 A brother of Marie who had embarrassed Vanessa has not ever washed. +6071 Those doctors who did not find a lot of newspaper articles have ever reacted. +6072 Those jackets that can not impress Meredith had ever appeared. +6073 Clyde's daughter who will scare Tara should not ever discover Jill. +6074 The dancers who were not praising waitresses have ever visited a high school. +6075 Some guy who is not annoying Rodney had ever sneezed. +6076 That actress's guests who had lifted the book could not ever cough. +6077 The adult that did not leave Gina might ever investigate what had evaporated. +6078 Those dogs that could scare Erin do not ever escape. +6079 Most museums that do not criticize April will ever declare Sally to shrug. +6080 Marie's contractors who have loved Kendra did not ever wave. +6081 Dancers that are not breaking that chair had ever pushed some governments to testify. +6082 Every friend of Stacey who was worrying students would not ever discuss Kathleen. +6083 Jerry's nieces that are talking to every guy did not ever joke around. +6084 Patients that will mess up many schools have not ever flirted. +6085 This cashier's colleagues who were buying this projector did not ever anticipate many pedestrians to retaliate. +6086 Some dancers that could not pass all malls had ever embraced. +6087 A customer who had referenced that waitress might not ever climb up those steps. +6088 Clyde's grandson who was passing that art gallery had not ever disagreed with Jerry. +6089 Some government that had disliked the lake does not ever surrender. +6090 The governments that are not hiring Gregory can ever boast about Guy. +6091 A library that would not stun Heidi had ever taken those reports. +6092 Ladies who had not hated Chad have ever boycotted this art gallery. +6093 A lot of associations that were not insulting a lot of museums have ever forgotten Liam. +6094 This bird that will astound Guy has not ever escaped. +6095 Some cousin of every pedestrian who is dropping by those rivers can not ever pressure Tina to break carriages. +6096 Kendra's classmates that could admire some art galleries should not ever attempt to fire Jacqueline. +6097 Some bank that should astound Noah had not ever discovered all ladies to communicate. +6098 Most daughters of Bruce who would criticize Raymond should not ever cry. +6099 Timothy's nephews that are not noticing Clyde had ever joked around. +6100 The couches that are not looking like most photographs had ever tipped over. +6101 Joseph's lawyers that have attacked Larry have not ever flirted. +6102 Some nephew of Dan that has not broken many cups has ever fired this patient. +6103 Those companies that were aggravating Julia had not ever preferred the tomato to bother Jane. +6104 An apple that is aggravating Tonya could not ever annoy Monet. +6105 Donald's cousins who did not praise a lot of people had ever prevented that play from boring Rodney. +6106 Caroline's French teachers that are not attacking Stephen had ever pressured Jodi to joke around. +6107 Some girls that had not concealed every carriage have ever responded. +6108 Some cart that was distracting Sandra had not ever turned. +6109 The dancers' tutor who is disturbing some ladies might not ever notice Matt. +6110 Some turtles that have climbed up the slope will not ever fall asleep. +6111 Ann's students that had discovered this story have not ever investigated Sally. +6112 Chad's colleagues that would examine stories can not ever need Vanessa to come here. +6113 Steve's senators who will drive to a lot of high schools had not ever ascertained all windows to annoy Grace. +6114 Adam's ex-wife that is investigating the girl has not ever revealed all carriages. +6115 Larry's father that has not looked like those pictures should ever hide. +6116 Many schools that were buying a college campus could not ever argue. +6117 All hills that were stunning Naomi can not ever confuse the waiters. +6118 Kathleen's French teachers who were hurting Becky did not ever weep. +6119 Those teachers who had hugged Mitchell would not ever meet. +6120 Liam's husband who was hiring Carla has not ever woken up. +6121 Eric's grandfather who was wearing the hat has not ever found the mountain. +6122 Melissa's piano teachers who are not boycotting the legislature had ever married. +6123 Sharon's contractor who should find Kayla would not ever fail to bore Becky. +6124 Jesus's partner that can admire Melanie did not ever press Sonia to hide. +6125 The actor that might run around all restaurants should not ever grin. +6126 Karen's grandson that was not distracting Jane had ever approached the ladies. +6127 Jeffrey's contractor that is not finding Amelia should ever study. +6128 Michelle's ex-girlfriends that will impress Eva had not ever murmured. +6129 Christopher's fathers that have not listened to Joseph had ever littered. +6130 These pictures of the malls that have embarrassed Cynthia did not ever confuse Martha. +6131 Florence's grandsons who should clean these banks could not ever listen to Tiffany. +6132 Friends of Victoria that had found Stacy might not ever marry. +6133 Some banana that did alarm customers did not ever grow. +6134 The customer who had not arrived at all art galleries can ever upset a school. +6135 Some door that has not resembled many drawings has ever shut. +6136 Irene's nephew who was stunning Andrea has not ever noticed an actor. +6137 This plane that can not bother Stacey has ever stopped. +6138 Some grocery stores that have alarmed Lisa will not ever confuse Kayla. +6139 That company that did not describe Timothy has ever responded. +6140 Veronica's employee that had not impressed Scott will ever wonder who sold many reports. +6141 Monica's ex-husband who has sounded like Kristen has not ever returned to Martha. +6142 Some girls that have seen Naomi could not ever help Martin. +6143 The libraries that have worked with Christina have not ever worked hard. +6144 Danielle's plumber that had ascended some hill could not ever attack Amelia. +6145 Some guy who had referenced Kimberley should not ever investigate who cared for Christina. +6146 Dana's dentist who has visited Ella does not ever find Travis. +6147 Sisters of Theresa who might not run around the high school can ever break this bike. +6148 Many grandsons of Grace who will walk through that mall had not ever taken many stairs. +6149 Some gate that was aggravating banks should not ever hurt Daniel. +6150 Most grandmothers of Paul that do not tour that glacier will ever break some screen. +6151 Carrie's ex-girlfriend who is discovering the book has not ever hoped to scream. 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ask some friend of Catherine to get fired. +6165 Many sweaters that were not disgusting Sherry had ever unfolded. +6166 That guy who has disturbed Allison has not ever imagined a lot of shoes loosen. +6167 Laura's teachers that are not disagreeing with Gregory have ever murmured. +6168 All waitresses who have scared a waitress could not ever examine those movies. +6169 That water that will confuse Kevin has not ever evaporated. +6170 All committees that have referenced Daniel could not ever profit. +6171 All grandmothers of the cashiers who were not praising Dennis had ever thought Walter had thought about Benjamin. +6172 This chicken that might not confuse Dennis had ever disappeared. +6173 Some teenager that will clean every screen could not ever doubt the Lutherans believe some screen to exist. +6174 A lot of brothers of Travis who do not remember some story had ever asked Christine to bake. +6175 Some gloves that do not resemble the print should ever soak. +6176 Susan's lawyer who had respected the actors could not ever learn. +6177 Amy's hairdressers who have cared for Linda did not ever respond. +6178 Elaine's nephews that are fleeing from Travis can not ever divorce. +6179 Jeffrey's French teacher that was kissing a boy can not ever descend these steps. +6180 Those jackets that were astounding Jodi had not ever frayed. +6181 That organization that had loved Naomi does not ever worry Benjamin. +6182 Every doctor that is breaking a glass will not ever sit down. +6183 The rice that will not resemble pictures had ever blackened. +6184 Beth's senator that has not lifted that candle has ever threatened to smile. +6185 A skirt that is bothering Adam would not ever shrink. +6186 Paula's grandfathers who are exploring every museum would not ever date. +6187 All actors that were not cleaning some cups have ever considered every popsicle to freeze. +6188 Leslie's dentist who could not disagree with Roger has ever dropped by every bank. +6189 Andrew's brother who has not watched Sandra had ever seen. +6190 The broccoli that is astounding Daniel has not ever bored the cashier. +6191 Some socks that should not embarrass Keith had ever stretched. +6192 All bananas that are not irritating Sabrina had ever grown. +6193 Those brothers of Rodney who are not distracting Meredith have ever nodded. +6194 Cheryl's brothers who have driven to most malls have not ever collaborated. +6195 A bank that can remember Naomi has not ever pressured Travis's teachers to waste away. +6196 Amanda's bosses who had broken some chairs have not ever researched that school. +6197 A sweater that should not bore Jodi had ever dried. +6198 Some coat that is not irritating Marla had ever soaked. +6199 Monet's supervisors that could not discover a cup can ever conceal every doctor. +6200 Scott's employees that are working with Marcus did not ever testify. +6201 Many screens that are scaring Tamara would not ever brighten. +6202 Karla's student who was upsetting Monica could not ever order Valerie to drop by the mountains. +6203 Some jacket that would distract Veronica had not ever soaked. +6204 Regina's mothers who were not bothering Gina had ever assembled. +6205 This boy that was investigating Amanda could not ever explain everything. +6206 A lot of skirts that would look like all prints have not ever vanished. +6207 These girls who would research that mall have not ever questioned Jane. +6208 Many mothers of Bruce that are not helping Paul had ever predicted the teenagers to correspond. +6209 Some guys' doctor that did not criticize Mark had ever laughed. +6210 A screen that had annoyed Stacy had not ever darkened. +6211 This daughter of Carl that had not talked to some patient should ever compel Roger to hate Ella. +6212 Laura's grandson that could not see Debra had ever disagreed with Becky. +6213 This book that might sound like these children could not ever disagree. +6214 Those children who were not hurting Emily had ever moved. +6215 This brother of this girl who had not remembered most hills has ever dropped by this grocery store. +6216 A lot of fathers of Heather who are healing Charles did not ever hope to kiss Eric. +6217 Benjamin's friends that can conceal Becky have not ever suffered. +6218 Many children who did not run around most high schools will ever alarm Laura. +6219 Some cucumber that is not alarming Steve had ever grown. +6220 Carlos's spouse who is not hugging Kirsten had ever doubted Richard has exercised. +6221 Some pedestrians who can not descend a lot of ladders had ever disagreed with Kevin. +6222 That student that was not disturbing Janice had ever expected Janice to exercise. +6223 An association that could not irritate William could ever threaten to bring couches. +6224 This actress who will not return to those boys has ever littered. +6225 That waitress who has climbed up a ladder had not ever noticed many gates. +6226 Those friends of Denise who had noticed Thomas can not ever interact. +6227 Windows that would not stun Eric had ever shattered. +6228 Cars that have aggravated Ruth might not ever accelerate. +6229 Organizations that had described Rebecca did not ever reply. +6230 A lot of shawls that will impress many companies have not ever dried. +6231 Larry's tutors that can discover Cynthia should not ever need these senators to play. +6232 A museum that did not work with an actor had ever discussed William. +6233 Dana's teacher that had explored a museum could not ever intend to bother the Lutherans. +6234 These actors that have loved a child do not ever love David. +6235 These cousins of Ruth that had skated around this mall would not ever fight. +6236 The nephew of Jessica who did buy most horses did not ever stand up. +6237 Many friends of the boy who can attack Sally would not ever lie. +6238 Most people's electrician that should not annoy Stacey has ever insulted Holly. +6239 The granddaughters of Dana who have examined some play can not ever need Erin to escape. +6240 Most lakes that were bothering some legislature had not ever evaporated. +6241 Bruce's doctor that is fixing this bike has not ever spoken up. +6242 These children that could not notice all ladders had ever disagreed with Cynthia. +6243 A cousin of Caroline that can like that boy did not ever cry. +6244 A hospital that could dislike drivers might not ever respond. +6245 Tonya's father who has hired Marla can not ever require all fathers of these waiters to bore Danielle. +6246 That bank that had shocked some teacher would not ever cope. +6247 Melanie's granddaughter that was working with Stephen had not ever walked through these closets. +6248 The ice cream that can not bother this teenager had ever vaporized. +6249 A lot of waiters who were not concealing most trucks have ever protested. +6250 Sheila's senators that have attacked Emily had not ever taken that high school. +6251 Brian's colleagues that did not impress a committee have ever fixed a lot of couches. +6252 Jennifer's teachers that would scan a lot of commentaries have not ever disappeared. +6253 Steak that has not irritated Carla had ever baked. +6254 Dancers that were boycotting these libraries have not ever talked. +6255 Some adult that does criticize Benjamin does not ever win. +6256 The girl who can astound Charles could not ever see. +6257 Most apples that should not hurt all boys had ever blackened. +6258 Steven's mothers who do not help Beverly have ever respected Raymond. +6259 Cynthia's students that can descend this ladder have not ever questioned Carolyn. +6260 The gate that was disgusting Christina had not ever closed. +6261 Irene's father who could worry this government will not ever want to go fishing. +6262 That plane that is impressing a guy does not ever crash. +6263 Candice's ex-girlfriend that was upsetting Adam has not ever forgotten who persuade the cashier to sing. +6264 Some grandson of that lady that has researched science would not ever slump over. +6265 A brother of Sonia who was not arguing about Catherine had ever sat down. +6266 Those dancers that do not hide Thomas should ever entreat Rachel to compromise. +6267 That carriage that should not worry Teresa had ever steered. +6268 Nieces of Martha that have not sounded like these girls have ever surrendered. +6269 Most teachers who have noticed Vincent could not ever like to meet. +6270 Most daughters of Sara that are not arriving at every bank have ever researched this book. +6271 Some drivers who were scanning the documentary had not ever visited Teresa. +6272 Nina's dermatologist who is distracting Kirsten would not ever respect Sheila. +6273 Russell's wife who is revealing some patient has not ever investigated Todd. +6274 The girl who would not shock Monica has ever hidden. +6275 Every car that is scaring Carla had not ever stopped. +6276 Many trucks that might bother Meredith might not ever crash. +6277 Stacy's daughters who will argue about Joel can not ever fail to disagree with Phillip. +6278 Rodney's granddaughters who were praising Carl might not ever drop by a cafe. +6279 Every lady that is talking to Stacey might not ever praise Cheryl. +6280 Suzanne's grandfather that was attacking some men does not ever wish to investigate Donna. +6281 Stacy's French teacher that had loved Phillip does not ever whisper. +6282 The window that should upset Pamela has not ever closed. +6283 The sister of Jason that was not breaking many planes had ever insulted Veronica. +6284 This grandson of Naomi that could not return to Michelle should ever want the actress to argue about Sandra. +6285 Ella's senators who had escaped from every driver should not ever die. +6286 The driver that was listening to Russell had not ever begged most dancers to collaborate. +6287 A lot of gloves that will not look like some pictures could ever impress the actresses. +6288 The customers who were describing Martin have not ever persuaded Donna to climb up the hill. +6289 That ice that is not irritating Valerie has ever condensed. +6290 That broccoli that did not stun Anna will ever blacken. +6291 The actor who was researching science can not ever care for Melinda. +6292 The actor's grandmother who had scared the banks had not ever seen most screens. +6293 Some shawls that had not impressed Amy have ever frayed. +6294 Bethany's daughters who are appreciating all lakes could not ever plan to disturb Diane. +6295 Some waitress that has alarmed Martin did not ever weep. +6296 The carriage that does not disgust most adults had ever turned. +6297 The organizations that had upset Kenneth might not ever require Theresa's senator to shout. +6298 Vanessa's brother that has questioned Diana had not ever considered some scarf to fray. +6299 Emily's sons who are insulting some mall did not ever describe the cafe. +6300 This guest's grandfathers who had described Larry could not ever read. +6301 Susan's father that did explore the public park would not ever escape from Gary. +6302 Carrie's tutors that were approaching Theresa have not ever looked like some pictures. +6303 Jason's grandmothers who did not clean some dish have ever gone fishing. +6304 Some grandfathers of Ella that had left Pamela had not ever exercised. +6305 Those vases that did impress many waiters do not ever slip. +6306 Sarah's students who had not hugged Catherine have ever prodded Paula to discuss Brad. +6307 Robert's classmates who had hated Sheila would not ever practice. +6308 John's plumbers that had cleaned some chairs have not ever slumped over. +6309 Every cart that had not stunned Stacey had ever slowed. +6310 Deanna's partner that was describing Erin had not ever wished to sneeze. +6311 An actress who did not climb up slopes had ever brought these birds. +6312 The father of Phillip who is climbing down every slope has not ever cleaned some plate. +6313 Some father of Jerry who did not talk to Bill has ever complained about Jane. +6314 Some girl's grandmothers who are not cleaning most windows might ever forfeit. +6315 That cake that is astounding William does not ever blacken. +6316 Catherine's grandfathers who are describing that cashier have not ever suffered. +6317 Alan's handyman who has scanned a play does not ever slump over. +6318 The bicycle that was upsetting Eric did not ever tip over. +6319 A museum that had sold these movie theaters can not ever force Scott to leave. +6320 Many sons of those teenagers who are not finding Eva had ever ascertained Julie to complain. +6321 Stacey's grandson who had insulted Jeffrey should not ever discuss Julie. +6322 Joel's daughters who did not boast about Tamara have ever toured some schools. +6323 The pepper that should not hurt Margaret should ever rot. +6324 Sarah's colleagues who could run around this bank have not ever surrendered. +6325 The Lutherans' nephew who does not alarm Kathleen has ever changed. +6326 All friends of Joseph who are not firing Michael had ever drunk. +6327 Every granddaughter of Galileo who can stun Karen has not ever noticed some mushrooms. +6328 Larry's dentist that is impressing a teacher did not ever study. +6329 The people that were lifting most dishes can not ever ask Ann's employees to practice. +6330 A lot of rugs that had distracted Diane have not ever bothered Keith. +6331 A customer who is researching all lakes can not ever admire the eggplant. +6332 Maria's electricians who will reveal this book should not ever return to the children. +6333 Some student who will not discover broccoli had ever discovered Leslie to hug Tanya. +6334 Melissa's tutors who have appreciated Carmen should not ever cough. +6335 Joseph's sons who had descended every hill will not ever oblige Michael's spouse to sit down. +6336 Diane's piano teacher that has boasted about these cashiers has not ever disappeared. +6337 Girls who might not discuss Deborah have ever kissed. +6338 Carlos's French teacher that had skated around hills will not ever swallow. +6339 Those drivers' hairdresser who has skated around that closet has not ever questioned Karla. +6340 Most doctors who were hiring Laura did not ever judge Becky to attack Helen. +6341 Janice's dentists who were investigating Andrea have not ever discovered Brian. +6342 The guy that is referencing Carolyn does not ever laugh. +6343 All cashiers who had discovered those cafes had not ever scanned all stories. +6344 Colleen's grandmothers who were arriving at restaurants should not ever cry. +6345 Timothy's dermatologist that is concealing Frank might not ever think about jackets. +6346 Some cousins of some student who could attack Brian have not ever testified. +6347 Sonia's daughters that have not hurt Renee had ever coped. +6348 Carmen's dermatologist that is not questioning some hospital will ever persuade Eric to learn. +6349 Chad's mentor who was annoying Jodi had not ever imagined some dancers train. +6350 Naomi's fathers that should aggravate Monica might not ever leave. +6351 A government that was criticizing Rebecca would not ever assemble. +6352 Vincent's associate who is healing Alice has not ever pressured Jennifer's supervisors to fix some mirror. +6353 Alicia's colleague who was helping Nancy does not ever seem to condense. +6354 All sons of Claire who did not praise Edward had ever hurt Rodney. +6355 This father of those actors that is not descending many ladders can ever donate. +6356 Candice's brothers who have examined those books have not ever returned to that guest. +6357 This car that could embarrass Roger has not ever stopped. +6358 A lot of sons of Jennifer that had not helped Timothy have ever come here. +6359 That person who could boast about Martin had not ever remembered Connie. +6360 Many libraries that are stunning that organization had not ever want to vanish. +6361 John's grandmother who had left a driver had not ever fired Kenneth. +6362 This niece of this guest that was escaping from a movie theater has not ever praised the Impressionists. +6363 Nina's electrician who has complained about Veronica should not ever blink. +6364 Mary's dentists that were not disturbing John had ever figured out who shouldn't implore Sally to climb up some slopes. +6365 Every waiter that was exploring the grocery stores does not ever conceal who swallow. +6366 Jacqueline's daughters who had visited Edward might not ever continue to play. +6367 An actress who would not drop by those cafes has ever cried. +6368 Guy's supervisors that had concealed Carla should not ever hire Roger. +6369 Mark's plumber that is not irritating Martha has ever cleaned some cup. +6370 These students that had criticized Jane can not ever reunite. +6371 Every man that had not disliked this child has ever liked to answer. +6372 Larry's sons who do escape from Amanda do not ever fall asleep. +6373 Patrick's electricians that are upsetting Alicia should not ever leave. +6374 A lot of cashiers who had left Karla had not ever yawned. +6375 Jane's son that has fixed a vase will not ever try to discover Patrick. +6376 Many adults who had kissed Theresa will not ever approve. +6377 Those girls' daughters who would not pass that high school have ever conferred. +6378 Some shoe that had disgusted Jill could not ever twist. +6379 All boys that are breaking every wheelbarrow did not ever hide Kevin. +6380 Timothy's contractor that does not remember Susan had ever investigated who discover Ruth to conceal a lot of snakes. +6381 Frank's teacher who was examining a documentary has not ever lied. +6382 Alexander's lawyer that is praising Leslie did not ever sigh. +6383 That shawl that might not shock Claire had ever loosened. +6384 The friend of Carolyn who would observe Ruth has not ever seen Debra. +6385 Melanie's lawyer who had taken every casserole might not ever arrive at that hospital. +6386 Jesus's contractor who is arguing about Kendra might not ever hate Diana. +6387 A cashier's guests who will not care for Tanya had ever embraced. +6388 These projectors that are embarrassing Amelia have not ever stunned Aaron. +6389 Sandra's dentists who have cured Kendra had not ever longed to salute. +6390 A nephew of Connie that had not looked like those paintings had ever forfeited. +6391 Craig's best friend who has questioned Cheryl did not ever get a job. +6392 Monet's grandson that was not scanning many movies had ever coughed. +6393 Every bank that had not respected Dana had ever failed to cooperate. +6394 All doctors that might describe this waiter have not ever left. +6395 Those museums that were talking about Joseph have not ever retaliated. +6396 Barbara's partner that is not examining the movies had ever insulted Dan. +6397 Beth's tutor who would drive to all cafes had not ever noticed Scott. +6398 Katherine's ex-wife that is describing Bradley can not ever find Karla to skate around the mall. +6399 Regina's guests who will hide Ronald might not ever debate. +6400 All friends of Nancy that are not arguing about Winston Churchill can ever fight. +6401 That icicle that can stun cashiers does not ever melt. +6402 Linda's dentist who might not watch Natalie can ever get a job. +6403 A niece of Melissa who did not stun Tracy can ever love most actresses. +6404 The daughter of Heidi that was messing up some rug would not ever depart. +6405 All lakes that have annoyed the schools would not ever condense. +6406 Those guests that do exit the lake did not ever observe these customers. +6407 Donald's dentists who were selling that light will not ever have these stairs. +6408 That doctor's dermatologist who does not like these shoes had ever read. +6409 All daughters of William who have loved Walter have not ever married. +6410 The senator's senators who will bike to some banks have not ever hugged Sarah. +6411 Some organization that had alarmed Diane does not ever joke around. +6412 That cafe that was aggravating Rose has not ever scared some pedestrian. +6413 All men's guests that might escape from Susan will not ever drop by the museums. +6414 Every lamp that has embarrassed Rose has not ever darkened. +6415 Every bank that could not praise Jeffrey had ever concealed Heidi. +6416 Every scarf that has not alarmed some pedestrian had ever distracted Melissa. +6417 A lot of schools that have concealed this book should not ever anticipate most projectors to brighten. +6418 The cups that are not bothering Jesus have ever worried Liam. +6419 The sweater that might distract Raymond should not ever loosen. +6420 Most art galleries that could disturb some lady can not ever vanish. +6421 Every student that has alarmed most drivers did not ever astound most adults. +6422 Larry's handyman that has not brought all banks has ever remembered what stops. +6423 The companies that have admired Susan do not ever force Leslie's dermatologist to disturb most boys. +6424 Some dancers that had looked like that print do not ever hide all dishes. +6425 Susan's plumber that was not scanning most stories has ever doubted Donna wouldn't break most computers. +6426 Tracy's mentor who was looking like that photograph has not ever researched literature. +6427 That mushroom that can not hurt every driver has ever grown. +6428 That association that can fire Douglas could not ever conspire. +6429 Those patients' grandfather who had questioned this library would not ever command Sandra to yell. +6430 Some actor's mothers who have not irritated Frank had ever left Natalie. +6431 The planes that are not annoying Marie had ever existed. +6432 Maria's handyman that has talked about Lawrence does not ever blink. +6433 Every pie that has confused Florence would not ever char. +6434 The art gallery that has worried Peter did not ever impress the pedestrian. +6435 A mother of Jesus that will drive to the malls had not ever performed. +6436 Pamela's grandmother who is discovering some actor has not ever learned. +6437 These fathers of Derek that were selling some pasta had not ever cooked. +6438 This patient who was not cleaning a lot of glasses had ever explained Peter wasn't waving. +6439 The mothers of Monet that could not tour cafes have ever exited that closet. +6440 Some son of Sonia that will respect Ella can not ever hide away. +6441 Some mother of Valerie who has cared for Roger should not ever yearn to attack Julie. +6442 That senator's supervisor who will not listen to Brad had ever broken a lot of trucks. +6443 Alexander's lawyers that will not drive to every library had ever talked. +6444 Nancy's grandson who can conceal all waiters will not ever respect Melinda. +6445 Alexander's mentors that have concealed Denise should not ever kiss Christine. +6446 The lady that was arguing about Irene did not ever judge Stacy to hate Colleen. +6447 This carriage that is boring Martin does not ever hurt Paul. +6448 Most doctors who have shocked Donna did not ever prevent every bank from changing. +6449 Every teenager's brothers who were confusing Sonia would not ever work hard. +6450 Catherine's husband who had not cleaned many public parks has ever nodded. +6451 Carmen's hairdresser that was insulting Susan does not ever fire Samuel. +6452 Jesus's piano teacher that is visiting the art gallery would not ever pass some art gallery. +6453 Some pedestrian who was buying some lamps could not ever turn out to exit some glacier. +6454 Most children that have escaped from some closet can not ever succeed. +6455 A doctor who does not break this screen has ever insulted Raymond. +6456 That shoe that is annoying most teenagers can not ever stretch. +6457 Those patients that have scared Elizabeth had not ever embraced. +6458 A cashier who is not concealing Andrea has ever played. +6459 Barbara's ex-husband who can pass this grocery store did not ever ask some daughters of Gerald to scan that newspaper article. +6460 Amanda's employees that could arrive at some lake do not ever figure out who climbed up some ladder. +6461 All vases that were not alarming Helen have ever stunned schools. +6462 That cashier who does not exit some high school had ever bothered Stacy. +6463 Grace's mothers that might think about Tammy did not ever wash. +6464 The association that is concealing Christine will not ever advise the nieces of Stephen to divorce. +6465 Windows that will not shock Dan should ever fling open. +6466 Some guests that would bore Rachel would not ever react. +6467 The son of Carolyn that had resembled this photograph could not ever prompt Wendy to visit Beverly. +6468 Martha's nieces who have alarmed Kendra will not ever sway Tanya to sit down. +6469 A lot of mouths that were not impressing a doctor had ever flung open. +6470 Granddaughters of Patrick who might exit that mall had not ever worked hard. +6471 Pamela's piano teacher who has fled from George had not ever happened to hire a lot of actors. +6472 Sonia's guest that has disagreed with Sonia will not ever move. +6473 Jesus's contractor who had not concealed these plays had ever screamed. +6474 Some senator who was dropping by glaciers can not ever explain everything. +6475 Most nieces of John that have approached Ruth might not ever concur. +6476 Debra's mother who had referenced ladies would not ever break this convertible. +6477 That driver who does not disgust this bank had ever escaped from a lot of glaciers. +6478 A mushroom that might bore Gary will not ever blacken. +6479 The gloves that have not astounded these children have ever shrunk. +6480 Those doctors who would not see Ruth have ever changed. +6481 That lake that was looking like every photograph should not ever freeze. +6482 Jodi's wife that is not bothering Carmen has ever donated. +6483 Many nieces of Emily who are climbing up a lot of slopes had not ever forced Marla's sisters to walk through most hospitals. +6484 Most dancers that are escaping from Kristen have not ever wondered who is confusing Jerry. +6485 Carol's electricians that have descended this mountain have not ever worked with the teenagers. +6486 Some mushroom that had bothered most teenagers would not ever bake. +6487 Some boy who will know Stacy has not ever hoped to alarm Nicole. +6488 Tamara's bosses that had not argued about literature have ever walked through the mountain. +6489 Many pedestrians who had bought every door should not ever hire Charles. +6490 Most shawls that do not alarm the girl can ever dry. +6491 James's ex-boyfriend that does not wear this jacket might ever need to study. +6492 Theodore's friend that could not dislike Dana has ever praised Rhonda. +6493 Every eggplant that had bothered Tammy had not ever ripened. +6494 This customer that is not exiting that museum has ever sneezed. +6495 Jennifer's handyman that had bothered Amelia would not ever find out what do steer. +6496 Cynthia's cousins that have left Natalie can not ever correspond. +6497 Those people who were disagreeing with Debra have not ever brought most trucks. +6498 The banana that was not alarming Carrie has ever charred. +6499 Most waiters that have not fixed some dishes had ever fired Curtis. +6500 The casseroles that were looking like some prints had not ever baked. +6501 That cousin of Becca that has healed some snake has not ever boasted about Regina. +6502 Tonya's classmates that have astounded that university did not ever press Stephanie to answer. +6503 All mirrors that would not impress Samuel had ever shattered. +6504 Some screen that might look like some painting had not ever faded. +6505 Those socks that are not upsetting Tanya have ever twirled. +6506 A blouse that had bored Jesus had not ever unfolded. +6507 A lot of brothers of Diana who can not attack Jodi have ever coped. +6508 Marcus's students who had not noticed the patients have ever replied. +6509 Actors that are attacking Jacqueline have not ever found out who forgets about what have shrunk. +6510 Elaine's ex-husband who was disagreeing with all women did not ever vanish. +6511 These women's ex-boyfriend who does not drop by that high school had ever insulted Gerald. +6512 Most people who were talking to Rachel might not ever hide. +6513 This mother of Craig who can complain about Tina does not ever learn. +6514 A lot of teenagers who do talk to Winston Churchill did not ever arrive at libraries. +6515 That cousin of William that had left Victoria should not ever practice. +6516 Every print of Danielle that had shocked Nicole should not ever look like college campuses. +6517 Every waiter that had bought the dresses might not ever sway Michael to bother Samantha. +6518 These waitresses' senators that were working with Christine could not ever break these couches. +6519 Many doctors that were not praising Pamela can ever fix a plane. +6520 Those teachers that had talked to Kathleen should not ever boast about Deborah. +6521 Elaine's ex-girlfriends who would not disagree with many cashiers have ever performed. +6522 The actress's supervisors that have climbed up all ladders had not ever worked with Dan. +6523 These women that have sold those pants can not ever explain Brian persuades Mark to bring some books. +6524 A mother of a doctor who is wearing some scarves can not ever notice Carmen. +6525 The teacher who had talked about Ruth could not ever get a job. +6526 Deborah's son that has kissed some cashiers has not ever talked about Joel. +6527 The socks that are annoying Nancy did not ever fray. +6528 That pasta that was not disgusting Melanie has ever stunned Becca. +6529 The unicycle that was not hurting Jennifer had ever slowed. +6530 Irene's supervisors that had toured every high school might not ever complain. +6531 Some lake that had hurt Laurie did not ever freeze. +6532 Many actresses' electrician that should take most coats did not ever shout. +6533 Edward's grandmothers that had not cared for Andrea had ever painted. +6534 That child that did not hug a lot of customers had ever happened to annoy Janet. +6535 Douglas's employees that are boring some doctors have not ever corresponded. +6536 Tamara's nephews who have disturbed Carmen had not ever competed. +6537 Gary's daughters that have not escaped from Michael had ever chuckled. +6538 Alexander's boss who can leave Benjamin had not ever talked about Gregory. +6539 Janice's teachers who are skated around that school could not ever attack William. +6540 Sheila's ex-boyfriend that is breaking many carriages does not ever sigh. +6541 Joel's mothers who would hate Galileo could not ever need to reunite. +6542 Most teenagers that will not alarm Sharon had ever appreciated Janet. +6543 Ann's friends that would not question Walter have ever flirted. +6544 This grandfather of Wayne who is distracting Renee had not ever spun around. +6545 A library that is confusing a lady does not ever look to testify. +6546 Sarah's piano teacher who was finding Guy can not ever tour a lot of movie theaters. +6547 The lake that was distracting Sonia has not ever frozen. +6548 The man that can discover Christopher could not ever hurt Angela. +6549 Joseph's supervisor who will argue about Brian had not ever run around many art galleries. +6550 Anne's grandson that has disgusted Noah had not ever slumped over. +6551 Some guys who were disgusting Irene have not ever concealed that unicycle. +6552 Rodney's best friend that is not questioning most girls has ever pressured the Borgias to look like this picture. +6553 This waitress that has found Deanna should not ever ascend those steps. +6554 A teacher who has walked through a museum has not ever swayed Peter to complain. +6555 This waitress's son that might boast about Rachel did not ever long to paint. +6556 Every teenager's fathers who have insulted all stories have not ever complained. +6557 Some teacher that was scaring Edward will not ever learn who couldn't slump over. +6558 A father of Craig who did drop by the grocery store did not ever stretch. +6559 Kevin's employees who had worn those coats had not ever cleaned a high school. +6560 This pepper that was boring Denise will not ever rot. +6561 Some cars that were alarming Jeffrey could not ever look like those paintings. +6562 Aaron's grandfathers who have taken these schools had not ever toured the hills. +6563 Rodney's handyman that is not lifting these ladders has ever imagined Steven won't donate. +6564 Rebecca's plumber who is buying these mouths did not ever implore some analyses to protest. +6565 Adam's student that was exiting all glaciers will not ever badger Maria's ex-girlfriends to spin around. +6566 Vincent's ex-wife that has examined the newspaper articles does not ever praise Gerald. +6567 Every doctor that had shocked the hospital has not ever cleaned these high schools. +6568 A lot of children that have cared for Margaret had not ever proven to hire Kristen. +6569 Nina's granddaughters who are touring some cafe do not ever climb down this hill. +6570 Dawn's electricians that are returning to that lake did not ever convince Patrick to love Monica. +6571 Lori's grandmother that is not embarrassing Pamela could ever think about every committee. +6572 Linda's doctor who has climbed down all ladders does not ever climb down those hills. +6573 Those drawings of Lucille that are not impressing a lot of governments have ever looked like the eggplant. +6574 John's ex-wives that had found many icicles should not ever fail to descend the mountain. +6575 All customers who are escaping from Tamara might not ever marry. +6576 Marcus's teachers that have examined the movie will not ever flirt. +6577 A lot of customers that were fixing a lot of bikes have not ever performed. +6578 Some newspaper article about politics that can embarrass Allison could not ever bore Larry. +6579 Meredith's French teachers that have examined a lot of documentaries have not ever reunited. +6580 The mothers of Aaron who are lifting the hat have not ever resembled many pictures. +6581 Rebecca's brothers that had messed up these cars would not ever hug Heather. +6582 Most actresses that are not concealing Roger had ever conspired. +6583 Carol's son who has hurt Stephanie did not ever admire Naomi. +6584 All libraries that had criticized Tonya had not ever appeared. +6585 A cousin of Stephen who has seen Steven could not ever compromise. diff --git a/zeroshot/qa_congruence_easy/eval_results.json b/zeroshot/qa_congruence_easy/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..0d917577e64ec10df4569b74fe0aaad37abb2a0c --- /dev/null +++ b/zeroshot/qa_congruence_easy/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.5625} \ No newline at end of file diff --git a/zeroshot/qa_congruence_easy/predictions.txt b/zeroshot/qa_congruence_easy/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..f27f424bef63fb407134949faee89fa53b451f87 --- /dev/null +++ b/zeroshot/qa_congruence_easy/predictions.txt @@ -0,0 +1,65 @@ +index prediction +0 What did you get?\nI got a chair. +1 A: What did you sell?\nB: Sarah. +2 David: What did you sell?\nSarah: I sold a chair. +3 A: What did you get?\nB: I got a piano. +4 David: What did you get?\nSarah: I got a doctor. +5 A: What did you break?\nB: I broke a bowl. +6 What did you make?\nA lamp. +7 What did you make?\nA lamp. +8 David: What did you make?\nSarah: Sarah. +9 David: What did you cook?\nSarah: Potatoes. +10 David: What did you eat?\nSarah: Sarah. +11 A: What did you cook?\nB: I cooked chicken. +12 What did you cook?\nPotatoes. +13 What did you close?\nI closed the gate. +14 What did you open?\nI opened the box. +15 What did you lock?\nI locked the window. +16 A: What did you close?\nB: The window. +17 A: What did you lock?\nB: The gate. +18 David: What did you close?\nSarah: I closed the window. +19 David: What did you close?\nSarah: David. +20 A: Who did you see?\nB: A teacher. +21 David: Who did you see?\nSarah: I saw a sofa. +22 A: Who helped you?\nB: Milk. +23 David: Who did you see?\nSarah: David. +24 A: Who did you see?\nB: I saw eggs. +25 Who is here?\nA car. +26 Who did you see?\nI saw a car. +27 Who did you meet?\nA doctor. +28 David: Who did you meet?\nSarah: Eggs. +29 David: Who kissed you?\nSarah: A chair kissed you. +30 Who is talking to you?\nSarah. +31 Who kissed you?\nA teacher. +32 David: Who shook your hand?\nSarah: A statue. +33 Who shook your hand?\nA statue. +34 A: Who kissed you?\nB: Sarah. +35 David: Who kissed you?\nSarah: David. +36 David: Who is sleeping?\nSarah: Milk is sleeping. +37 Who is sleeping?\nEggs. +38 David: Who is sleeping?\nSarah: Sarah. +39 A: Who is sleeping?\nB: A teacher. +40 A: Who is playing the guitar?\nB: A piano is playing the guitar. +41 A: Who painted that picture?\nB: Milk. +42 David: Who is playing the guitar?\nSarah: Sarah. +43 Who is drawing?\nEggs. +44 David: Who is drawing?\nSarah: A teacher is drawing. +45 A: Where did you send the package?\nB: I sent the package a computer. +46 Where did you send the package?\nI sent the package to europe. +47 A: Where did you go?\nB: I went across the country. +48 David: Where did you go?\nSarah: Milk. +49 A: Where did you travel to?\nB: To the other side of town. +50 David: Where did you send the package?\nSarah: I sent the package across the country. +51 Where did you send the package?\nMilk. +52 A: Where are you headed?\nB: Across the country. +53 A: Where did you drive to?\nB: A chair. +54 Where are you headed?\nAcross the country. +55 A: Where did you travel to?\nB: I traveled to europe. +56 Where did you find it?\nI found it a car. +57 A: Where did you put it?\nB: I put it on the table. +58 David: Where did you put it?\nSarah: Eggs. +59 A: Where did you leave it?\nB: A book. +60 David: Where did you leave it?\nSarah: I left it behind the sofa. +61 Where did you put it?\nI put it a piano. +62 A: Where did you leave it?\nB: A table. +63 Where did you find it?\nI found it on the table. diff --git a/zeroshot/qa_congruence_tricky/eval_results.json b/zeroshot/qa_congruence_tricky/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..5b05370cfa4467e4e056100d678ba40380f2b2a4 --- /dev/null +++ b/zeroshot/qa_congruence_tricky/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.3515151515151515} \ No newline at end of file diff --git a/zeroshot/qa_congruence_tricky/predictions.txt b/zeroshot/qa_congruence_tricky/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..b74055f921fa2004677b68383ceb7424345e9b0f --- /dev/null +++ b/zeroshot/qa_congruence_tricky/predictions.txt @@ -0,0 +1,166 @@ +index prediction +0 Who cleaned?\nDavid cleaned. +1 A: Who cleaned?\nB: The patio. +2 A: Who cleaned?\nB: The tiles cleaned. +3 David: Who wrote?\nSarah: Sarah wrote. +4 A: Who wrote?\nB: An essay. +5 David: Who read?\nSarah: A book. +6 David: Who wrote?\nSarah: An essay. +7 A: Who wrote?\nB: An essay wrote. +8 A: Who read?\nB: A letter. +9 Who read?\nA book read. +10 A: Who read?\nB: A letter. +11 David: Who wrote?\nSarah: David wrote. +12 A: Who read?\nB: An essay read. +13 Who ate?\nA doctor. +14 Who ate?\nDinner ate. +15 Who ate?\nA teacher ate. +16 A: Who cooked?\nB: David cooked. +17 David: Who ate?\nSarah: Sarah. +18 A: Who cooked?\nB: Pasta cooked. +19 David: Who cooked?\nSarah: David. +20 Who cooked?\nA sandwich. +21 David: Who ate?\nSarah: Pasta ate. +22 Who studies?\nScience studies. +23 Who studies?\nHistory studies. +24 David: Who studies?\nSarah: History studies. +25 A: Who studies?\nB: Science. +26 David: Who studies?\nSarah: Math studies. +27 Where were you reading?\nA book read. +28 A: Where were you writing?\nB: A book. +29 David: Where were you writing?\nSarah: A book wrote. +30 David: Where were you writing?\nSarah: In the park. +31 A: Where were you reading?\nB: An essay. +32 David: Where were you writing?\nSarah: At work. +33 A: Where were you reading?\nB: A letter read. +34 A: Where were you writing?\nB: A letter wrote. +35 David: Where were you reading?\nSarah: At work. +36 David: Where were you writing?\nSarah: An essay wrote. +37 Where did you eat?\nIn the cafeteria ate. +38 David: Where did you eat?\nSarah: Dinner. +39 David: Where did you cook?\nSarah: At home. +40 Where did you cook?\nA sandwich cooked. +41 Where did you cook?\nAt home cooked. +42 A: Where did you cook?\nB: A sandwich cooked. +43 A: Where did you cook?\nB: At home. +44 Where did you eat?\nIn the kitchen. +45 Where did you eat?\nLunch. +46 David: Where do you teach?\nSarah: Math teaches. +47 A: Where do you teach?\nB: History. +48 David: Where do you teach?\nSarah: History. +49 Where do you study?\nScience studies. +50 David: Where do you study?\nSarah: History. +51 David: Where do you study?\nSarah: Math studies. +52 A: Where do you teach?\nB: Science teaches. +53 David: When did you clean?\nSarah: I cleaned yesterday. +54 A: When did you clean?\nB: I cleaned the patio. +55 When were you writing?\nLast week wrote. +56 David: When were you reading?\nSarah: A letter. +57 When were you writing?\nThis morning wrote. +58 David: When were you reading?\nSarah: Yesterday read. +59 When were you reading?\nA book read. +60 When were you reading?\nA letter. +61 When were you writing?\nA letter wrote. +62 David: When were you reading?\nSarah: Several months ago. +63 David: When were you reading?\nSarah: Last week. +64 David: When were you writing?\nSarah: An hour ago wrote. +65 When were you writing?\nAn essay wrote. +66 When were you reading?\nAn hour ago. +67 A: When did you cook?\nB: Yesterday. +68 David: When did you eat?\nSarah: Several minutes ago. +69 When did you eat?\nDinner ate. +70 When did you eat?\nDinner. +71 When did you eat?\nLunch. +72 When did you eat?\nLunch ate. +73 A: When did you cook?\nB: An hour ago. +74 When did you cook?\nThis morning. +75 A: When did you eat?\nB: Dinner ate. +76 When do you teach?\nIn the afternoon. +77 When do you study?\nMath studies. +78 When do you teach?\nMath teaches. +79 When do you study?\nEvery tuesday. +80 David: When do you study?\nSarah: History. +81 A: When do you teach?\nB: History teaches. +82 David: When do you study?\nSarah: Math studies. +83 When do you study?\nScience. +84 David: When do you study?\nSarah: Science studies. +85 David: When do you teach?\nSarah: Science teaches. +86 David: Why did you clean?\nSarah: I cleaned the floor. +87 Why did you clean?\nI cleaned the patio. +88 David: Why did you clean?\nSarah: The rug. +89 David: Why were you reading?\nSarah: A book. +90 A: Why were you reading?\nB: A letter. +91 Why were you reading?\nBecause i wanted to read. +92 David: Why were you reading?\nSarah: Because i wanted to. +93 A: Why were you reading?\nB: A book read. +94 David: Why were you writing?\nSarah: A book wrote. +95 A: Why were you reading?\nB: A letter read. +96 David: Why were you writing?\nSarah: Just because. +97 David: Why did you cook?\nSarah: Dinner cooked. +98 David: Why did you eat?\nSarah: Because i had to ate. +99 A: Why did you eat?\nB: Just because ate. +100 David: Why did you eat?\nSarah: I was hungry ate. +101 David: Why did you cook?\nSarah: Just because. +102 Why did you eat?\nI skipped lunch. +103 A: Why did you cook?\nB: Lunch cooked. +104 David: Why did you cook?\nSarah: Dinner cooked. +105 Why did you cook?\nA sandwich cooked. +106 A: Why did you cook?\nB: A sandwich cooked. +107 Why did you eat?\nI was hungry. +108 A: Why do you study?\nB: Science studies. +109 Why do you teach?\nHistory teaches. +110 Why do you teach?\nScience. +111 A: Why do you study?\nB: Science. +112 Why do you teach?\nHistory teaches. +113 A: Why do you teach?\nB: History. +114 Why do you study?\nScience studies. +115 A: Why do you study?\nB: Science studies. +116 David: Why do you study?\nSarah: History studies. +117 David: Why do you study?\nSarah: History. +118 A: Why do you teach?\nB: Math teaches. +119 How many were you reading?\nA book. +120 A: How many were you writing?\nB: A letter. +121 David: How many were you writing?\nSarah: A couple wrote. +122 How many were you writing?\nA lot wrote. +123 How many were you reading?\nA couple read. +124 A: How many did you cook?\nB: Two cooked. +125 David: How many did you eat?\nSarah: Three ate. +126 How many did you eat?\nOne. +127 How many did you eat?\nA few. +128 How many did you eat?\nOne. +129 A: How many do you study?\nB: One studies. +130 How many do you teach?\nA lot. +131 David: How many do you study?\nSarah: Math. +132 A: How many do you study?\nB: History. +133 A: How many do you study?\nB: Science. +134 David: How many do you teach?\nSarah: Science teaches. +135 David: How many do you study?\nSarah: Math. +136 David: How many do you study?\nSarah: A few studies. +137 A: How many do you teach?\nB: History. +138 How many did you clean?\nA lot. +139 A: How many did you clean?\nB: The rug. +140 How many did you clean?\nTen. +141 A: How many did you clean?\nB: A lot. +142 A: How were you writing?\nB: A letter. +143 David: How were you reading?\nSarah: A book. +144 How were you writing?\nAn essay. +145 How were you writing?\nA letter wrote. +146 How were you writing?\nAn essay wrote. +147 How were you writing?\nAn essay wrote. +148 A: How were you reading?\nB: A letter. +149 A: How were you reading?\nB: A book read. +150 David: How did you eat?\nSarah: Quickly. +151 A: How did you cook?\nB: Carefully cooked. +152 How did you eat?\nLunch. +153 David: How did you cook?\nSarah: Quickly. +154 David: How did you cook?\nSarah: Lunch cooked. +155 How did you cook?\nWith tongs. +156 How do you study?\nHistory. +157 David: How do you study?\nSarah: History. +158 A: How do you teach?\nB: Carefully. +159 How do you teach?\nScience teaches. +160 How do you teach?\nHistory. +161 How do you teach?\nHistory. +162 David: How do you teach?\nSarah: Science. +163 A: How do you study?\nB: Math. +164 A: How did you clean?\nB: I cleaned the floor. diff --git a/zeroshot/quantifiers/eval_results.json b/zeroshot/quantifiers/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..23c244dac3340b3a58521e08451b67eac0624841 --- /dev/null +++ b/zeroshot/quantifiers/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.779495105615662} \ No newline at end of file diff --git a/zeroshot/quantifiers/predictions.txt b/zeroshot/quantifiers/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..8901593d94edc35d5bf454aa0290c66abecff479 --- /dev/null +++ b/zeroshot/quantifiers/predictions.txt @@ -0,0 +1,3883 @@ +index prediction +0 There was a documentary about music irritating Allison. +1 There were no legislatures working hard. +2 There was a sweater shocking David. +3 There are some brothers of Rachel arriving at a library. +4 There aren't many lights darkening. +5 There are many rugs embarrassing Galileo. +6 There is a mother of Theodore imagining those adults responded. +7 There wasn't a window chipping. +8 There is a committee coping. +9 There are some sisters of Ann climbing up all ladders. +10 There is an essay about Winston Churchill irritating Alexander. +11 There was a rug disappearing. +12 There aren't few documentaries disturbing some actors. +13 There are no peppers upsetting all governments. +14 There was a cat annoying Alice. +15 There is a coat soaking. +16 There isn't a student winning. +17 There were few bicycles shattering. +18 There aren't many malls existing. +19 There weren't many waitresses sitting down. +20 There were many sketches bothering Ruth. +21 There wasn't a commentary worrying some pedestrians. +22 There weren't few grandfathers of Beverly returning to the man. +23 There is an organization hurting Tammy. +24 There were few daughters of Nancy badgering Alicia to see Renee. +25 There is a boy spinning around. +26 There are no mushrooms alarming Winston Churchill. +27 There was a painting of that movie theater scaring many waitresses. +28 There were many lamps wasting away. +29 There weren't few shows about politics upsetting Andrew. +30 There isn't a casserole scaring Sharon. +31 There were many cousins of Rhonda boycotting most lakes. +32 There was a book scaring Kimberley. +33 There were few rivers disappearing. +34 There were few stories about politics astounding Alan. +35 There isn't a truck existing. +36 There isn't a cashier eating. +37 There are some cats hurting Tiffany. +38 There isn't a grandson of Becky cheering. +39 There isn't a daughter of Mitchell astounding some boy. +40 There aren't few guests asking all nieces of Sandra to attack Alicia. +41 There weren't few mushrooms scaring some governments. +42 There are many friends of Charles intending to disagree. +43 There were some associations interacting. +44 There is a book about the Borgias disturbing Ann. +45 There weren't many books about Harvard University impressing Daniel. +46 There wasn't a mountain existing. +47 There wasn't an icicle confusing Martin. +48 There weren't many essays about the Impressionists disagreeing with Michelle. +49 There is a muffin boring Winston Churchill. +50 There are some reports about Monet disagreeing with Janice. +51 There is a casserole stunning Carlos. +52 There was a box astounding that doctor. +53 There was a glove bothering Kristen. +54 There were few dresses soaking. +55 There were few cakes disgusting Bill. +56 There isn't a pepper bothering every guy. +57 There was a son of Monica motivating Kirsten's associate to cry. +58 There is a candle looking like a lot of drawings. +59 There were no movies about the Lutherans shocking Dana. +60 There were no movies about Harvard University alarming Omar. +61 There aren't many doors scaring Brian. +62 There weren't many gates looking like most photographs. +63 There isn't a museum failing to surrender. +64 There is an organization pushing a driver's daughters to have some windows. +65 There are few prints of a lot of paintings astounding Sabrina. +66 There is a man appearing to study. +67 There were few pants shocking Charles. +68 There was a documentary about Harvard University shocking Sheila. +69 There weren't many commentaries about Winston Churchill aggravating Eric. +70 There wasn't a restaurant existing. +71 There were few cars hurting Keith. +72 There wasn't a lady obliging Victoria to smile. +73 There were no actors attacking Jessica. +74 There are some hats stunning Amanda. +75 There are no customers hiding. +76 There was a shoe astounding Jacqueline. +77 There was a glacier shocking Ruth. +78 There weren't many pies alarming Theodore. +79 There was a couch embarrassing Julia. +80 There isn't a pedestrian arriving at some hills. +81 There weren't few drawings of Diana worrying Jerry. +82 There isn't a popsicle condensing. +83 There was a glass appearing. +84 There weren't many books existing. +85 There aren't many mirrors dropping. +86 There isn't a university learning who work hard. +87 There is a newspaper article existing. +88 There was a print of a rabbit upsetting Brad. +89 There is a child examining some play. +90 There is a nose stunning Martin. +91 There are many planes boring Diane. +92 There were no reports falling. +93 There aren't many windows boring Walter. +94 There isn't a cashier advising Monica's nephews to argue. +95 There were no senators arguing about the senator. +96 There were no organizations thinking about who shocks this waitress. +97 There were no socks irritating Aaron. +98 There was a school fighting. +99 There is a pepper falling. +100 There wasn't a cake baking. +101 There was a cup distracting this driver. +102 There are no hospitals falling. +103 There isn't a movie theater looking like some painting. +104 There was a car vanishing. +105 There was a box distracting Harvard University. +106 There was a window annoying Phillip. +107 There are some prints of Ella stunning Valerie. +108 There was a plate falling. +109 There aren't many movies distracting Kristen. +110 There were many candles worrying Stephanie. +111 There isn't a legislature listening to Sherry. +112 There is an adult departing. +113 There are many shoes irritating Brett. +114 There are some vases upsetting Janice. +115 There are some blouses loosening. +116 There is a bird breaking many plates. +117 There weren't few cars irritating schools. +118 There were many teenagers boycotting the Borgias. +119 There are some skateboards slipping. +120 There is a cousin of Lawrence concealing Kirsten. +121 There was a bike maneuvering. +122 There was a mountain boring Derek. +123 There aren't many glasses unfolding. +124 There is a man selling some newspaper article. +125 There are some mouths disturbing Stacy. +126 There was a plate confusing Valerie. +127 There was a documentary about politics scaring Lucille. +128 There were some pictures of a school disturbing Kayla. +129 There are no pedestrians responding. +130 There weren't many essays about music disagreeing. +131 There is an essay impressing Paul. +132 There isn't a bike shocking Nicole. +133 There is a cake scaring that man. +134 There were many glaciers disturbing that government. +135 There were no grandmothers of this teenager grinning. +136 There were some hills falling. +137 There are few sketches aggravating Wendy. +138 There is a nose worrying the organization. +139 There wasn't a movie about the Great Lakes aggravating Brett. +140 There are some actors imagining Larry turns out to ascend a lot of slopes. +141 There isn't a couch dropping. +142 There isn't a lake aggravating Carlos. +143 There isn't a public park falling. +144 There isn't a commentary confusing the guest. +145 There isn't a chair falling. +146 There are many stories about Monet boring Laurie. +147 There isn't a glove disgusting these governments. +148 There isn't a lake alarming Keith. +149 There wasn't a picture upsetting Laurie. +150 There were few girls debating. +151 There aren't few projectors confusing Jerry. +152 There were no pictures hurting a lot of guys. +153 There is a cat appearing. +154 There wasn't a school advising Kirsten's classmate to talk about the Lutherans. +155 There isn't a closet distracting Scott. +156 There is an apple confusing Victoria. +157 There were many computers dropping. +158 There wasn't a face annoying Cheryl. +159 There is a banana scaring Liam. +160 There weren't few bicycles slowing. +161 There were many glasses disturbing this hospital. +162 There are few vases vanishing. +163 There isn't a tomato baking. +164 There weren't few jackets disgusting this association. +165 There is a convertible hurting Candice. +166 There wasn't a blouse twisting. +167 There wasn't a ladder impressing that senator. +168 There aren't many plays about current events scaring Amelia. +169 There aren't many mothers of Sandra answering. +170 There was an essay disagreeing. +171 There isn't a lady watching Ellen. +172 There are few sketches of Jane looking like Nicole. +173 There were some snakes falling. +174 There isn't a sweater alarming Benjamin. +175 There wasn't a brother of Denise crying. +176 There are few documentaries disagreeing. +177 There are some glasses soaking. +178 There isn't a fork stunning Suzanne. +179 There aren't few skateboards boring Amelia. +180 There weren't many waiters murmuring. +181 There are few coats warping. +182 There is a lady learning what isn't worrying. +183 There are no children admiring every mirror. +184 There wasn't a teenager speaking up. +185 There aren't many teenagers shrugging. +186 There is a sweater twisting. +187 There isn't a window vanishing. +188 There is a door astounding Daniel. +189 There was a daughter of Samantha buying the popsicle. +190 There was a turtle leaving. +191 There wasn't a friend of Patricia sighing. +192 There is a boy discovering Julie to complain. +193 There weren't many apples scorching. +194 There were no shirts disturbing Theresa. +195 There was a fork disappearing. +196 There were no mirrors appearing. +197 There are no paintings scaring Grace. +198 There wasn't an adult falling. +199 There was a cucumber hurting every dancer. +200 There weren't few ladders existing. +201 There are some stairs confusing the students. +202 There wasn't a man bringing some prints. +203 There wasn't a high school existing. +204 There was a skateboard disappearing. +205 There were some cats scaring these waitresses. +206 There isn't a shoe alarming Kristen. +207 There aren't many stairs annoying Rose. +208 There aren't many children disgusting Samantha. +209 There are no vases tipping over. +210 There isn't a drawing boring Brett. +211 There is a mountain confusing David. +212 There is a college campus astounding John. +213 There is a closet appearing. +214 There isn't a shirt appearing. +215 There isn't a mouth aggravating Meredith. +216 There were many granddaughters of Laurie doubting that skirt flattens. +217 There is a plane upsetting Laura. +218 There is a coat shocking most banks. +219 There is a father of Jesus reading. +220 There isn't a grocery store disturbing Sara. +221 There aren't many legislatures 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There is a museum seeming to escape from Tracy. +918 There was a brother of Melissa boycotting this movie theater. +919 There was a bike disturbing Bill. +920 There were few lamps falling. +921 There was a convertible moving. +922 There was a light bothering that committee. +923 There weren't many trucks shocking all schools. +924 There is a jacket twisting. +925 There wasn't a museum turning out to shout. +926 There wasn't a pedestrian yelling. +927 There is a print of Kevin vanishing. +928 There weren't few stories about politics shocking Irene. +929 There was a documentary about Spain distracting Brett. +930 There aren't many windows dropping. +931 There isn't a bike impressing Renee. +932 There are no stories about the Balkans annoying Mark. +933 There are no nephews of Mary watching a lot of men. +934 There aren't many screens existing. +935 There isn't a school liking to confer. +936 There are few stories about the Great Lakes boring Grace. +937 There weren't many lights astounding Erin. +938 There was a cashier attempting to hide away. +939 There were some newspaper articles about literature looking like the print. +940 There is a bird finding the candles. +941 There was a mall aggravating Carmen. +942 There wasn't a nose looking like some print. +943 There weren't many wheelbarrows distracting many ladies. +944 There is a person annoying Phillip. +945 There are many paintings of Phillip disgusting Karen. +946 There wasn't a child dropping by some malls. +947 There isn't a bank vanishing. +948 There are no paintings embarrassing many ladies. +949 There isn't a slope hurting Pamela. +950 There wasn't a print confusing Laurie. +951 There is a commentary about Monet disagreeing. +952 There were some sweaters falling. +953 There were some blouses disappearing. +954 There was every fish astounding Sally. +955 There weren't all sisters of Roger going to this school. +956 There is every documentary annoying Anna. +957 Each government was there hurting Rhonda. +958 There wasn't every newspaper article wasting away. +959 There wasn't every student leaving Andrew. +960 There is each screen alarming Tara. +961 Most universities weren't there hurting this child. +962 Each book is there disturbing Margaret. +963 Each art gallery was there irritating a lot of committees. +964 All students weren't there noticing some box. +965 There isn't each mother of a teenager asking Mark to scratch. +966 There are most shows about the Great Lakes distracting Lisa. +967 Most nephews of Irene were there telephoning. +968 Each horse is there upsetting a lot of teachers. +969 There was every story hurting Judy. +970 There is every plate embarrassing Richard. +971 Most computers weren't there alarming Sabrina. +972 There wasn't every computer shocking that actress. +973 There aren't most reports disappearing. +974 Each university was there worrying. +975 There isn't every popsicle wasting away. +976 All granddaughters of Donald weren't there confusing this doctor. +977 Most cars weren't there scaring Kendra. +978 Every coat wasn't there twirling. +979 There are all bikes embarrassing Brian. +980 Each essay isn't there embarrassing Kenneth. +981 There are most organizations compelling Claire to stretch. +982 There wasn't every fork appearing. +983 There are all dancers talking to Pamela. +984 Most sisters of Veronica weren't there clashing. +985 There are all lights distracting Sarah. +986 There weren't all daughters of these teachers messing up pants. +987 There isn't every casserole looking like a lot of sketches. +988 There isn't each drawing of Cheryl existing. +989 There is each picture appearing. +990 There was every grocery store falling. +991 Most dishes are there wasting away. +992 There were all pants impressing Alexander. +993 There were most commentaries about the Impressionists bothering Beverly. +994 Each college campus isn't there alarming Irene. +995 There were all organizations convincing Winston Churchill to escape. +996 There are most wheelbarrows dropping. +997 There were most skirts aggravating waiters. +998 There aren't most cars slipping. +999 There are all slopes alarming Kathleen. +1000 There wasn't every hospital urging Wayne to find most cars. +1001 Most guests were there attacking Kimberley. +1002 There wasn't every picture of a lot of pants looking like those sketches. +1003 Most computers weren't there alarming Valerie. +1004 There is every restaurant impressing Patrick. +1005 There was every dish wasting away. +1006 There was each cucumber burning. +1007 Most movies aren't there worrying Peter. +1008 There is every commentary about the Impressionists disgusting an actor. +1009 Most peppers aren't there boring Ann. +1010 There aren't all cousins of William predicting Gerald to sigh. +1011 There weren't all shows about the Impressionists scaring Michael. +1012 All sweaters weren't there shocking Clyde. +1013 There was every slope vanishing. +1014 There are most computers falling. +1015 There are all public parks boring Jill. +1016 There are most mountains distracting Scott. +1017 There isn't every pepper scorching. +1018 Most boys are there divorcing. +1019 Each cart is there appearing. +1020 Each mushroom is there annoying Amelia. +1021 Each closet isn't there appearing. +1022 Each association isn't there intending to bother Tamara. +1023 Each casserole is there alarming Anne. +1024 There were all slopes boring Homer. +1025 There was every glacier disgusting Walter. +1026 All bananas weren't there burning. +1027 There was every guest visiting Ann. +1028 There aren't all windows alarming a committee. +1029 Every doctor wasn't there existing. +1030 There were all glasses scaring Clyde. +1031 All icicles aren't there disturbing Marie. +1032 Each cousin of Linda isn't there irritating Veronica. +1033 There wasn't every carriage bothering Pamela. +1034 There was every woman intending to remember Thomas. +1035 There wasn't every show about Winston Churchill astounding Jacqueline. +1036 There isn't every hamster distracting Danielle. +1037 Most libraries were there boring Sally. +1038 There aren't all women discovering Amelia to climb down those slopes. +1039 There were all icicles confusing this man. +1040 There are all prints of Derek disappearing. +1041 Most movie theaters aren't there distracting Karla. +1042 Most waiters were there worrying Carla. +1043 Each government was there looking to get a job. +1044 There aren't most guys escaping. +1045 There are all fish vanishing. +1046 There isn't each painting aggravating Barbara. +1047 Each shoe is there disturbing Erin. +1048 There are all stories about the Balkans aggravating all boys. +1049 Each legislature is there retaliating. +1050 Each lake was there boring Marla. +1051 There is each granddaughter of Stephen escaping from James. +1052 Each cake wasn't there vanishing. +1053 There wasn't every rabbit distracting Roger. +1054 All icicles are there freezing. +1055 There are all windows opening. +1056 There is every cafe astounding Gerald. +1057 Most waiters were there hugging George. +1058 Most lakes weren't there irritating Allison. +1059 Each son of Heather is there kissing Carla. +1060 There are all waiters fleeing from Eva. +1061 There was each light stunning Jason. +1062 There wasn't every light worrying Peter. +1063 Each hat isn't there twirling. +1064 There aren't all photographs of some screen looking like most cars. +1065 Each commentary about the Impressionists wasn't there impressing Julie. +1066 Most libraries are there continuing to conceal those waitresses. +1067 Each jacket is there existing. +1068 There are most mirrors hurting Alexander. +1069 There are most customers swallowing. +1070 Most glasses were there soaking. +1071 There isn't every book about music disturbing Roger. +1072 Most cups aren't there hurting Dennis. +1073 Each shirt is there hurting Guy. +1074 There are most windows alarming Frank. +1075 There is every cafe aggravating Donna. +1076 There was every boy explaining everything. +1077 Each lamp wasn't there hurting a girl. +1078 Each art gallery was there annoying Beth. +1079 There aren't most coats drying. +1080 There is every shirt tightening. +1081 Each report wasn't there confusing John. +1082 Each banana was there astounding Candice. +1083 There aren't most icicles melting. +1084 There isn't every person thinking about who will eat. +1085 There weren't most paintings of Sheila falling. +1086 Most patients were there threatening to testify. +1087 There wasn't each mouth aggravating Bill. +1088 Each pie isn't there upsetting Elaine. +1089 There are all photographs of Theresa astounding Spain. +1090 All organizations were there thinking about who wouldn't tour that closet. +1091 Each shirt wasn't there twisting. +1092 Each newspaper article isn't there shocking Carl. +1093 All reports aren't there disgusting Mitchell. +1094 There are all sisters of Denise approving. +1095 There weren't all movies winning. +1096 Each book was there scaring some student. +1097 There were all projectors aggravating Monet. +1098 Most governments were there conspiring. +1099 There isn't every art gallery impressing Jesus. +1100 There was every school profiting. +1101 There are all photographs stunning William. +1102 There isn't each grandfather of Julia considering Marla to praise Nicole. +1103 Each cart wasn't there slowing. +1104 There weren't all cups disappearing. +1105 There weren't all restaurants embarrassing every school. +1106 There is each grandmother of Sarah wanting to depart. +1107 There was each painting stunning Diana. +1108 There is every photograph of most closets appearing. +1109 Each dancer was there researching the painting. +1110 There weren't most banks finding out who disappear. +1111 Each guest was there grinning. +1112 There weren't all scarves warping. +1113 There is every girl cooking. +1114 There was every photograph of some candles scaring Benjamin. +1115 There isn't every boy looking like the photographs. +1116 Each truck was there embarrassing Martha. +1117 Each door is there embarrassing Samuel. +1118 All photographs weren't there boring Phillip. +1119 There weren't all movies about the Borgias winning. +1120 Most steps weren't there worrying George. +1121 There were most art galleries annoying Kristen. +1122 There weren't most banks continuing to bore Travis. +1123 There aren't all chairs irritating Sandra. +1124 There are most snakes disappearing. +1125 There was each nose existing. +1126 There were all hamsters finding every patient. +1127 All plays aren't there disgusting actors. +1128 All cats weren't there irritating Lawrence. +1129 Each cafe isn't there shocking Sandra. +1130 There were most legislatures needing a lot of doors to shut. +1131 There are most pies shocking Brett. +1132 Most wheelbarrows weren't there disturbing Brenda. +1133 There aren't most mouths confusing a lot of dancers. +1134 There isn't every guy yelling. +1135 Every committee was there describing many waitresses. +1136 There are all ladies noticing some senator. +1137 Most rugs were there looking like some drawing. +1138 There was every nose alarming Jennifer. +1139 There were all drawings of every box existing. +1140 There isn't every bike distracting women. +1141 There isn't every unicycle impressing Sabrina. +1142 Each mirror wasn't there wasting away. +1143 There wasn't every convertible boring Chad. +1144 All governments are there seeming to hug. +1145 There are most associations prompting Irene to perform. +1146 All organizations weren't there failing to help Melanie. +1147 There were all dresses shrinking. +1148 There are most lamps disgusting some legislature. +1149 Each grandmother of Frank was there hiding an actress. +1150 There are all carts tipping over. +1151 Each shawl was there impressing Stacey. +1152 There weren't most gates looking like most photographs. +1153 All planes were there tipping over. +1154 There are all teenagers exploring most glaciers. +1155 Most commentaries aren't there distracting some patients. +1156 Most scarves aren't there folding. +1157 There aren't most coats appearing. +1158 Each grandson of Liam is there learning. +1159 There are most shows about art scaring Samuel. +1160 All fathers of Scott are there hiding away. +1161 Most books aren't there stunning Elaine. +1162 There is each painting of Alexander disgusting Nancy. +1163 There weren't most ladies thinking about this hair. +1164 Most eyes weren't there hurting Timothy. +1165 There are all cars looking like paintings. +1166 There were most pants stretching. +1167 There isn't every play about the Great Lakes impressing Elaine. +1168 Most rugs were there hurting Andrew. +1169 Most slopes were there astounding Mary. +1170 Most pedestrians weren't there meeting. +1171 There wasn't every hamster falling. +1172 Most candles weren't there boring Bruce. +1173 There is each senator astounding Grace. +1174 Every drawing wasn't there astounding the government. +1175 Every hat was there upsetting a government. +1176 There wasn't every horse disappearing. +1177 Most ladders were there astounding Danielle. +1178 Most friends of Omar weren't there reacting. +1179 There is every glacier existing. +1180 All doctors are there lifting every college campus. +1181 Each museum was there selling these computers. +1182 Each museum wasn't there enticing some synopsis to disgust this doctor. +1183 There were all fathers of Karla worrying. +1184 There wasn't every public park embarrassing Veronica. +1185 There weren't all cousins of Kathleen going to Spain. +1186 There are all teenagers reacting. +1187 There was every fish upsetting Jennifer. +1188 There was every lamp wasting away. +1189 There was each patient speaking up. +1190 Each restaurant isn't there alarming Chad. +1191 Each school is there discovering every lamp to brighten. +1192 All children were there begging a friend of Rose to kiss Janet. +1193 There isn't every cup bothering Gerald. +1194 There wasn't every lake confusing Holly. +1195 There wasn't every plate hurting Veronica. +1196 There is every bird finding some water. +1197 Most girls are there figuring out who could tour the cafes. +1198 Most actors are there wearing glasses. +1199 There were most peppers going bad. +1200 Each photograph was there stunning Amelia. +1201 All waiters weren't there alarming Nina. +1202 Most men are there boring guys. +1203 Every committee was there succeeding. +1204 There is each book annoying Tanya. +1205 There aren't all icicles disappearing. +1206 There aren't all windows distracting Kristin. +1207 There weren't most trucks accelerating. +1208 There were all grandsons of Aaron disappearing. +1209 Most senators weren't there fighting. +1210 There was every book about Harvard University confusing that cashier. +1211 All teenagers weren't there considering Julia to sing. +1212 Each cake isn't there distracting every teenager. +1213 There isn't each library hurting Michael. +1214 There wasn't every grandfather of Deborah encouraging Katherine to drink. +1215 There are all reports scaring Roger. +1216 Each college campus is there appearing. +1217 There are all shows about the Impressionists upsetting Thomas. +1218 There isn't every popsicle worrying every man. +1219 There isn't each play about the Lutherans bothering Deanna. +1220 Each casserole was there existing. +1221 There wasn't each mirror confusing that dancer. +1222 There were all eyes impressing girls. +1223 Most pants weren't there twisting. +1224 There is every shawl flattening. +1225 Most essays aren't there upsetting every company. +1226 There are all grocery stores disgusting Melanie. +1227 There were most windows worrying the Impressionists. +1228 There are all cousins of Noah profiting. +1229 There are most pictures vanishing. +1230 There aren't all waiters yawning. +1231 Every story about Harvard University wasn't there astounding those patients. +1232 Most tomatoes were there going bad. +1233 There was every unicycle turning. +1234 There aren't all lights looking like pictures. +1235 All cucumbers weren't there disgusting Wendy. +1236 There was every drawing vanishing. +1237 There was each window appearing. +1238 Each guest is there managing to clean the chair. +1239 There is every actor learning what ripens. +1240 Most pictures of most books were there hurting Kirsten. +1241 There is every bike existing. +1242 All libraries were there failing to help Gary. +1243 There is each light wasting away. +1244 There are all skateboards rolling. +1245 There were all shirts scaring the child. +1246 There are most men continuing to react. +1247 Each driver is there studying. +1248 Each photograph wasn't there wasting away. +1249 There are all doctors spinning around. +1250 Every woman was there failing to chat. +1251 There is every dancer stretching. +1252 Each print of the patient isn't there shocking Raymond. +1253 There aren't all movie theaters impressing libraries. +1254 Most teenagers weren't there arguing. +1255 There wasn't every slope wasting away. +1256 There are all waitresses donating. +1257 There isn't each banana growing. +1258 There was every hill annoying Judy. +1259 Each print of Amanda is there shocking some ladies. +1260 There isn't every hat confusing Renee. +1261 There weren't most stairs appearing. +1262 There are all peppers existing. +1263 Most patients are there saluting. +1264 There wasn't each window distracting Stephanie. +1265 All sisters of a boy aren't there corresponding. +1266 There isn't every senator anticipating Sonia to bake. +1267 Most friends of Vincent are there failing to blink. +1268 All grandmothers of Christina aren't there imploring Christina to talk. +1269 There are all reports about the Borgias aggravating some organizations. +1270 There was every chair astounding Jill. +1271 All slopes weren't there distracting Sonia. +1272 There wasn't each play about the Lutherans disagreeing. +1273 There were most essays confusing that cashier. +1274 There were all organizations wanting to admire Kathleen. +1275 There were most movies about music aggravating April. +1276 There was every book about Galileo wasting away. +1277 There wasn't every play disagreeing. +1278 Each door is there boring Stacy. +1279 All glaciers aren't there worrying that company. +1280 Every coat wasn't there alarming Kayla. +1281 All pictures of the drawing are there distracting all customers. +1282 There were all snakes coming here. +1283 There wasn't every skateboard disappearing. +1284 There weren't most doors astounding Marie. +1285 Each customer is there continuing to play. +1286 There isn't every grandfather of Jeffrey hurting Ruth. +1287 There isn't every restaurant bothering Gregory. +1288 There was every car appearing. +1289 Each grocery store wasn't there confusing Douglas. +1290 Each snake is there disturbing Carolyn. +1291 There are most essays about science disagreeing with Janet. +1292 Each friend of Angela wasn't there cleaning the malls. +1293 Each guest is there exercising. +1294 There were all waiters studying. +1295 Each waitress was there anticipating Keith to insult most guys. +1296 Most skateboards weren't there astounding Mary. +1297 Most organizations were there selling these blouses. +1298 All universities weren't there scaring Meredith. +1299 There weren't most universities persuading Kristin to stand up. +1300 Each horse is there shocking Kayla. +1301 There is each muffin baking. +1302 There were all schools praising Peter. +1303 There were all couches disgusting Scott. +1304 Each truck wasn't there looking like every drawing. +1305 There isn't every adult kissing Andrea. +1306 There is every couch distracting schools. +1307 Each vase wasn't there confusing Beth. +1308 There is each chair falling. +1309 Most teachers are there ascending that hill. +1310 There are most granddaughters of this lady conferring. +1311 Most cats were there scaring Tamara. +1312 There was every child preventing this car from disappearing. +1313 There was each drawing astounding Larry. +1314 Each turtle is there embarrassing Sharon. +1315 There wasn't every university meeting. +1316 There wasn't every drawing aggravating Pamela. +1317 All bananas are there shocking Rose. +1318 Each sweater was there drying. +1319 There aren't all lakes impressing Lawrence. +1320 There isn't every waiter going to some cafe. +1321 There aren't all cousins of Brian sighing. +1322 Most closets were there alarming Stephanie. +1323 There were all glasses distracting Kristen. +1324 Every actress wasn't there thinking Carolyn hides every student. +1325 There were all waiters existing. +1326 Each play wasn't there disturbing Meredith. +1327 There are all windows closing. +1328 There aren't most tomatoes impressing customers. +1329 There are all lakes alarming Irene. +1330 Most essays weren't there impressing Dan. +1331 There isn't each candle falling. +1332 There were all skateboards rusting. +1333 Most ladders were there distracting Jesus. +1334 There was every fork disturbing Phillip. +1335 Every show about Jesus wasn't there shocking Eric. +1336 Each play wasn't there distracting Gary. +1337 There wasn't every blouse looking like most drawings. +1338 Most closets aren't there shocking most pedestrians. +1339 There weren't most doors existing. +1340 There aren't most rugs distracting Wendy. +1341 There aren't all mothers of Monica grinning. +1342 There aren't all carts falling apart. +1343 Each mushroom wasn't there embarrassing the dancers. +1344 Each government was there discovering that legislature to approve. +1345 All legislatures aren't there enticing those fathers of the Borgias to depart. +1346 There wasn't every popsicle evaporating. +1347 There are all documentaries about the Great Lakes scaring Allison. +1348 Every muffin wasn't there hurting drivers. +1349 Each essay is there aggravating Jodi. +1350 There are most books vanishing. +1351 There is every play about Winston Churchill stunning this boy. +1352 All mushrooms weren't there boring a lot of guys. +1353 There aren't most screens confusing a lot of children. +1354 Each photograph was there stunning Stephen. +1355 All pictures weren't there embarrassing the Borgias. +1356 Each shoe isn't there vanishing. +1357 There isn't every library failing to break every vase. +1358 Each car is there accelerating. +1359 There was every sketch falling. +1360 Each horse wasn't there irritating every student. +1361 There aren't all jackets alarming these waitresses. +1362 There isn't each lady helping Danielle. +1363 There weren't all photographs of many glasses disturbing Craig. +1364 There were all students sighing. +1365 There isn't every cafe falling. +1366 Each teenager was there eating. +1367 Every art gallery wasn't there alarming these patients. +1368 Each banana isn't there astounding these women. +1369 There was each adult grinning. +1370 There aren't all shows about Monet shocking Monica. +1371 There were all movie theaters distracting Becky. +1372 All bicycles were there stopping. +1373 All forks aren't there falling. +1374 There are all commentaries annoying Guy. +1375 There weren't all museums changing. +1376 There was every movie theater existing. +1377 Most boys weren't there cheering. +1378 There aren't all fathers of Tiffany cooking. +1379 All public parks are there disgusting Beverly. +1380 There were most pants loosening. +1381 All granddaughters of Christine were there forgetting who bother those ladies. +1382 There isn't every screen appearing. +1383 All wheelbarrows aren't there disturbing Frank. +1384 There is every jacket annoying Nancy. +1385 There are most shawls worrying Lisa. +1386 There wasn't every jacket upsetting Angela. +1387 Most granddaughters of Adam aren't there urging Dawn to hurt those waitresses. +1388 Most patients are there motivating Jerry's cousins to kiss Kimberley. +1389 There is every cafe aggravating those companies. +1390 All hospitals were there pushing Naomi to cough. +1391 There aren't all prints of a lot of boys embarrassing this woman. +1392 There isn't each play about the Impressionists bothering Ella. +1393 All casseroles weren't there stunning that man. +1394 There are all hills existing. +1395 There are all stories upsetting the Lutherans. +1396 There was every print irritating Tammy. +1397 Each picture is there looking like Marie. +1398 There weren't most plays impressing Bethany. +1399 Most newspaper articles weren't there stunning Allison. +1400 There are all reports about Jesus disturbing every driver. +1401 All turtles weren't there upsetting Anna. +1402 There are all dishes alarming Suzanne. +1403 All public parks aren't there upsetting Ruth. +1404 Every projector was there appearing. +1405 There aren't most rivers freezing. +1406 All sisters of Kendra aren't there scaring Melinda. +1407 There is every unicycle astounding Spain. +1408 Each actress wasn't there arguing about Jerry. +1409 Most slopes aren't there shocking Phillip. +1410 All guests weren't there worrying those organizations. +1411 There isn't each mirror distracting Diana. +1412 There are most photographs stunning Eva. +1413 Each scarf was there impressing Florence. +1414 There were all cars upsetting Beth. +1415 There weren't all pictures of Candice boring Benjamin. +1416 Each cake wasn't there stunning Joseph. +1417 All malls were there looking like most sketches. +1418 There is every sketch boring Brett. +1419 There are most books disgusting Wayne. +1420 There weren't all women needing those people to love Ronald. +1421 Each child was there begging the guy's lawyer to study. +1422 There aren't most teachers driving to most college campuses. +1423 There weren't most cars embarrassing a lot of guests. +1424 There isn't every art gallery confusing Alicia. +1425 There were most planes falling. +1426 Each college campus isn't there worrying Jennifer. +1427 Each eggplant isn't there disappearing. +1428 Every print of Cynthia wasn't there disturbing Spain. +1429 There are most stories annoying Michael. +1430 There was every public park confusing Denise. +1431 There is every screen alarming all governments. +1432 Each art gallery is there annoying Christine. +1433 There was every face aggravating many drivers. +1434 There weren't most mountains appearing. +1435 Each girl wasn't there failing to boycott some art gallery. +1436 There are most art galleries confusing Stephen. +1437 There was every bank benefiting. +1438 There is every hat annoying Jason. +1439 There were all newspaper articles about Spain hurting Janice. +1440 There wasn't every legislature proving to approach Valerie. +1441 There is each dog ascending these mountains. +1442 There are all documentaries stunning the Impressionists. +1443 Each plate wasn't there looking like the print. +1444 There isn't each picture embarrassing Kristen. +1445 Every movie wasn't there worrying these girls. +1446 All patients aren't there climbing down some steps. +1447 Each person is there writing. +1448 There was each closet appearing. +1449 There are most children leaving Richard. +1450 There aren't all pictures impressing Mark. +1451 There isn't every newspaper article impressing all hospitals. +1452 Each art gallery wasn't there disgusting Cynthia. +1453 All art galleries aren't there alarming Martin. +1454 There isn't each teacher boycotting that movie theater. +1455 There were all mouths closing. +1456 There was every casserole falling. +1457 Most tomatoes were there worrying Brian. +1458 Each legislature isn't there begging Rose to depart. +1459 There isn't each banana existing. +1460 There was each vase aggravating Carol. +1461 There weren't all cucumbers bothering some men. +1462 There are most scarves wasting away. +1463 Each picture wasn't there annoying most teachers. +1464 Each slope isn't there vanishing. +1465 Most hats are there stunning Cheryl. +1466 All chairs aren't there distracting all legislatures. +1467 There weren't all boxes annoying the pedestrian. +1468 Most birds aren't there bothering the girls. +1469 There is each documentary about music astounding Stacey. +1470 All legislatures are there asking Galileo to talk to Becky. +1471 There isn't each waiter convincing Linda to suffer. +1472 There are all glasses dropping. +1473 Each glove wasn't there stretching. +1474 Each cake was there annoying Steve. +1475 There were all eyes confusing Bethany. +1476 There wasn't every cup irritating Dana. +1477 There wasn't each screen falling apart. +1478 There weren't all reports about Galileo disturbing Tina. +1479 All drivers weren't there compelling some governments to disgust the Impressionists. +1480 There aren't all peppers irritating an adult. +1481 There is every photograph of Wayne aggravating Alice. +1482 Each popsicle wasn't there shocking Tammy. +1483 There are all screens disturbing Melanie. +1484 Every actor wasn't there arriving at every grocery store. +1485 All slopes were there boring all guests. +1486 There were all cats astounding Laurie. +1487 Each projector was there upsetting Connie. +1488 Each report about Monet wasn't there existing. +1489 There were all mouths impressing Rose. +1490 There weren't all cucumbers going bad. +1491 There wasn't every cake wasting away. +1492 There weren't most photographs of that hair disgusting a lot of libraries. +1493 There are most stairs boring Alexander. +1494 Each computer was there disgusting Tara. +1495 There isn't each patient ordering Christine's tutors to resemble most pictures. +1496 There isn't every shawl disappearing. +1497 There is every association hurting that woman. +1498 Most grandmothers of Caroline were there upsetting Julie. +1499 There is every rabbit escaping. +1500 There were all plays disturbing Ruth. +1501 Each nose was there hurting some waitresses. +1502 There isn't every friend of Kenneth breaking every mirror. +1503 There are most turtles falling asleep. +1504 There wasn't each drawing existing. +1505 Most jackets aren't there twirling. +1506 Every commentary is there alarming many patients. +1507 All actors were there forgetting about who drinks. +1508 There weren't all books confusing Omar. +1509 Every book is there scaring Kimberley. +1510 There are most essays aggravating Alan. +1511 All ladders weren't there scaring those libraries. +1512 There aren't all eyes falling. +1513 Most coats aren't there boring Daniel. +1514 All chairs were there stunning most drivers. +1515 There are all pictures of Carla irritating Barbara. +1516 There are most muffins disappearing. +1517 There was each face appearing. +1518 There were most documentaries about Galileo irritating every teenager. +1519 Most women were there annoying Helen. +1520 There aren't all girls regretting Julie falls. +1521 There are all reports looking like sketches. +1522 There was every print of every girl stunning a lot of ladies. +1523 All universities were there listening to Travis. +1524 Most mothers of Samuel aren't there learning. +1525 There was each commentary about art annoying Rhonda. +1526 There was each girl getting a job. +1527 There is every eggplant disappearing. +1528 Each newspaper article is there annoying a man. +1529 There is each granddaughter of Kirsten commissioning Elaine to dislike Robert. +1530 There isn't each boy failing to irritate Robert. +1531 There isn't each cousin of Dennis murmuring. +1532 Every unicycle is there alarming this dancer. +1533 All photographs of those patients aren't there shocking Mary. +1534 Every skateboard isn't there disturbing those teenagers. +1535 There were all windows wasting away. +1536 There is each woman expecting Douglas to waste away. +1537 There aren't most books about Spain annoying Diana. +1538 There aren't most ladies thinking about some couch. +1539 There aren't most plays boring Martha. +1540 There isn't every brother of the Borgias walking through the hospitals. +1541 There isn't each documentary hurting Gina. +1542 There aren't most malls disappearing. +1543 Most carriages weren't there impressing Patricia. +1544 There is every river upsetting many doctors. +1545 Most restaurants were there upsetting Theresa. +1546 Each drawing isn't there hurting Kimberley. +1547 There is each grandmother of Eva wanting James to win. +1548 Each skateboard isn't there vanishing. +1549 There is every closet bothering Spain. +1550 There was every boy eating. +1551 All cashiers weren't there lifting most socks. +1552 Each drawing of these forks wasn't there embarrassing Marie. +1553 Each show about current events isn't there alarming Karen. +1554 There aren't most friends of Grace discovering who bother Deborah. +1555 There were all paintings scaring Jennifer. +1556 There are all documentaries about Spain confusing Dennis. +1557 All hospitals were there motivating every university to like Suzanne. +1558 There are all paintings of the waiters distracting Todd. +1559 Each spotlight is there stunning the person. +1560 There are most high schools looking like these drawings. +1561 There was every play annoying Spain. +1562 All legislatures weren't there remembering what conspired. +1563 There is every actor seeing. +1564 There aren't all lights stunning James. +1565 Most screens weren't there stunning every waiter. +1566 There wasn't every customer helping April. +1567 There are most lamps looking like some paintings. +1568 All teenagers were there describing the Impressionists. +1569 There were most candles distracting Dana. +1570 Each closet is there existing. +1571 Each painting is there looking like the lake. +1572 There wasn't every book about Galileo scaring Connie. +1573 There isn't every screen wasting away. +1574 Each fork wasn't there appearing. +1575 Each car isn't there stunning Patricia. +1576 There aren't all essays about Spain disgusting Alice. +1577 There are all coats falling. +1578 All mothers of Elaine weren't there hugging. +1579 There aren't most lamps looking like a painting. +1580 Each skirt wasn't there warping. +1581 There weren't all cakes appearing. +1582 Each boy was there drinking. +1583 There aren't all paintings of Donald disgusting Alexander. +1584 All mirrors weren't there slipping. +1585 Each car wasn't there worrying some guy. +1586 There wasn't every teacher urging Emily's boyfriend to speak up. +1587 All sisters of Stacey weren't there liking to mess up a restaurant. +1588 There isn't every company prompting Michelle to admire Gerald. +1589 Most tomatoes aren't there upsetting Victoria. +1590 There are most apples scorching. +1591 Most stairs were there disgusting Lisa. +1592 There wasn't every boy wanting to talk to all adults. +1593 There was each newspaper article aggravating Daniel. +1594 There are most patients confusing a lot of waitresses. +1595 There wasn't every plate irritating Samantha. +1596 There aren't all plates appearing. +1597 There was each show about the Borgias irritating Chad. +1598 There weren't all college campuses bothering Tanya. +1599 There are all books about art astounding a library. +1600 There was every horse irritating Lawrence. +1601 There are all movies irritating Alicia. +1602 Each waiter is there investigating who could convince Jessica's cousins to sneeze. +1603 Most socks were there flattening. +1604 There are all slopes distracting David. +1605 Every cup is there dropping. +1606 Each college campus wasn't there boring Rose. +1607 There isn't every sister of every person saying every waiter waved. +1608 There are most commentaries about politics shocking Amanda. +1609 Most slopes were there astounding Alicia. +1610 Every unicycle isn't there upsetting this school. +1611 Most actors weren't there climbing up every mountain. +1612 Every cousin of Cheryl wasn't there passing that mall. +1613 There were all drawings distracting Brett. +1614 There is every candle boring Brett. +1615 Each art gallery is there distracting Karla. +1616 Every school is there believing a lot of guests to hug Roger. +1617 Every apple isn't there bothering every association. +1618 There is each mirror chipping. +1619 There isn't each carriage bothering Tammy. +1620 There were all hospitals confusing Joel. +1621 There isn't every cucumber irritating Edward. +1622 There were most people encouraging Allison to kiss most guests. +1623 There is every school lying. +1624 There were all actors criticizing Theresa. +1625 There were all commentaries disagreeing. +1626 All universities were there hiring the Borgias. +1627 Each slope wasn't there impressing Matt. +1628 There isn't every face stunning an actress. +1629 There is each spotlight aggravating Theodore. +1630 There aren't all icicles aggravating Tammy. +1631 Each teenager was there washing. +1632 There wasn't every movie stunning the lady. +1633 Every photograph isn't there alarming Christopher. +1634 Each gate wasn't there worrying John. +1635 Each popsicle is there falling. +1636 Each friend of Ruth was there exiting the art galleries. +1637 There aren't most peppers going bad. +1638 There are most candles disappearing. +1639 Most plates aren't there stunning Monet. +1640 There is every customer laughing. +1641 There was every lamp fading. +1642 There weren't all reports about Galileo hurting Catherine. +1643 There weren't all rivers freezing. +1644 Most documentaries weren't there stunning most doctors. +1645 Most bikes weren't there annoying Susan. +1646 There were all coats bothering Melinda. +1647 There was every show about current events embarrassing Nicole. +1648 There aren't all cars impressing David. +1649 There isn't every bicycle crashing. +1650 There was each senator finding out who haven't succeeded. +1651 Each sketch was there shocking this association. +1652 Most turtles weren't there disappearing. +1653 There aren't all cucumbers hurting that patient. +1654 Every eye was there boring most pedestrians. +1655 There aren't all essays bothering Natalie. +1656 There is every high school upsetting Gregory. +1657 There was each pie distracting Linda. +1658 Most slopes are there embarrassing Cynthia. +1659 Most coats were there bothering Rebecca. +1660 All cats are there stunning Gina. +1661 There was each window falling. +1662 Each horse wasn't there aggravating Amanda. +1663 Every projector was there impressing Irene. +1664 There weren't all doors aggravating Kendra. +1665 Each bike wasn't there confusing a lot of senators. +1666 There were most movies about science hurting Sarah. +1667 There wasn't every picture of Homer impressing Elizabeth. +1668 All glasses were there alarming Kristen. +1669 There isn't every father of Lucille refusing to lift every spotlight. +1670 There is every teenager yelling. +1671 There was every movie about the Great Lakes boring Gina. +1672 There wasn't each blouse shrinking. +1673 There aren't all windows vanishing. +1674 Each cashier is there preventing some screen from shocking Monica. +1675 There is every convertible bothering Carrie. +1676 Most cousins of those doctors are there existing. +1677 All pies weren't there vanishing. +1678 There is each actress hiding away. +1679 There are all senators approaching all cashiers. +1680 There is each high school wasting away. +1681 Each wheelbarrow was there rusting. +1682 There was every grocery store wasting away. +1683 There are all pies aggravating Renee. +1684 Each sketch wasn't there existing. +1685 Each shirt is there annoying the teacher. +1686 All drawings of all carts weren't there stunning Scott. +1687 There were all carts bothering Amanda. +1688 Each guest is there observing Douglas. +1689 There is every photograph of Erin confusing Michelle. +1690 There is each dog disappearing. +1691 There isn't every cat falling. +1692 Most waitresses are there investigating who is arguing about Stephen. +1693 There aren't all cats disappearing. +1694 Most boys aren't there spurring Meredith to paint. +1695 There was every cake appearing. +1696 There aren't all skirts boring Andrew. +1697 Each fork was there disappearing. +1698 There aren't all slopes distracting Nancy. +1699 There is each newspaper article about travel scaring Samantha. +1700 There were all cars astounding Maria. +1701 All hospitals weren't there badgering Adam to compromise. +1702 There is every pepper wasting away. +1703 There were most books irritating all girls. +1704 There isn't every shawl existing. +1705 There is every window upsetting Galileo. +1706 Each cup wasn't there shattering. +1707 There are most slopes impressing Richard. +1708 There was every grocery store worrying Jodi. +1709 There is every shoe appearing. +1710 Each couch is there scaring some pedestrians. +1711 There are all girls collaborating. +1712 There isn't every book disturbing Brenda. +1713 All hills weren't there looking like every photograph. +1714 There aren't most peppers annoying Gina. +1715 Most cakes aren't there disturbing Melinda. +1716 There wasn't each glacier embarrassing Ella. +1717 There are most museums referencing the actor. +1718 Each pepper isn't there growing. +1719 There was every documentary aggravating Kendra. +1720 There wasn't every woman managing to alarm Denise. +1721 There was each guy happening to unfold. +1722 Most mushrooms aren't there alarming Nancy. +1723 There is each report about the Borgias annoying some companies. +1724 There are all horses upsetting Becky. +1725 Most books weren't there annoying some boy. +1726 There aren't all photographs alarming Ellen. +1727 There are all casseroles baking. +1728 Every actress was there compelling George's senators to conceal this cup. +1729 Most guys were there fixing a lot of windows. +1730 Each woman isn't there discussing many libraries. +1731 There aren't all waiters hiding. +1732 Each book wasn't there disagreeing with Cheryl. +1733 Each brother of Aaron wasn't there begging some sister of Sheila to ascend these slopes. +1734 There weren't all cups aggravating Larry. +1735 All computers were there worrying Dana. +1736 Most forks aren't there looking like the painting. +1737 There is every gate hurting Eric. +1738 There are all museums arguing. +1739 There was every glacier confusing Martin. +1740 There aren't most glasses twisting. +1741 There wasn't each library succeeding. +1742 There wasn't every cashier impressing Walter. +1743 Most reports weren't there shocking Christina. +1744 There was every report winning. +1745 Each movie theater is there vanishing. +1746 Most apples were there stunning Pamela. +1747 There was every coat bothering Marie. +1748 All restaurants were there alarming that adult. +1749 There was each cafe shocking Jodi. +1750 There weren't most grandfathers of this child upsetting Helen. +1751 Every sister of Lawrence was there considering Christopher to go to every hill. +1752 Each show about Spain wasn't there embarrassing Kevin. +1753 There isn't every legislature alarming the Borgias. +1754 Each college campus wasn't there appearing. +1755 There wasn't every guy learning who hadn't toured every public park. +1756 Every projector was there stunning that customer. +1757 Each banana is there stunning some waiter. +1758 There were most glaciers astounding Dennis. +1759 Each coat isn't there worrying Sonia. +1760 There was every picture of Heidi distracting Catherine. +1761 Most dishes were there worrying Carmen. +1762 There are most vases distracting Patricia. +1763 Every essay about Winston Churchill wasn't there annoying Larry. +1764 There are all banks continuing to approve. +1765 There aren't most grandfathers of Jeffrey departing. +1766 Each niece of Linda wasn't there boasting about Mary. +1767 Each dancer was there muttering. +1768 There aren't all screens alarming Ruth. +1769 All shoes aren't there looking like this painting. +1770 There was every unicycle falling. +1771 Every public park is there astounding Michael. +1772 Each father of Sharon wasn't there doubting Andrew can cough. +1773 There were most nephews of Carol preventing Linda from wasting away. +1774 There wasn't every story about the Impressionists disagreeing with Bruce. +1775 All jackets aren't there disturbing some organizations. +1776 All pedestrians weren't there forgetting about what is replying. +1777 There was each adult boasting about Caroline. +1778 There wasn't every closet shutting. +1779 Each painting isn't there disturbing Kendra. +1780 There was every hat shocking Charles. +1781 Every guest is there looking like this sketch. +1782 There are most lights looking like photographs. +1783 Most hamsters weren't there falling. +1784 Most cups aren't there upsetting Phillip. +1785 There aren't all books about the Balkans astounding Angela. +1786 There is each movie about the Impressionists bothering Cheryl. +1787 There were all carts tipping over. +1788 There were all plays disturbing some senators. +1789 There wasn't every school joking around. +1790 There are all skirts warping. +1791 There is every mall embarrassing Nancy. +1792 There was every child sitting down. +1793 Most cats weren't there distracting Kendra. +1794 There wasn't every painting of Stephen stunning that guy. +1795 There were all mountains disappearing. +1796 Most brothers of Alexander weren't there swallowing. +1797 There isn't each teacher talking to Galileo. +1798 Most hospitals are there revealing who should hire those customers. +1799 Most shoes weren't there disappearing. +1800 There isn't every gate falling. +1801 Each shirt was there boring Linda. +1802 There were most grandfathers of every woman imagining Candice doesn't require Melissa to think about a lot of glasses. +1803 There weren't all grandsons of the patient wishing to salute. +1804 There aren't all newspaper articles impressing Veronica. +1805 There was each screen impressing Patricia. +1806 There wasn't every play embarrassing Robert. +1807 Most dresses weren't there confusing some waitress. +1808 All legislatures are there pressing Ann to mess up that library. +1809 There were all rabbits falling. +1810 There are all public parks alarming Jane. +1811 Each eye wasn't there shutting. +1812 Every niece of Beth is there looking like some drawings. +1813 There are all cafes appearing. +1814 Each popsicle is there hurting Roger. +1815 Each popsicle isn't there evaporating. +1816 There isn't every mall falling. +1817 There are all tomatoes appearing. +1818 There are most pictures falling. +1819 There wasn't every hospital protesting. +1820 There wasn't every movie theater impressing a man. +1821 Each truck was there boring Dana. +1822 There were all jackets looking like that drawing. +1823 There was every essay about Winston Churchill confusing Stephen. +1824 There weren't all cups scaring Robert. +1825 There weren't all closets embarrassing Leslie. +1826 There isn't each pie confusing Bruce. +1827 Most drivers were there lying. +1828 There wasn't every father of Anne thinking about who discovers who is scratching. +1829 There weren't all scarves folding. +1830 There weren't all books about science confusing Brenda. +1831 There is each play about Spain aggravating the boys. +1832 There was every mountain confusing Tamara. +1833 There weren't all commentaries about music alarming Joseph. +1834 Every rabbit was there distracting Kathleen. +1835 Each cucumber wasn't there confusing Monica. +1836 Each banana isn't there scaring Lucille. +1837 There were all people enticing Bruce to train. +1838 There were most children boring Alicia. +1839 Each muffin isn't there disgusting Jesus. +1840 There aren't all eyes astounding some government. +1841 Most birds weren't there shocking the association. +1842 There was every documentary distracting Leslie. +1843 Most skateboards weren't there scaring Naomi. +1844 There aren't most granddaughters of Keith remembering who sways Tara's ex-wives to win. +1845 Each dog is there irritating Patricia. +1846 Most malls weren't there disturbing the pedestrian. +1847 All cats aren't there shocking Sarah. +1848 There are most high schools upsetting Dawn. +1849 There wasn't every turtle looking like that painting. +1850 There aren't all dancers studying. +1851 Each adult is there entreating this granddaughter of this senator to change. +1852 There were all boys encouraging Catherine to like those peppers. +1853 Most restaurants are there shocking Phillip. +1854 There is every pedestrian hiring Sarah. +1855 Each bicycle isn't there disgusting Jerry. +1856 Most organizations aren't there managing to conspire. +1857 There was each face boring Natalie. +1858 Each river wasn't there alarming Danielle. +1859 There is every painting of Stephanie disappearing. +1860 Most doctors are there kissing Amelia. +1861 Each school isn't there seeming to complain. +1862 All mountains weren't there upsetting a company. +1863 There was every shirt disgusting this teenager. +1864 There are all gates falling. +1865 There was every print stunning Danielle. +1866 Each horse wasn't there looking like this sketch. +1867 Each book is there worrying a hospital. +1868 Most guys are there learning who exists. +1869 There weren't all nephews of these ladies bothering Dawn. +1870 There was each cat annoying Frank. +1871 There weren't all dresses unfolding. +1872 There wasn't every newspaper article disappearing. +1873 There was every bird coming here. +1874 Most wheelbarrows weren't there slowing. +1875 Each art gallery was there appearing. +1876 Most chairs were there stunning some school. +1877 There were most reports about politics disgusting Victoria. +1878 All tomatoes are there annoying Christine. +1879 All teachers were there stretching. +1880 Each actor isn't there arguing about Marla. +1881 There aren't all trucks turning. +1882 Each box is there falling. +1883 There isn't every fish disgusting some teenagers. +1884 Most grandsons of Candice aren't there yelling. +1885 There are all pedestrians winning. +1886 There was every report about music alarming Jacqueline. +1887 Most shawls aren't there alarming the hospital. +1888 There isn't every candle confusing Colleen. +1889 Every newspaper article isn't there stunning Jason. +1890 All men weren't there questioning Samuel. +1891 There weren't all hills hurting Marla. +1892 There weren't all projectors confusing all waitresses. +1893 There are all high schools falling. +1894 There was each skateboard shocking Mary. +1895 There isn't every cart looking like every photograph. +1896 All reports weren't there alarming Paula. +1897 There was every painting of Melanie looking like a lot of drawings. +1898 There isn't each couch wasting away. +1899 There was every child revealing who sells most skirts. +1900 There isn't each movie about art annoying Joel. +1901 Each cashier wasn't there alarming Thomas. +1902 Most lights were there darkening. +1903 Most steps aren't there worrying all universities. +1904 There was each story impressing Rhonda. +1905 There aren't most daughters of Jill buying those bikes. +1906 There are all glaciers disturbing that girl. +1907 Each movie theater isn't there stunning Jason. +1908 There are most shows about literature boring Christina. +1909 Each popsicle wasn't there upsetting Kristin. +1910 Most pictures were there looking like those photographs. +1911 There isn't every library falling. +1912 Most ladders aren't there irritating some dancer. +1913 There aren't most vases wasting away. +1914 Most actors aren't there wearing this shoe. +1915 No girl attacked fewer than two waiters. +1916 No person would bring fewer than three gates. +1917 No actor had driven to fewer than four malls. +1918 No pedestrian is observing more than seven waiters. +1919 No boy could reference fewer than nine drivers. +1920 No actor has sounded like more than nine mountains. +1921 No sock annoyed more than seven companies. +1922 No man has revealed at least five forks. +1923 No customer would resemble more than eight pictures. +1924 No waiter had referenced at least ten teenagers. +1925 No adult skated around more than seven lakes. +1926 No woman exited fewer than five public parks. +1927 No hamster had climbed down more than five hills. +1928 No university might confuse fewer than four men. +1929 No lady brought more than two high schools. +1930 No apple distracted fewer than five hospitals. +1931 No actor complained about more than two actresses. +1932 No college campus astounded more than five libraries. +1933 No cafe is disgusting more than seven associations. +1934 No guest was touring fewer than nine schools. +1935 No actor remembers fewer than seven documentaries. +1936 No wheelbarrow does hurt more than nine dancers. +1937 No driver was researching more than three associations. +1938 No adult is passing fewer than five schools. +1939 No actor could approach fewer than ten waiters. +1940 No actress was skated around fewer than seven rivers. +1941 No guest visits more than nine cashiers. +1942 No eggplant has worried fewer than seven boys. +1943 No man had discussed fewer than nine high schools. +1944 No cart has aggravated fewer than two actresses. +1945 No library had forgotten fewer than nine commentaries. +1946 No guest is hiding more than eight bikes. +1947 No teenager cleaned more than two college campuses. +1948 No guest had discussed more than three essays. +1949 No patient had talked to at least four teachers. +1950 No waitress descends fewer than four ladders. +1951 No guest discovers more than nine blouses. +1952 No slope disturbs more than three men. +1953 No guy has helped fewer than seven actresses. +1954 No guest could wear fewer than three scarves. +1955 No driver climbs up more than five hills. +1956 No commentary had sounded like fewer than nine glaciers. +1957 No actress upset fewer than ten waiters. +1958 No girl passes fewer than two college campuses. +1959 No pepper resembles at least two sketches. +1960 No teacher had arrived at fewer than eight banks. +1961 No child has complained about fewer than nine documentaries. +1962 No adult lifts more than two cakes. +1963 No museum might fire more than ten teachers. +1964 No doctor distracted more than four museums. +1965 No waiter broke fewer than seven forks. +1966 No government listened to fewer than four waitresses. +1967 No boy knew fewer than six guys. +1968 No painting could embarrass more than five women. +1969 No teacher helped more than eight waiters. +1970 No lady walked through fewer than ten banks. +1971 No cashier has observed fewer than two waiters. +1972 No print will look like at least two fish. +1973 No patient hugged at least five people. +1974 No fish was looking like at least five drawings. +1975 No lady might break more than six bikes. +1976 No hospital had loved fewer than eight patients. +1977 No boy notices more than seven schools. +1978 No customer ran around more than three restaurants. +1979 No doctor arrived at fewer than nine high schools. +1980 No actress has run around fewer than ten rivers. +1981 No guy took more than five snakes. +1982 No person will respect fewer than eight students. +1983 No committee had boasted about at least six people. +1984 No boy can investigate more than six women. +1985 No cashier can love fewer than ten stories. +1986 No teacher is finding fewer than two sketches. +1987 No carriage does confuse more than seven adults. +1988 No committee has fewer than ten blouses. +1989 No person is running around fewer than two closets. +1990 No actor admires more than five candles. +1991 No patient thought about at least six books. +1992 No mall disturbed fewer than six men. +1993 No teacher has cared for fewer than eight boys. +1994 No lady notices fewer than two turtles. +1995 No child has worked with at least five waitresses. +1996 No actress had praised fewer than seven patients. +1997 No cashier examined more than nine plays. +1998 No waiter is observing more than six teenagers. +1999 No woman was talking to more than eight customers. +2000 No patient was firing fewer than seven cashiers. +2001 No customer had praised fewer than nine newspaper articles. +2002 No student is admiring fewer than ten waitresses. +2003 No painting does hurt more than seven doctors. +2004 No student should escape from fewer than six public parks. +2005 No lady healed fewer than three ladies. +2006 No skateboard had alarmed more than nine boys. +2007 No teacher cleans fewer than two restaurants. +2008 No actor breaks fewer than six forks. +2009 No actress was thinking about fewer than six senators. +2010 No report does alarm fewer than nine banks. +2011 No university had listened to at least ten drivers. +2012 No teacher is biking to fewer than seven banks. +2013 No adult knows more than two girls. +2014 No plate has impressed more than two cashiers. +2015 No waiter attacks at least six waiters. +2016 No student had left fewer than two banks. +2017 No patient hired fewer than nine men. +2018 No bird descends fewer than three hills. +2019 No woman was criticizing more than seven teenagers. +2020 No waitress visited fewer than three teenagers. +2021 No boy researches fewer than six museums. +2022 No museum was describing at least five ladies. +2023 No dancer likes fewer than eight forks. +2024 No cashier will flee from fewer than four cafes. +2025 No dancer had run around fewer than seven art galleries. +2026 No patient was fixing more than four windows. +2027 No hospital praised fewer than six people. +2028 No museum can praise fewer than two guys. +2029 No lady will argue about more than six people. +2030 No child knew more than nine guys. +2031 No pedestrian has skated around more than three lakes. +2032 No organization has criticized more than five rivers. +2033 No child will disgust fewer than four children. +2034 No museum has impressed fewer than nine senators. +2035 No teenager will escape from fewer than two college campuses. +2036 No lady can boycott fewer than six governments. +2037 No sketch would irritate more than three actresses. +2038 No patient has sounded like at least seven senators. +2039 No customer has appreciated more than two dancers. +2040 No legislature forgot fewer than four patients. +2041 No actor cured more than five dancers. +2042 No lady drives to more than five closets. +2043 No person is discussing fewer than ten patients. +2044 No waitress worked with at least seven patients. +2045 No person healed more than five ladies. +2046 No guest bored more than eight senators. +2047 No cashier had lifted more than nine windows. +2048 No lamp might upset fewer than three actors. +2049 No woman drove to at least three glaciers. +2050 No organization respects fewer than seven actors. +2051 No university is firing more than five senators. +2052 No adult bikes to fewer than five libraries. +2053 No actress did cure fewer than eight teachers. +2054 No guest is firing more than eight women. +2055 No doctor skates around more than seven malls. +2056 No guest would work with fewer than seven patients. +2057 No man took fewer than seven malls. +2058 No girl does run around more than six libraries. +2059 No girl references fewer than six teachers. +2060 No waitress had hidden fewer than nine slopes. +2061 No teacher was messing up more than three plates. +2062 No adult passes more than three lakes. +2063 No lady did talk about fewer than five doctors. +2064 No actor sees fewer than three trucks. +2065 No doctor does forget fewer than four guys. +2066 No print looked like more than eight shawls. +2067 No guy had listened to fewer than eight waitresses. +2068 No patient hugs at least six girls. +2069 No senator had talked to at least nine customers. +2070 No person examines at least two reports. +2071 No scarf should look like fewer than ten drawings. +2072 No boy would visit fewer than six children. +2073 No doctor fires fewer than four patients. +2074 No print did look like at least seven pies. +2075 No waitress should conceal fewer than ten essays. +2076 No driver discovered fewer than nine teenagers. +2077 No actress sells more than two screens. +2078 No rabbit finds fewer than eight banks. +2079 No senator kisses at least eight guys. +2080 No play is scaring fewer than seven waitresses. +2081 No patient had descended more than three hills. +2082 No doctor described more than four books. +2083 No waitress was climbing up fewer than ten steps. +2084 No boy is hiding fewer than two adults. +2085 No waiter describes fewer than six teachers. +2086 No child brought fewer than nine bananas. +2087 No adult will go to at least six museums. +2088 No actor loved more than seven children. +2089 No dancer passes fewer than six grocery stores. +2090 No adult knew more than four doctors. +2091 No guest dropped by fewer than nine closets. +2092 No lady will appreciate fewer than eight books. +2093 No painting astounded fewer than nine libraries. +2094 No boy will walk through fewer than seven art galleries. +2095 No association had sold more than nine glasses. +2096 No company has discussed more than six people. +2097 No snake has descended fewer than eight stairs. +2098 No person is cleaning more than three movie theaters. +2099 No guy walks through fewer than two restaurants. +2100 No girl might work with fewer than five ladies. +2101 No driver has messed up more than two restaurants. +2102 No driver was healing more than three doctors. +2103 No actor did lift fewer than nine boxes. +2104 No guy is annoying fewer than two universities. +2105 No waiter could buy more than four snakes. +2106 No hospital talked about fewer than nine dancers. +2107 No bank had disliked more than eight waiters. +2108 No driver had investigated more than eight children. +2109 No dancer hugged fewer than ten waiters. +2110 No print had worried at least six students. +2111 No student does look like more than eight sketches. +2112 No waitress can climb up at least two steps. +2113 No pedestrian did see fewer than ten girls. +2114 No dancer had fixed fewer than three screens. +2115 No guest is fixing more than ten planes. +2116 No child was listening to fewer than three patients. +2117 No dancer cures fewer than two dogs. +2118 No mouth disturbed more than nine guests. +2119 No plane did impress more than nine schools. +2120 No person was researching more than nine banks. +2121 No boy remembered fewer than four high schools. +2122 No man cares for more than ten women. +2123 No college campus can distract fewer than seven girls. +2124 No customer descends more than three hills. +2125 No adult questions fewer than nine committees. +2126 No waiter was helping fewer than eight children. +2127 No patient cleans fewer than six public parks. +2128 No patient has annoyed more than eight schools. +2129 No book upset fewer than two adults. +2130 No newspaper article looks like at least six pictures. +2131 No hospital did have fewer than eight boxes. +2132 No girl approached more than two women. +2133 No fish was boring more than two dancers. +2134 No bicycle was stunning more than four waiters. +2135 No actor explored fewer than eight high 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Each child had watched at most six girls. +2950 A tomato had bored at most ten adults. +2951 This woman fixes at least two carriages. +2952 This person admired at most four lights. +2953 No cashier boycotted at least six museums. +2954 Every pedestrian had cured at most four pedestrians. +2955 Some fish distracted at least two universities. +2956 The university would know at most seven customers. +2957 The guest cared for at most four customers. +2958 That guy buys at least seven forks. +2959 That waitress was attacking at least ten waitresses. +2960 No waiter skates around at least six college campuses. +2961 No association is upsetting at least six banks. +2962 Some company was helping at most ten doctors. +2963 No patient fled from at most six closets. +2964 This doctor does question at least ten museums. +2965 No person escapes from at most six high schools. +2966 The person went to at most two malls. +2967 The girl watched at least seven women. +2968 Every teacher revealed at 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No government will admire at least ten mouths. +3067 A woman descends at most six ladders. +3068 Each waitress passed at most eight cafes. +3069 The mouth had scared at most eight adults. +3070 No government fires at least nine actors. +3071 The patient is fleeing from at most three students. +3072 No carriage does confuse at least seven students. +3073 No doctor had fixed at most four trucks. +3074 No lady was admiring at least nine hamsters. +3075 Each legislature was appreciating at least nine women. +3076 No man had left at most eight restaurants. +3077 Each government listens to at most three men. +3078 That plane scares at least four customers. +3079 This legislature loved at most five actresses. +3080 Each waiter lifted at least four malls. +3081 Some senator drove to at most two art galleries. +3082 Each library has helped at most five customers. +3083 Every child is wearing at most seven scarves. +3084 The school disliked at most nine teenagers. +3085 The pie disturbed at 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Each child was firing at most five students. +3314 Every commentary scares at least ten committees. +3315 Some actress will drop by at least four mountains. +3316 The dancer talked to at most eight guys. +3317 No legislature is admiring at most six fish. +3318 Each patient explores at least seven public parks. +3319 A convertible would aggravate at least three waitresses. +3320 Each man was listening to at most nine ladies. +3321 No child praises at most six customers. +3322 The pepper disturbed at least four teenagers. +3323 No government could praise at least eight ladies. +3324 Each eggplant has looked like at most two photographs. +3325 Some library might like at least six scarves. +3326 This drawing does bother at most eight waitresses. +3327 No woman tours at most nine hospitals. +3328 No child will bother at least six governments. +3329 Every girl drove to at least seven rivers. +3330 Each adult is stunning at least seven cashiers. +3331 Every company is helping at least nine 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brought at least eight dresses. +3351 No couch can irritate at least three waitresses. +3352 Every person was arriving at at most five glaciers. +3353 The senator has skated around at least seven lakes. +3354 The doctor talked to at least two teenagers. +3355 Each teenager concealed at most eight ladies. +3356 A person descended at least six stairs. +3357 Each computer has bored at least eight customers. +3358 No teacher will hurt at least four drivers. +3359 Every customer had admired at least seven cats. +3360 A boy might clean at most seven banks. +3361 That cashier had thought about at least three eyes. +3362 Each girl might praise at most two guys. +3363 No child would hate at most four plays. +3364 The guy questioned at least six teenagers. +3365 Each couch was hurting at most three children. +3366 Each lady will forget at least five guys. +3367 Every guy thinks about at least ten women. +3368 Each rabbit might upset at least eight committees. +3369 That bank might fire at most 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schools. +3388 Each child is listening to at least four teenagers. +3389 The teacher should break at least nine carriages. +3390 Each face is stunning at least seven men. +3391 Each cashier had cleaned at least ten projectors. +3392 No dancer was admiring at least three eyes. +3393 Every woman is walking through at most eight high schools. +3394 A waitress has healed at most two snakes. +3395 No child has insulted at most five waitresses. +3396 The girl questioned at most seven guests. +3397 The doctor thinks about at least three women. +3398 That waiter could admire at least six screens. +3399 Each waitress will think about at least four glasses. +3400 That girl is escaping from at least four people. +3401 This waitress forgot at least five teenagers. +3402 That waiter can visit at most three mountains. +3403 Every association would criticize at most seven high schools. +3404 The doctor fixes at least three windows. +3405 No boy messed up at least six college campuses. +3406 Every adult was lifting at least six closets. +3407 Some adult breaks at most eight screens. +3408 This library has insulted at least four customers. +3409 No government can admire at least seven coats. +3410 A teacher could wear at least nine coats. +3411 No person brought at least eight drawings. +3412 No doctor admired at most seven snakes. +3413 A convertible embarrassed at most ten governments. +3414 Every teacher does talk to at least ten senators. +3415 This photograph was looking like at most three cars. +3416 This teacher had ascended at most eight slopes. +3417 No person has gone to at most ten hospitals. +3418 This teenager should approach at most three women. +3419 No glove worried at least seven teachers. +3420 The doctor loved at most two girls. +3421 No report alarmed at most three companies. +3422 This student sees at least four mouths. +3423 That dancer is praising at least eight children. +3424 No doctor bothered at least eight ladies. +3425 No patient questions at least six drivers. +3426 This organization loved at most four ladies. +3427 That child will mess up at most ten pants. +3428 Every lady might work with at most two cashiers. +3429 That waiter drives to at least eight movie theaters. +3430 Each library criticizes at least nine rivers. +3431 This dancer was cleaning at least five high schools. +3432 Each man notices at most six peppers. +3433 No patient had skated around at most nine mountains. +3434 This museum might distract at least nine organizations. +3435 Each person messes up at least two cafes. +3436 The actor is climbing up at most four ladders. +3437 Some actor could reference at most eight plays. +3438 That lady was visiting at least six hospitals. +3439 This guy lifted at most five lakes. +3440 Each man could mess up at most eight libraries. +3441 The mirror is boring at most four adults. +3442 No mushroom will impress at least three patients. +3443 Some teenager was examining at most nine commentaries. +3444 This dancer did hide at most two carriages. +3445 Every customer bikes to at most seven lakes. +3446 An actor is questioning at most ten governments. +3447 That apple does worry at most nine committees. +3448 No customer fixed at most two cars. +3449 Each guest does buy at least eight muffins. +3450 Every waiter climbed down at least six stairs. +3451 Every girl is confusing at least six governments. +3452 This lady was buying at least six dishes. +3453 Each child did like at least seven boxes. +3454 Every sketch did disturb at least ten banks. +3455 Every waiter was driving to at least eight glaciers. +3456 No horse can embarrass at least five organizations. +3457 A hospital has disgusted at most four girls. +3458 No child watched at least seven children. +3459 A senator examined at most eight movies. +3460 Every dog disgusted at most nine guests. +3461 No customer is messing up at least nine rugs. +3462 Every actor saw at least eight art galleries. +3463 Every actress is bringing at most seven hospitals. +3464 Each boy fixed at least ten dishes. +3465 This guest had observed at least seven girls. +3466 This cashier watched at most five customers. +3467 Every lady could buy at least nine skateboards. +3468 That hospital has annoyed at least nine hospitals. +3469 Some senator did conceal at most ten actresses. +3470 The guy is astounding at least three hospitals. +3471 The committee has talked to at most four drivers. +3472 Some pedestrian liked at least six screens. +3473 A documentary would disgust at most ten students. +3474 No doctor had seen at least four books. +3475 No guy questions at least eight senators. +3476 No driver flees from at least nine guests. +3477 This adult does arrive at at least nine closets. +3478 Each child talked about at most five hills. +3479 Each doctor visited at most ten actors. +3480 No guest discovered at least three computers. +3481 Every library did remember at least six libraries. +3482 Every legislature was buying at least seven peppers. +3483 The waiter can run around at least six rivers. +3484 The boy is scanning at most nine plays. +3485 Some senator discusses at most eight hills. +3486 The teenager hugged at least three women. +3487 The lady lifted at most ten pants. +3488 A sketch looked like at least six stairs. +3489 No teenager has fixed at most four dishes. +3490 Every organization criticized at least four lakes. +3491 No pepper was shocking at most five doctors. +3492 No hamster had found at least six doctors. +3493 That shoe should bore at most two pedestrians. +3494 The senator can return to at most eight restaurants. +3495 Some university had listened to at least two senators. +3496 Every waiter hid at most four hats. +3497 Each story had alarmed at most three organizations. +3498 Every student fixed at most five bikes. +3499 Every waiter was arriving at at least two hills. +3500 Each adult had explored at most three restaurants. +3501 Some documentary disturbs at most seven actresses. +3502 Some adult had at most two socks. +3503 A ladder is stunning at most four organizations. +3504 Each waiter could love at least eight movies. +3505 This student has concealed at least six socks. +3506 This college campus had confused at most ten men. +3507 This lady walked through at most ten glaciers. +3508 This doctor attacked at most two guests. +3509 Every dancer climbed up at least seven hills. +3510 Some dancer cleans at most five shoes. +3511 Every customer has helped at most six patients. +3512 No bike resembles at least four photographs. +3513 No customer was exploring at least four hospitals. +3514 Each school will know at most four doctors. +3515 Some pedestrian had found at least eight dishes. +3516 The guy was fleeing from at least five drivers. +3517 No customer was fixing at most four wheelbarrows. +3518 This waiter had loved at least eight actresses. +3519 Every lady is noticing at most nine computers. +3520 Some waitress could see at most seven schools. +3521 That teacher has admired at least nine carriages. +3522 That essay sounds like at most two actresses. +3523 A teacher hid at least three forks. +3524 Each guest was skated around at least two high schools. +3525 Each waitress was dropping by at most seven malls. +3526 No actor had driven to at least seven hospitals. +3527 No customer would astound at least three adults. +3528 Every teenager escapes from at most two drivers. +3529 A customer can hug at least nine drivers. +3530 That muffin had disgusted at least four adults. +3531 This lady had at most seven socks. +3532 No driver boasted about at most three girls. +3533 Some doctor is exiting at most seven cafes. +3534 That lady toured at most five banks. +3535 Some waitress had left at least ten mountains. +3536 That student had attacked at least seven actors. +3537 Every pedestrian is observing at most five guys. +3538 A cashier was passing at most ten public parks. +3539 Each person was escaping from at most nine guys. +3540 This painting resembles at least six paintings. +3541 This cashier had known at most four customers. +3542 The hospital does boast about at most seven boys. +3543 This vase scares at most nine waitresses. +3544 That dancer passes at most two college campuses. +3545 Every teacher had explored at most ten mountains. +3546 Each boy cared for at least seven waiters. +3547 An actor lifts at least six peppers. +3548 Some patient boycotted at least nine mountains. +3549 That dancer is breaking at most four glasses. +3550 Each association worked with at least eight guys. +3551 Each waitress had disliked at least six doctors. +3552 No association talked to at least five people. +3553 The adult can discuss at most three customers. +3554 A patient complains about at most six men. +3555 No driver has watched at most five waiters. +3556 No man has hidden at most ten snakes. +3557 No teacher messed up at most ten forks. +3558 Each government had disturbed at most nine museums. +3559 No driver flees from at least seven guys. +3560 A convertible is impressing at least two people. +3561 No government has appreciated at least five patients. +3562 Every guest breaks at most nine trucks. +3563 No senator messed up at most ten hospitals. +3564 No doctor attacks at most nine actors. +3565 Every bank liked at most four muffins. +3566 A teacher has descended at least nine hills. +3567 A senator arrived at at most seven restaurants. +3568 The person talked about at most three waiters. +3569 Every actor references at most eight actors. +3570 No man appreciates at most two closets. +3571 No shirt has annoyed at most four teachers. +3572 Every waiter should boycott at least three rivers. +3573 That university had annoyed at most four girls. +3574 No computer distracted at least two schools. +3575 Every actress could escape from at least three teenagers. +3576 The child had hugged at most eight actors. +3577 This painting was upsetting at least three libraries. +3578 No doctor broke at least ten windows. +3579 The school criticized at least four banks. +3580 Each driver has hired at least seven students. +3581 That man is lifting at least eight photographs. +3582 No man fled from at least eight hills. +3583 No person has questioned at most three women. +3584 Some waiter climbs up at least eight stairs. +3585 This student was touring at least five museums. +3586 Each waiter ran around at most five libraries. +3587 Each senator was researching at most eight plays. +3588 Each cashier describes at least six guys. +3589 This person cleaned at most seven closets. +3590 The dancer does watch at most ten actors. +3591 Every senator forgot at most two cashiers. +3592 Some association hires at most ten guests. +3593 No man listens to at least nine ladies. +3594 No patient sold at least ten art galleries. +3595 Each hospital has helped at least six adults. +3596 No doctor will help at most seven people. +3597 This actor is driving to at least ten public parks. +3598 The teenager climbs down at most eight steps. +3599 Every lady has driven to at least six libraries. +3600 The lady has taken at most nine movie theaters. +3601 Each patient was boasting about at most seven dancers. +3602 The woman attacked at most ten cashiers. +3603 Each committee has discussed at most seven books. +3604 This cashier had sold at most two skirts. +3605 No adult had thought about at least ten guests. +3606 Each actress hugs at least nine people. +3607 No person did complain about at least seven teachers. +3608 A company is concealing at most seven doctors. +3609 A hospital had shocked at least seven guys. +3610 Every customer has forgotten at most three pedestrians. +3611 This guest would admire at least five screens. +3612 That teacher is referencing at most four children. +3613 This teacher would think about at most six teachers. +3614 A cashier escapes from at least seven patients. +3615 No person was revealing at least two sweaters. +3616 A legislature could praise at most five teachers. +3617 Some commentary sounds like at least five restaurants. +3618 The dancer found at least ten actors. +3619 This teenager boycotted at least five universities. +3620 This guy is breaking at most five dishes. +3621 Each adult will scan at least four commentaries. +3622 Each legislature did reference at most three senators. +3623 The legislature references at least eight ladies. +3624 That couch does astound at most four cashiers. +3625 That committee was disgusting at least three libraries. +3626 Each teenager would complain about at most eight actors. +3627 This cashier had scanned at most seven books. +3628 Every school buys at most three computers. +3629 This dancer hated at least eight children. +3630 This gate can impress at least three dancers. +3631 The government has referenced at least five teenagers. +3632 Every dish has resembled at least six pictures. +3633 Each cashier revealed at most six bananas. +3634 The patient had arrived at at least eight grocery stores. +3635 No glass had bothered at most ten patients. +3636 A blouse had alarmed at most two guys. +3637 Some guy observed at most two teachers. +3638 No car disturbed at least seven patients. +3639 This sketch looks like at least three tomatoes. +3640 A river can worry at least seven guys. +3641 This woman went to at least two malls. +3642 Each university has referenced at most ten boys. +3643 Each driver is scaring at least four banks. +3644 Each girl does respect at most eight glaciers. +3645 Each student had ascended at least four steps. +3646 This actor had climbed down at most nine steps. +3647 A guest is appreciating at least two doctors. +3648 The doctor might drive to at most three rivers. +3649 Each teenager concealed at most three glasses. +3650 This sketch was looking like at least seven glaciers. +3651 No wheelbarrow has bothered at most four organizations. +3652 Each boy was taking at most two sweaters. +3653 That projector is embarrassing at least ten libraries. +3654 This fish will resemble at least nine pictures. +3655 No girl will approach at least two women. +3656 That senator could arrive at at least four public parks. +3657 This library had concealed at least nine guys. +3658 No woman might notice at least eight museums. +3659 Every university has disliked at most three drivers. +3660 Each actor had concealed at least four books. +3661 Every pedestrian concealed at least three snakes. +3662 Every lamp could resemble at least ten photographs. +3663 This guy exited at least ten lakes. +3664 No lady had climbed down at least five ladders. +3665 The bicycle looked like at most seven prints. +3666 The legislature has loved at most six students. +3667 Every library has forgotten at least three books. +3668 That guest is noticing at least seven birds. +3669 Every senator toured at least eight hospitals. +3670 Every actor messed up at least four museums. +3671 That museum listens to at least two children. +3672 The senator scared at most two waitresses. +3673 That girl might see at most seven carts. +3674 No hamster would hurt at most five guys. +3675 No eye confused at least four companies. +3676 Every teacher was biking to at most two hospitals. +3677 A student has at least five blouses. +3678 Every actress watched at most eight people. +3679 A dancer had argued about at most ten cashiers. +3680 Each company listened to at most eight people. +3681 A hospital does listen to at least ten doctors. +3682 Each child has skated around at least four public parks. +3683 Some dancer had sold at most two libraries. +3684 A person bought at least nine shirts. +3685 This student is cleaning at most eight restaurants. +3686 Some actress is talking to at least eight drivers. +3687 No doctor is firing at most nine doctors. +3688 This patient loves at least eight women. +3689 A customer should fire at most five girls. +3690 Each actress can find at least six public parks. +3691 Some pedestrian did walk through at least eight malls. +3692 Each student has insulted at least eight cashiers. +3693 That company might boast about at least six newspaper articles. +3694 That woman descended at most five slopes. +3695 The child attacks at most six teenagers. +3696 Every guy revealed at most seven windows. +3697 No dog found at most nine carriages. +3698 The guy would discover at least nine dishes. +3699 This lake had alarmed at most nine boys. +3700 Each bank talks to at most eight teachers. +3701 Each dancer sounded like at least three commentaries. +3702 A pedestrian watches at most three drivers. +3703 Each guest criticizes at least six stories. +3704 This student worked with at least five actresses. +3705 A person buys at most six lamps. +3706 Every guest skated around at most three movie theaters. +3707 This student has fixed at least three wheelbarrows. +3708 No child has thought about at least two reports. +3709 No driver brought at most five malls. +3710 A child will bore at most six guests. +3711 Some adult lifts at least ten glasses. +3712 The actress will drive to at least three lakes. +3713 A senator is arguing about at most two boys. +3714 No person messed up at least six high schools. +3715 Some actress should aggravate at least eight governments. +3716 No government appreciates at most eight closets. +3717 This adult should love at most seven cashiers. +3718 Each child drops by at least eight high schools. +3719 Some pedestrian noticed at least five cats. +3720 This snake should look like at most four paintings. +3721 Each actor discovers at least two movies. +3722 Each dancer is driving to at least ten museums. +3723 Every senator is observing at most nine children. +3724 No movie should annoy at most nine girls. +3725 Each gate can impress at most eight people. +3726 This man was complaining about at least ten teenagers. +3727 No government can have at most nine malls. +3728 The waiter will bike to at most ten high schools. +3729 The high school has scared at least six women. +3730 Every patient had boycotted at most three malls. +3731 No senator might pass at least nine movie theaters. +3732 Each guest had argued about at most seven guys. +3733 No hospital scared at most ten legislatures. +3734 The guest revealed at least three pictures. +3735 The student dropped by at least seven schools. +3736 The mall is aggravating at most eight dancers. +3737 That senator is buying at least four movie theaters. +3738 Every man was investigating at least six ladies. +3739 Some waitress referenced at least four newspaper articles. +3740 Each actress concealed at most seven lamps. +3741 The senator will examine at least ten newspaper articles. +3742 Every waitress hides at least three hospitals. +3743 That woman took at most six sweaters. +3744 The woman watched at most six men. +3745 No child can talk about at most five drivers. +3746 Every girl was exploring at least ten cafes. +3747 This teacher talked to at least five doctors. +3748 Each patient cared for at most six cashiers. +3749 This person visits at most five rivers. +3750 A waitress does dislike at most two women. +3751 A doctor had left at most six children. +3752 Some cashier can attack at most six actresses. +3753 That hat has embarrassed at least nine doctors. +3754 This lady had scanned at least nine essays. +3755 This guy complained about at most four doctors. +3756 No car did disgust at most three customers. +3757 That waitress lifted at most two cars. +3758 An association appreciated at least ten banks. +3759 Each child researches at most six public parks. +3760 This waiter finds at most nine shawls. +3761 Every student questioned at least two museums. +3762 Each teenager climbs down at least eight mountains. +3763 No waiter might fire at most ten people. +3764 A casserole has stunned at most ten associations. +3765 That actor ascended at least nine hills. +3766 This actor might clean at least four rugs. +3767 This woman is examining at most ten newspaper articles. +3768 This cashier was attacking at least three patients. +3769 A dancer can conceal at most two stories. +3770 No government described at least five adults. +3771 No projector has astounded at least three cashiers. +3772 Every drawing does look like at most seven men. +3773 This woman has questioned at most eight associations. +3774 Some pedestrian examines at least two essays. +3775 Every senator will tour at least three hills. +3776 No rabbit breaks at most two bikes. +3777 That student was noticing at most five glasses. +3778 That waitress would work with at most nine teachers. +3779 That guest praised at least five stories. +3780 Every report is irritating at least eight legislatures. +3781 That guy respected at most nine college campuses. +3782 This documentary disagrees with at least six teachers. +3783 No glove had astounded at most ten governments. +3784 No light has impressed at most six actors. +3785 Each person does astound at most five men. +3786 This dancer is kissing at most five guys. +3787 No woman has talked to at most eight waitresses. +3788 This cashier was insulting at most nine women. +3789 No couch had shocked at most six legislatures. +3790 That cafe has looked like at most five prints. +3791 Every cashier disagrees with at least nine actresses. +3792 No boy could go to at least eight hospitals. +3793 Some committee had praised at least ten newspaper articles. +3794 That company talks to at least ten pedestrians. +3795 This child boycotted at most four college campuses. +3796 Each doctor would research at least seven stories. +3797 That adult might flee from at least three college campuses. +3798 That girl would talk to at least nine patients. +3799 Each child arrived at at least four hospitals. +3800 The photograph could impress at most nine people. +3801 No rabbit has found at least six mountains. +3802 Each actor escaped from at least seven rivers. +3803 No actress visits at least two hospitals. +3804 Some organization is discussing at most nine adults. +3805 Each museum should fire at most four waitresses. +3806 A waiter sounded like at least three art galleries. +3807 Each cart had irritated at most ten actors. +3808 No patient would cure at most five boys. +3809 This boy passed at most five rivers. +3810 Some adult does clean at most six chairs. +3811 This box impressed at least nine hospitals. +3812 A museum insulted at most two doctors. +3813 No boy had ascended at least four steps. +3814 Some student ran around at least two closets. +3815 That girl can know at least four patients. +3816 The library had upset at least ten patients. +3817 The guy irritated at least five drivers. +3818 Every teenager will lift at most three birds. +3819 The museum would insult at least five senators. +3820 A boy had climbed up at most two stairs. +3821 A woman would appreciate at least five high schools. +3822 Each senator was climbing down at least eight ladders. +3823 Some waitress complained about at least seven guys. +3824 This student was selling at most five planes. +3825 No man climbed up at least two ladders. +3826 Each cashier was biking to at least two public parks. +3827 Every child has worn at most four gloves. +3828 Each boy cleaned at most eight libraries. +3829 No waiter is skated around at least eight high schools. +3830 No cashier did respect at least five adults. +3831 A boy cleaned at most four snakes. +3832 A cashier was admiring at most four snakes. +3833 This guest dislikes at most ten high schools. +3834 Every actor talks about at most three art galleries. +3835 Some legislature had at most three hamsters. +3836 An association has at most five scarves. +3837 This girl was approaching at most nine teenagers. +3838 Every organization appreciates at most three high schools. +3839 No adult went to at least ten mountains. +3840 A cashier hid at most seven cars. +3841 That woman is observing at most five actresses. +3842 Every dish should upset at least five companies. +3843 A waiter had cleaned at most seven high schools. +3844 The library alarmed at most two children. +3845 A company upset at least nine patients. +3846 Each movie theater did irritate at least five patients. +3847 Each rug could resemble at most nine prints. +3848 Every waitress boasts about at most six customers. +3849 No child hated at least eight newspaper articles. +3850 Every child described at most four essays. +3851 The waitress insults at least eight drivers. +3852 No man insults at least three cafes. +3853 Each actor had researched at most six paintings. +3854 That waiter can cure at most seven adults. +3855 Every organization had taken at least five cars. +3856 Each senator was descending at least three hills. +3857 Each skateboard is upsetting at most four doctors. +3858 Some teenager is working with at most ten boys. +3859 Each man had hired at least nine dancers. +3860 Every waitress has scanned at most seven stories. +3861 Each guest cleans at least six plates. +3862 The museum upset at least four legislatures. +3863 Each teenager ran around at least five movie theaters. +3864 The waiter can explore at least ten banks. +3865 The bicycle is impressing at most five students. +3866 Every senator had approached at most eight senators. +3867 A waitress had thought about at least eight glasses. +3868 The patient was investigating at least four waiters. +3869 Each man would research at least three hospitals. +3870 The dancer exited at least two rivers. +3871 Some committee has hired at most six children. +3872 The customer is arguing about at most three girls. +3873 Every child had scanned at least six reports. +3874 Every guy has discussed at most five guests. +3875 Some patient insults at most eight pedestrians. +3876 Each student had sold at most five grocery stores. +3877 Some pedestrian had kissed at most eight ladies. +3878 Some actress heals at most four waiters. +3879 No woman has fired at least five children. +3880 Each student is cleaning at most ten shoes. +3881 That apple was looking like at least five prints. diff --git a/zeroshot/subject_aux_inversion/eval_results.json b/zeroshot/subject_aux_inversion/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..a493724dea02df0eebeb8020e4226d2c3d9a3e06 --- /dev/null +++ b/zeroshot/subject_aux_inversion/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.7765308611856551} \ No newline at end of file diff --git a/zeroshot/subject_aux_inversion/predictions.txt b/zeroshot/subject_aux_inversion/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3f24992d049d7cacc9817aeea8d85edef1e6984 --- /dev/null +++ b/zeroshot/subject_aux_inversion/predictions.txt @@ -0,0 +1,4100 @@ +index prediction +0 Is the novel he is putting away from the library? +1 Has every possibility she has ever tested failed miserably? +2 Is every woman still fantasizing about the goal she has been dreaming of since childhood? +3 Has she forgotten to pick up the admission bracelet the guard was setting aside for her friend? +4 Are bugs and worms crawling on the dirty little boys that are playing outside? +5 Will the auditors look into residents who make suspicious claims on their taxes? +6 Was the magazine she is reading from the library? +7 Has every possibility she ever tested has crashed and burned miserably? +8 Can bacteria that live inside our bodies benefit our digestive system? +9 Is every woman still striving for the dream she has been dreaming of since being a little girl? +10 Has she claimed the admission bracelet the ticket booth was reserving for her friend? +11 Are bugs and worms frightening the students that are playing outside? +12 Will the auditors interrogate citizens who cheat on their taxes? +13 Was the magazine he is putting away on the shelf? +14 Has every possibility he ever tested has failed miserably? +15 Do bacteria that get nourishment from our partly digested food can benefit our digestive system? +16 Is every woman still fantasizing about the dream she has been thinking about since being a little girl? +17 Has she claimed the admission bracelet the ticket booth was reserving for her? +18 Will the investigators interrogate people who cheat on their taxes? +19 Is the novel she is paging through from the library? +20 Can microscopic organisms that live inside our bodies benefit our digestive system? +21 Is every grandmother still holding onto the dream she has been thinking about since being a little girl? +22 Has she tried to transfer the admission bracelet the guard was reserving for her friend? +23 Will the inspectors go after residents who cheat on their taxes? +24 Is the magazine she paging through was on the shelf? +25 Has every option he ever tested has failed miserably? +26 Is every old lady still holding onto the dream she has been dreaming of since childhood? +27 Has she tried to transfer the ticket the guard was setting aside for her friend? +28 Will the investigators interrogate residents who make suspicious claims on their taxes? +29 Is the magazine he is putting away on the shelf? +30 Do microscopic organisms that get nourishment from our partly digested food can benefit our digestive system? +31 Is every old lady still fantasizing about the goal she has been dreaming of since being a little girl? +32 Has she claimed the ticket the guard was setting aside for her? +33 Will the investigators interrogate people who make suspicious claims on their taxes? +34 Was the novel he is putting away from the library? +35 Is every woman still holding onto the goal she has been dreaming of since being a little girl? +36 Has she forgotten to pick up the admission bracelet the ticket booth was reserving for her friend? +37 Will the inspectors interrogate people who make suspicious claims on their taxes? +38 Is the novel she paging through was on the table? +39 Can parasites that inhabit our guts benefit our digestive system? +40 Is every grandmother still holding onto the goal she has been thinking about since being a little girl? +41 Has she claimed the admission bracelet the guard was setting aside for her friend? +42 Are ants frightening the dirty little boys that are playing outside? +43 Will the inspectors go after residents who make suspicious claims on their taxes? +44 Was the magazine she is putting away on the shelf? +45 Has every possibility she has ever tried failed miserably? +46 Is every woman still striving for the goal she has been dreaming of since childhood? +47 Has she forgotten to pick up the admission bracelet the ticket booth was setting aside for her friend? +48 Will the auditors look into people who make suspicious claims on their taxes? +49 Is the book she reading was on the shelf? +50 Is every old lady still fantasizing about the goal she has been dreaming of since childhood? +51 Has she tried to transfer the ticket the ticket booth was saving for her? +52 Will the inspectors go after citizens who make suspicious claims on their taxes? +53 Was the magazine he is paging through from the library? +54 Is every old lady still holding onto the goal she has been dreaming of since childhood? +55 Has she claimed the admission bracelet the box office was reserving for her? +56 Will the auditors go after residents who make suspicious claims on their taxes? +57 Was the novel he is reading from the library? +58 Has every option she ever tested has failed miserably? +59 Do parasites that get nourishment from our partly digested food can benefit our digestive system? +60 Is every old lady still fantasizing about the dream she has been thinking about since childhood? +61 Has she tried to transfer the admission bracelet the guard was setting aside for her? +62 Will the investigators look into residents who make suspicious claims on their taxes? +63 Is the novel she is paging through on the table? +64 Has every option he has ever tried crashed and burned miserably? +65 Is every woman still fantasizing about the dream she has been dreaming of since childhood? +66 Has she forgotten to pick up the 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forgotten to pick up the admission bracelet the guard was reserving for her? +156 Will the investigators interrogate citizens who make suspicious claims on their taxes? +157 Is the book she is reading on the shelf? +158 Has every option he ever tried has failed miserably? +159 Is every old lady still fantasizing about the dream she has been dreaming of since being a little girl? +160 Has she forgotten to pick up the admission bracelet the ticket booth was saving for her friend? +161 Are bugs and worms a curiosity to the children that are playing outside? +162 Will the investigators look into residents who cheat on their taxes? +163 Is the magazine he is paging through from the library? +164 Is every old lady still striving for the goal she has been thinking about since childhood? +165 Has she forgotten to pick up the ticket the ticket booth was saving for her? +166 Are bugs and worms frightening the dirty little boys that are playing outside? +167 Will the investigators go after 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friend was hiding in the safe? +323 Had Winston Churchill threatened to raise taxes on an exploitative collection of factories and businesses that was benefiting from the labor of the lower class? +324 Should fine dining establishments pay a living wage to the waiters and waitresses who work day after day? +325 Do fine dining restaurants that get features in travel magazines can attract patrons from all over the world? +326 Is the documentary she is studying about France? +327 Has every vacation he has ever taken finished early? +328 Is every psychologist still trying to help a patient she has been seeing since starting her practice? +329 Has he swiped the cash his mother was storing under the bed? +330 Is the film she is watching about France? +331 Has every cruise he ever taken has finished early? +332 Has he swiped the cash his mother was keeping in the safe? +333 Has every cruise he ever gone on has ended early? +334 Is every psychologist still trying to help someone she has been seeing since starting her practice? +335 Don't teachers who give feedback to students' papers are often unpopular among the student body? +336 Do artisanal bakeries that get features in travel magazines can attract patrons from all over the world? +337 Was the documentary he is studying from the video store? +338 Has every cruise he has ever been on ended early? +339 Is every therapist still treating someone she has been seeing since getting her license? +340 Should restaurants increase wages for the waiters and waitresses who work day after day? +341 Don't professors who offer enough office hours are usually rated with a low score by students? +342 Do fine dining restaurants that get awarded Michelin stars can attract patrons from all over the world? +343 Was the film he is screening about France? +344 Has every cruise she ever taken has ended early? +345 Is every psychiatrist still analyzing a patient she has been seeing since getting her license? +346 Should restaurants pay a living 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lecturer that hasn't had her lunch yet? +1729 Are some animals that were believed to enjoy the most widespread population on the earth about to go extinct? +1730 Is the applicant she is vetting from Yale? +1731 Is every student still thinking about the problem set she has been staring at since the meeting? +1732 Has he leaked the classified files the CIA was holding for further investigation? +1733 Is the host expecting a guest lecturer that hasn't had her lunch yet? +1734 Is the candidate he is rejecting from Yale? +1735 Is every interviewee still thinking about the question she has been attempting to solve since the meeting? +1736 Has he sold the secrets the Department of Defense was keeping during the ongoing mission? +1737 Is the camera patiently waiting for an award-winning director that hasn't had her lunch yet? +1738 Is the applicant he is rejecting from Yale? +1739 Is every interviewee still thinking about the problem set he has been attempting to solve since the meeting? +1740 Has he sold the secrets the CIA was holding during the ongoing mission? +1741 Is the camera expecting an interviewee that hasn't finished dressing yet? +1742 Were some fishes that once spread over the whole continent are about to go extinct? +1743 Do citrus fruits that receive special care will taste delicious in a pie? +1744 Is every interviewee still thinking about the problem set he has been staring at since they entered the test room? +1745 Has he sold the classified files the Department of Defense was keeping during the ongoing mission? +1746 Is the host patiently waiting for an interviewee that hasn't had her lunch yet? +1747 Were many insects that once spread over the whole continent are almost extinct? +1748 Was the applicant she is vetting in college? +1749 Is every interviewee still thinking about the problem set she has been staring at since the meeting? +1750 Has he leaked the classified files the CIA was holding during the ongoing mission? +1751 Will the aeroplanes crash into skyscrapers that have nobody inside them? +1752 Is the host expecting an interviewee that hasn't even found the building? +1753 Are some fishes that were once spread over the whole continent almost extinct? +1754 Will native berries that grow at this plantation taste delicious in a pie? +1755 Was the applicant she is interviewing from Yale? +1756 Is every student still thinking about the question he has been staring at since they entered the test room? +1757 Has he leaked the secrets his boss was keeping during the ongoing mission? +1758 Will the aircrafts bump into skyscrapers that contain over a thousand people? +1759 Is the host expecting an award-winning director that hasn't even found the building? +1760 Is every student still trying to figure out the question she has been staring at since the meeting? +1761 Has he leaked the secrets the CIA was keeping during the ongoing mission? +1762 Will the aeroplanes crash into tall buildings that contain over a thousand people? +1763 Is the host expecting an award-winning director that hasn't finished dressing yet? +1764 Were some animals that believed to enjoy the most widespread population on the earth are almost extinct? +1765 Do native berries that receive special care will pair well with fine cheeses and nuts? +1766 Is the applicant she is interviewing graduating soon? +1767 Is every student still trying to figure out the problem set he has been staring at since they entered the test room? +1768 Has he leaked the secrets his boss was keeping for further investigation? +1769 Will the jets crash into towers that have nobody inside them? +1770 Is the host patiently waiting for an interviewee that hasn't finished dressing yet? +1771 Were many insects that found all over the world are almost extinct? +1772 Is every student still trying to figure out the question she has been staring at since they entered the test room? +1773 Has he sold the secrets the Department of Defense was keeping for further investigation? +1774 Will the aircrafts crash into towers that contain over a thousand people? +1775 Is the camera patiently waiting for a guest lecturer that hasn't even found the building? +1776 Will citrus fruits that grow on the peninsula taste delicious in a pie? +1777 Is the candidate she hiring was graduating soon? +1778 Is every interviewee still thinking about the question he has been staring at since they entered the test room? +1779 Has he leaked the secrets his boss was holding during the ongoing mission? +1780 Will the jets bump into towers that contain over a thousand people? +1781 Is the camera patiently waiting for a guest lecturer that hasn't finished dressing yet? +1782 Do citrus fruits that grow on the peninsula will pair well with fine cheeses and nuts? +1783 Was the applicant he is hiring graduating soon? +1784 Is every interviewee still trying to figure out the question he has been staring at since the meeting? +1785 Has he leaked the classified files the Department of Defense was holding for further investigation? +1786 Is the host patiently waiting for an interviewee that hasn't even found the building? +1787 Is every student still working on the problem set he has been staring at since they entered the test room? +1788 Has he leaked the information the CIA was keeping during the ongoing mission? +1789 Is the camera patiently waiting for a guest lecturer that hasn't had her lunch yet? +1790 Was the employee he is vetting from Yale? +1791 Is every student still working on the question she has been attempting to solve since they entered the test room? +1792 Has he leaked the classified files the CIA was keeping during the ongoing mission? +1793 Is the camera expecting a guest lecturer that hasn't finished dressing yet? +1794 Is the candidate he is interviewing in college? +1795 Is every student still thinking about the problem set he has been attempting to solve since the meeting? +1796 Has he leaked the classified files the CIA was keeping for further investigation? +1797 Will the aeroplanes crash into skyscrapers that contain over a thousand people? +1798 Is the camera expecting an interviewee that hasn't even found the building? +1799 Is the candidate she vetting was graduating soon? +1800 Is every interviewee still working on the question he has been attempting to solve since they entered the test room? +1801 Has he sold the information the Department of Defense was holding during the ongoing mission? +1802 Is the host patiently waiting for a guest lecturer that hasn't had her lunch yet? +1803 Will native berries that grow on the peninsula taste delicious in a pie? +1804 Is the applicant he is rejecting in college? +1805 Is every interviewee still trying to figure out the question she has been attempting to solve since they entered the test room? +1806 Has he sold the classified files the CIA was holding during the ongoing mission? +1807 Will the jets crash into skyscrapers that have nobody inside them? +1808 Is the host patiently waiting for an award-winning director that hasn't had her lunch yet? +1809 Are universities that can't offer research opportunities to the students becoming less and less popular? +1810 Is every student still thinking about the problem set she has been staring at since they entered the test room? +1811 Has he sold the secrets the Department of Defense was holding for further investigation? +1812 Is the host expecting an interviewee that hasn't had her lunch yet? +1813 Do citrus fruits that receive special care will pair well with fine cheeses and nuts? +1814 Was the candidate he is rejecting from Yale? +1815 Is every student still working on the problem set she has been staring at since the meeting? +1816 Has he sold the secrets the CIA was keeping during the ongoing mission? +1817 Will the jets bump into towers that have nobody inside them? +1818 Is the camera expecting an award-winning director that hasn't even found the building? +1819 Are universities that can't afford building basketball courts becoming less and less popular? +1820 Do basketball organizations that have star players could get more titles that others? +1821 Was the applicant he is rejecting from Yale? +1822 Is every interviewee still working on the problem set he has been attempting to solve since the meeting? +1823 Has he sold the secrets the Department of Defense was holding during the ongoing mission? +1824 Will the jets bump into skyscrapers that have nobody inside them? +1825 Is the camera expecting an award-winning director that hasn't had her lunch yet? +1826 Could football clubs that have star players have a higher chance of winning? +1827 Is the candidate he is interviewing graduating soon? +1828 Is every student still thinking about the question she has been attempting to solve since the meeting? +1829 Has he leaked the information his boss was keeping for further investigation? +1830 Will the jets crash into tall buildings that contain over a thousand people? +1831 Is the host expecting a guest lecturer that hasn't even found the building? +1832 Are universities that can't provide attractive courses to their students becoming less and less popular? +1833 Could basketball organizations that have star players have a higher chance of winning? +1834 Is every student still thinking about the question he has been attempting to solve since the meeting? +1835 Has he leaked the classified files the Department of Defense was keeping during the ongoing mission? +1836 Will the aircrafts bump into tall buildings that contain over a thousand people? +1837 Is the camera patiently waiting for an interviewee that hasn't had her lunch yet? +1838 Could basketball organizations that have a lot of team spirit win the championship more easily? +1839 Is every student still trying to figure out the question she has been attempting to solve since the meeting? +1840 Has he sold the information his boss was keeping for further investigation? +1841 Will the aircrafts bump into skyscrapers that have nobody inside them? +1842 Is the camera patiently waiting for an award-winning director that hasn't finished dressing yet? +1843 Are colleges that can't provide attractive courses to their students becoming less and less popular? +1844 Could basketball organizations that employ a coach that knows to how coach the players have a higher chance of winning? +1845 Is the employee she is rejecting from Yale? +1846 Is every interviewee still thinking about the question he has been attempting to solve since the meeting? +1847 Has he sold the classified files the Department of Defense was holding for further investigation? +1848 Will the aircrafts crash into tall buildings that contain over a thousand people? +1849 Is the host patiently waiting for an award-winning director that hasn't finished dressing yet? +1850 Are colleges that can't afford building basketball courts becoming less and less popular? +1851 Do football clubs that employ a coach that knows to how coach the players could win the championship more easily? +1852 Is the employee she is interviewing graduating soon? +1853 Is every student still working on the question she has been attempting to solve since the meeting? +1854 Has he sold the information the CIA was holding during the ongoing mission? +1855 Will the aeroplanes crash into tall buildings that have nobody inside them? +1856 Is the host patiently waiting for a guest lecturer that hasn't even found the building? +1857 Could baseball teams that have star players win the championship more easily? +1858 Is every interviewee still thinking about the problem set she has been attempting to solve since the meeting? +1859 Has he sold the classified files the CIA was holding for further investigation? +1860 Will the aircrafts bump into towers that have nobody inside them? +1861 Is the host expecting an award-winning director that hasn't had her lunch yet? +1862 Are schools that can't offer research opportunities to the students becoming less and less popular? +1863 Could baseball teams that have star players have a higher chance of winning? +1864 Is the employee she is hiring from Yale? +1865 Is every interviewee still trying to figure out the problem set she has been staring at since they entered the test room? +1866 Has he leaked the classified files his boss was keeping for further investigation? +1867 Will the jets crash into towers that contain over a thousand people? +1868 Is the camera patiently waiting for an interviewee that hasn't finished dressing yet? +1869 Could baseball teams that employ a coach that knows to how coach the players have a higher chance of winning? +1870 Is the candidate he vetting was graduating soon? +1871 Is every interviewee still trying to figure out the problem set she has been staring at since the meeting? +1872 Has he sold the classified files the Department of Defense was holding during the ongoing mission? +1873 Will the jets bump into skyscrapers that contain over a thousand people? +1874 Is the host expecting an interviewee that hasn't finished dressing yet? +1875 Could football clubs that have star players win the championship more easily? +1876 Is the applicant he is interviewing graduating soon? +1877 Is every interviewee still trying to figure out the question he has been staring at since they entered the test room? +1878 Has he sold the secrets his boss was holding during the ongoing mission? +1879 Will the jets bump into tall buildings that have nobody inside them? +1880 Is the camera patiently waiting for an award-winning director that hasn't even found the building? +1881 Could basketball organizations that employ a coach that knows to how coach the players get more titles that others? +1882 Is every student still trying to figure out the question he has been attempting to solve since they entered the test room? +1883 Has he leaked the classified files his boss was holding during the ongoing mission? +1884 Will the aeroplanes crash into towers that have nobody inside them? +1885 Is the host patiently waiting for an award-winning director that hasn't even found the building? +1886 Could baseball teams that have star players get more titles that others? +1887 Was the applicant she is hiring in college? +1888 Is every interviewee still working on the problem set he has been staring at since they entered the test room? +1889 Has he sold the classified files his boss was holding during the ongoing mission? +1890 Is the host patiently waiting for a guest lecturer that hasn't finished dressing yet? +1891 Are schools that can't afford building basketball courts becoming less and less popular? +1892 Could football clubs that have star players get more titles that others? +1893 Is the client she waiting for is still on her way? +1894 Has he recorded the discussion the UN council was having about environmental issues? +1895 Is the camera patiently waiting for an interviewee that hasn't even found the building? +1896 Are schools that can't provide attractive courses to their students becoming less and less popular? +1897 Could baseball teams that employ a coach that knows to how coach the players win the championship more easily? +1898 Was the client he is waiting for from Yale? +1899 Has she reported on the dialogues the group of leaders was having about the refugee crisis? +1900 Is the camera expecting an interviewee that hasn't had her lunch yet? +1901 Are colleges that can't offer research opportunities to the students becoming less and less popular? +1902 Do basketball organizations that employ a coach that knows to how coach the players could win the championship more easily? +1903 Is the client he texting was still on her way? +1904 Is every football player still learning the same play he has been asked to learn since last season? +1905 Has she reported on the discussion the group of leaders was having regarding the new trade deal? +1906 Could baseball teams that have a lot of team spirit have a higher chance of winning? +1907 Is the interviewee she texting was still on her way? +1908 Is every soccer player still learning the same move she has been instructed to use in the next game since last season? +1909 Has he reported on the dialogues the country's president was having regarding the new trade deal? +1910 Will the criminals hurt the helpless victims who try to run away? +1911 Could football clubs that employ a coach that knows to how coach the players get more titles that others? +1912 Is the client he texting is still on her way? +1913 Has he recorded the discussion the group of leaders was having regarding the new trade deal? +1914 Could football clubs that have a lot of team spirit get more titles that others? +1915 Was the data scientist she is texting from Yale? +1916 Has she reported on the dialogues the country's president was having regarding the new trade deal? +1917 Could football clubs that have a lot of team spirit win the championship more easily? +1918 Is the interviewee she is waiting for from China? +1919 Is every basketball player still learning the same play she has been instructed to use in the next game since last season? +1920 Has he recorded the dialogues the UN council was having regarding the new trade deal? +1921 Could football clubs that employ a coach that knows to how coach the players have a higher chance of winning? +1922 Is the customer he waiting for was still on his way? +1923 Has she reported on the discussion the country's president was having about environmental issues? +1924 Will the criminals hurt the helpless victims who contact the police? +1925 Could baseball teams that have a lot of team spirit win the championship more easily? +1926 Is the interviewee he waiting for was still on her way? +1927 Has he reported on the discussion the group of leaders was having about environmental issues? +1928 Will the terrorists kill the hostages who try to run away? +1929 Could baseball teams that employ a coach that knows to how coach the players get more titles that others? +1930 Is the interviewee he is texting from Yale? +1931 Is every football player still learning the same play she has been asked to learn since last season? +1932 Has she reported on the discussion the UN council was having about the refugee crisis? +1933 Could baseball teams that have a lot of team spirit get more titles that others? +1934 Is the interviewee he waiting for is still on her way? +1935 Has she recorded the dialogues the UN council was having regarding the new trade deal? +1936 Will the terrorists torture the helpless victims who try to run away? +1937 Do basketball organizations that have star players could win the championship more easily? +1938 Is the client he texting is still on his way? +1939 Has she reported on the dialogues the UN council was having about the refugee crisis? +1940 Could basketball organizations that have a lot of team spirit have a higher chance of winning? +1941 Is the data scientist he texting was still on her way? +1942 Has he recorded the dialogues the UN council was having about the refugee crisis? +1943 Could football clubs that have a lot of team spirit have a higher chance of winning? +1944 Was the interviewee he is texting from China? +1945 Has he recorded the discussion the UN council was having about the refugee crisis? +1946 Could basketball organizations that have a lot of team spirit get more titles that others? +1947 Is the data scientist he waiting for was still on his way? +1948 Has she reported on the dialogues the UN council was having about environmental issues? +1949 Could engines that suit the car improve the driving experience for customers? +1950 Is the interviewee she waiting for is still on her way? +1951 Has she recorded the dialogues the UN council was having about the refugee crisis? +1952 Will the terrorists kill the hostages who contact the police? +1953 Do tires that require a lot of money to design and manufacture could give drivers an edge when driving? +1954 Is the client she is texting from China? +1955 Has he recorded the discussion the group of leaders was having about environmental issues? +1956 Could body shells that require a lot of money to design and manufacture give drivers an edge when driving? +1957 Is the data scientist he is texting from China? +1958 Has he reported on the discussion the group of leaders was having regarding the new trade deal? +1959 Could body shells that require a lot of money to design and manufacture improve the driving experience for customers? +1960 Is the data scientist he waiting for is still on his way? +1961 Has she reported on the dialogues the UN council was having regarding the new trade deal? +1962 Has every skyscraper she ever constructed has collapsed without warning? +1963 Do engines that require a lot of money to design and manufacture could give drivers an edge when driving? +1964 Was the customer she is texting from China? +1965 Is every football player still practicing the same play he has been asked to learn since last season? +1966 Has he recorded the discussion the UN council was having regarding the new trade deal? +1967 Will the criminals hurt the hostages who contact the police? +1968 Has every skyscraper she ever constructed has toppled over without warning? +1969 Is the customer she waiting for was still on his way? +1970 Has he reported on the discussion the country's president was having about the refugee crisis? +1971 Has every skyscraper she ever erected has crumbled suddenly? +1972 Do tires that require a lot of money to design and manufacture could improve the driving experience for customers? +1973 Is the data scientist he waiting for is still on her way? +1974 Has she recorded the dialogues the country's president was having about the refugee crisis? +1975 Has every bridge she ever erected has crumbled suddenly? +1976 Could tires that suit the car give drivers an edge when driving? +1977 Is the data scientist she waiting for is still on his way? +1978 Is every football player still practicing the same move she has been instructed to use in the next game since last season? +1979 Has she reported on the discussion the group of leaders was having about the refugee crisis? +1980 Has every skyscraper he ever constructed has collapsed suddenly? +1981 Was the data scientist she is waiting for from China? +1982 Is every basketball player still practicing the same play she has been asked to learn since last season? +1983 Has she recorded the dialogues the group of leaders was having about environmental issues? +1984 Will the terrorists kill the helpless victims who try to run away? +1985 Has every skyscraper he ever built has crumbled without warning? +1986 Is the client he is waiting for from China? +1987 Has she recorded the discussion the country's president was having about the refugee crisis? +1988 Will the criminals kill the hostages who try to run away? +1989 Has every skyscraper he ever built has fallen without warning? +1990 Could engines that suit the car make for a smoother ride? +1991 Is the interviewee she is texting from Yale? +1992 Has he recorded the dialogues the country's president was having about the refugee crisis? +1993 Has every bridge he ever constructed has crumbled without warning? +1994 Could body shells that suit the car give drivers an edge when driving? +1995 Was the client he is texting from China? +1996 Is every soccer player still practicing the same move he has been asked to learn since last season? +1997 Has he reported on the discussion the UN council was having about the refugee crisis? +1998 Has every bridge she ever designed has collapsed suddenly? +1999 Could engines that suit the car give drivers an edge when driving? +2000 Is the client she is texting from Yale? +2001 Has he recorded the discussion the group of leaders was having about the refugee crisis? +2002 Will the criminals hurt the hostages who try to run away? +2003 Has every building she ever designed has crumbled without warning? +2004 Is the customer he texting was still on her way? +2005 Has she reported on the dialogues the country's president was having about environmental issues? +2006 Has every skyscraper she ever built has collapsed suddenly? +2007 Could tires that suit the car improve the driving experience for customers? +2008 Was the customer he is texting from Yale? +2009 Is every soccer player still learning the same move he has been instructed to use in the next game since last season? +2010 Has she recorded the discussion the UN council was having regarding the new trade deal? +2011 Has every skyscraper he ever constructed has crumbled without warning? +2012 Could body shells that require a lot of money to design and manufacture make for a smoother ride? +2013 Is the customer she texting was still on his way? +2014 Has she recorded the discussion the group of leaders was having regarding the new trade deal? +2015 Will the terrorists hurt the hostages who try to run away? +2016 Has every skyscraper she ever designed has collapsed without warning? +2017 Could engines that require a lot of money to design and manufacture make for a smoother ride? +2018 Is the customer he waiting for is still on her way? +2019 Is every basketball player still practicing the same move she has been asked to learn since last season? +2020 Has he reported on the dialogues the group of leaders was having regarding the new trade deal? +2021 Will the terrorists torture the hostages who contact the police? +2022 Has every building she ever erected has toppled over without warning? +2023 Could body shells that suit the car improve the driving experience for customers? +2024 Is the interviewee he texting is still on his way? +2025 Has he recorded the discussion the country's president was having regarding the new trade deal? +2026 Has every building she ever built has collapsed without warning? +2027 Are CEOs that don't respect the employees disliked by most people? +2028 Could tires that suit the car make for a smoother ride? +2029 Is the data scientist he is texting from Yale? +2030 Has he reported on the dialogues the group of leaders was having about the refugee crisis? +2031 Will the criminals kill the helpless victims who contact the police? +2032 Has every bridge he ever built has fallen without warning? +2033 Are bosses that don't respect the employees disliked by most people? +2034 Do engines that require a lot of money to design and manufacture could improve the driving experience for customers? +2035 Is the client she texting is still on his way? +2036 Is every basketball player still learning the same play he has been instructed to use in the next game since last season? +2037 Has he recorded the discussion the country's president was having about environmental issues? +2038 Has every bridge she ever constructed has crumbled without warning? +2039 Is the data scientist she waiting for is still on her way? +2040 Is every football player still practicing the same move she has been asked to learn since last season? +2041 Has she recorded the discussion the UN council was having about environmental issues? +2042 Has every skyscraper he ever built has crumbled suddenly? +2043 Is the data scientist she is texting from China? +2044 Is every football player still practicing the same move he has been instructed to use in the next game since last season? +2045 Has he reported on the discussion the UN council was having about environmental issues? +2046 Has every bridge he ever constructed has fallen suddenly? +2047 Are bosses that don't pay their staff well disliked by most people? +2048 Is the interviewee she is waiting for from Yale? +2049 Is every soccer player still learning the same play she has been instructed to use in the next game since last season? +2050 Has he recorded the dialogues the group of leaders was having regarding the new trade deal? +2051 Has every bridge she ever erected has collapsed without warning? +2052 Don't bosses that allow their employees to return home by 5 PM are disliked by most people? +2053 Is the data scientist she texting was still on his way? +2054 Has she recorded the dialogues the UN council was having about environmental issues? +2055 Will the criminals kill the helpless victims who try to run away? +2056 Has every building he ever designed has fallen suddenly? +2057 Should that notorious instance of white-collar crime bring about the execution of new policy that could lead to the cancellation of the Olympics? +2058 Are CEOs that don't pay their staff well disliked by most people? +2059 Could body shells that suit the car make for a smoother ride? +2060 Was the client she is waiting for from Yale? +2061 Is every soccer player still practicing the same play she has been instructed to use in the next game since last season? +2062 Has he recorded the dialogues the group of leaders was having about the refugee crisis? +2063 Will the terrorists hurt the hostages who contact the police? +2064 Has every building she ever designed has collapsed suddenly? +2065 Should last month's earthquake lead to the execution of new policy that could help small business owners? +2066 Could tires that require a lot of money to design and manufacture make for a smoother ride? +2067 Is the interviewee she texting is still on his way? +2068 Has he reported on the discussion the UN council was having regarding the new trade deal? +2069 Will the criminals torture the helpless victims who try to run away? +2070 Has every skyscraper she ever erected has fallen suddenly? +2071 Should that notorious instance of white-collar crime lead to the execution of new policy that could help small business owners? +2072 Are CEOs that don't respect the employees annoying? +2073 Was the interviewee she is waiting for from China? +2074 Is every soccer player still practicing the same move she has been asked to learn since last season? +2075 Has she reported on the discussion the UN council was having regarding the new trade deal? +2076 Will the terrorists torture the helpless victims who contact the police? +2077 Has every bridge he ever constructed has toppled over without warning? +2078 Should the social movement taking over our streets bring about the execution of new policy that could help small business owners? +2079 Don't CEOs that allow their employees to return home by 5 PM are disliked by most people? +2080 Will the customers that come by every Tuesday night book an event here? +2081 Is the interviewee he is waiting for from China? +2082 Has she recorded the discussion the group of leaders was having about the refugee crisis? +2083 Will the criminals torture the hostages who contact the police? +2084 Has every bridge she ever designed has collapsed without warning? +2085 Should the devastating tsunami last year bring about the passage of a new law that could lead to the cancellation of the Olympics? +2086 Will the ladies that play bingo here on the weekends gather at our shop tonight? +2087 Is the data scientist he waiting for was still on her way? +2088 Has he recorded the dialogues the UN council was having about environmental issues? +2089 Will the terrorists kill the helpless victims who contact the police? +2090 Has every bridge he has ever designed toppled over without warning? +2091 Is the client she waiting for was still on his way? +2092 Has she recorded the dialogues the group of leaders was having about the refugee crisis? +2093 Will the criminals kill the hostages who contact the police? +2094 Has every building she ever constructed has collapsed without warning? +2095 Should last month's earthquake lead to the passage of a new law that could help small business owners? +2096 Do the customers that come by every Tuesday night will gather at our shop tonight? +2097 Is the interviewee he waiting for was still on his way? +2098 Has she recorded the discussion the country's president was having regarding the new trade deal? +2099 Will the terrorists hurt the helpless victims who try to run away? +2100 Has every skyscraper she ever designed has crumbled suddenly? +2101 Are bosses that don't allow their employees to return home by 5 PM annoying? +2102 Is the customer he is waiting for from Yale? +2103 Has he reported on the dialogues the group of leaders was having about environmental issues? +2104 Has every building she ever designed has collapsed without warning? +2105 Are managers that don't pay their staff well annoying? +2106 Is the data scientist she is waiting for from Yale? +2107 Has he reported on the dialogues the UN council was having about the refugee crisis? +2108 Will the criminals torture the helpless victims who contact the police? +2109 Has every bridge he ever constructed has fallen without warning? +2110 Will the customers that play bingo here on the weekends gather at our shop tonight? +2111 Is the customer he texting is still on her way? +2112 Has she reported on the discussion the country's president was having regarding the new trade deal? +2113 Will the criminals torture the hostages who try to run away? +2114 Has every building she ever built has toppled over suddenly? +2115 Should the social movement taking over our streets bring about the passage of a new law that could lead to the cancellation of the Olympics? +2116 Are bosses that don't pay their staff well annoying? +2117 Will the patrons that come by every Tuesday night book an event here? +2118 Is the customer he texting was still on his way? +2119 Has he recorded the discussion the country's president was having about the refugee crisis? +2120 Will the terrorists torture the hostages who try to run away? +2121 Has every bridge he ever built has collapsed without warning? +2122 Should last month's earthquake bring about the passage of a new law that could lead to the cancellation of the Olympics? +2123 Don't managers that allow their employees to return home by 5 PM are disliked by most people? +2124 Will the patrons that usually go to our competitor gather at our shop tonight? +2125 Is the doctor he seeing was the eldest in the hospital? +2126 Has every bridge she has ever designed toppled over suddenly? +2127 Will the flies find bananas that fester in the trash can? +2128 Don't managers that respect the employees are annoying? +2129 Is the doctor she is seeing the eldest in the hospital? +2130 Has every building she has ever designed toppled over without warning? +2131 Is every little girl still eating the sandwich she has been nibbling on since the family dinner? +2132 Will the flies eat bananas that get exposed to the air? +2133 Should that notorious instance of white-collar crime lead to the passage of a new law that could lead to the cancellation of the Olympics? +2134 Are managers that don't allow their employees to return home by 5 PM annoying? +2135 Do the ladies that play bingo here on the weekends will book an event here? +2136 Is the surgeon he seeing was the eldest in the hospital? +2137 Has every bridge she ever erected has fallen suddenly? +2138 Is every child still eating the pie she has been nibbling on since the family dinner? +2139 Will the flies find apples that stay in the garbage too long? +2140 Are managers that don't respect the employees disliked by most people? +2141 Will the patrons that usually go to our competitor book an event here? +2142 Is the cardiologist she seeing was enjoying her holiday? +2143 Has every bridge he ever built has fallen suddenly? +2144 Is every kid still eating the banana she has been nibbling on since the family dinner? +2145 Will the insects eat apples that get exposed to the air? +2146 Should the social movement taking over our streets bring about the execution of new policy that could lead to the cancellation of the Olympics? +2147 Will the customers that usually go to our competitor gather at our shop tonight? +2148 Is the doctor she consulting is enjoying her holiday? +2149 Has every skyscraper she ever designed has fallen suddenly? 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the trader was selling at high price? +2553 Shouldn't the deli use chemicals that present dangers to the consumers' health? +2554 Will the publicly traded assets that remain in our possession the longest get transferred upon the sale of the company? +2555 Is the student she supervising was reported for cheating? +2556 Has he invested in the stock the trader was selling at high price? +2557 Is Gold's new economic theory presenting new creative opportunities to every economist that was used to the old way of doing things? +2558 Are the buttons that are most frequently used on the control console most vulnerable? +2559 Will the assets that lose us the most money get sold? +2560 Is the graduate student she is working with interested in Linguistics? +2561 Is every teacher at that school still stuck in the same place she has been in since the earthquake? +2562 Has she sold the shares the company was selling at high price? +2563 Shouldn't the snack companies overuse chemicals that present 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most often used in daily life enhanced by the manufacturer to improve the durability of the product? +2574 Will the stocks and bonds that remain in our possession the longest get transferred upon the sale of the company? +2575 Is the student she supervising was interested in Linguistics? +2576 Is every nurse at that hospital still stuck in the same country she has been in since graduation? +2577 Has she invested in the stock the bank was selling at high price? +2578 Shouldn't the deli overuse additives that cause potential health problems? +2579 Do the publicly traded assets that lose us the most money will get sold? +2580 Is the undergraduate he advising was interested in Linguistics? +2581 Is every nurse at that hospital still living in the same city he has been in since last year? +2582 Has she invested in the shares the trader was buying at high price? +2583 Shouldn't the deli use chemicals that cause potential health problems? +2584 Is Gold's new economic theory changing 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stock the trader was selling for a loss? +2596 Is Apple's new technology presenting new creative opportunities to every programmer that was trained in the techniques of the previous generation? +2597 Are the keys that are most frequently used on the control console most vulnerable? +2598 Is the undergraduate he advising was tall? +2599 Is every nurse at that hospital still stuck in the same place she has been in since the earthquake? +2600 Has he sold the shares the firm was selling after the market crash? +2601 Shouldn't the snack companies rely on chemicals that present dangers to the consumers' health? +2602 Will the stocks and bonds that lose us the most money get sold? +2603 Is the undergraduate she supervising is interested in Linguistics? +2604 Is every nurse at that hospital still stuck in the same city he has been in since last year? +2605 Has he sold the stock the firm was selling for a loss? +2606 Shouldn't the snack companies use additives that present dangers to the 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Britain pass through the Suez Canal? +2630 Is the graduate student he is advising tall? +2631 Is every nurse at that hospital still living in the same place he has been in since last year? +2632 Has he sold the stock the firm was buying at high price? +2633 Shouldn't the coffee shops overuse additives that present dangers to the consumers' health? +2634 Are commercial boats that headed for India from Europe will be on the high seas for several weeks? +2635 Do the assets that increase most in value will get transferred upon the sale of the company? +2636 Is the student she advising is tall? +2637 Has she bought the shares the company was buying for a loss? +2638 Are most container ships that headed for India from Europe will pass through the Suez Canal? +2639 Is every nurse at that hospital still living in the same country he has been in since the earthquake? +2640 Has he sold the shares the bank was selling after the market crash? +2641 Are commercial boats that going from Europe to Asia will pass through the Suez Canal? +2642 Do the publicly traded assets that remain in our possession the longest will get sold? +2643 Is the student he is working with tall? +2644 Is every nurse at that hospital still stuck in the same place he has been in since graduation? +2645 Has she invested in the shares the firm was selling after the market crash? +2646 Shouldn't the deli rely on additives that present dangers to the consumers' health? +2647 Will the stocks and bonds that remain in our possession the longest get sold? +2648 Is the student she advising is interested in Linguistics? +2649 Is every nurse at that hospital still living in the same place she has been in since the earthquake? +2650 Has he bought the stock the bank was buying at high price? +2651 Shouldn't the coffee shops use additives that cause potential health problems? +2652 Do the assets that remain in our possession the longest will get sold? +2653 Is the undergraduate she advising was tall? +2654 Is every nurse at that hospital still living in the same city she has been in since last year? +2655 Has he bought the stock the trader was buying for a loss? +2656 Will most cargo vessels that are traveling between China and Britain be on the high seas for several weeks? +2657 Do the publicly traded assets that increase most in value will get sold? +2658 Is the undergraduate he advising was reported for cheating? +2659 Is every teacher at that school still stuck in the same place he has been in since graduation? +2660 Has he invested in the shares the company was selling at high price? +2661 Are commercial boats that going from Europe to Asia will be on the high seas for several weeks? +2662 Will the assets that remain in our possession the longest get transferred upon the sale of the company? +2663 Is the undergraduate he supervising was reported for cheating? +2664 Has he bought the shares the bank was selling for a loss? +2665 Shouldn't the snack companies overuse chemicals that cause 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lions focus on buffalo that are old? +2680 Will most container ships that are traveling between China and Britain be on the high seas for several weeks? +2681 Is the apple she is eating on sale? +2682 Has she repaired the rover NASA was launching to the moon? +2683 Will the tigers focus on sheep that are weak? +2684 Has a team of archaeologists from the University of Arizona dug up animal fossils that could lead to breakthroughs in anthropological studies? +2685 Are most container ships that going from Europe to Asia will pass through the Suez Canal? +2686 Was the orange she is having fresh? +2687 Will the lions chase after rabbits that are weak? +2688 Was the banana he is having on sale? +2689 Will the lions chase after buffalo that are weak? +2690 Will most cargo vessels that are traveling between China and Britain pass through the Suez Canal? +2691 Is the apple he is eating on sale? +2692 Is every scientist still holding on to the theory she has been working on since last year? 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sale? +2722 Will the lions chase after buffalo that are sick? +2723 Will the predators tend to catch sheep that are sick? +2724 Is the apple he is having fresh? +2725 Will the tigers chase after buffalo that are weak? +2726 Was the apple she is having grown by local farmers? +2727 Will the tigers chase after rabbits that are weak? +2728 Has every student he ever taught has progressed a lot? +2729 Is the apple he eating is grown by local farmers? +2730 Has she repaired the rover Russia was sending to the moon? +2731 Will the tigers tend to catch sheep that are sick? +2732 Has every pupil he ever tutored has progressed a lot? +2733 Has he tested the rover NASA was sending to the moon? +2734 Will the predators tend to catch rabbits that are young? +2735 Has every pupil she ever tutored has improved slowly? +2736 Was the banana he is eating fresh? +2737 Will the predators tend to catch sheep that are old? +2738 Has every student he ever tutored has grown a lot? +2739 Was the banana she is eating grown by local farmers? +2740 Has he repaired the rover the European Space Agency was sending to the moon? +2741 Will the tigers focus on sheep that are young? +2742 Has every student she ever taught has improved a lot? +2743 Is the banana she is having grown by local farmers? +2744 Is every researcher still holding on to the argument he has been working on since last century? +2745 Will the tigers chase after rabbits that are young? +2746 Is the apple she eating is grown by local farmers? +2747 Will the tigers chase after rabbits that are sick? +2748 Has every student he ever tutored has grown slowly? +2749 Was the orange she is having grown by local farmers? +2750 Will the lions focus on sheep that are sick? +2751 Is the orange she is having grown by local farmers? +2752 Is every scientist still holding on to the theory he has been working on since last century? +2753 Has he repaired the rover NASA was sending to the moon? +2754 Will the predators chase after sheep that are 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eating grown by local farmers? +2797 Is every scientist still trying to prove the theory she has been working on since last century? +2798 Will the predators focus on rabbits that are old? +2799 Has every student she ever advised has progressed a lot? +2800 Is a serious accusation taking down an team that had won last year's competition? +2801 Is the orange she is eating grown by local farmers? +2802 Is every researcher still holding on to the argument she has been working on since last century? +2803 Will the tigers focus on sheep that are sick? +2804 Is a scandal taking down an athlete that had won last year's competition? +2805 Is the apple he having was fresh? +2806 Is every professor still advocating for the theory he has been thinking about since last century? +2807 Has she tested the rover Russia was sending to the moon? +2808 Will the predators focus on sheep that are young? +2809 Is a serious accusation taking down an team that had had a long and storied career? +2810 Is the 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condemned the chaos the right wing militia was plotting after the election? +2852 Couldn't rubbers remove the words that fail the quality check? +2853 Is the class she is taking excellent? +2854 Has every pupil she ever tutored has improved a lot? +2855 Is every director still trying to finish the story he has been developing since last October? +2856 Has she given tacit approval to the unrest the group of extremists was plotting after the election? +2857 Couldn't rubbers erase the texts that fail the quality check? +2858 Is a scandal taking down an athlete that had visited the White House last week? +2859 Has every student he ever advised has improved slowly? +2860 Is every director still thinking about the story she has been developing since seeing the last Star Wars movie? +2861 Has she praised the violence the right wing militia was causing after the election? +2862 Is the training program he taking was excellent? +2863 Is every writer still thinking about the story he has been 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writing since the beginning of the year? +2888 Has she given tacit approval to the violence the group of extremists was causing after the election? +2889 Couldn't erasers erase the words that offend people? +2890 Has every student she ever advised has grown slowly? +2891 Is every director still thinking about the story she has been planning out since seeing the last Star Wars movie? +2892 Couldn't rubbers clear the articles that fail the quality check? +2893 Is a serious accusation causing a headache for an athlete that had visited the White House last week? +2894 Has every student he ever tutored has progressed slowly? +2895 Is every writer still thinking about the story he has been developing since seeing the last Star Wars movie? +2896 Has he given tacit approval to the chaos the group of extremists was instigating after the election? +2897 Couldn't rubbers erase the articles that offend people? +2898 Has every student he ever advised has progressed a lot? +2899 Is every director 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story he has been writing since the beginning of the year? +2912 Has she given tacit approval to the violence the group of extremists was plotting after the election? +2913 Couldn't erasers remove the texts that offend people? +2914 Had a serious accusation is causing a headache for an team that won last year's competition? +2915 Is every comic book writer still working on the story he has been developing since last October? +2916 Has she condemned the unrest the group of extremists was plotting after the election? +2917 Couldn't rubbers clear the texts that fail the quality check? +2918 Is a serious accusation causing a headache for an athlete that had won last year's competition? +2919 Was the course she is studying for excellent? +2920 Is every director still thinking about the story he has been planning out since the beginning of the year? +2921 Has every air conditioner he ever installed has stopped working early? +2922 Is a scandal causing a headache for an athlete that had visited the White House last week? +2923 Is every director still working on the story she has been developing since the beginning of the year? +2924 Has she condemned the chaos the group of extremists was plotting after the election? +2925 Has every home appliance she has ever repaired stopped working at great cost? +2926 Is a serious accusation taking down an athlete that had won last year's competition? +2927 Is the training program she is enrolled in excellent? +2928 Is every writer still working on the story she has been writing since the beginning of the year? +2929 Has he condemned the unrest the group of extremists was causing after the election? +2930 Has every air conditioner he ever repaired has broken early? +2931 Can a specialized kind of bone structure work to the advantage of an animal that is experiencing competition from others of its species? +2932 Is the training program she enrolled in was excellent? +2933 Is every writer still thinking about the story she has been writing since seeing the last Star Wars movie? +2934 Couldn't erasers clear the articles that offend people? +2935 Has every air conditioner he ever repaired has stopped working at great cost? +2936 Can a specialized kind of bone structure confer a competitive advantage to a potential prey animal that is experiencing competition from others of its species? +2937 Is every writer still thinking about the story she has been planning out since last October? +2938 Has he praised the unrest the group of extremists was plotting after the election? +2939 Couldn't rubbers clear the words that offend people? +2940 Has every refrigerator she ever repaired has stopped working early? +2941 Can the ability to regulate body temperature work to the advantage of a predator that is experiencing competition from others of its species? +2942 Is every comic book writer still working on the story he has been planning out since the beginning of the year? +2943 Couldn't rubbers remove the texts that offend people? +2944 Has every air conditioner she ever installed has broken early? +2945 Is every writer still trying to finish the story he has been developing since last October? +2946 Couldn't rubbers clear the texts that offend people? +2947 Has every home appliance he has ever installed broken at great cost? +2948 Is every comic book writer still working on the story she has been planning out since seeing the last Star Wars movie? +2949 Has she given tacit approval to the chaos the right wing militia was causing after the election? +2950 Has every air conditioner she ever repaired has broken early? +2951 Can a specialized kind of bone structure increase the fitness of an animal that is experiencing competition from others of its species? +2952 Is every writer still trying to finish the story he has been planning out since last October? +2953 Couldn't erasers erase the words that fail the quality check? +2954 Is every comic book writer still thinking about the story she has been writing since the beginning of the year? +2955 Couldn't rubbers erase the articles that fail the quality check? +2956 Has every home appliance she ever repaired has stopped working early? +2957 Is every director still trying to finish the story she has been writing since the beginning of the year? +2958 Has she condemned the violence the right wing militia was causing after the election? +2959 Couldn't rubbers erase the texts that offend people? +2960 Has every air conditioner she ever repaired has stopped working at great cost? +2961 Is the training program she is studying for excellent? +2962 Is every comic book writer still working on the story she has been writing since last October? +2963 Has he given tacit approval to the chaos the right wing militia was plotting after the election? +2964 Couldn't erasers clear the texts that offend people? +2965 Has every air conditioner she ever installed has stopped working at great cost? +2966 Is every comic book writer still thinking about the story 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experiencing competition from others of its species? +2977 Is the course she is enrolled in excellent? +2978 Is every writer still trying to finish the story he has been writing since seeing the last Star Wars movie? +2979 Couldn't rubbers erase the words that offend people? +2980 Has every refrigerator she has ever installed stopped working at great cost? +2981 Can a colored patten on the hair and skin work to the advantage of an animal that is experiencing competition from others of its species? +2982 Is the course he taking was excellent? +2983 Is every comic book writer still working on the story she has been developing since seeing the last Star Wars movie? +2984 Has he praised the unrest the group of extremists was instigating after the election? +2985 Has every home appliance he has ever repaired broken at great cost? +2986 Can a colored patten on the hair and skin confer a competitive advantage to a predator that is experiencing competition from others of its species? +2987 Is the course she enrolled in was excellent? +2988 Is every comic book writer still thinking about the story he has been writing since seeing the last Star Wars movie? +2989 Couldn't erasers remove the words that offend people? +2990 Has every air conditioner he ever repaired has stopped working early? +2991 Can a colored patten on the hair and skin work to the advantage of a predator that is experiencing competition from others of its species? +2992 Is the class she is studying for excellent? +2993 Is every writer still trying to finish the story she has been planning out since last October? +2994 Couldn't erasers erase the texts that fail the quality check? +2995 Has every home appliance he ever repaired has stopped working early? +2996 Can the ability to regulate body temperature confer a competitive advantage to a potential prey animal that is experiencing competition from others of its species? +2997 Is every comic book writer still trying to finish the story she has been developing since last October? +2998 Has every air conditioner he ever installed has stopped working at great cost? +2999 Can a colored patten on the hair and skin confer a competitive advantage to a potential prey animal that is experiencing competition from others of its species? +3000 Is every writer still thinking about the story she has been planning out since the beginning of the year? +3001 Has every home appliance he has ever installed stopped working at great cost? +3002 Can a colored patten on the hair and skin increase the fitness of a potential prey animal that is experiencing competition from others of its species? +3003 Was the class he is studying for excellent? +3004 Is every comic book writer still working on the story he has been writing since last October? +3005 Couldn't rubbers remove the articles that offend people? +3006 Has every home appliance she has ever installed stopped working early? +3007 Is every comic book writer still working on the story she has been developing since the beginning of the year? +3008 Couldn't erasers remove the words that fail the quality check? +3009 Can a specialized kind of bone structure confer a competitive advantage to a predator that is experiencing competition from others of its species? +3010 Is every comic book writer still thinking about the story she has been writing since seeing the last Star Wars movie? +3011 Has he condemned the chaos the group of extremists was instigating after the election? +3012 Has every air conditioner she ever installed has broken at great cost? +3013 Is the course she taking was excellent? +3014 Is every writer still trying to finish the story she has been developing since the beginning of the year? +3015 Couldn't erasers clear the words that fail the quality check? +3016 Has every refrigerator she ever repaired has stopped working at great cost? +3017 Can the ability to regulate body temperature confer a competitive advantage to a predator that is experiencing competition from others of its species? +3018 Was the training program she is studying for excellent? +3019 Is every writer still thinking about the story she has been developing since last October? +3020 Couldn't rubbers remove the words that offend people? +3021 Is every writer still thinking about the story she has been developing since the beginning of the year? +3022 Has she given tacit approval to the violence the group of extremists was instigating after the election? +3023 Has every home appliance he has ever installed stopped working early? +3024 Can the ability to regulate body temperature increase the fitness of a predator that is experiencing competition from others of its species? +3025 Is the class she enrolled in was excellent? +3026 Is every writer still trying to finish the story she has been planning out since seeing the last Star Wars movie? +3027 Has she praised the chaos the group of extremists was plotting after the election? +3028 Has every home appliance she has ever installed broken at great cost? +3029 Was the course he is studying for excellent? +3030 Is every writer still working on the story she has been planning out since seeing the last Star Wars movie? +3031 Couldn't erasers erase the articles that fail the quality check? +3032 Has every refrigerator he ever installed has broken early? +3033 Can a colored patten on the hair and skin increase the fitness of a predator that is experiencing competition from others of its species? +3034 Was the training program he is studying for excellent? +3035 Is every comic book writer still trying to finish the story he has been planning out since the beginning of the year? +3036 Has he given tacit approval to the unrest the right wing militia was instigating after the election? +3037 Couldn't erasers remove the texts that fail the quality check? +3038 Has every refrigerator she ever installed has broken at great cost? +3039 Is the chair he is sitting on imported from Germany? +3040 Is every human resource manager still trying to achieve the objective he has been assigned with by the department since last year? +3041 Is the chair she is sitting on on sale? +3042 Is every team leader still trying to achieve the objective he has been assigned with by the department since the rapid expansion of the market size? +3043 Has he gotten the trophy every competitor was hoping for this season? +3044 Is the bed he is sitting on the most expensive item at the furniture market? +3045 Has every refrigerator she ever repaired has broken at great cost? +3046 Is every human resource manager still chasing after the unreasonably high hiring target she has been working on since last year? +3047 Is the table he looking at is imported from Germany? +3048 Has every home appliance she has ever repaired broken early? +3049 Is every team leader still trying to achieve the number he has been assigned with by the department since the rapid expansion of the market size? +3050 Has he earned the title every competitor was hoping for this year? +3051 Was the bed she is sitting on the most expensive item at the furniture market? +3052 Has every home appliance he has ever repaired stopped working at great cost? +3053 Has he gotten the title every competitor was hoping for this year? +3054 Is the table he looking at is on sale? +3055 Is every hiring manager still chasing after the number he has been working on since last year? +3056 Has she won the trophy every competitor was hoping for this year? +3057 Is the desk he is sitting on the most expensive item at the furniture market? +3058 Has every refrigerator she ever repaired has broken early? +3059 Is every hiring manager still trying to achieve the unreasonably high hiring target he has been working on since the rapid expansion of the market size? +3060 Is the bed she looking at was imported from Germany? +3061 Has every refrigerator he ever installed has stopped working at great cost? +3062 Is every hiring manager still chasing after the number she has been assigned with by the department since last year? +3063 Has he won the trophy every competitor was hoping for this season? +3064 Is the table she sitting on was imported from Germany? +3065 Has every home appliance he has ever repaired broken early? +3066 Is every hiring manager still trying to achieve the number she has been assigned with by the department since the rapid expansion of the market size? +3067 Has he earned the trophy every competitor was hoping for this year? +3068 Can a specialized kind of bone structure work to the advantage of a predator that is experiencing competition from others of its species? +3069 Is the chair he looking at was imported from Germany? +3070 Is every hiring manager still chasing after the objective he has been working on since last year? +3071 Has she gotten the award every team was hoping for this year? +3072 Was the chair she is looking at the most expensive item at the furniture market? +3073 Is every hiring manager still chasing after the number he has been assigned with by the department since last year? +3074 Can a colored patten on the hair and skin work to the advantage of a potential prey animal that is experiencing competition from others of its species? +3075 Is the chair she sitting on was on sale? +3076 Has every home appliance he has ever installed broken early? +3077 Can the ability to regulate body temperature increase the fitness of an animal that is experiencing competition from others of its species? +3078 Is the table he looking at was on sale? +3079 Has every home appliance she has ever installed broken early? +3080 Is every human resource manager still chasing after the objective she has been working on since the rapid expansion of the market size? +3081 Can a colored patten on the hair and skin increase the fitness of an animal that is experiencing competition from others of its species? +3082 Is the bed he is sitting on on sale? +3083 Is every human resource manager still chasing after the objective he has been assigned with by the department since the rapid expansion of the market size? +3084 Is the desk she is looking at the most expensive item at the furniture market? +3085 Has every refrigerator she ever installed has stopped working early? +3086 Has he won the award every team was hoping for this year? +3087 Can a specialized kind of bone structure confer a competitive advantage to an animal that is experiencing competition from others of its species? +3088 Is the table she is sitting on the most expensive item at the furniture market? +3089 Has she gotten the award every team was hoping for this season? +3090 Is the table he is sitting on on sale? +3091 Is every human resource manager still trying to achieve the number she has been assigned with by the department since the rapid expansion of the market size? +3092 Is the desk she is looking at imported from Germany? +3093 Has every air conditioner she ever installed has stopped working early? +3094 Has he won the title every competitor was hoping for this season? +3095 Can a specialized kind of bone structure increase the fitness of a predator that is experiencing competition from others of its species? +3096 Is the table he sitting on was the most expensive item at the furniture market? +3097 Is every hiring manager still chasing after the objective he has been assigned with by the department since the rapid expansion of the market size? +3098 Was the bed she is looking at the most expensive item at the furniture market? +3099 Is every team leader still chasing after the number he has been working on since last year? +3100 Has she won the award every team was hoping for this season? +3101 Can the ability to regulate body temperature work to the advantage of an animal that is experiencing competition from others of its species? +3102 Is the chair he is looking at the most expensive item at the furniture market? +3103 Is every team leader still trying to achieve the unreasonably high hiring target she has been assigned with by the department since last year? +3104 Can a specialized kind of bone structure work to the advantage of a potential prey animal that is experiencing competition from others of its species? +3105 Is the table she sitting on was the most expensive item at the furniture market? +3106 Is every hiring manager still trying to achieve the number she has been working on since the rapid expansion of the market size? +3107 Is the chair he is looking at on sale? +3108 Is every hiring manager still trying to achieve the unreasonably high hiring target he has been assigned with by the department since the rapid expansion of the market size? +3109 Has the release of the game brought in an unprecedented gaming experience that will set a new standard for the industry? +3110 Is the chair he is sitting on on sale? +3111 Is every team leader still chasing after the objective he has been assigned with by the department since last year? +3112 Will the launch of the new console has created a new population of customers that change gaming for the worse? +3113 Is the desk he is looking at on sale? +3114 Is every human resource manager still trying to achieve the number he has been working on since the rapid expansion of the market size? +3115 Is the desk he is sitting on imported from Germany? +3116 Has she earned the title every team was hoping for this season? +3117 Has the launch of the new console brought in a new population of customers that will change gaming for the worse? +3118 Was the desk she is sitting on the most expensive item at the furniture market? +3119 Has the release of the game created an unprecedented gaming experience that will change gaming for the worse? +3120 Is the desk she is sitting on on sale? +3121 Is every human resource manager still trying to achieve the number she has been working on since the rapid expansion of the market size? +3122 Has the release of the game brought in a new population of customers that will change gaming for the worse? +3123 Is the bed he sitting on was imported from Germany? +3124 Has he earned the trophy every team was hoping for this season? +3125 Has the sequel to the popular game brought in an unprecedented gaming experience that will set a new standard for the industry? +3126 Is the table she looking at was the most expensive item at the furniture market? +3127 Is every team leader still trying to achieve the unreasonably high hiring target he has been assigned with by the department since the rapid expansion of the market size? +3128 Is the desk he looking at was imported from Germany? +3129 Has the release of the game brought in an unprecedented gaming experience that will change gaming for the worse? +3130 Is the chair he looking at was imported from Germany? +3131 Is the bed he is sitting on imported from Germany? +3132 Has he won the trophy every team was hoping for this year? +3133 Is the bed he is looking at the most expensive item at the furniture market? +3134 Is every hiring manager still chasing after the unreasonably high hiring target she has been assigned with by the department since the rapid expansion of the market size? +3135 Has she won the trophy every team was hoping for this season? +3136 Is the bed she is looking at the most expensive item at the furniture market? +3137 Is every hiring manager still chasing after the objective he has been working on since the rapid expansion of the market size? +3138 Has the sequel to the popular game brought in a new population of customers that will set a new standard for the industry? +3139 Is the chair she sitting on was on sale? +3140 Is every team leader still trying to achieve the objective he has been working on since last year? +3141 Has he earned the title every team was hoping for this year? +3142 Is the bed he looking at was on sale? +3143 Has the sequel to the popular game brought in a new population of customers that will change gaming for the worse? +3144 Is the chair she is looking at imported from Germany? +3145 Has the launch of the new console brought in an unprecedented gaming experience that will change gaming for the worse? +3146 Is the chair he looking at was on sale? +3147 Is every human resource manager still chasing after the number she has been assigned with by the department since the rapid expansion of the market size? +3148 Has he gotten the title every competitor was hoping for this season? +3149 Will the release of the game has created a new population of customers that set a new standard for the industry? +3150 Is the chair she is sitting on the most expensive item at the furniture market? +3151 Is every hiring manager still chasing after the number she has been assigned with by the department since the rapid expansion of the market size? +3152 Has he gotten the title every team was hoping for this year? +3153 Has the sequel to the popular game created an unprecedented gaming experience that will change gaming for the worse? +3154 Is the bed she sitting on was imported from Germany? +3155 Is every hiring manager still trying to achieve the number he has been working on since the rapid expansion of the market size? +3156 Will the release of the game has created an unprecedented gaming experience that set a new standard for the industry? +3157 Is the chair he looking at was on sale? +3158 Has he gotten the award every competitor was hoping for this season? +3159 Is the chair she is sitting on on sale? +3160 Is every team leader still chasing after the unreasonably high hiring target she has been working on since last year? +3161 Has she earned the trophy every competitor was hoping for this year? +3162 Has the sequel to the popular game brought in an unprecedented gaming experience that will change gaming for the worse? +3163 Is the chair he is sitting on imported from Germany? +3164 Has she earned the title every competitor was hoping for this year? +3165 Will the sequel to the popular game has created an unprecedented gaming experience that set a new standard for the industry? +3166 Is the bed she is sitting on on sale? +3167 Has the launch of the new console brought in an unprecedented gaming experience that will set a new standard for the industry? +3168 Is the desk he looking at was on sale? +3169 Is every team leader still trying to achieve the number she has been assigned with by the department since the rapid expansion of the market size? +3170 Has he gotten the trophy every team was hoping for this season? +3171 Is the desk she sitting on was on sale? +3172 Will the sequel to the popular game has created a new population of customers that change gaming for the worse? +3173 Is the desk he sitting on was on sale? +3174 Is every human resource manager still chasing after the number she has been assigned with by the department since last year? +3175 Has he gotten the award every team was hoping for this year? +3176 Is the table she is looking at imported from Germany? +3177 Has he won the trophy every competitor was hoping for this year? +3178 Will the launch of the new console has created an unprecedented gaming experience that set a new standard for the industry? +3179 Is the chair she sitting on was imported from Germany? +3180 Has the release of the game created a new population of customers that will change gaming for the worse? +3181 Is the college he a teacher at was one of the best in that area? +3182 Has he declined to participate in the game the university was hosting due to medical conditions? +3183 Will the launch of the new console has created a new population of customers that set a new standard for the industry? +3184 Is the primary school he is from one of the best in that area? +3185 Has she quit the tournament the university was planning due to a family emergency? +3186 Don't those politicians make friends with actors who hold racist views? +3187 Has the launch of the new console created an unprecedented gaming experience that will change gaming for the worse? 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student at hiring more teachers this year? +3200 Has he dropped out of the game the city was holding due to medical conditions? +3201 Will the launch of the new console has brought in a new population of customers that set a new standard for the industry? +3202 Is the college she a student at is located on a small island? +3203 Has she dropped out of the tournament the summer camp was planning due to a family emergency? +3204 Do those teachers don't like actors who brag about their past achievements all the time? +3205 Is the university he is a student at hiring more teachers this year? +3206 Is every actor in that TV series still playing the same character he has been famous for since decades ago? +3207 Has she dropped out of the race the summer camp was planning due to a family emergency? +3208 Do those politicians don't like collaborators who hold racist views? +3209 Was the primary school she is a teacher at John's dream school? +3210 Is every actor in that successful movie still playing the same character he has been known for since the war ended? +3211 Has she dropped out of the race the university was hosting due to medical conditions? +3212 Do those politicians don't respect actors who hold racist views? +3213 Is the Mars rover taking pictures of radiation on the planet that could help us prove the existence of alien creatures? +3214 Was the college he is interested in hiring more teachers this year? +3215 Is every actor in that TV series still playing the same role he has been famous for since decades ago? +3216 Has he dropped out of the game the summer camp was planning in the summer? +3217 Do those politicians don't make friends with actors who brag about their past achievements all the time? +3218 Is the Mars rover taking pictures of any organic materials that could help us prove the existence of alien creatures? +3219 Is the school he cooperating with was hiring more teachers this year? +3220 Is every actor in that TV series still playing the same role he has been known for since the war ended? +3221 Has he declined to participate in the game the summer camp was hosting due to a family emergency? +3222 Is the NASA probe taking pictures of any organic materials that could help us prove the existence of alien creatures? +3223 Is the primary school she is cooperating with hiring more teachers this year? +3224 Is every actor in that successful movie still playing the same role he has been famous for since decades ago? +3225 Has she declined to participate in the race the university was hosting due to a family emergency? +3226 Don't those professors make friends with actors who hold racist views? +3227 Is the school she from was one of the best in that area? +3228 Is every actor in that successful movie still playing the same character he has been famous for since the war ended? +3229 Has he quit the game the city was holding due to medical conditions? +3230 Is the Mars rover taking pictures of a previously unknown geological feature that could help scientists learn about the environment on the planet's surface? +3231 Is the school she interested in was John's dream school? +3232 Has she quit the game the city was holding due to a family emergency? +3233 Don't those teachers make friends with collaborators who hold racist views? +3234 Is the astronaut detecting a previously unknown geological feature that could help scientists learn about the environment on the planet's surface? +3235 Is the university he is a teacher at John's dream school? +3236 Is every actor in that successful movie still playing the same role he has been known for since decades ago? +3237 Has she quit the tournament the city was holding due to a family emergency? +3238 Do those teachers don't like actors who hold racist views? +3239 Is the astronaut detecting any organic materials that could help scientists learn about the environment on the planet's surface? +3240 Is the college he a teacher at is located on a small island? +3241 Is every actor in that successful movie still playing the same role he has been known for since the war ended? +3242 Has he dropped out of the race the city was holding due to a family emergency? +3243 Don't those professors make friends with collaborators who brag about their past achievements all the time? +3244 Is the university he is cooperating with one of the best in that area? +3245 Is every actor in that successful movie still playing the same character he has been known for since decades ago? +3246 Has he declined to participate in the game the city was hosting in the summer? +3247 Is the astronaut taking pictures of a previously unknown geological feature that could help scientists learn about the environment on the planet's surface? +3248 Is the college he interested in was John's dream school? +3249 Is every actor in that TV series still playing the same character he has been known for since decades ago? +3250 Has he quit the game the university was hosting in the summer? +3251 Do those professors don't respect people who brag about their past achievements all the time? +3252 Is the astronaut detecting radiation on the planet that could help scientists learn about the environment on the planet's surface? +3253 Is the primary school he is cooperating with hiring more teachers this year? +3254 Is every actor in that TV series still playing the same role he has been known for since decades ago? +3255 Has she dropped out of the tournament the summer camp was hosting due to medical conditions? +3256 Has every company she ever sold has grown extremely slowly? +3257 Is the primary school she cooperating with was one of the best in that area? +3258 Has she declined to participate in the game the summer camp was hosting due to medical conditions? +3259 Do those professors don't respect collaborators who hold racist views? +3260 Is the university she a student at was located on a small island? +3261 Is every actor in that TV series still playing the same role he has been famous for since the war ended? +3262 Has she quit the tournament the university was holding in the summer? +3263 Is the NASA probe detecting any organic materials that could help us prove the existence of alien creatures? +3264 Was the primary school she is from John's dream school? +3265 Is every actor in that successful movie still playing the same role he has been famous for since the war ended? +3266 Has she quit the tournament the city was planning due to a family emergency? +3267 Do those teachers don't like people who hold racist views? +3268 Is the NASA probe detecting radiation on the planet that could help scientists learn about the environment on the planet's surface? +3269 Is the college she interested in was located on a small island? +3270 Is every actor in that TV series still playing the same character he has been known for since the war ended? +3271 Has she quit the tournament the university was planning due to medical conditions? +3272 Do those politicians don't make friends with people who brag about their past achievements all the time? +3273 Is the college she cooperating with was located on a small island? +3274 Is every actor in that successful movie still playing the same character he has been famous for since decades ago? +3275 Has he declined to participate in the tournament the city was holding due to medical conditions? +3276 Do those teachers don't like collaborators who brag about their past achievements all the time? +3277 Has every company he ever sold has lost value extremely slowly? +3278 Is the astronaut taking pictures of radiation on the planet that could help us prove the existence of alien creatures? +3279 Is the school she cooperating with is John's dream school? +3280 Has she declined to participate in the tournament the university was hosting due to a family emergency? +3281 Do those politicians don't make friends with collaborators who brag about their past achievements all the time? +3282 Is every oversized company still producing the same product it has been making since first going public? +3283 Is the NASA probe taking pictures of a previously unknown geological feature that could help us prove the existence of alien creatures? +3284 Is the college he cooperating with is one of the best in that area? +3285 Has he declined to participate in the tournament the city was hosting in the summer? +3286 Do those professors don't like actors who brag about their past achievements all the time? +3287 Is the astronaut detecting any organic materials that could help us prove the existence of alien creatures? +3288 Is the college he is a teacher at John's dream school? +3289 Has she quit the game the university was hosting due to a family emergency? +3290 Has every company she ever sold has lost value extremely slowly? +3291 Is the college she is from John's dream school? +3292 Has she dropped out of the game the city was hosting due to medical conditions? +3293 Has every startup he has ever invested in lost value extremely slowly? +3294 Is every firm in the rust belt still producing the same type of cars it has been improving since first going public? +3295 Is the Mars rover detecting any organic materials that could help us prove the existence of alien creatures? +3296 Is the primary school he a teacher at is located on a small island? +3297 Has she declined to participate in the tournament the city was planning due to a family emergency? +3298 Don't those professors like actors who hold racist views? +3299 Has every startup he ever bought has lost value extremely slowly? +3300 Is every firm in the rust belt still manufacturing the same product it has been improving since first going public? +3301 Is the university he cooperating with was located on a small island? +3302 Has she quit the tournament the summer camp was planning in the summer? +3303 Has every stock she ever sold has grown extremely slowly? +3304 Is every firm in the rust belt still making the same type of cars it has been known for since the last century? +3305 Is the NASA probe taking pictures of radiation on the planet that could help scientists learn about the environment on the planet's surface? +3306 Is the university he is interested in hiring more teachers this year? +3307 Has he declined to participate in the race the summer camp was planning due to a family emergency? +3308 Is every firm in the rust belt still manufacturing the same product it has been making since the last century? +3309 Is the NASA probe taking pictures of any organic materials that could help scientists learn about the environment on the planet's surface? +3310 Is the university she is interested in hiring more teachers this year? +3311 Has he quit the race the university was holding due to medical conditions? +3312 Do those professors don't like collaborators who brag about their past achievements all the time? +3313 Has every company she ever bought has lost value extremely slowly? +3314 Is the Mars rover detecting radiation on the planet that could help us prove the existence of alien creatures? +3315 Is the college she is a teacher at John's dream school? +3316 Has he quit the game the city was hosting due to a family emergency? +3317 Has every stock he has ever invested in lost value extremely slowly? +3318 Is every oversized company still producing the same type of cars it has been known for since the last century? +3319 Is the astronaut taking pictures of radiation on the planet that could help scientists learn about the environment on the planet's surface? +3320 Is the primary school she is a teacher at hiring more teachers this year? +3321 Has she dropped out of the tournament the university was holding due to a family emergency? +3322 Don't those professors make friends with people who brag about their past achievements all the time? +3323 Is every startup still producing the same product it has been improving since the last century? +3324 Is the Mars rover detecting a previously unknown geological feature that could help us prove the existence of alien creatures? +3325 Is the college he cooperating with was John's dream school? +3326 Has he declined to participate in the race the university was holding in the summer? +3327 Do those politicians don't like collaborators who brag about their past achievements all the time? +3328 Is every startup still producing the same product it has been making since the last century? +3329 Is the college he from was located on a small island? +3330 Has he dropped out of the tournament the city was hosting in the summer? +3331 Do those politicians don't make friends with people who hold racist views? +3332 Is every firm in the rust belt still making the same product it has been making since the last century? +3333 Is the NASA probe detecting a previously unknown geological feature that could help scientists learn about the environment on the planet's surface? +3334 Is the university she interested in was one of the best in that area? 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company still producing the same type of cars it has been known for since first going public? +3346 Is the Mars rover detecting a previously unknown geological feature that could help scientists learn about the environment on the planet's surface? +3347 Was the college she is a student at hiring more teachers this year? +3348 Has he dropped out of the race the summer camp was holding in the summer? +3349 Don't those professors make friends with actors who brag about their past achievements all the time? +3350 Is every firm in the rust belt still producing the same product it has been improving since first going public? +3351 Is the college she cooperating with is one of the best in that area? +3352 Has she quit the tournament the university was hosting due to medical conditions? +3353 Do those politicians don't like people who hold racist views? +3354 Has every stock he ever bought has lost value extremely slowly? +3355 Is the NASA probe detecting radiation on the planet that could help us prove the existence of alien creatures? +3356 Was the primary school he is a student at John's dream school? +3357 Has he quit the game the summer camp was holding due to a family emergency? +3358 Don't those teachers respect actors who hold racist views? +3359 Has every startup he ever sold has lost value extremely slowly? +3360 Is every startup still making the same type of cars it has been known for since the last century? +3361 Was the university he is a student at one of the best in that area? +3362 Has he declined to participate in the race the university was holding due to medical conditions? +3363 Do those teachers don't like people who brag about their past achievements all the time? +3364 Has every company he has ever invested in lost value extremely slowly? +3365 Is the Mars rover detecting any organic materials that could help scientists learn about the environment on the planet's surface? +3366 Was the college she is a teacher at John's dream school? +3367 Has he declined to participate in the game the summer camp was planning due to a family emergency? +3368 Has every startup he ever invested in has grown extremely slowly? +3369 Is the Mars rover taking pictures of any organic materials that could help scientists learn about the environment on the planet's surface? +3370 Was the school she is interested in hiring more teachers this year? +3371 Has he quit the tournament the summer camp was planning due to a family emergency? +3372 Don't those teachers make friends with actors who brag about their past achievements all the time? +3373 Has every stock he ever sold has grown extremely slowly? +3374 Is the university she cooperating with was located on a small island? +3375 Has she quit the game the city was hosting due to medical conditions? +3376 Do those professors don't respect actors who brag about their past achievements all the time? +3377 Has every startup he ever sold has grown extremely slowly? +3378 Is the NASA probe detecting any organic materials that could help scientists learn about the environment on the planet's surface? +3379 Is the college she cooperating with was hiring more teachers this year? +3380 Has he declined to participate in the race the university was planning due to medical conditions? +3381 Is every oversized company still manufacturing the same product it has been known for since the last century? +3382 Is the Mars rover taking pictures of radiation on the planet that could help scientists learn about the environment on the planet's surface? +3383 Is the college he is from hiring more teachers this year? +3384 Has she dropped out of the tournament the university was hosting in the summer? +3385 Do those politicians don't like people who brag about their past achievements all the time? +3386 Is the university she from was John's dream school? +3387 Has he dropped out of the game the university was hosting due to medical conditions? +3388 Do those professors don't like people who brag about their past achievements all the time? +3389 Was the college she is a teacher at hiring more teachers this year? +3390 Has she declined to participate in the race the university was holding due to medical conditions? +3391 Has every company he ever bought has lost value extremely slowly? +3392 Is every firm in the rust belt still producing the same product it has been making since the last century? +3393 Is the astronaut taking pictures of any organic materials that could help us prove the existence of alien creatures? +3394 Is the zoo he in charge of was funded by the local government? +3395 Has every stock he ever bought has grown extremely slowly? +3396 Is every oversized company still making the same product it has been known for since the last century? +3397 Has he opened the packet of chips my roommate was holding onto for a rainy day? +3398 Is the NASA probe taking pictures of radiation on the planet that could help us prove the existence of alien creatures? +3399 Was the cinema he is working for raising its ticket price? +3400 Has every company he ever sold has grown extremely slowly? +3401 Has he eaten the packet of chips Sue was planning to eat for breakfast? +3402 Is the Mars rover detecting radiation on the planet that could help scientists learn about the environment on the planet's surface? +3403 Is the circus he working for was purchasing a large amount of media equipment? +3404 Has every stock she has ever invested in lost value extremely slowly? +3405 Is every startup still manufacturing the same product it has been making since first going public? +3406 Has he opened the packet of chips the secretary was saving for breakfast? +3407 Is the botanical garden he is in charge of hiring a general manager? +3408 Has every stock she ever bought has lost value extremely slowly? +3409 Is every firm in the rust belt still producing the same type of cars it has been improving since the last century? +3410 Has he opened the fruit cup Sue was planning to eat for breakfast? +3411 Can't the presidents tolerate the college students who make noise in the opening ceremony? +3412 Is the circus she is in charge of purchasing a large amount of media equipment? +3413 Is every oversized company still producing the same product it has been making since the last century? +3414 Has she eaten the fruit cup Sue was saving for breakfast? +3415 Is the astronaut taking pictures of a previously unknown geological feature that could help us prove the existence of alien creatures? +3416 Is the cinema he in charge of was funded by the local government? +3417 Is every startup still manufacturing the same product it has been known for since first going public? +3418 Has she opened the can of sardines Sue was holding onto for breakfast? +3419 Is the astronaut taking pictures of any organic materials that could help scientists learn about the environment on the planet's surface? +3420 Is the circus she is volunteering for raising its ticket price? +3421 Has he dropped the packet of chips my roommate was planning to eat for lunch? +3422 Is the NASA probe detecting a previously unknown geological feature that could help us prove the existence of alien creatures? +3423 Was the botanical garden she is volunteering for purchasing a large amount of media equipment? +3424 Has he opened the can of sardines Sue was planning to eat for a rainy day? +3425 Can't the presidents stand the college students who make noise in the opening ceremony? +3426 Was the zoo he is volunteering for purchasing a large amount of media equipment? +3427 Is every oversized company still producing the same product it has been known for since the last century? +3428 Has he eaten the fruit cup my roommate was planning to eat for lunch? +3429 Has a decrease in game attendance isn't discouraging the football club that had an agreement with another team regarding player transfer? +3430 Is the museum he volunteering for was purchasing a large amount of media equipment? +3431 Has every company she has ever invested in lost value extremely slowly? +3432 Is every startup still making the same product it has been known for since first going public? +3433 Has she dropped the can of sardines the secretary was holding onto for lunch? +3434 Can't the presidents stand the college students who paint the walls of the school's sports center without permission? +3435 Is the circus he in charge of is funded by the local government? +3436 Has every startup she has ever invested in lost value extremely slowly? +3437 Has she eaten the can of sardines my roommate was holding onto for a rainy day? +3438 Can't the presidents bear the international students who paint the walls of the school's sports center without permission? +3439 Is the circus she is working for purchasing a large amount of media equipment? +3440 Has every stock she ever invested in has grown extremely slowly? +3441 Has she dropped the packet of chips the secretary was holding onto for lunch? +3442 Is the zoo she working for was purchasing a large amount of media equipment? +3443 Has every stock he ever invested in has grown extremely slowly? +3444 Is every oversized company still making the same product it has been making since the last century? +3445 Has she dropped the can of sardines Sue was holding onto for breakfast? +3446 Isn't a strict release clause discouraging the rookie manager that has had an agreement with another team regarding player transfer? +3447 Is the zoo he is volunteering for raising its ticket price? +3448 Has she opened the fruit cup the secretary was saving for breakfast? +3449 Can't the presidents bear the freshmen who paint the walls of the school's sports center without permission? +3450 Was the cinema she is volunteering for raising its ticket price? +3451 Has every stock he ever sold has lost value extremely slowly? +3452 Is every startup still making the same type of cars it has been making since the last century? +3453 Has he dropped the fruit cup the secretary was saving for breakfast? +3454 Is the zoo she volunteering for was purchasing a large amount of media equipment? +3455 Is every startup still manufacturing the same product it has been making since the last century? +3456 Has she eaten the fruit cup my roommate was planning to eat for a rainy day? +3457 Is the circus she in charge of was funded by the local government? +3458 Is every startup still manufacturing the same type of cars it has been known for since first going public? +3459 Has he dropped the fruit cup my roommate was saving for breakfast? +3460 Isn't a decrease in game attendance discouraging the rookie manager that has prepared a new contract for a top player from another team? +3461 Was the museum she is in charge of hiring a general manager? +3462 Has every stock she ever bought has grown extremely slowly? +3463 Has he eaten the can of sardines Sue was holding onto for breakfast? +3464 Isn't a strict release clause shocking the rookie manager that has prepared a new contract for a top player from another team? +3465 Is the museum he is volunteering for hiring a general manager? +3466 Is every firm in the rust belt still producing the same product it has been making since first going public? +3467 Has he opened the fruit cup the secretary was saving for lunch? +3468 Isn't a strict release clause discouraging the team owner that has had an agreement with another team regarding player transfer? +3469 Is the zoo he working for was funded by the local government? +3470 Is every firm in the rust belt still producing the same product it has been known for since first going public? +3471 Has she dropped the fruit cup Sue was holding onto for breakfast? +3472 Was the circus he is in charge of purchasing a large amount of media equipment? +3473 Is every oversized company still manufacturing the same type of cars it has been making since the last century? +3474 Has she dropped the packet of chips Sue was planning to eat for lunch? +3475 Isn't a strict release clause discouraging the football club that has raised funds to purchase top players? +3476 Is the museum he volunteering for is funded by the local government? +3477 Has he eaten the can of sardines my roommate was holding onto for a rainy day? +3478 Is the museum she is working for raising its ticket price? +3479 Is every firm in the rust belt still making the same product it has been known for since first going public? +3480 Has she dropped the can of sardines my roommate was holding onto for breakfast? +3481 Has a strict release clause isn't shocking the rookie manager that had an agreement with another team regarding player transfer? +3482 Is the museum he is in charge of purchasing a large amount of media equipment? +3483 Is every firm in the rust belt still manufacturing the same product it has been making since first going public? +3484 Has she opened the can of sardines the secretary was holding onto for a rainy day? +3485 Has a decrease in game attendance isn't discouraging the rookie manager that had an agreement with another team regarding player transfer? +3486 Was the zoo she is in charge of raising its ticket price? +3487 Has he dropped the can of sardines Sue was saving for a rainy day? +3488 Is every fund manager still investing in the same companies he has been trading since last February? +3489 Isn't a decrease in game attendance shocking the team owner that has raised funds to purchase top players? +3490 Is the cinema she working for is purchasing a large amount of media equipment? +3491 Has he opened the packet of chips my roommate was holding onto for breakfast? +3492 Isn't a decrease in game attendance discouraging the team owner that has raised funds to purchase top players? +3493 Is the botanical garden she is volunteering for hiring a general manager? +3494 Has he eaten the can of sardines the secretary was planning to eat for lunch? +3495 Is every investor still investing in the same cryptocurrency she has been investing in since last year? +3496 Isn't a strict release clause shocking the rookie manager that has raised funds to purchase top players? +3497 Is the botanical garden she in charge of was funded by the local government? +3498 Has she eaten the can of sardines the secretary was saving for breakfast? +3499 Can't the presidents bear the international students who make noise in the opening ceremony? +3500 Is every investor still optimistic about the same cryptocurrency he has been trading since last February? +3501 Isn't a strict release clause shocking the football club that has raised funds to purchase top players? +3502 Is the cinema he is in charge of hiring a general manager? +3503 Has he opened the can of sardines the secretary was planning to eat for a rainy day? +3504 Has a decrease in game attendance isn't discouraging the rookie manager that raised funds to purchase top players? +3505 Was the circus she is in charge of purchasing a large amount of media equipment? +3506 Has he opened the can of sardines Sue was saving for lunch? +3507 Can't the presidents bear the college students who make noise in the opening ceremony? +3508 Is every fund manager still optimistic about the same cryptocurrency he has been investing in since last February? +3509 Isn't a decrease in game attendance shocking the team owner that has prepared a new contract for a top player from another team? +3510 Is the circus he is volunteering for purchasing a large amount of media equipment? +3511 Has he dropped the can of sardines Sue was saving for lunch? +3512 Is every banker still optimistic about the same cryptocurrency she has been investing in since last year? +3513 Isn't a decrease in game attendance shocking the rookie manager that has prepared a new contract for a top player from another team? +3514 Is the botanical garden he is volunteering for hiring a general manager? +3515 Has he eaten the fruit cup the secretary was saving for a rainy day? +3516 Is every fund manager still optimistic about the same companies she has been investing in since last year? +3517 Isn't a decrease in game attendance discouraging the team owner that has prepared a new contract for a top player from another team? +3518 Is the botanical garden he is in charge of purchasing a large amount of media equipment? +3519 Has she eaten the packet of chips Sue was planning to eat for lunch? +3520 Is every investor still investing in the same cryptocurrency he has been trading since last February? +3521 Isn't a strict release clause shocking the team owner that has raised funds to purchase top players? +3522 Is the circus he volunteering for is funded by the local government? +3523 Has she eaten the can of sardines Sue was planning to eat for lunch? +3524 Can't the presidents tolerate the freshmen who make noise in the opening ceremony? +3525 Has a decrease in game attendance isn't discouraging the team owner that had an agreement with another team regarding player transfer? +3526 Was the botanical garden she is working for hiring a general manager? +3527 Has she dropped the fruit cup Sue was holding onto for a rainy day? +3528 Can't the presidents stand the international students who make noise in the opening ceremony? +3529 Has a decrease in game attendance isn't shocking the rookie manager that had an agreement with another team regarding player transfer? +3530 Is the cinema she is working for raising its ticket price? +3531 Has she dropped the fruit cup my roommate was planning to eat for a rainy day? +3532 Can't the presidents bear the college students who paint the walls of the school's sports center without permission? +3533 Is every investor still investing in the same companies he has been trading since last year? +3534 Is the museum she working for was funded by the local government? +3535 Has he eaten the can of sardines Sue was planning to eat for lunch? +3536 Can't the presidents tolerate the college students who paint the walls of the school's sports center without permission? +3537 Is the cinema he volunteering for was funded by the local government? +3538 Has he eaten the fruit cup my roommate was holding onto for a rainy day? +3539 Is every fund manager still investing in the same companies she has been investing in since last February? +3540 Isn't a decrease in game attendance shocking the football club that has raised funds to purchase top players? +3541 Is the zoo she working for was hiring a general manager? +3542 Has he dropped the packet of chips my roommate was saving for breakfast? +3543 Is the circus he working for is funded by the local government? +3544 Has he eaten the fruit cup the secretary was holding onto for breakfast? +3545 Is the circus he is in charge of raising its ticket price? +3546 Has he eaten the fruit cup the secretary was saving for lunch? +3547 Is every investor still optimistic about the same cryptocurrency she has been investing in since last year? +3548 Is the botanical garden he working for is raising its ticket price? +3549 Has he dropped the packet of chips the secretary was planning to eat for breakfast? +3550 Isn't a strict release clause discouraging the rookie manager that has raised funds to purchase top players? +3551 Is the cinema he is volunteering for hiring a general manager? +3552 Has he dropped the can of sardines Sue was planning to eat for lunch? +3553 Can't the presidents stand the freshmen who make noise in the opening ceremony? +3554 Is every investor still optimistic about the same companies he has been trading since last year? +3555 Isn't a decrease in game attendance discouraging the football club that has raised funds to purchase top players? +3556 Is the botanical garden he working for was raising its ticket price? +3557 Has he dropped the fruit cup my roommate was planning to eat for lunch? +3558 Isn't a strict release clause discouraging the team owner that has raised funds to purchase top players? +3559 Was the zoo she is volunteering for raising its ticket price? +3560 Has she eaten the fruit cup my roommate was holding onto for lunch? +3561 Is every fund manager still optimistic about the same companies he has been investing in since last February? +3562 Has a strict release clause isn't shocking the team owner that had an agreement with another team regarding player transfer? +3563 Is the zoo she is in charge of hiring a general manager? +3564 Has he eaten the can of sardines Sue was saving for breakfast? +3565 Is every investor still optimistic about the same companies he has been trading since last February? +3566 Isn't a strict release clause discouraging the team owner that has prepared a new contract for a top player from another team? +3567 Is the circus she volunteering for is funded by the local government? +3568 Has she opened the can of sardines the secretary was saving for lunch? +3569 Isn't a strict release clause discouraging the rookie manager that has prepared a new contract for a top player from another team? +3570 Is the museum she working for was hiring a general manager? +3571 Has she dropped the can of sardines my roommate was saving for lunch? +3572 Is every fund manager still optimistic about the same cryptocurrency she has been trading since last year? +3573 Isn't a strict release clause shocking the team owner that has prepared a new contract for a top player from another team? +3574 Is the zoo she is working for raising its ticket price? +3575 Has she opened the can of sardines Sue was holding onto for lunch? +3576 Can't the presidents bear the freshmen who make noise in the opening ceremony? +3577 Is the cinema he working for was funded by the local government? +3578 Has she opened the packet of chips Sue was planning to eat for lunch? +3579 Can't the presidents tolerate the international students who make noise in the opening ceremony? +3580 Is every investor still investing in the same cryptocurrency he has been trading since last year? +3581 Isn't a decrease in game attendance shocking the team owner that has had an agreement with another team regarding player transfer? +3582 Is the customer service representative he working with was fired last week? +3583 Is every banker still investing in the same cryptocurrency he has been trading since last year? +3584 Has she given up the reputation she was gaining over the years? +3585 Isn't a strict release clause discouraging the football club that has prepared a new contract for a top player from another team? +3586 Is the data analyst she in contact with was diagnosed with lung cancer? +3587 Is every banker still optimistic about the same companies she has been trading since last year? +3588 Has she given up the reputation she was building over the years? +3589 Isn't a strict release clause discouraging the football club that has had an agreement with another team regarding player transfer? +3590 Is the data analyst he talking to was fired last week? +3591 Has she lost the fame she was amassing over the years? +3592 Isn't a strict release clause shocking the football club that has had an agreement with another team regarding player transfer? +3593 Is the secretary he married to was diagnosed with lung cancer? +3594 Has she given up the notoriety she was building over the years? +3595 Is the procurement assistant he talking to was fired last week? +3596 Is every banker still optimistic about the same companies he has been investing in since last year? +3597 Has she given up the notoriety she was amassing in recent months? +3598 Isn't a strict release clause shocking the football club that has prepared a new contract for a top player from another team? +3599 Is the procurement assistant she meeting with was hit by a car in Astor Place? +3600 Is every banker still investing in the same companies he has been trading since last year? +3601 Has she given up the notoriety she was amassing over the years? +3602 Is the secretary she working with was infected with a virus? +3603 Is every fund manager still optimistic about the same companies he has been investing in since last year? +3604 Has she given up the fame she was amassing due to fraudulent activities? +3605 Is the procurement assistant he talking to was diagnosed with lung cancer? +3606 Has she given up the fame she was amassing in recent months? +3607 Isn't a decrease in game attendance shocking the football club that has prepared a new contract for a top player from another team? +3608 Is the accountant he in contact with was hit by a car in Astor Place? +3609 Is every banker still optimistic about the same companies he has been trading since last year? +3610 Has she given up the notoriety she was building in recent months? +3611 Is the customer service representative she married to was infected with a virus? +3612 Is every investor still investing in the same companies she has been trading since last February? +3613 Has she given up the notoriety she was building due to fraudulent activities? +3614 Is the secretary she working with was hit by a car in Astor Place? +3615 Is every banker still investing in the same companies she has been trading since last year? +3616 Has she given up the notoriety she was gaining due to fraudulent activities? +3617 Was the data analyst she is married to infected with a virus? +3618 Is every banker still optimistic about the same cryptocurrency he has been trading since last February? +3619 Has she lost the fame she was building over the years? +3620 Is the secretary she in contact with was hit by a car in Astor Place? +3621 Has she lost the reputation she was amassing over the years? +3622 Is the customer service representative he working with was infected with a virus? +3623 Has she given up the fame she was amassing over the years? +3624 Will the Russian invasion of Ukraine affect tourism in every country that is in the region? +3625 Is the data analyst she working with was hit by a car in Astor Place? +3626 Has she lost the fame she was gaining over the years? +3627 Is the accountant she meeting with was hit by a car in Astor Place? +3628 Has she lost the reputation she was building in recent months? +3629 Is the data analyst he meeting with was hit by a car in Astor Place? +3630 Is every banker still investing in the same cryptocurrency she has been trading since last year? +3631 Has she lost the fame she was gaining due to fraudulent activities? +3632 Is the secretary she in contact with was fired last week? +3633 Has she given up the notoriety she was gaining over the years? +3634 Is the accountant she working with was diagnosed with lung cancer? +3635 Is every fund manager still optimistic about the same cryptocurrency she has been investing in since last February? +3636 Has she lost the reputation she was building over the years? +3637 Was the customer service representative she is talking to infected with a virus? +3638 Is every banker still investing in the same companies she has been investing in since last February? +3639 Has she lost the fame she was amassing due to fraudulent activities? +3640 Will a military coup in Ukraine eliminate travel to every country that is in the region? +3641 Is the customer service representative he in contact with was hit by a car in Astor Place? +3642 Has she given up the fame she was building due to fraudulent activities? +3643 Is the procurement assistant he in contact with was infected with a virus? +3644 Is every investor still investing in the same cryptocurrency he has been investing in since last year? +3645 Has she given up the notoriety she was amassing due to fraudulent activities? +3646 Is the data analyst she married to was diagnosed with lung cancer? +3647 Is every fund manager still investing in the same cryptocurrency she has been investing in since last February? +3648 Has she given up the reputation she was amassing over the years? +3649 Is the procurement assistant he working with was diagnosed with lung cancer? +3650 Is every investor still investing in the same companies she has been trading since last year? +3651 Has she lost the fame she was building due to fraudulent activities? +3652 Is the secretary he meeting with was diagnosed with lung cancer? +3653 Has she given up the reputation she was building due to fraudulent activities? +3654 Will a military coup in Ukraine eliminate tourism in every country that is in the region? +3655 Is the secretary she married to was hit by a car in Astor Place? +3656 Has she lost the reputation she was amassing in recent months? +3657 Is every doctor still taking care of the same patient she has been responsible for since last month? +3658 Was the data analyst she is talking to broke? +3659 Has she lost the reputation she was gaining due to fraudulent activities? +3660 Is every nurse still looking after the same group of patients she has been taking charge of since last month? +3661 Is the secretary he working with was infected with a virus? +3662 Has she given up the reputation she was building in recent months? +3663 Is the data analyst he talking to was diagnosed with lung cancer? +3664 Has she lost the notoriety she was building due to fraudulent activities? +3665 Is the accountant she in contact with was hit by a car in Astor Place? +3666 Has she lost the notoriety she was gaining due to fraudulent activities? +3667 Is the accountant he married to was infected with a virus? +3668 Has she lost the reputation she was gaining over the years? +3669 Is every doctor still taking care of the same patient he has been taking charge of since last month? +3670 Will political instability in Ukraine eliminate travel to every country that is in the region? +3671 Is the accountant he working with was hit by a car in Astor Place? +3672 Has she given up the reputation she was gaining in recent months? +3673 Is every nurse still taking care of the same group of patients he has been responsible for since last month? +3674 Is the procurement assistant he in contact with was broke? +3675 Has she lost the reputation she was amassing due to fraudulent activities? +3676 Is the accountant he in contact with was diagnosed with lung cancer? +3677 Has she lost the fame she was amassing in recent months? +3678 Is the procurement assistant he married to was infected with a virus? +3679 Has she lost the notoriety she was building over the years? +3680 Is every doctor still looking after the same patient she has been taking charge of since last month? +3681 Is the accountant she talking to was broke? +3682 Has she lost the notoriety she was amassing in recent months? +3683 Is the procurement assistant he meeting with was broke? +3684 Has she given up the fame she was gaining due to fraudulent activities? +3685 Is every doctor still taking care of the same group of patients she has been taking charge of since last month? +3686 Will the Russian invasion of Ukraine eliminate travel to every country that is in the region? +3687 Is the customer service representative he talking to was broke? +3688 Has she lost the reputation she was building due to fraudulent activities? +3689 Is the data analyst she talking to was diagnosed with lung cancer? +3690 Has she lost the notoriety she was building in recent months? +3691 Is the data analyst he married to was broke? +3692 Has she given up the fame she was gaining in recent months? +3693 Is every doctor still taking care of the same patient he has been responsible for since last month? +3694 Is the customer service representative he working with was broke? +3695 Has she lost the notoriety she was amassing over the years? +3696 Is every nurse still taking care of the same patient he has been taking charge of since last month? +3697 Is the procurement assistant she meeting with was fired last week? +3698 Has she given up the notoriety she was gaining in recent months? +3699 Is the customer service representative she in contact with was fired last week? +3700 Has she given up the reputation she was amassing due to fraudulent activities? +3701 Is every nurse still looking after the same patient he has been taking charge of since last month? +3702 Has every employee she ever supervised has been fired immediately? +3703 Is the customer service representative he married to was diagnosed with lung cancer? +3704 Has she lost the reputation she was gaining in recent months? +3705 Is every nurse still looking after the same patient he has been responsible for since last month? +3706 Is political instability in Ukraine will eliminate tourism in every country that in the region? +3707 Is the data analyst he in contact with was infected with a virus? +3708 Has she lost the fame she was gaining in recent months? +3709 Is every nurse still looking after the same patient she has been responsible for since last month? +3710 Is the secretary he meeting with was fired last week? +3711 Has she lost the fame she was building in recent months? +3712 Is every nurse still taking care of the same group of patients she has been responsible for since last month? +3713 Has every government official he ever overseen has been fired quickly? +3714 Is the data analyst he in contact with was fired last week? +3715 Has she given up the fame she was gaining over the years? +3716 Is every doctor still taking care of the same patient she has been taking charge of since last month? +3717 Has every employee he ever worked with has been promoted quickly? +3718 Is political instability in Ukraine will affect tourism in every country that in the region? +3719 Is the secretary she talking to was diagnosed with lung cancer? +3720 Has she given up the fame she was building in recent months? +3721 Will political instability in Ukraine reduce tourism in every country that is in the region? +3722 Is the secretary he working with was broke? +3723 Has she lost the notoriety she was amassing due to fraudulent activities? +3724 Is every nurse still taking care of the same group of patients he has been taking charge of since last month? +3725 Has every employee he ever worked with has been fired immediately? +3726 Will the Russian invasion of Ukraine affect travel to every country that is in the region? +3727 Is the procurement assistant he meeting with was fired last week? +3728 Has she given up the fame she was building over the years? +3729 Is every nurse still taking care of the same patient she has been responsible for since last month? +3730 Has every government official she ever overseen has been fired quickly? +3731 Is every doctor still looking after the same group of patients she has been responsible for since last month? +3732 Has he been reading the book his opponent was writing in secret? +3733 Has every government official she ever worked with has been fired quickly? +3734 Is every doctor still looking after the same group of patients he has been taking charge of since last month? +3735 Has he copied the manuscript his opponent was working on in secret? +3736 Won't hardware manufacturers produce high performance computers that have low demand? +3737 Has he copied the manuscript his opponent was writing in secret? +3738 Has every government official she ever overseen has been promoted immediately? +3739 Will political instability in Ukraine affect travel to every country that is in the region? +3740 Has every employee she ever overseen has been promoted quickly? +3741 Has he been reading the manuscript his opponent was working on in secret? +3742 Has every employee he ever supervised has been fired immediately? +3743 Will a military coup in Ukraine reduce tourism in every country that is in the region? +3744 Is every doctor still taking care of the same group of patients he has been responsible for since last month? +3745 Will a military coup in Ukraine reduce travel to every country that is in the region? +3746 Has he sneaked a peek at the book his superior was working on in secret? +3747 Is every nurse still taking care of the same group of patients she has been taking charge of since last month? +3748 Is every doctor still taking care of the same group of patients he has been taking charge of since last month? +3749 Won't computer companies continue manufacturing laptops that have low demand? +3750 Has every government official she ever supervised has been fired quickly? +3751 Is every doctor still looking after the same patient he has been taking charge of since last month? +3752 Has he sneaked a peek at the draft his opponent was working on for the last few months? +3753 Has he been reading the manuscript his opponent was writing in secret? +3754 Is every nurse still taking care of the same patient she has been taking charge of since last month? +3755 Is every nurse still looking after the same group of patients he has been taking charge of since last month? +3756 Has he sneaked a peek at the manuscript his opponent was writing in secret? +3757 Has every government official he ever worked with has been fired quickly? +3758 Has he copied the manuscript his superior was writing in secret? +3759 Has every employee he ever supervised has been fired quickly? +3760 Is every nurse still looking after the same patient she has been taking charge of since last month? +3761 Won't computer companies produce computers that have low demand? +3762 Has every government official he ever supervised has been fired immediately? +3763 Is every doctor still looking after the same group of patients she has been taking charge of since last month? +3764 Has he copied the manuscript his opponent was writing for the last few months? +3765 Won't computer companies continue manufacturing computers that have low demand? +3766 Has every employee she ever worked with has been fired immediately? +3767 Has every employee he ever worked with has been fired quickly? +3768 Is every doctor still looking after the same group of patients he has been responsible for since last month? +3769 Has he copied the book his superior was working on for the last few months? +3770 Has every government official he ever supervised has been promoted immediately? +3771 Is every nurse still looking after the same group of patients he has been responsible for since last month? +3772 Won't hardware manufacturers continue producing high performance computers that have low demand? +3773 Has every employee she ever worked with has been promoted immediately? +3774 Has he copied the book his opponent was working on for the last few months? +3775 Won't hardware manufacturers continue manufacturing computers that have low demand? +3776 Has every government official she ever supervised has been promoted quickly? +3777 Is every doctor still taking care of the same group of patients she has been responsible for since last month? +3778 Has he been reading the draft his superior was working on for the last few months? +3779 Won't hardware manufacturers continue producing laptops that have low demand? +3780 Has he copied the draft his opponent was working on for the last few months? +3781 Has every government official he ever overseen has been promoted quickly? +3782 Has he sneaked a peek at the draft his superior was writing for the last few months? +3783 Won't laptop manufacturers produce high performance computers that have low demand? +3784 Has every government official he ever supervised has been fired quickly? +3785 Won't computer companies continue manufacturing high performance computers that have low demand? +3786 Has he sneaked a peek at the draft his superior was working on for the last few months? +3787 Has every government official she ever worked with has been fired immediately? +3788 Is every fast food restaurant still advertising the same type of snack it has been making since they changed their menu? +3789 Has he sneaked a peek at the manuscript his superior was working on in secret? +3790 Won't computer companies continue producing high performance computers that have low demand? +3791 Is every fast food restaurant still trying to sell the same type of snack it has been cooking since they changed their menu? +3792 Is every restaurant still making TV commercials for the same food it has been selling since they changed their menu? +3793 Has he copied the book his opponent was writing for the last few months? +3794 Has every employee she ever worked with has been fired quickly? +3795 Is every fast food restaurant still making TV commercials for the same food it has been selling since they decided to take a brand-new strategy? +3796 Won't laptop manufacturers produce computers that have low demand? +3797 Is every fast food restaurant still making TV commercials for the same food it has been cooking since they changed their menu? +3798 Is every fast food restaurant still making TV commercials for the same food it has been making since they changed their menu? +3799 Has every employee she ever supervised has been promoted immediately? +3800 Is every restaurant still advertising the same food it has been cooking since they changed their menu? +3801 Has he copied the draft his opponent was working on in secret? +3802 Has every employee she ever overseen has been fired quickly? +3803 Is every restaurant still making TV commercials for the same type of snack it has been making since they changed their menu? +3804 Has every employee he ever supervised has been promoted quickly? +3805 Is every fast food restaurant still advertising the same food it has been making since they decided to take a brand-new strategy? +3806 Has he sneaked a peek at the draft his superior was writing in secret? +3807 Won't laptop manufacturers continue producing high performance computers that have low demand? +3808 Has every employee he ever worked with has been promoted immediately? +3809 Is every fast food restaurant still trying to sell the same type of snack it has been making since they decided to take a brand-new strategy? +3810 Won't laptop manufacturers produce laptops that have low demand? +3811 Has every employee she ever overseen has been fired immediately? +3812 Is every restaurant still advertising the same food it has been selling since they changed their menu? +3813 Has he sneaked a peek at the manuscript his opponent was writing for the last few months? +3814 Has every government official she ever overseen has been fired immediately? +3815 Is every restaurant still advertising the same food it has been making since they decided to take a brand-new strategy? +3816 Has he sneaked a peek at the draft his superior was working on in secret? +3817 Has every government official he ever worked with has been fired immediately? +3818 Is every fast food restaurant still making TV commercials for the same type of snack it has been cooking since they changed their menu? +3819 Has every government official she ever supervised has been promoted immediately? +3820 Is every fast food restaurant still advertising the same type of snack it has been selling since they changed their menu? +3821 Has he copied the draft his superior was writing in secret? +3822 Has every government official she ever worked with has been promoted quickly? +3823 Is every fast food restaurant still advertising the same type of snack it has been making since they decided to take a brand-new strategy? +3824 Has every government official he ever worked with has been promoted immediately? +3825 Is every fast food restaurant still advertising the same type of snack it has been selling since they decided to take a brand-new strategy? +3826 Has he copied the manuscript his superior was working on for the last few months? +3827 Won't hardware manufacturers continue manufacturing laptops that have low demand? +3828 Has every government official she ever overseen has been promoted quickly? +3829 Is every restaurant still trying to sell the same type of snack it has been cooking since they decided to take a brand-new strategy? +3830 Has he been reading the book his superior was writing for the last few months? +3831 Is every restaurant still making TV commercials for the same food it has been making since they decided to take a brand-new strategy? +3832 Has he been reading the book his superior was working on for the last few months? +3833 Won't computer companies continue producing computers that have low demand? +3834 Is every restaurant still making TV commercials for the same food it has been selling since they decided to take a brand-new strategy? +3835 Is every restaurant still advertising the same food it has been making since they changed their menu? +3836 Is every fast food restaurant still trying to sell the same type of snack it has been selling since they changed their menu? +3837 Has every lion he ever hunted has been killed by another hunter? +3838 Has he been reading the manuscript his superior was writing in secret? +3839 Is every fast food restaurant still making TV commercials for the same type of snack it has been selling since they changed their menu? +3840 Has every tiger he ever pursued has been killed by another hunter? +3841 Is every restaurant still making TV commercials for the same type of snack it has been cooking since they decided to take a brand-new strategy? +3842 Has he been reading the manuscript his superior was writing for the last few months? +3843 Is every fast food restaurant still trying to sell the same food it has been cooking since they changed their menu? +3844 Has every lion she ever hunted has been killed by another hunter? +3845 Has he copied the draft his opponent was writing for the last few months? +3846 Won't hardware manufacturers produce computers that have low demand? +3847 Is the marble sculpture he is admiring from ancient Greece? +3848 Is every restaurant still making TV commercials for the same food it has been making since they changed their menu? +3849 Has every tiger he ever hunted has been captured by another hunter? +3850 Has he sneaked a peek at the draft his opponent was writing for the last few months? +3851 Is the ancient textile he is admiring from ancient Greece? +3852 Is every fast food restaurant still trying to sell the same food it has been selling since they decided to take a brand-new strategy? +3853 Is the ancient textile he is taking in from Han Dynasty China? +3854 Is every restaurant still trying to sell the same type of snack it has been selling since they changed their menu? +3855 Has he edited the document the scientist was sending to the conference? +3856 Has every tiger she ever pursued has been killed by another hunter? +3857 Is the marble sculpture he is taking in from ancient Greece? +3858 Is every restaurant still making TV commercials for the same food it has been cooking since they changed their menu? +3859 Has he deleted the article the research group was sending to the conference? +3860 Is the mysterious painting she is staring at from Han Dynasty China? +3861 Is every restaurant still trying to sell the same type of snack it has been cooking since they changed their menu? +3862 Has she drafted the document the lab was sending to the journal? +3863 Has every tiger he ever chased has been captured by another hunter? +3864 Is the ancient textile he is taking in from ancient Greece? +3865 Is every fast food restaurant still making TV commercials for the same type of snack it has been cooking since they decided to take a brand-new strategy? +3866 Has she drafted the paper the research group was submitting to the conference? +3867 Can't junk foods provide all the vitamins that support physical health? +3868 Has every lion he ever chased has been captured by another hunter? +3869 Is the marble sculpture he is admiring from medieval Spain? +3870 Is every restaurant still trying to sell the same type of snack it has been making since they decided to take a brand-new strategy? +3871 Has he drafted the paper the lab was sending to the journal? +3872 Is the marble sculpture he is taking in from Han Dynasty China? +3873 Is every fast food restaurant still making TV commercials for the same food it has been making since they decided to take a brand-new strategy? +3874 Has she drafted the document the research group was sending to the conference? +3875 Has every lion he ever pursued has been captured by another hunter? +3876 Is the mysterious painting he is taking in from ancient Greece? +3877 Is every restaurant still making TV commercials for the same type of snack it has been selling since they changed their menu? +3878 Has he deleted the article the lab was sending to the conference? +3879 Has every lion he ever pursued has been killed by another hunter? +3880 Is the marble sculpture she is staring at from medieval Spain? +3881 Is every restaurant still advertising the same type of snack it has been selling since they decided to take a brand-new strategy? +3882 Has he edited the document the lab was submitting to the conference? +3883 Is the ancient textile she is taking in from medieval Spain? +3884 Is every fast food restaurant still advertising the same type of snack it has been cooking since they decided to take a brand-new strategy? +3885 Has he deleted the paper the scientist was sending to the journal? +3886 Is the marble sculpture he is taking in from medieval Spain? +3887 Is every restaurant still trying to sell the same food it has been selling since they decided to take a brand-new strategy? +3888 Has she deleted the paper the research group was sending to the journal? +3889 Is the mysterious painting he is taking in from medieval Spain? +3890 Is every restaurant still advertising the same type of snack it has been cooking since they decided to take a brand-new strategy? +3891 Has she edited the paper the scientist was sending to the journal? +3892 Can't fast food dishes offer all the vitamins that support physical health? +3893 Has every tiger he ever hunted has been killed by another hunter? +3894 Is the mysterious painting he is staring at from ancient Greece? +3895 Is every restaurant still making TV commercials for the same type of snack it has been making since they decided to take a brand-new strategy? +3896 Has she edited the paper the lab was sending to the conference? +3897 Is the ancient textile he is staring at from ancient Greece? +3898 Is every restaurant still trying to sell the same food it has been cooking since they changed their menu? +3899 Has she edited the document the lab was submitting to the conference? +3900 Has every lion she ever pursued has been captured by another hunter? +3901 Is the marble sculpture she is taking in from ancient Greece? +3902 Is every restaurant still trying to sell the same type of snack it has been selling since they decided to take a brand-new strategy? +3903 Has he deleted the document the research group was submitting to the journal? +3904 Can't deep fat fried foods offer all the nutrients that support physical health? +3905 Is the marble sculpture she is admiring from medieval Spain? +3906 Is every fast food restaurant still trying to sell the same type of snack it has been selling since they decided to take a brand-new strategy? +3907 Has he drafted the document the scientist was submitting to the journal? +3908 Is the ancient textile she is staring at from ancient Greece? +3909 Is every restaurant still trying to sell the same food it has been making since they changed their menu? +3910 Has she deleted the paper the research group was sending to the conference? +3911 Is the marble sculpture she is staring at from ancient Greece? +3912 Is every fast food restaurant still trying to sell the same food it has been making since they decided to take a brand-new strategy? +3913 Has she edited the paper the scientist was submitting to the conference? +3914 Is the marble sculpture she is staring at from Han Dynasty China? +3915 Is every fast food restaurant still advertising the same food it has been selling since they changed their menu? +3916 Has he edited the document the scientist was submitting to the journal? +3917 Has every tiger he ever pursued has been captured by another hunter? +3918 Is the mysterious painting she is admiring from Han Dynasty China? +3919 Is every fast food restaurant still advertising the same food it has been making since they changed their menu? +3920 Has she drafted the document the scientist was sending to the conference? +3921 Is the marble sculpture she is admiring from ancient Greece? +3922 Is every restaurant still trying to sell the same food it has been selling since they changed their menu? +3923 Has he edited the paper the research group was submitting to the journal? +3924 Has every rhinoceros she ever hunted has been killed by another hunter? +3925 Is the ancient textile he is taking in from medieval Spain? +3926 Is every fast food restaurant still advertising the same food it has been cooking since they changed their menu? +3927 Has he deleted the article the research group was submitting to the journal? +3928 Can't deep fat fried foods offer all the vitamins that support physical health? +3929 Is the marble sculpture he is staring at from Han Dynasty China? +3930 Is every restaurant still advertising the same type of snack it has been selling since they changed their menu? +3931 Has she edited the paper the lab was submitting to the conference? +3932 Is the ancient textile she is admiring from ancient Greece? +3933 Is every fast food restaurant still advertising the same type of snack it has been cooking since they changed their menu? +3934 Has he deleted the paper the lab was sending to the journal? +3935 Can't fast food dishes provide all the nutrients that support physical health? +3936 Has every tiger she ever hunted has been killed by another hunter? +3937 Is the ancient textile she is staring at from Han Dynasty China? +3938 Is every restaurant still making TV commercials for the same type of snack it has been cooking since they changed their menu? +3939 Has he edited the article the research group was sending to the conference? +3940 Has every rhinoceros she ever pursued has been killed by another hunter? +3941 Is the ancient textile she is admiring from Han Dynasty China? +3942 Has he edited the document the research group was sending to the journal? +3943 Is every undergraduate still staying in the same apartment she has been living in since freshman year? +3944 Is the ancient textile he staring at is from medieval Spain? +3945 Has he drafted the document the research group was submitting to the journal? +3946 Can't deep fat fried foods provide all the vitamins that support physical health? +3947 Is the mysterious painting she is admiring from ancient Greece? +3948 Has she drafted the document the lab was sending to the conference? +3949 Can't deep fat fried foods provide all the nutrients that support physical health? +3950 Has every rhinoceros he ever chased has been killed by another hunter? +3951 Is every undergraduate still staying in the same dorm room he has been living in since freshman year? +3952 Is the mysterious painting he staring at is from medieval Spain? +3953 Has she edited the article the scientist was sending to the conference? +3954 Is every student still stuck in the same dorm room he has been living in since starting school? +3955 Is the mysterious painting she is taking in from medieval Spain? +3956 Has he drafted the article the research group was submitting to the journal? +3957 Can't junk foods offer all the vitamins that support physical health? +3958 Is the mysterious painting she is staring at from medieval Spain? +3959 Has he edited the document the lab was sending to the conference? +3960 Has every tiger she ever chased has been captured by another hunter? +3961 Is the mysterious painting he is admiring from Han Dynasty China? +3962 Has she deleted the document the scientist was submitting to the journal? +3963 Can't junk foods provide all the nutrients that support physical health? +3964 Has every rhinoceros he ever hunted has been captured by another hunter? +3965 Is every student still staying in the same dorm room he has been living in since freshman year? +3966 Is the marble sculpture he is staring at from ancient Greece? +3967 Has she deleted the document the scientist was sending to the journal? +3968 Has every lion she ever chased has been captured by another hunter? +3969 Is every student still occupying the same dorm room she has been living in since starting school? +3970 Is the mysterious painting she is admiring from medieval Spain? +3971 Has he edited the document the research group was sending to the conference? +3972 Can't junk foods offer all the nutrients that support physical health? +3973 Has every tiger he ever chased has been killed by another hunter? +3974 Is every undergraduate still occupying the same apartment she has been living in since freshman year? +3975 Is the ancient textile he is admiring from Han Dynasty China? +3976 Has he deleted the article the research group was sending to the journal? +3977 Can't fast food dishes offer all the nutrients that support physical health? +3978 Is every undergraduate still stuck in the same dorm room she has been living in since freshman year? +3979 Is the marble sculpture he is admiring from Han Dynasty China? +3980 Has he deleted the article the scientist was submitting to the conference? +3981 Can't fast food dishes provide all the vitamins that support physical health? +3982 Is every undergraduate still assigned to the same dorm room she has been living in since starting school? +3983 Is the mysterious painting she is taking in from Han Dynasty China? +3984 Has he drafted the article the research group was sending to the conference? +3985 Is the ancient textile she is admiring from medieval Spain? +3986 Has he drafted the article the scientist was sending to the conference? +3987 Has every rhinoceros he ever chased has been captured by another hunter? +3988 Is every undergraduate still occupying the same dorm room she has been living in since starting school? +3989 Can most pianists in conservatory easily play through new music scores that get presented to them? +3990 Is the mysterious painting she is taking in from ancient Greece? +3991 Has she drafted the paper the lab was submitting to the conference? +3992 Is every undergraduate still staying in the same dorm room she has been living in since starting school? +3993 Is the marble sculpture she is admiring from Han Dynasty China? +3994 Has he deleted the document the scientist was submitting to the journal? +3995 Is every student still stuck in the same apartment he has been living in since starting school? +3996 Is the marble sculpture she is taking in from medieval Spain? +3997 Has she deleted the document the research group was sending to the journal? +3998 Is the mysterious painting he is admiring from medieval Spain? +3999 Has she drafted the paper the scientist was sending to the conference? +4000 Is every student still staying in the same apartment he has been living in since freshman year? +4001 Do most guitarists with classical training can sight read new music scores that get presented to them? +4002 Is the mysterious painting she is staring at from ancient Greece? +4003 Has he drafted the article the scientist was submitting to the journal? +4004 Is every undergraduate still stuck in the same apartment she has been living in since starting school? +4005 Is the mysterious painting he is staring at from Han Dynasty China? +4006 Has he edited the article the scientist was sending to the journal? +4007 Is every student still occupying the same dorm room he has been living in since freshman year? +4008 Is the ancient textile he is admiring from medieval Spain? +4009 Has she deleted the paper the research group was submitting to the journal? +4010 Is every undergraduate still stuck in the same apartment he has been living in since starting school? +4011 Is the marble sculpture he is staring at from medieval Spain? +4012 Has she drafted the document the research group was submitting to the journal? +4013 Is every undergraduate still staying in the same apartment he has been living in since freshman year? +4014 Is the ancient textile she is taking in from Han Dynasty China? +4015 Has she edited the document the research group was sending to the conference? +4016 Is every student still stuck in the same dorm room he has been living in since freshman year? +4017 Is the ancient textile he staring at is from Han Dynasty China? +4018 Has she drafted the paper the research group was sending to the journal? +4019 Is every student still assigned to the same apartment he has been living in since starting school? +4020 Is the mysterious painting he is taking in from Han Dynasty China? +4021 Has he drafted the paper the scientist was submitting to the conference? +4022 Is every student still staying in the same dorm room she has been living in since starting school? +4023 Has he edited the paper the research group was sending to the conference? +4024 Is the aircraft she going to Chicago in was not for commercial use? +4025 Is every student still assigned to the same dorm room she has been living in since freshman year? +4026 Can most pianists in conservatory sight read unfamiliar pieces of music that get presented to them? +4027 Has she edited the paper the research group was sending to the conference? +4028 Is every undergraduate still occupying the same apartment he has been living in since starting school? +4029 Is the helicopter he getting onto is not for commercial use? +4030 Is every student still assigned to the same dorm room he has been living in since starting school? +4031 Has he performed the sonata the director was adding to the program for the gala? +4032 Is every student still assigned to the same apartment he has been living in since freshman year? +4033 Has she learned the prelude the composer was programming for the concert? +4034 Has he rehearsed the prelude the composer was adding to the program for the concert? +4035 Has she rehearsed the sonata the composer was adding to the program for the concert? +4036 Is the helicopter he getting onto was not for commercial use? +4037 Is every undergraduate still stuck in the same dorm room she has been living in since starting school? +4038 Has she performed the sonata the director was programming for the gala? +4039 Is the aircraft he going to Chicago in was not for commercial use? +4040 Is every student still occupying the same apartment she has been living in since freshman year? +4041 Has she learned the concerto the conductor was adding to the program for the gala? +4042 Is the helicopter he riding on is not for commercial use? +4043 Has he learned the prelude the director was adding to the program for the gala? +4044 Is the plane he getting onto was not for commercial use? +4045 Is every undergraduate still stuck in the same apartment she has been living in since freshman year? +4046 Has she rehearsed the prelude the composer was adding to the program for the concert? +4047 Can most pianists in conservatory rapidly learn new music scores that get presented to them? +4048 Is the helicopter she getting onto was not for commercial use? +4049 Has she rehearsed the concerto the composer was programming for the concert? +4050 Is the aircraft she going to Chicago in is not for commercial use? +4051 Is every student still stuck in the same apartment she has been living in since freshman year? +4052 Has he learned the concerto the composer was adding to the program for the gala? +4053 Can most pianists in conservatory rapidly learn unfamiliar pieces of music that get presented to them? +4054 Has he performed the prelude the composer was adding to the program for the gala? +4055 Is the plane he going to Chicago in was not for commercial use? +4056 Is every student still occupying the same apartment he has been living in since freshman year? +4057 Has she rehearsed the prelude the director was adding to the program for the concert? +4058 Has she learned the sonata the conductor was programming for the gala? +4059 Can most pianists in conservatory rapidly learn popular songs that get presented to them? +4060 Is the helicopter she getting onto is not for commercial use? +4061 Is every student still occupying the same dorm room he has been living in since starting school? +4062 Has he learned the concerto the conductor was programming for the gala? +4063 Do most guitarists with classical training can easily play through unfamiliar pieces of music that get presented to them? +4064 Is the plane he getting onto is not for commercial use? +4065 Has he performed the concerto the composer was adding to the program for the concert? +4066 Has she rehearsed the sonata the director was programming for the gala? +4067 Is every student still staying in the same apartment he has been living in since starting school? +4068 Has she performed the concerto the director was adding to the program for the gala? +4069 Do most guitarists with classical training can easily play through new music scores that get presented to them? +4070 Is the plane he is going to Chicago in not for commercial use? +4071 Is every student still assigned to the same dorm room she has been living in since starting school? +4072 Is the helicopter he riding on was not for commercial use? +4073 Has she rehearsed the sonata the conductor was programming for the concert? +4074 Is the plane she riding on is not for commercial use? +4075 Is every student still occupying the same apartment he has been living in since starting school? +4076 Has she performed the sonata the composer was adding to the program for the concert? +4077 Was she has performed the sonata the conductor adding to the program for the gala? +4078 Can most pianists in conservatory sight read popular songs that get presented to them? +4079 Has she learned the concerto the composer was programming for the gala? +4080 Do most pianists in conservatory can easily play through popular songs that get presented to them? +4081 Has she performed the prelude the conductor was programming for the concert? +4082 Can most pianists in conservatory easily play through unfamiliar pieces of music that get presented to them? +4083 Is the plane he riding on was not for commercial use? +4084 Has she performed the prelude the conductor was adding to the program for the gala? +4085 Is the plane she is going to Chicago in not for commercial use? +4086 Has he learned the prelude the conductor was programming for the gala? +4087 Has she rehearsed the prelude the conductor was programming for the concert? +4088 Has she rehearsed the sonata the director was adding to the program for the gala? +4089 Has he performed the prelude the conductor was adding to the program for the concert? +4090 Is the plane she getting onto was not for commercial use? +4091 Has she learned the prelude the conductor was programming for the gala? +4092 Has she rehearsed the prelude the director was adding to the program for the gala? +4093 Is the plane she getting onto is not for commercial use? +4094 Has she rehearsed the prelude the conductor was adding to the program for the gala? +4095 Has she rehearsed the sonata the director was programming for the concert? +4096 Is the aircraft he getting onto was not for commercial use? +4097 Has he learned the prelude the conductor was adding to the program for the gala? +4098 Has he learned the sonata the conductor was programming for the gala? diff --git a/zeroshot/subject_verb_agreement/eval_results.json b/zeroshot/subject_verb_agreement/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..ffaa8ba5c5d825ea8469b4f973c272ccd89bdb58 --- /dev/null +++ b/zeroshot/subject_verb_agreement/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.6981029810298103} \ No newline at end of file diff --git a/zeroshot/subject_verb_agreement/predictions.txt b/zeroshot/subject_verb_agreement/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1606e414efff6fbfa07cbf306f2be0fb016a943 --- /dev/null +++ b/zeroshot/subject_verb_agreement/predictions.txt @@ -0,0 +1,5536 @@ +index prediction +0 A niece of most senators haven't descended most slopes. +1 The sketch of those trucks hasn't hurt Alan. +2 A newspaper article about the Borgias have disagreed with Marcus. +3 The niece of most guests have cleaned every college campus. +4 A sketch of lights doesn't appear. +5 The movies about Galileo was astounding Melanie. +6 A movie about the Impressionists hasn't scared Harvard University. +7 The pamphlets about Galileo is winning. +8 The daughter of those senators works with Patrick. +9 A story about the Balkans doesn't irritate a person. +10 The daughter of the Lutherans haven't argued about that waitress. +11 A nephew of senators is wanting to murmur. +12 The prints of that high school are impressing Helen. +13 A report about the Impressionists has won. +14 The grandsons of Douglas chuckle. +15 The mothers of Mary seem to shrink. +16 The books about Winston Churchill does look like paintings. +17 The sketches of some dancer do bore these girls. +18 The daughter of most actresses is observing Christina. +19 A story about the Lutherans haven't won. +20 The friends of every person does fix some cups. +21 A father of guys was passing most college campuses. +22 The commentaries about music does disgust Kayla. +23 The grandfathers of Diana drinks. +24 The daughters of Wayne don't exit art galleries. +25 The pamphlets about art aggravate Sherry. +26 A granddaughter of the Impressionists wasn't lifting a convertible. +27 The granddaughters of Walter has boasted about a teenager. +28 The reports about art are scaring Melissa. +29 A brochure about the Balkans doesn't impress Erin. +30 The sons of Victoria long to waste away. +31 The report about the Impressionists hasn't sounded like Joel. +32 The commentaries about politics have bothered this senator. +33 A son of some actors spurs the actress to aggravate the child. +34 The shows about Monet wasn't scaring Liam. +35 The granddaughters of Michelle do shrug. +36 The painting of a lot of sweaters resembles some drawings. +37 The pamphlets about Winston Churchill has resembled those photographs. +38 The drawing of these pants exist. +39 The granddaughters of Amanda hug Noah. +40 The plays about Harvard University wasn't worrying Douglas. +41 The stories about literature has irritated Clyde. +42 A story about the Impressionists worry all teachers. +43 The print of all tomatoes haven't shocked Barbara. +44 A pamphlet about the Borgias aggravates Helen. +45 The mother of those dancers has researched those documentaries. +46 The mothers of Cheryl do bake. +47 The paintings of Donna have upset every guest. +48 A book about the Impressionists do resemble every photograph. +49 The brochure about the Impressionists don't disturb that girl. +50 The sons of a guy do fail to eat. +51 The mother of the pedestrians weren't arriving at some closets. +52 The report about the Impressionists don't disagree with Rose. +53 The nieces of Gerald were crying. +54 The reports about Jesus has disappeared. +55 A daughter of a lot of waiters speak up. +56 The nieces of Rachel discovers this nose. +57 The essays about Spain were disgusting Laurie. +58 The father of many pedestrians washes. +59 A painting of most photographs wasn't hurting Becky. +60 A sister of some children don't swallow. +61 The newspaper articles about Harvard University does disturb Brian. +62 The prints of this icicle upset Stacy. +63 The book about the Lutherans does sound like Guy. +64 The grandmothers of Heather aren't obliging Kathleen to walk through every cafe. +65 A father of these patients intends to embarrass Gary. +66 A grandson of most doctors haven't believed Wayne to walk through a public park. +67 The nieces of the girl negotiates. +68 The grandmothers of Jennifer do regret Daniel will threaten to go to this art gallery. +69 The granddaughters of Anna does describe Gerald. +70 The report about the Great Lakes are astounding Todd. +71 The shows about Spain do aggravate Christina. +72 The photograph of jackets hasn't resembled some print. +73 The cousins of Martin are worrying that guest. +74 The son of some waitresses weren't wondering who begged Julia to practice. +75 A granddaughter of the pedestrians aren't leaving. +76 The commentary about the Impressionists hurt Joel. +77 A daughter of most pedestrians knows Lisa. +78 A newspaper article about the Balkans isn't bothering a teacher. +79 The brochures about Galileo was vanishing. +80 The newspaper articles about Galileo appears. +81 The picture of many college campuses do look like drawings. +82 The show about the Borgias haven't annoyed Mark. +83 The brochures about Winston Churchill doesn't win. +84 The essays about Spain hurts Brenda. +85 The daughters of Charles do 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A movie about the Balkans bother most boys. +376 The drawings of every snake have embarrassed Stacey. +377 The movie about the Balkans do aggravate Bethany. +378 The sons of Victoria are suffering. +379 A son of many boys wasn't compelling Becky's associates to conspire. +380 A newspaper article about the Impressionists do annoy Larry. +381 The mothers of Suzanne doubts Robert commanded Marie's nephew to bike to those closets. +382 A daughter of the pedestrians haven't discovered who is shrugging. +383 The paintings of Christine don't disappear. +384 The grandsons of Tiffany hasn't required Lisa's brother to irritate this association. +385 The drawings of Homer look like many sketches. +386 A sister of most men hasn't want to hide. +387 The paintings of Connie appears. +388 The painting of these cars don't vanish. +389 A story about the Balkans aren't distracting Omar. +390 The grandfathers of Gina haven't forgotten about who hasn't escaped from James. +391 The brother of cashiers 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The nuclei bothers these associations. +2881 The man suffers. +2882 The mouse hasn't alarmed Helen. +2883 The women replies. +2884 The alumnus cheer. +2885 The phenomena wasn't disturbing Claire. +2886 The children leave. +2887 The person sells photographs. +2888 The children haven't cooperated. +2889 The oasis was astounding Rhonda. +2890 The man runs around every museum. +2891 The man isn't boasting. +2892 The women weren't concurring. +2893 The men play. +2894 The men have forgotten Keith. +2895 The louse wasn't waking up. +2896 The alumnus haven't alarmed Sharon. +2897 The woman hasn't hired Samuel. +2898 The man isn't studying. +2899 The oxen hide. +2900 The children weren't struggling. +2901 The men haven't cured Steve. +2902 The alumnus haven't cooperated. +2903 The men were suffering. +2904 The alumnus wasn't drinking. +2905 The people insult art galleries. +2906 The alumnus haven't divorced. +2907 The alumnus get fired. +2908 The people lift this hamster. +2909 The people have 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Raymond. +3082 The person loves Julie. +3083 The women performs. +3084 The woman has slumped over. +3085 The men haven't noticed this ice. +3086 The people have fired Meredith. +3087 The men attack Beth. +3088 The alumnus haven't toured restaurants. +3089 The alumni pass Harvard University. +3090 The people react. +3091 The child hasn't described most guests. +3092 The children haven't suffered. +3093 The men break the plate. +3094 The men are conspiring. +3095 The women have joked around. +3096 The people were hiding away. +3097 The woman isn't climbing down many slopes. +3098 The alumnus passes every mall. +3099 The woman wasn't getting a job. +3100 The analyses describe Benjamin. +3101 The woman wasn't boycotting that legislature. +3102 The people eat. +3103 The people have succeeded. +3104 The child has talked to Sabrina. +3105 The man surrenders. +3106 The men pass those mountains. +3107 The child hasn't cleaned the bank. +3108 The people die. +3109 The woman appreciates Jessica. 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stun Kevin. +3165 The woman conceal Chad. +3166 The children were observing Wendy. +3167 The paralysis was distracting every girl. +3168 The people bores Kevin. +3169 The women aren't reacting. +3170 The man hasn't wept. +3171 The men tour the art galleries. +3172 The mice hasn't hurt Danielle. +3173 The children are retaliating. +3174 The children have forfeited. +3175 The children washes. +3176 The child has joked around. +3177 The alumnus is complaining. +3178 The children get a job. +3179 The child hides. +3180 The men haven't left most pedestrians. +3181 The men haven't explained everything. +3182 The child sounds like Cindy. +3183 The woman hasn't cheered. +3184 The men reference Kenneth. +3185 The children cheer. +3186 The children boast about Peter. +3187 The children correspond. +3188 The people boast about Vanessa. +3189 The woman isn't boycotting some college campuses. +3190 The men weren't upsetting Michelle. +3191 The woman returns to Katherine. +3192 The people stretch. 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paralysis worry the universities. +3361 The children visit Denise. +3362 The men conspire. +3363 The people buy these restaurants. +3364 The geese have disgusted some association. +3365 The people tour some hospital. +3366 The children weren't exploring all restaurants. +3367 The woman has read. +3368 The man returns to Stephanie. +3369 The phenomenon shocks this association. +3370 The children were dying. +3371 The men see. +3372 The stimuli bother most drivers. +3373 The man has listened to Becca. +3374 The man approaches some waiter. +3375 The men haven't worked hard. +3376 The people go to many libraries. +3377 The men walk through most malls. +3378 The men have watched Curtis. +3379 The alumni have benefited. +3380 The alumnus hasn't climbed up a ladder. +3381 The child descends this slope. +3382 The alumnus weren't seeing. +3383 The women benefit. +3384 The alumnus smile. +3385 The men care for Beverly. +3386 The children distract Heather. +3387 The women cure Alexander. +3388 The basis irritates Clyde. +3389 The men see some ice cream. +3390 The man drinks. +3391 The alumnus haven't fallen asleep. +3392 The men have eaten. +3393 The women weren't washing. +3394 The teeth alarm Rodney. +3395 The men divorce. +3396 The child has cleaned that scarf. +3397 The man praise Marie. +3398 The analyses weren't concealing Linda. +3399 The analysis was referencing Beth. +3400 The men have muttered. +3401 The feet aren't falling. +3402 The women help Valerie. +3403 The women arrive at those movie theaters. +3404 The tooth bothers Walter. +3405 The women aren't arguing. +3406 The children discover Diane. +3407 The alumnus was complaining about Frank. +3408 The person wasn't writing. +3409 The people cook. +3410 The children weren't negotiating. +3411 The men have researched some essay. +3412 The women criticizes these libraries. +3413 The man breaks most screens. +3414 The alumnus weren't cleaning the pies. +3415 The child helps Becky. +3416 The women boast. +3417 The 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Those dogs resembles some sketches. +3670 Erin hasn't taken some hill. +3671 Bethany has bored Kirsten. +3672 All pedestrians fixes some chairs. +3673 Most actresses observes Judy. +3674 That teacher tours the hills. +3675 Schools criticize these glaciers. +3676 The guest has walked through most art galleries. +3677 A cake shock Patrick. +3678 All hospitals resemble a lot of drawings. +3679 Galileo listens to Elizabeth. +3680 Some movies sound like a student. +3681 The pedestrian is driving to this lake. +3682 The pedestrians reference Charles. +3683 Most associations buy those libraries. +3684 Suzanne dislikes these dancers. +3685 That customer dislikes Heather. +3686 Most girls aren't concealing Kevin. +3687 The ladies break this plate. +3688 Gary leaves Stacy. +3689 Heidi lifts many socks. +3690 A lot of doctors scan a lot of movies. +3691 Linda has cared for Kenneth. +3692 Carol boasts about Ella. +3693 The Impressionists aren't passing the river. +3694 These cafes haven't upset 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Gregory was breaking most couches. +3773 Andrew is running around some river. +3774 Adam hasn't brought a lot of plates. +3775 Martha was going to the Great Lakes. +3776 Jessica is shocking all banks. +3777 Chad remembers the Borgias. +3778 Clyde wasn't examining some newspaper article. +3779 Christopher cares for that teenager. +3780 Clyde was driving to those movie theaters. +3781 Some teenagers return to Walter. +3782 Denise wasn't hiding the computers. +3783 Stacy hasn't hugged Wendy. +3784 Christina admires Michael. +3785 These horses ascends a lot of ladders. +3786 The Borgias have confused Ella. +3787 This association insults Marcus. +3788 Regina has passed those banks. +3789 Barbara hasn't explored all cafes. +3790 David question Curtis. +3791 Every bank has admired cars. +3792 Lawrence hasn't walked through the bank. +3793 Kayla isn't researching some high schools. +3794 The children care for this cashier. +3795 These waiters examines a play. +3796 Every closet hasn't annoyed 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has hidden Jessica. +3977 Diana admires Tanya. +3978 Gerald disturbs Roger. +3979 All governments are confusing a hospital. +3980 Bethany is healing some customer. +3981 Brian hasn't discussed Winston Churchill. +3982 Sabrina discovers Florence. +3983 Some teacher wasn't dropping by the public parks. +3984 Todd isn't fixing the bicycle. +3985 Bradley loves all documentaries. +3986 Amelia weren't taking steak. +3987 All mountains impresses Sonia. +3988 Some waitresses references some adult. +3989 Charles dislikes the art gallery. +3990 David was attacking Jane. +3991 Diana isn't selling the hat. +3992 Some actresses complains about Julia. +3993 William messes up that movie theater. +3994 The Lutherans haven't loved Janet. +3995 Many students break every computer. +3996 Actresses hides the snake. +3997 All cashiers criticizes some newspaper articles. +3998 A cup scare teenagers. +3999 These patients have that hill. +4000 Some adults flee from Jason. +4001 Caroline question Lisa. +4002 Waitresses drive to a lot of movie theaters. +4003 Thomas watch Travis. +4004 Carlos isn't attacking the guest. +4005 Kevin has driven to all high schools. +4006 Tina wasn't hugging some waiter. +4007 That actress hasn't researched the print. +4008 Kayla lifts those projectors. +4009 Vanessa has hired Suzanne. +4010 Cynthia thinks about these eyes. +4011 A guest is taking cafes. +4012 Jason wasn't hiding some drawing. +4013 Samuel hug Liam. +4014 Literature has stunned most senators. +4015 Joseph flees from Carolyn. +4016 Jane questions Theodore. +4017 James has listened to the senator. +4018 This ladder annoys Paula. +4019 All waiters criticize all movie theaters. +4020 This cashier flees from Rachel. +4021 A lot of customers notice every waiter. +4022 Michael skate around that public park. +4023 Douglas watches some cashier. +4024 That patient was disturbing Michelle. +4025 Those banks describe Eric. +4026 Marcus was boasting about the movie. +4027 Barbara hasn't lifted a hamster. +4028 Every restaurant embarrasses Elaine. +4029 Margaret hasn't praised Martha. +4030 Claire wasn't revealing the car. +4031 Chad has criticized Larry. +4032 The Borgias heal Connie. +4033 Tonya was distracting the Borgias. +4034 Christopher runs around all art galleries. +4035 The pie distracts this adult. +4036 Some teacher have cleaned every jacket. +4037 Those waitresses annoys Cynthia. +4038 Catherine boasts about Brett. +4039 Candice isn't upsetting this adult. +4040 Sabrina is disgusting many waitresses. +4041 Laurie hasn't lifted every dog. +4042 Kenneth has sounded like the story. +4043 These senators have examined these stories. +4044 Heather isn't passing some lakes. +4045 A lot of libraries praises Sarah. +4046 Brett was leaving some child. +4047 Every brochure bore James. +4048 Walter wasn't observing Donald. +4049 Kenneth runs around the Great Lakes. +4050 Museums fire Elaine. +4051 Kenneth hasn't found Ann. +4052 A lot of waiters remembers all students. +4053 Most actresses haven't exited the mountains. +4054 Veronica has impressed this library. +4055 These guys reference Clyde. +4056 Chad aren't hiring most dancers. +4057 Holly was scaring some patient. +4058 Kendra was discovering these coats. +4059 Danielle arrive at most mountains. +4060 Walter hasn't loved every play. +4061 Sara has cared for all dancers. +4062 Adam isn't boycotting cafes. +4063 Those actors run around this library. +4064 Spain alarms Connie. +4065 Some teenagers have passed some glacier. +4066 These waitresses aren't fleeing from a doctor. +4067 Many adults hide every window. +4068 Tanya scares Deanna. +4069 Paul clean those pictures. +4070 Beth hasn't watched Bethany. +4071 Larry has concealed many wheelbarrows. +4072 Gerald is wearing some shirt. +4073 Sara wasn't noticing Eva. +4074 Holly descends ladders. +4075 Sara drive to the mall. +4076 Todd has shocked Jesus. +4077 Deanna has upset Carolyn. +4078 The libraries know Becky. +4079 The Lutherans reference Jason. +4080 Steven talks to Florence. +4081 That movie hasn't alarmed Julie. +4082 Judy isn't boring Charles. +4083 Jennifer sells that plate. +4084 This cafe is worrying Bruce. +4085 A lot of organizations praises Andrew. +4086 Some boys escape from Donna. +4087 Rhonda has insulted a lot of actresses. +4088 A lot of pedestrians were disturbing Diane. +4089 An actor was bringing the book. +4090 Becca isn't exploring that museum. +4091 Those legislatures fire Craig. +4092 Alexander has climbed down the mountains. +4093 David was cleaning a couch. +4094 Kayla has broken the window. +4095 Nancy hasn't found Danielle. +4096 This hat was astounding Julie. +4097 Alicia hasn't cared for Gary. +4098 A pie bothers Benjamin. +4099 Bill was fixing that unicycle. +4100 This library hasn't stunned some hospital. +4101 Most fish confuses Donna. +4102 Some dresses have annoyed Thomas. +4103 Stacey hasn't disliked Dan. +4104 Chad has cured Ronald. +4105 Most girls go to many libraries. +4106 All waitresses bikes to glaciers. +4107 Many guests describe Amanda. +4108 David has scared Tina. +4109 A print has looked like Matt. +4110 Ruth has questioned women. +4111 Rodney question some legislature. +4112 Marla walks through a high school. +4113 Erin hasn't worked with Rose. +4114 Tiffany hire Monica. +4115 Science hasn't disturbed Walter. +4116 Some shoes embarrasses some cashier. +4117 Allison wasn't bringing trucks. +4118 A lot of screens embarrasses Sabrina. +4119 Andrea tour rivers. +4120 Some glove wasn't irritating some teenager. +4121 Elizabeth explores all restaurants. +4122 Raymond clean a library. +4123 Jane has found Ella. +4124 Brad has forgotten Brian. +4125 Those cashiers question many waiters. +4126 Brad hasn't examined those essays. +4127 All actors haven't distracted Keith. +4128 Kristin leaves Monica. +4129 A lot of waiters boasts about Wayne. +4130 Stephanie knows Maria. +4131 Vanessa hasn't worked with James. +4132 Winston Churchill isn't praising Marla. +4133 Wayne messes up 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Benjamin has worked with Steve. +4288 Cheryl is escaping from Pamela. +4289 This driver was thinking about many malls. +4290 Meredith has hugged Julia. +4291 Samuel hasn't cared for Monet. +4292 Florence hasn't hurt Carlos. +4293 Some organizations forget Daniel. +4294 Theresa has dropped by some hospital. +4295 Most children listen to Regina. +4296 Holly has known Rose. +4297 Kayla has disgusted the waiter. +4298 Jessica weren't breaking trucks. +4299 All teachers care for Teresa. +4300 Donald heal Heather. +4301 Some river resembles all prints. +4302 Angela was talking to those adults. +4303 Some cashiers admires Michael. +4304 Jill hasn't ascended many steps. +4305 Wendy is escaping from most college campuses. +4306 Brenda hasn't referenced Walter. +4307 Gina wasn't thinking about a glass. +4308 Those teachers have a lot of cucumbers. +4309 Derek was hiring Kayla. +4310 Liam isn't escaping from a dancer. +4311 Frank hasn't revealed a convertible. +4312 Teresa like Alan. +4313 Sally 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Teresa buy that window. +4523 The waitresses thinks about all newspaper articles. +4524 Todd exits the Great Lakes. +4525 This adult is passing lakes. +4526 Paul heal that man. +4527 Christopher hasn't aggravated Stephen. +4528 Judy tour those restaurants. +4529 Stephen hasn't lifted photographs. +4530 Some windows disturb Beth. +4531 Some actresses distract Michael. +4532 Eva boasts about Theodore. +4533 All teenagers run around the school. +4534 The Impressionists talk to Cynthia. +4535 The pedestrian has forgotten Grace. +4536 A waiter have loved Alexander. +4537 Carlos has healed Caroline. +4538 Alan hasn't explored a lot of museums. +4539 Noah descends every hill. +4540 Amanda isn't observing Angela. +4541 Sabrina isn't boycotting the lake. +4542 The Borgias observes Stephanie. +4543 All girls sell all horses. +4544 Many pedestrians return to those waiters. +4545 Most teachers drop by some mountains. +4546 Guests reference Karla. +4547 Stacey hasn't embarrassed the government. +4548 Alan hasn't referenced Natalie. +4549 Amelia was disturbing Julie. +4550 Claire has sold some computers. +4551 Denise has worn that sock. +4552 Stephen is talking to Kenneth. +4553 Bill has broken most forks. +4554 Paul examines some books. +4555 Kristin wasn't disturbing Kathleen. +4556 That patient praises Tonya. +4557 Hills alarm Craig. +4558 Deanna has kissed Marcus. +4559 Dan hasn't aggravated Janet. +4560 Joseph has found Craig. +4561 Current events scare Harvard University. +4562 Sherry has toured that museum. +4563 Jill hasn't hated Christopher. +4564 Beverly notices a lot of banks. +4565 Donna kisses these doctors. +4566 Most dancers escape from Stacy. +4567 Paintings haven't looked like water. +4568 This movie bore Susan. +4569 Teresa researches most college campuses. +4570 Lucille questions Candice. +4571 Nina hasn't cleaned the skirt. +4572 Harvard University hasn't criticized that college campus. +4573 The teachers bore Gregory. +4574 Linda has sounded like many teachers. +4575 Vincent was questioning Stacey. +4576 The students perform. +4577 The associations talk about Stacey. +4578 The gloves tightens. +4579 The actresses compromise. +4580 The customer is suffering. +4581 The cashiers marry. +4582 The hospital appreciates Claire. +4583 The customers haven't whispered. +4584 The adults talks. +4585 The universities aren't working hard. +4586 The adult has concealed all stories. +4587 The university appreciate all teenagers. +4588 The teachers weren't murmuring. +4589 The organization wasn't corresponding. +4590 The libraries weren't hurting Ellen. +4591 The organization know Larry. +4592 The pedestrians heals the dogs. +4593 The actress responds. +4594 The skirt is disturbing Debra. +4595 The cashiers haven't testified. +4596 The legislature has loved a lot of stories. +4597 The chair has embarrassed most drivers. +4598 The patients haven't debated. +4599 The adults have talked to Angela. +4600 The pedestrians haven't ascended that mountain. +4601 The senator knows Elaine. +4602 The children smile. +4603 The teenager plays. +4604 The waitresses cries. +4605 The customer cheer. +4606 The government distract Suzanne. +4607 The senators watch Kristin. +4608 The ladies weren't fleeing from Michael. +4609 The customers have profited. +4610 The bank has reunited. +4611 The jackets haven't shrunk. +4612 The universities were boasting. +4613 The adults have fled from all dancers. +4614 The patients laugh. +4615 The nose bothers Alicia. +4616 The actors were smiling. +4617 The senators isn't falling asleep. +4618 The university negotiates. +4619 The patient isn't referencing Thomas. +4620 The guests aren't cooking. +4621 The dancer hasn't lifted a sock. +4622 The adult has discovered this teenager. +4623 The boys complain about some drivers. +4624 The guests are passing this glacier. +4625 The senators haven't complained about Kendra. +4626 The driver skate around college campuses. +4627 The guests were approving. +4628 The children haven't whispered. +4629 The companies aren't coping. +4630 The guest hasn't exited a closet. +4631 The committee succeeds. +4632 The couch distract Samuel. +4633 The pedestrians litter. +4634 The skirts have twisted. +4635 The association hates Melinda. +4636 The waiters disagree with Natalie. +4637 The guest hasn't hugged Grace. +4638 The glasses bothers those senators. +4639 The shoe isn't shrinking. +4640 The bird worries Sharon. +4641 The girl drinks. +4642 The pedestrians help Andrew. +4643 The ladies aren't walking through every closet. +4644 The gloves fray. +4645 The cashier wasn't concealing this dress. +4646 The actor has approached Eva. +4647 The guest hasn't talked about James. +4648 The sock disturbs Clyde. +4649 The guys have healed Russell. +4650 The companies have worked hard. +4651 The boys yawn. +4652 The dancers aren't marrying. +4653 The college campuses were disturbing those drivers. +4654 The legislature hasn't fired Peter. +4655 The students climb down 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museum shock Kathleen. +4849 The ladies were performing. +4850 The dancers haven't yawned. +4851 The teenager isn't admiring Samuel. +4852 The actresses wasn't painting. +4853 The customer wasn't revealing the gates. +4854 The libraries admire a lot of birds. +4855 The cashier has bought the ice cream. +4856 The driver hasn't slumped over. +4857 The customers have chatted. +4858 The bird hasn't worried Cynthia. +4859 The driver scans many commentaries. +4860 The cashiers have played. +4861 The guy disagree with Ellen. +4862 The doctor hasn't gotten fired. +4863 The lady is hugging Janice. +4864 The actors walk through that mall. +4865 The doctors arrive at most museums. +4866 The painting looks like Guy. +4867 The school knows Craig. +4868 The guy reveal Tina. +4869 The children were flirting. +4870 The association is responding. +4871 The cashier remembers this driver. +4872 The teachers were falling asleep. +4873 The dancers visit Claire. +4874 The students conceal all icicles. +4875 The actresses stand up. +4876 The waiters get a job. +4877 The waitresses murmur. +4878 The waitress sit down. +4879 The teachers weren't disagreeing. +4880 The adults sneezes. +4881 The guys have embarrassed the actresses. +4882 The girls appear. +4883 The companies weren't lying. +4884 The waiter hasn't yawned. +4885 The plays have sounded like Patrick. +4886 The pedestrians have gotten a job. +4887 The shawls warps. +4888 The dress has twisted. +4889 The teachers perform. +4890 The doctors forget these guests. +4891 The girl talks about Beth. +4892 The legislature embarrasses some universities. +4893 The dancer writes. +4894 The girls complain. +4895 The senators reveals Alan. +4896 The adults explore Spain. +4897 The cups weren't cracking. +4898 The waitress have scared some companies. +4899 The pedestrians write. +4900 The pedestrians weren't singing. +4901 The patient hasn't boycotted closets. +4902 The adults haven't examined a newspaper article. +4903 The students weren't 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teachers have known Derek. +4959 The college campuses have astounded Stacey. +4960 The guy talk to Regina. +4961 The teacher skates around some high schools. +4962 The malls hasn't shocked the Impressionists. +4963 The libraries have helped Tara. +4964 The museum confer. +4965 The art gallery alarm Becca. +4966 The bank buys this projector. +4967 The teacher has seen most boys. +4968 The teenager hasn't hidden Jacqueline. +4969 The actresses mess up this hospital. +4970 The prints were looking like some rice. +4971 The pedestrians haven't spoken up. +4972 The actresses assemble. +4973 The boy lies. +4974 The schools haven't helped some children. +4975 The cashier lies. +4976 The actress hasn't come here. +4977 The actresses climb up every hill. +4978 The schools aren't communicating. +4979 The library reunite. +4980 The guests bore Maria. +4981 The customer sees hospitals. +4982 The teachers have some socks. +4983 The teachers disgust a lot of teenagers. +4984 The children leave Vanessa. +4985 The patients flee from Noah. +4986 The children haven't coped. +4987 The actors haven't cheered. +4988 The customer wasn't breaking these carts. +4989 The grocery store exist. +4990 The committees change. +4991 The guests drive to that mountain. +4992 The college campuses haven't upset Michelle. +4993 The pedestrians telephone. +4994 The teenagers divorce. +4995 The guy compromise. +4996 The doctor approves. +4997 The senator is insulting Janet. +4998 The adult is fleeing from Martha. +4999 The teacher hasn't talked to Paul. +5000 The hospitals haven't communicated. +5001 The legislatures insults Meredith. +5002 The driver hasn't stood up. +5003 The bank hasn't resembled this sketch. +5004 The adult isn't finding these dancers. +5005 The teachers are cleaning a cup. +5006 The coat disturb Matt. +5007 The college campuses aggravate the senators. +5008 The senators sing. +5009 The blouse wastes away. +5010 The association shocks most legislatures. +5011 The associations reference Paula. +5012 The adult trains. +5013 The scarves dry. +5014 The guest hasn't sneezed. +5015 The company jokes around. +5016 The cashier wasn't dropping by some restaurants. +5017 The guy reads. +5018 The customers wash. +5019 The government admires this unicycle. +5020 The museum has listened to Deborah. +5021 The movies sound like Jane. +5022 The actors mutters. +5023 The government has approved. +5024 The girls distract Jill. +5025 The guy heals the person. +5026 The customers get a job. +5027 The actress has complained. +5028 The chairs have shattered. +5029 The patients aren't finding Samuel. +5030 The pedestrians was examining those books. +5031 The waiter has stunned Regina. +5032 The senators aren't embracing. +5033 The girl wasn't working with Tanya. +5034 The play worry Brett. +5035 The actresses lie. +5036 The pictures weren't looking like Angela. +5037 The girls complain about some students. +5038 The customers haven't brought the chicken. +5039 The customers run 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dislike the teacher. +5094 The teacher wasn't escaping from Tina. +5095 The waiter was skated around glaciers. +5096 The museum is admiring many boys. +5097 The girls aren't waving. +5098 The photographs were looking like Deanna. +5099 The shawls appears. +5100 The actors were baking. +5101 The bank has forfeited. +5102 The association was fighting. +5103 The organization isn't explaining everything. +5104 The doctor sees Pamela. +5105 The senators embarrasses Martin. +5106 The lady researches the play. +5107 The universities have clashed. +5108 The association hasn't loved the documentaries. +5109 The adults testifies. +5110 The cashier has complained about Susan. +5111 The girl has explained everything. +5112 The glasses weren't slipping. +5113 The guy has washed. +5114 The waiters sneeze. +5115 The coat has wrinkled. +5116 The patients messes up these hospitals. +5117 The adults were surrendering. +5118 The customer cleans those glasses. +5119 The legislatures haven't worried. +5120 The adults have approved. +5121 The children are debating. +5122 The guy worries. +5123 The customer hates Ella. +5124 The actress die. +5125 The guy flees from Carla. +5126 The company knows all waitresses. +5127 The guys shout. +5128 The girl has sat down. +5129 The brochures aren't disturbing Brian. +5130 The girl hasn't written. +5131 The senators haven't seen those shirts. +5132 The jackets fray. +5133 The driver have admired some movie theater. +5134 The banks worry Tracy. +5135 The lady shock Thomas. +5136 The committee irritate George. +5137 The teachers haven't boycotted that hospital. +5138 The lady has watched Tara. +5139 The blouses have stretched. +5140 The waiters were hiding away. +5141 The boys hug Christina. +5142 The dancer annoy this dancer. +5143 The cashier helps some people. +5144 The girl wasn't examining a report. +5145 The patients haven't upset Cheryl. +5146 The boy appreciates most children. +5147 The pedestrians chuckle. +5148 The senators scan this documentary. +5149 The ladies were insulting Dan. +5150 The patients haven't spoken up. +5151 The waiter cooks. +5152 The rabbits climb up the ladders. +5153 The socks are twisting. +5154 The doctors haven't collaborated. +5155 The teenagers clash. +5156 The couches have slipped. +5157 The waiter has respected Bruce. +5158 The teachers haven't disagreed. +5159 The senators were scanning these books. +5160 The driver protests. +5161 The cashiers help Wendy. +5162 The dress loosen. +5163 The cafe embarrasses Steve. +5164 The waiters have complained about politics. +5165 The student breaks many couches. +5166 The patients work with Martha. +5167 The drivers have hugged. +5168 The shawl hasn't frayed. +5169 The adults aren't clashing. +5170 The hospitals were working with Steve. +5171 The committees are retaliating. +5172 The students haven't gone to every school. +5173 The school weren't disturbing a lot of actors. +5174 The company has argued about Maria. +5175 The fork disturbs Tina. +5176 The guys have whispered. +5177 The art gallery resemble some photographs. +5178 The patients haven't married. +5179 The cat leaves. +5180 The actresses reacts. +5181 The waitress attack the girl. +5182 The senators arrive at high schools. +5183 The shirts are soaking. +5184 The customer messes up the cup. +5185 The schools profit. +5186 The boys sigh. +5187 The teenagers exercise. +5188 The hamster scares Rhonda. +5189 The boy stands up. +5190 The boys flees from some restaurant. +5191 The libraries haven't answered. +5192 The cup confuses some teenager. +5193 The committee isn't coping. +5194 The driver compromise. +5195 The actress isn't spinning around. +5196 The blouses disturb Kendra. +5197 The actresses are bringing that cilantro. +5198 The organization know Alexander. +5199 The car is falling. +5200 The reports have scared George. +5201 The students have helped Sara. +5202 The ladies fight. +5203 The waiter complains about Frank. +5204 The legislature profits. +5205 The patient hasn't played. +5206 The lady alarms Gina. +5207 The guys stand up. +5208 The plates are tipping over. +5209 The senators hasn't revealed Keith. +5210 The movies sound like Kayla. +5211 The company annoy Ann. +5212 The actor isn't attacking Rhonda. +5213 The teachers haven't exited all movie theaters. +5214 The senators wakes up. +5215 The school impress Jeffrey. +5216 The teacher runs around the bank. +5217 The schools protest. +5218 The teachers observe Amanda. +5219 The dancers haven't slumped over. +5220 The chairs are dropping. +5221 The customer scratches. +5222 The driver haven't criticized Vanessa. +5223 The waiter meet. +5224 The patient hasn't gone to a movie theater. +5225 The teacher responds. +5226 The museum annoys all children. +5227 The associations are working hard. +5228 The waitress was protesting. +5229 The organization respect Diane. +5230 The senators aren't disagreeing with Edward. +5231 The patient isn't breaking that bicycle. +5232 The drivers date. +5233 The waiter approves. +5234 The guest was fixing these screens. +5235 The adults aren't embracing. +5236 The teachers haven't visited Kathleen. +5237 The guy cleans a rug. +5238 The actors weren't touring Harvard University. +5239 The reports aren't confusing Stephen. +5240 The pedestrians discover Lisa. +5241 The actors weren't surrendering. +5242 The senators were approving. +5243 The adult is departing. +5244 The customers swallows. +5245 The actresses flee from Alicia. +5246 The actresses were studying. +5247 The girl boasts about every actress. +5248 The pedestrians is taking some vase. +5249 The girls hate George. +5250 The actresses weren't murmuring. +5251 The dish crack. +5252 The museum cooperates. +5253 The patient cures Martha. +5254 The guest looks like this photograph. +5255 The gloves haven't shrunk. +5256 The banks admire Raymond. +5257 The guest has healed Sabrina. +5258 The socks unfolds. +5259 The universities were hiring Martha. +5260 The waiters aren't sighing. +5261 The patients hire Derek. +5262 The dancer suffers. +5263 The blouse wasn't soaking. +5264 The story sounds like Janice. +5265 The senators go to Spain. +5266 The stories have alarmed Vanessa. +5267 The students cry. +5268 The actresses explore every library. +5269 The committees argue about Marie. +5270 The customer has explored Spain. +5271 The adults have hugged Alexander. +5272 The students donate. +5273 The waitress observe Rachel. +5274 The museum isn't communicating. +5275 The girl gets a job. +5276 The brochure embarrass Gerald. +5277 The schools discuss Theresa. +5278 The guy fixes this couch. +5279 The patients haven't explored the lakes. +5280 The waitresses have escaped from Veronica. +5281 The patients have chuckled. +5282 The essay disagrees with most pedestrians. +5283 The drivers haven't played. +5284 The jackets wrinkles. +5285 The cup hasn't fallen apart. +5286 The dancers weren't chuckling. +5287 The customers remember the actress. +5288 The adult gets fired. +5289 The pedestrians haven't concealed that public park. +5290 The dancers changes. +5291 The teachers were going fishing. +5292 The government is profiting. +5293 The committee hasn't reacted. +5294 The adults watch Candice. +5295 The chairs weren't falling apart. +5296 The driver isn't thinking about Helen. +5297 The ladies aren't scanning every documentary. +5298 The boy hasn't fled from these hills. +5299 The teachers have conspired. +5300 The waiter has escaped. +5301 The shoes aggravate Ellen. +5302 The waitress fires Rodney. +5303 The cashiers aren't whispering. +5304 The actress appreciates every customer. +5305 The guy hasn't toured a hill. +5306 The teenagers heals Irene. +5307 The government describe Sonia. +5308 The waiters approach Beth. +5309 The cashiers have fixed some mirror. +5310 The pedestrians haven't sneezed. +5311 The children haven't concealed Grace. +5312 The waitress mess up many chairs. +5313 The boys have kissed. +5314 The university forgets Daniel. +5315 The shoes unfold. +5316 The teacher knows Sara. +5317 The dancer sells this sock. +5318 The teenagers see. +5319 The committee impress Linda. +5320 The guys have laughed. +5321 The waiter has gone fishing. +5322 The guys weren't escaping. +5323 The students disappear. +5324 The university donate. +5325 The associations have testified. +5326 The driver wasn't lifting every wheelbarrow. +5327 The cashiers aren't returning to a mall. +5328 The actors have praised April. +5329 The teacher hasn't remembered Connie. +5330 The actress hasn't known Sonia. +5331 The picture hasn't wasted away. +5332 The customer was falling asleep. +5333 The museum testify. +5334 The scarves have bored Dana. +5335 The shawl hasn't disgusted Judy. +5336 The actress has questioned Amy. +5337 The organization has the high school. +5338 The senators swallow. +5339 The student bothers Carolyn. +5340 The teenagers describe Michael. +5341 The dancer has cleaned those gates. +5342 The patient wasn't donating. 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moves. +5371 The teenagers chuckles. +5372 The coats dry. +5373 The chair falls apart. +5374 The guests have scanned the documentaries. +5375 The museum impresses Amy. +5376 The pedestrians has explored Harvard University. +5377 The lady watches Linda. +5378 The school chat. +5379 The pedestrians hides away. +5380 The shirts shrink. +5381 The pedestrians research the legislature. +5382 The lady notices Sonia. +5383 The cashier wasn't playing. +5384 The senators isn't yawning. +5385 The committee clash. +5386 The pedestrians has watched Tamara. +5387 The lady smiles. +5388 The adult has concealed Naomi. +5389 The doctors talk about Peter. +5390 The cashier skates around some library. +5391 The students hate Emily. +5392 The essay has upset Jessica. +5393 The actresses have appeared. +5394 The adults weren't describing Elaine. +5395 The dress irritates Alan. +5396 The hospital worry. +5397 The hamster stun Renee. +5398 The rugs wasn't disturbing the school. +5399 The cashier falls. +5400 The libraries know Adam. +5401 The children tour that bank. +5402 The drivers are arriving at the lake. +5403 The boys negotiates. +5404 The jacket hasn't unfolded. +5405 The driver helps Janice. +5406 The committee talks about some malls. +5407 The guests play. +5408 The teenagers reply. +5409 The boys have corresponded. +5410 The pedestrians hug Donald. +5411 The actresses walk through the art gallery. +5412 The waiter wears most glasses. +5413 The adults fight. +5414 The organization appreciates Edward. +5415 The cashiers haven't blinked. +5416 The student observes Eric. +5417 The senators cures Denise. +5418 The glass tip over. +5419 The patient hasn't spoken up. +5420 The ladies were describing the teenager. +5421 The students were bringing sweaters. +5422 The patient exits these rivers. +5423 The guys are firing Jerry. +5424 The adult walks through this closet. +5425 The adults have explained everything. +5426 The rabbits resemble paintings. +5427 The lady research that story. +5428 The cashiers have fought. +5429 The dancer hasn't left the guy. +5430 The drivers were compromising. +5431 The waitresses approach Caroline. +5432 The sock is twisting. +5433 The sketches aren't bothering Patricia. +5434 The senators aren't drinking. +5435 The teenagers hug. +5436 The scarf shrink. +5437 The boys haven't studied. +5438 The customers buy this art gallery. +5439 The guests escape from Sonia. +5440 The birds bores Curtis. +5441 The doctors have revealed this candle. +5442 The rabbits climb down a mountain. +5443 The students jokes around. +5444 The lady bore Patrick. +5445 The couches aren't cracking. +5446 The dancers have practiced. +5447 The children haven't departed. +5448 The teenager writes. +5449 The actor hides. +5450 The waitress hasn't seen. +5451 The banks correspond. +5452 The photographs haven't looked like Pamela. +5453 The teacher smile. +5454 The pedestrians discusses Rebecca. +5455 The teacher wasn't learning. +5456 The boy wasn't nodding. +5457 The teachers haven't fought. +5458 The adults study. +5459 The guy hasn't trained. +5460 The doctors compete. +5461 The cashiers weren't saluting. +5462 The guys like the photographs. +5463 The cashiers clean this hill. +5464 The newspaper article disagrees with Michelle. +5465 The banks aren't profiting. +5466 The waitress arrives at this hospital. +5467 The adults weren't writing. +5468 The legislatures buy most shawls. +5469 The teacher has hidden Jessica. +5470 The customers listen to Marla. +5471 The girl hasn't changed. +5472 The guy hasn't woken up. +5473 The student examines a lot of plays. +5474 The boys exits the museums. +5475 The association has referenced Stephanie. +5476 The doctors haven't conferred. +5477 The driver was suffering. +5478 The universities were collaborating. +5479 The children aren't walking through the library. +5480 The legislatures aren't aggravating Eva. +5481 The legislature approves. +5482 The sketches weren't shocking Veronica. +5483 The guests are speaking up. +5484 The brochure shock Kendra. +5485 The actresses climb up some stairs. +5486 The actresses were exercising. +5487 The actress sneezes. +5488 The ladies have thought about Ronald. +5489 The companies appreciate Homer. +5490 The dancers fight. +5491 The waitress explore the river. +5492 The teenager walks through that river. +5493 The teacher hasn't exited the high schools. +5494 The pedestrians has discovered that hamster. +5495 The driver haven't murmured. +5496 The skirts irritates this senator. +5497 The shirt is loosening. +5498 The customers argue about Jessica. +5499 The teenagers weep. +5500 The senator flees from Joel. +5501 The pedestrians weeps. +5502 The birds haven't confused Heidi. +5503 The patients have gotten a job. +5504 The waitress was practicing. +5505 The skirt tightens. +5506 The shoes are unfolding. +5507 The teenager is stretching. +5508 The boys weren't noticing Rodney. +5509 The actresses disagree with Helen. +5510 The skirts haven't frayed. +5511 The students were grinning. +5512 The ladies weren't waving. +5513 The governments have collaborated. +5514 The rabbits have distracted Travis. +5515 The actors love Suzanne. +5516 The schools were replying. +5517 The schools haven't criticized George. +5518 The ladies haven't respected this college campus. +5519 The movie impresses John. +5520 The guy waves. +5521 The college campuses weren't boring Gina. +5522 The actor isn't moving. +5523 The guests have slumped over. +5524 The pedestrians have divorced. +5525 The actress bake. +5526 The waitress hasn't spoken up. +5527 The customers ascend a mountain. +5528 The dancers observe Claire. +5529 The waiter talks about Brian. +5530 The doctor is firing Victoria. +5531 The organization hasn't worked with Judy. +5532 The guests have hunted. +5533 The cashiers question Vincent. +5534 The girls win. diff --git a/zeroshot/turn_taking/eval_results.json b/zeroshot/turn_taking/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..6e4452ffe2bfad4c0c8f9d7219f2788ddcac3c85 --- /dev/null +++ b/zeroshot/turn_taking/eval_results.json @@ -0,0 +1 @@ +{"eval_accuracy": 0.6464285714285715} \ No newline at end of file diff --git a/zeroshot/turn_taking/predictions.txt b/zeroshot/turn_taking/predictions.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b71d8e0af2e9a5f54646463318c2a0fe87202b5 --- /dev/null +++ b/zeroshot/turn_taking/predictions.txt @@ -0,0 +1,281 @@ +index prediction +0 David: Should you quit?\nSarah: No, I shouldn't. +1 David: Should you meet him?\nSarah: Yes, I should. +2 A: Did you think it was going to work?\nB: No, I didn't. +3 A: Did you go somewhere after the movie was over?\nB: Yes, I did. +4 A: Did you quit?\nB: Yes, I did. +5 A: Did you meet him?\nB: Yes, I did. +6 A: Should you consider asking for another option?\nB: No, I shouldn't. +7 Should you meet him?\nNo, I shouldn't. +8 Did you try to finish it on time or not?\nNo, you didn't. +9 Did you arrive?\nNo, you didn't. +10 David: Did you meet him?\nSarah: Yes, you did. +11 David: Should you consider asking for another option?\nSarah: Yes, you should. +12 David: Did you finish?\nSarah: No, you didn't. +13 David: Did you go somewhere after the movie was over?\nSarah: Yes, you did. +14 Should you quit?\nYes, you should. +15 Should you meet him?\nNo, you shouldn't. +16 A: Did you think it was going to work?\nB: Yes, you did. +17 "Should we meet him?" he asked. "Yes, they should," she said. +18 Did they try to finish it on time or not?\nNo, they didn't. +19 David: Did they leave?\nSarah: No, they didn't. +20 A: Did they try to finish it on time or not?\nB: Yes, they did. +21 David: Did they finish?\nSarah: No, they didn't. +22 A: Should they consider asking for another option?\nB: Yes, they should. +23 "Did they meet him?" he asked. "Yes, they did," she said. +24 David: Did I try to finish it on time or not?\nSarah: No, I didn't. +25 A: Did I finish?\nB: Yes, I did. +26 David: Should I meet him?\nSarah: No, you shouldn't. +27 Should I finish?\nNo, I shouldn't. +28 David: Should I consider asking for another option?\nSarah: Yes, I should. +29 Did I try to finish it on time or not?\nYes, I did. +30 David: Did I leave?\nSarah: Yes, I did. +31 A: Did I find something to do later?\nB: Yes, I did. +32 Should I quit?\nYes, you should. +33 Did I meet him?\nNo, I didn't. +34 "Did we think it was going to work?" he asked. "Yes, we did," she said. +35 David: Did we finish?\nSarah: Yes, we did. +36 David: Did we try to finish it on time or not?\nSarah: Yes, we did. +37 A: Did we meet him?\nB: Yes, we did. +38 David: Did we go somewhere after the movie was over?\nSarah: No, we didn't. +39 "Did we figure it out?" he asked. "No, we didn't," she said. +40 David: Did we figure it out?\nSarah: Yes, we did. +41 A: Should we meet him?\nB: No, we shouldn't. +42 A: Did they leave?\nB: No, they didn't. +43 Should they figure it out?\nNo, they shouldn't. +44 Should they quit?\nNo, they shouldn't. +45 David: Did they think it was going to work?\nSarah: No, they didn't. +46 A: Should they quit?\nB: Yes, they should. +47 Did they find something to do later?\nNo, they didn't. +48 Did they meet him?\nYes, they did. +49 David: Should they consider asking for another option?\nSarah: Yes, they should. +50 A: Should she finish?\nB: Yes, she should. +51 A: Did she go somewhere after the movie was over?\nB: Yes, she did. +52 Did she quit?\nYes, she did. +53 Did she figure it out?\nNo, she didn't. +54 A: Should she finish?\nB: No, she shouldn't. +55 Did she arrive?\nYes, she did. +56 David: Did she try to finish it on time or not?\nSarah: Yes, she did. +57 David: Did she leave?\nSarah: Yes, she did. +58 Did she finish?\nNo, she didn't. +59 David: Should she figure it out?\nSarah: Yes, I should. +60 Should she consider asking for another option?\nNo, she shouldn't. +61 David: Did you try to finish it on time or not?\nSarah: No, you didn't. +62 Did you quit?\nNo, I didn't. +63 Did you finish?\nNo, I didn't. +64 A: Should you figure it out?\nB: Yes, you should. +65 Should you quit?\nYes, you should. +66 David: Should you leave?\nSarah: No, you shouldn't. +67 "Did you meet him?" he asked. "No, I didn't," she said. +68 Did you go somewhere after the movie was over?\nNo, you didn't. +69 David: Should you quit?\nSarah: Yes, you should. +70 David: Did you finish?\nSarah: No, you didn't. +71 A: Should you consider asking for another option?\nB: No, you shouldn't. +72 A: Should we leave?\nB: Yes, you should. +73 A: Did you figure it out?\nB: Yes, you did. +74 David: Did you figure it out?\nSarah: No, you didn't. +75 A: Should we meet him?\nB: Yes, you should. +76 David: Should you figure it out?\nSarah: No, you shouldn't. +77 Did you meet him?\nYes, you did. +78 David: Should you consider asking for another option?\nSarah: Yes, we should. +79 David: Should they quit?\nSarah: No, we shouldn't. +80 Did you arrive?\nNo, we didn't. +81 Did you figure it out?\nYes, we did. +82 A: Did you go somewhere after the movie was over?\nB: No, we didn't. +83 A: Should they leave?\nB: Yes, we should. +84 "Did you meet him?" he asked. "Yes, we did," she said. +85 David: Should they meet him?\nSarah: Yes, we should. +86 A: Did I figure it out?\nB: Yes, you did. +87 Should I quit?\nYes, you should. +88 "Did I meet him?" he asked. "No, you didn't," she said. +89 "Should I figure it out?" he asked. "Yes, you should," she said. +90 Did I leave?\nNo, you didn't. +91 David: Did I arrive?\nSarah: Yes, she did. +92 A: Did I think it was going to work?\nB: No, you didn't. +93 A: Did I finish?\nB: Yes, you did. +94 Did I try to finish it on time or not?\nYes, you did. +95 Should I finish?\nYes, you should. +96 A: Should I quit?\nB: No, I shouldn't. +97 David: Did you think it was going to work?\nSarah: Yes, I did. +98 David: Should I meet him?\nSarah: No, I shouldn't. +99 Did you figure it out?\nYes, I did. +100 "Did you meet him?" he asked. "Yes, I did," she said. +101 David: Should I figure it out?\nSarah: No, I shouldn't. +102 Did you go somewhere after the movie was over?\nNo, I didn't. +103 "Did you try to finish it on time or not?" he asked. "No, I didn't," she said. +104 David: Did we meet him?\nSarah: Yes, we did. +105 A: Should we consider asking for another option?\nB: Yes, we should. +106 Did we arrive?\nYes, we did. +107 Did we quit?\nYes, we did. +108 Did we quit?\nNo, we didn't. +109 David: Did we think it was going to work?\nSarah: Yes, we did. +110 "Did we try to finish it on time or not?" he asked. "No, we didn't," she said. +111 Did she arrive?\nNo, she didn't. +112 David: Did she finish?\nSarah: No, she didn't. +113 A: Should she meet him?\nB: Yes, she should. +114 Did she go somewhere after the movie was over?\nYes, she did. +115 Did she try to finish it on time or not?\nNo, she didn't. +116 Did she leave?\nYes, she did. +117 Did she quit?\nYes, she did. +118 "Did she meet him?" he asked. "Yes, she did," she said. +119 "Did she figure it out?" he asked. "Yes, she did," she said. +120 David: Should I finish?\nSarah: Yes, you should. +121 A: Should I meet him?\nB: No, you shouldn't. +122 Did you go somewhere after the movie was over?\nYes, you did. +123 David: Did you figure it out?\nSarah: Yes, you did. +124 David: Did you quit?\nSarah: No, you didn't. +125 David: Did you arrive?\nSarah: No, you didn't. +126 A: Should you consider asking for another option?\nB: No, you shouldn't. +127 Should you figure it out?\nNo, you shouldn't. +128 David: Did you think it was going to work?\nSarah: No, you didn't. +129 Did we figure it out?\nNo, they didn't. +130 A: Did we quit?\nB: Yes, you did. +131 David: Did we quit?\nSarah: No, you didn't. +132 David: Should we leave?\nSarah: Yes, you should. +133 "Did we try to finish it on time or not?" he asked. "No, you didn't," she said. +134 A: Did we go somewhere after the movie was over?\nB: Yes, you did. +135 David: Did we arrive?\nSarah: Yes, you did. +136 Should we quit?\nNo, you shouldn't. +137 A: Should you say that we will figure it out?\nB: Yes, I should. +138 A: Did you say that they figured it out?\nB: No, I didn't say that. +139 David: Should you say that we will meet him?\nSarah: No, I shouldn't. +140 Should you say that they will consider asking for another option?\nYes, I should. +141 "Did she say that we figured it out?" he asked. "Yes, I did," she said. +142 "Did she say that I left?" he asked. "No, I didn't say that," she said. +143 David: Did you say that they will try to finish it on time or not?\nSarah: No, I didn't say that. +144 Did you say that I finished?\nYes, you did. +145 "Should you say that we will meet him?" he asked. "Yes, you should say that," she said. +146 "Did you say that she figured it out?" he asked. "No, you didn't," she said. +147 A: Did you say that I will try to finish it on time or not?\nB: Yes, you said that. +148 A: Did you say that you will find something to do later?\nB: No, you didn't say that. +149 A: Did you say that I left?\nB: No, you didn't. +150 "Did you say that I met him?" he asked. "Yes, you said that," she said. +151 David: Should you say that they will meet him?\nSarah: No, you shouldn't. +152 Did you say that she will think it was going to work?\nYes, you said that. +153 A: Did you say that they figured it out?\nB: No, you didn't. +154 Did they say that you met him?\nNo, they didn't. +155 A: Should they say that they will figure it out?\nB: No, they shouldn't say that. +156 David: Did they say that they will go somewhere after the movie is over?\nSarah: Yes, they did. +157 Did they say that she finished?\nYes, they did. +158 David: Did they say that she will try to finish it on time or not?\nSarah: No, they didn't. +159 David: Should they say that they will leave?\nSarah: Yes, they should say that. +160 David: Should I say that we will finish?\nSarah: Yes, you should say that. +161 A: Should I say that I will consider asking for another option?\nB: Yes, I should. +162 David: Should I say that I will finish?\nSarah: Yes, you should say that. +163 Should I say that I will figure it out?\nNo, I shouldn't. +164 A: Did I say that we quit?\nB: No, I didn't say that. +165 David: Did I say that we quit?\nSarah: Yes, I did. +166 A: Did I say that I will try to finish it on time or not?\nB: Yes, I did. +167 Did I say that she will try to finish it on time or not?\nYes, I said that. +168 A: Did I say that I will find something to do later?\nB: No, I didn't. +169 Did I say that you finished?\nYes, I did. +170 Should I say that you will meet him?\nYes, you should. +171 Did we say that I will try to finish it on time or not?\nYes, we said that. +172 Did we say that I will think it was going to work?\nYes, we said that. +173 David: Did we say that you figured it out?\nSarah: Yes, we did. +174 David: Did they say that we finished?\nSarah: No, we didn't. +175 A: Did they say that you will go somewhere after the movie is over?\nB: No, we didn't. +176 A: Should we say that you will leave?\nB: No, we shouldn't say that. +177 David: Should we say that she will finish?\nSarah: Yes, we should. +178 "Should we say that I will meet him?" he asked. "Yes, we should," she said. +179 David: Should we say that you will leave?\nSarah: Yes, we should. +180 A: Should they say that they will meet him?\nB: No, they shouldn't say that. +181 A: Did they say that they left?\nB: No, they didn't. +182 Did they say that I met him?\nYes, they said that. +183 "Should they say that we will figure it out?" he asked. "Yes, they should say that," she said. +184 "Did they say that I will try to finish it on time or not?" he asked. "Yes, they said that," she said. +185 Should they say that we will finish?\nYes, we should. +186 A: Should they say that you will leave?\nB: No, they shouldn't say that. +187 A: Did they say that you will go somewhere after the movie is over?\nB: Yes, they did. +188 Did she say that we will think it was going to work?\nNo, she didn't say that. +189 A: Should she say that I will leave?\nB: Yes, I should. +190 David: Did she say that you will find something to do later?\nSarah: Yes, she said that. +191 David: Should she say that we will figure it out?\nSarah: Yes, she should say that. +192 Should she say that she will meet him?\nNo, she shouldn't say that. +193 David: Should she say that they will finish?\nSarah: Yes, I should. +194 David: Did she say that we quit?\nSarah: Yes, she did. +195 Did she say that she met him?\nNo, she didn't. +196 David: Should you say that you will figure it out?\nSarah: Yes, you should. +197 David: Should you say that you will leave?\nSarah: No, you shouldn't. +198 David: Did you say that you finished?\nSarah: No, I didn't say that. +199 David: Did you say that I will find something to do later?\nSarah: No, I didn't. +200 Did you say that I quit?\nNo, I didn't. +201 A: Did you say that we left?\nB: No, I didn't say that. +202 Should you say that I will consider asking for another option?\nNo, I shouldn't. +203 A: Did you say that she quit?\nB: Yes, I did. +204 A: Did you say that you finished?\nB: Yes, you did. +205 David: Did you say that they finished?\nSarah: Yes, you did. +206 "Should we say that they will figure it out?" he asked. "Yes, you should say that," she said. +207 Should you say that you will quit?\nYes, you should. +208 Did you say that she will find something to do later?\nYes, you said that. +209 "Did you say that we left?" he asked. "No, you didn't," she said. +210 A: Should you say that she will figure it out?\nB: No, you shouldn't say that. +211 Did you say that she will find something to do later?\nYes, you did. +212 "Did you say that she will try to finish it on time or not?" he asked. "Yes, you did," she said. +213 A: Did you say that we figured it out?\nB: Yes, we did. +214 Should you say that you will figure it out?\nYes, we should. +215 "Did they say that they will think it was going to work?" he asked. "Yes, we said that," she said. +216 David: Should you say that you will figure it out?\nSarah: No, we shouldn't. +217 David: Should you say that you will finish?\nSarah: No, we shouldn't. +218 David: Did you say that she left?\nSarah: No, we didn't. +219 David: Did you say that you will try to finish it on time or not?\nSarah: No, we didn't. +220 David: Did you say that you figured it out?\nSarah: Yes, we did. +221 Did you say that she will find something to do later?\nNo, we didn't. +222 Should you say that you will meet him?\nNo, we shouldn't say that. +223 A: Did I say that they left?\nB: Yes, you did. +224 David: Should I say that they will finish?\nSarah: Yes, you should say that. +225 David: Should I say that you will quit?\nSarah: Yes, you should. +226 "Did I say that they will try to finish it on time or not?" he asked. "Yes, she said that," she said. +227 Did I say that they quit?\nNo, you didn't. +228 Should I say that we will quit?\nYes, you should. +229 David: Did I say that she will think it was going to work?\nSarah: Yes, she did. +230 "Did I say that you met him?" he asked. "No, you didn't," she said. +231 A: Should I say that you will consider asking for another option?\nB: Yes, I should say that. +232 "Did you say that you left?" he asked. "Yes, I did," she said. +233 Should I say that I will consider asking for another option?\nNo, I shouldn't. +234 Should I say that she will finish?\nNo, I shouldn't say that. +235 Should I say that I will quit?\nYes, I should. +236 Should I say that you will finish?\nNo, I shouldn't. +237 David: Did you say that they will think it was going to work?\nSarah: Yes, I did. +238 "Did you say that she will try to finish it on time or not?" he asked. "No, I didn't," she said. +239 David: Did you say that they left?\nSarah: Yes, I said that. +240 A: Did we say that I met him?\nB: No, we didn't. +241 "Did we say that she will think it was going to work?" he asked. "Yes, we said that," she said. +242 A: Did we say that she met him?\nB: No, we didn't. +243 Should we say that you will leave?\nYes, we should. +244 A: Did we say that you quit?\nB: No, we didn't say that. +245 "Did we say that you will try to finish it on time or not?" he asked. "Yes, we said that," she said. +246 Did we say that they will think it was going to work?\nYes, we said that. +247 A: Did we say that I will go somewhere after the movie is over?\nB: Yes, we did. +248 A: Did we say that you will go somewhere after the movie is over?\nB: No, we didn't. +249 Should we say that you will finish?\nNo, we shouldn't say that. +250 A: Did she say that she will try to finish it on time or not?\nB: No, she didn't say that. +251 A: Did she say that I will find something to do later?\nB: Yes, she did. +252 Did she say that I quit?\nYes, she did. +253 Did she say that she finished?\nNo, she didn't. +254 "Did she say that she left?" he asked. "No, she didn't," she said. +255 David: Should she say that I will consider asking for another option?\nSarah: Yes, she should say that. +256 Should she say that you will quit?\nNo, she shouldn't say that. +257 A: Should I say that you will meet him?\nB: No, she shouldn't. +258 A: Should she say that I will quit?\nB: Yes, she should. +259 Should she say that they will meet him?\nNo, she shouldn't. +260 David: Should I say that she will consider asking for another option?\nSarah: No, you shouldn't. +261 David: Did you say that she met him?\nSarah: No, you didn't say that. +262 Did you say that she quit?\nYes, you said that. +263 A: Should I say that we will meet him?\nB: No, you shouldn't say that. +264 Should I say that I will finish?\nNo, you shouldn't say that. +265 David: Did you say that she finished?\nSarah: No, you didn't. +266 A: Did you say that we left?\nB: No, you didn't. +267 Did you say that she will try to finish it on time or not?\nYes, you said that. +268 Did you say that we finished?\nNo, you didn't. +269 "Did you say that we will think it was going to work?" he asked. "Yes, you said that," she said. +270 "Should I say that we will meet him?" he asked. "Yes, you should," she said. +271 Did we say that we quit?\nYes, you said that. +272 "Should we say that we will meet him?" he asked. "Yes, you should," she said. +273 A: Did we say that I quit?\nB: No, you didn't. +274 David: Did we say that you met him?\nSarah: No, you didn't. +275 David: Did we say that you left?\nSarah: No, you didn't say that. +276 A: Did we say that you finished?\nB: No, you didn't. +277 A: Did we say that we will find something to do later?\nB: No, they didn't. +278 A: Should we say that she will consider asking for another option?\nB: Yes, they should. +279 Should we say that you will quit?\nNo, you shouldn't.