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"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'''The RoBERTa Model transformer with early exiting (DeeRoBERTa). ''' , lowercase , )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = RobertaConfig
__snake_case = '''roberta'''
def __init__( self : List[Any] , __UpperCAmelCase : Any ) ->Union[str, Any]:
"""simple docstring"""
super().__init__(__UpperCAmelCase )
a = RobertaEmbeddings(__UpperCAmelCase )
self.init_weights()
@add_start_docstrings(
'''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. ''' , lowercase , )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = RobertaConfig
__snake_case = '''roberta'''
def __init__( self : str , __UpperCAmelCase : str ) ->Any:
"""simple docstring"""
super().__init__(__UpperCAmelCase )
a = config.num_labels
a = config.num_hidden_layers
a = DeeRobertaModel(__UpperCAmelCase )
a = nn.Dropout(config.hidden_dropout_prob )
a = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : Any=None , __UpperCAmelCase : str=None , __UpperCAmelCase : int=None , __UpperCAmelCase : int=None , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=-1 , __UpperCAmelCase : Optional[Any]=False , ) ->int:
"""simple docstring"""
a = self.num_layers
try:
a = self.roberta(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , position_ids=__UpperCAmelCase , head_mask=__UpperCAmelCase , inputs_embeds=__UpperCAmelCase , )
a = outputs[1]
a = self.dropout(__UpperCAmelCase )
a = self.classifier(__UpperCAmelCase )
a = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
a = e.message
a = e.exit_layer
a = outputs[0]
if not self.training:
a = entropy(__UpperCAmelCase )
a = []
a = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
a = MSELoss()
a = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
a = CrossEntropyLoss()
a = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
a = []
for highway_exit in outputs[-1]:
a = highway_exit[0]
if not self.training:
highway_logits_all.append(__UpperCAmelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
a = MSELoss()
a = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
a = CrossEntropyLoss()
a = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__UpperCAmelCase )
if train_highway:
a = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
a = (loss,) + outputs
if not self.training:
a = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
a = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 359 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _a ( a :Union[str, Any] , a :List[Any] ) -> List[Any]:
a = checkpoint
a = {}
a = vae_state_dict['''encoder.conv_in.weight''']
a = vae_state_dict['''encoder.conv_in.bias''']
a = vae_state_dict['''encoder.conv_out.weight''']
a = vae_state_dict['''encoder.conv_out.bias''']
a = vae_state_dict['''encoder.norm_out.weight''']
a = vae_state_dict['''encoder.norm_out.bias''']
a = vae_state_dict['''decoder.conv_in.weight''']
a = vae_state_dict['''decoder.conv_in.bias''']
a = vae_state_dict['''decoder.conv_out.weight''']
a = vae_state_dict['''decoder.conv_out.bias''']
a = vae_state_dict['''decoder.norm_out.weight''']
a = vae_state_dict['''decoder.norm_out.bias''']
a = vae_state_dict['''quant_conv.weight''']
a = vae_state_dict['''quant_conv.bias''']
a = vae_state_dict['''post_quant_conv.weight''']
a = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(a )
}
# Retrieves the keys for the decoder up blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(a )
}
for i in range(a ):
a = [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]
if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.weight""" )
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.bias""" )
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
for i in range(a ):
a = num_up_blocks - 1 - i
a = [
key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
]
if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.weight"""
]
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.bias"""
]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
return new_checkpoint
def _a ( a :str , a :str , ) -> List[str]:
# Only support V1
a = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
a = io.BytesIO(r.content )
a = OmegaConf.load(a )
a = 512
a = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
a = {}
with safe_open(a , framework='''pt''' , device='''cpu''' ) as f:
for key in f.keys():
a = f.get_tensor(a )
else:
a = torch.load(a , map_location=a )['''state_dict''']
# Convert the VAE model.
a = create_vae_diffusers_config(a , image_size=a )
a = custom_convert_ldm_vae_checkpoint(a , a )
a = AutoencoderKL(**a )
vae.load_state_dict(a )
vae.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
UpperCAmelCase__ = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 360 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''image_processor''', '''tokenizer''']
__snake_case = '''CLIPImageProcessor'''
__snake_case = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Dict , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
a = kwargs.pop('''feature_extractor''' )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : str ) ->Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
a = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) ->Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def __lowerCAmelCase ( self : int ) ->List[str]:
"""simple docstring"""
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __UpperCAmelCase , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __UpperCAmelCase , )
return self.image_processor
| 26 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''fnet'''
def __init__( self : Tuple , __UpperCAmelCase : Optional[int]=32_000 , __UpperCAmelCase : Tuple=768 , __UpperCAmelCase : Any=12 , __UpperCAmelCase : str=3_072 , __UpperCAmelCase : Optional[int]="gelu_new" , __UpperCAmelCase : List[str]=0.1 , __UpperCAmelCase : List[Any]=512 , __UpperCAmelCase : List[str]=4 , __UpperCAmelCase : int=0.02 , __UpperCAmelCase : str=1e-1_2 , __UpperCAmelCase : str=False , __UpperCAmelCase : Optional[int]=512 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : Optional[int]=1 , __UpperCAmelCase : List[str]=2 , **__UpperCAmelCase : str , ) ->List[Any]:
"""simple docstring"""
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
a = vocab_size
a = max_position_embeddings
a = hidden_size
a = num_hidden_layers
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = initializer_range
a = type_vocab_size
a = layer_norm_eps
a = use_tpu_fourier_optimizations
a = tpu_short_seq_length
| 361 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 26 | 0 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCAmelCase__ = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def _a ( a :Dict ) -> Optional[Any]:
a = test_results.split(''' ''' )
a = 0
a = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
a = expressions[-2] if '''=''' in expressions[-1] else expressions[-1]
for i, expression in enumerate(a ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def _a ( a :Optional[int] ) -> List[str]:
a = {}
a = None
a = False
for line in failures_short_lines.split('''\n''' ):
if re.search(r'''_ \[doctest\]''' , a ):
a = True
a = line.split(''' ''' )[2]
elif in_error and not line.split(''' ''' )[0].isdigit():
a = line
a = False
return failures
class lowercase_ :
'''simple docstring'''
def __init__( self : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : Dict ) ->List[Any]:
"""simple docstring"""
a = title
a = doc_test_results['''time_spent'''].split(''',''' )[0]
a = doc_test_results['''success''']
a = doc_test_results['''failures''']
a = self.n_success + self.n_failures
# Failures and success of the modeling tests
a = doc_test_results
@property
def __lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
a = [self._time_spent]
a = 0
for time in time_spent:
a = time.split(''':''' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(__UpperCAmelCase ) == 1:
a = [0, 0, time_parts[0]]
a , a , a = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3_600 + minutes * 60 + seconds
a , a , a = total_secs // 3_600, (total_secs % 3_600) // 60, total_secs % 60
return F"""{int(__UpperCAmelCase )}h{int(__UpperCAmelCase )}m{int(__UpperCAmelCase )}s"""
@property
def __lowerCAmelCase ( self : str ) ->Dict:
"""simple docstring"""
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def __lowerCAmelCase ( self : Optional[int] ) ->Dict:
"""simple docstring"""
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F"""🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.""",
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
},
}
@property
def __lowerCAmelCase ( self : Dict ) ->Dict:
"""simple docstring"""
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F"""There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"""
F""" {self.time}."""
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
},
}
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
a = 40
a = {k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
a = ''''''
for category, failures in category_failures.items():
if len(__UpperCAmelCase ) == 0:
continue
if report != "":
report += "\n\n"
report += F"""*{category} failures*:""".ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(__UpperCAmelCase )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F"""The following examples had failures:\n\n\n{report}\n""",
},
}
@property
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(__UpperCAmelCase )
@staticmethod
def __lowerCAmelCase ( ) ->Tuple:
"""simple docstring"""
a = [
{
'''type''': '''section''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''There was an issue running the tests.''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
'''url''': F"""https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
},
}
]
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(__UpperCAmelCase )} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text='''There was an issue running the tests.''' , blocks=__UpperCAmelCase , )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(self.payload )} ) )
a = F"""{self.n_failures} failures out of {self.n_tests} tests,""" if self.n_failures else '''All tests passed.'''
a = client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , blocks=self.payload , text=__UpperCAmelCase , )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : List[str] ) ->Optional[int]:
"""simple docstring"""
a = ''''''
for key, value in failures.items():
a = value[:200] + ''' [Truncated]''' if len(__UpperCAmelCase ) > 250 else value
failures_text += F"""*{key}*\n_{value}_\n\n"""
a = job_name
a = {'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}
if job_link is not None:
a = {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
'''url''': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
if self.thread_ts is None:
raise ValueError('''Can only post reply if a post has been made.''' )
a = self.doc_test_results.pop('''job_link''' )
self.doc_test_results.pop('''failures''' )
self.doc_test_results.pop('''success''' )
self.doc_test_results.pop('''time_spent''' )
a = sorted(self.doc_test_results.items() , key=lambda __UpperCAmelCase : t[0] )
for job, job_result in sorted_dict:
if len(job_result['''failures'''] ):
a = F"""*Num failures* :{len(job_result['failed'] )} \n"""
a = job_result['''failures''']
a = self.get_reply_blocks(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , text=__UpperCAmelCase )
print('''Sending the following reply''' )
print(json.dumps({'''blocks''': blocks} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text=F"""Results for {job}""" , blocks=__UpperCAmelCase , thread_ts=self.thread_ts['''ts'''] , )
time.sleep(1 )
def _a ( ) -> Tuple:
a = os.environ['''GITHUB_RUN_ID''']
a = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"""
a = requests.get(a ).json()
a = {}
try:
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
a = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(a ):
a = requests.get(url + F"""&page={i + 2}""" ).json()
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return jobs
except Exception as e:
print('''Unknown error, could not fetch links.''' , a )
return {}
def _a ( a :str ) -> Dict:
a = {}
if os.path.exists(a ):
a = os.listdir(a )
for file in files:
try:
with open(os.path.join(a , a ) , encoding='''utf-8''' ) as f:
a = f.read()
except UnicodeDecodeError as e:
raise ValueError(F"""Could not open {os.path.join(a , a )}.""" ) from e
return _artifact
def _a ( ) -> int:
class lowercase_ :
'''simple docstring'''
def __init__( self : Optional[int] , __UpperCAmelCase : str ) ->List[Any]:
"""simple docstring"""
a = name
a = []
def __str__( self : int ) ->Optional[int]:
"""simple docstring"""
return self.name
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : str ) ->int:
"""simple docstring"""
self.paths.append({'''name''': self.name, '''path''': path} )
a = {}
a = filter(os.path.isdir , os.listdir() )
for directory in directories:
a = directory
if artifact_name not in _available_artifacts:
a = Artifact(a )
_available_artifacts[artifact_name].add_path(a )
return _available_artifacts
if __name__ == "__main__":
UpperCAmelCase__ = get_job_links()
UpperCAmelCase__ = retrieve_available_artifacts()
UpperCAmelCase__ = collections.OrderedDict(
[
("*.py", "API Examples"),
("*.md", "MD Examples"),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
UpperCAmelCase__ = {
v: {
"failed": [],
"failures": {},
}
for v in docs.values()
}
# Link to the GitHub Action job
UpperCAmelCase__ = github_actions_job_links.get("run_doctests")
UpperCAmelCase__ = available_artifacts["doc_tests_gpu_test_reports"].paths[0]
UpperCAmelCase__ = retrieve_artifact(artifact_path["name"])
if "stats" in artifact:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = handle_test_results(artifact["stats"])
UpperCAmelCase__ = failed
UpperCAmelCase__ = success
UpperCAmelCase__ = time_spent[1:-1] + ", "
UpperCAmelCase__ = extract_first_line_failure(artifact["failures_short"])
for line in artifact["summary_short"].split("\n"):
if re.search("FAILED", line):
UpperCAmelCase__ = line.replace("FAILED ", "")
UpperCAmelCase__ = line.split()[0].replace("\n", "")
if "::" in line:
UpperCAmelCase__ , UpperCAmelCase__ = line.split("::")
else:
UpperCAmelCase__ , UpperCAmelCase__ = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
UpperCAmelCase__ = docs[file_regex]
doc_test_results[category]["failed"].append(test)
UpperCAmelCase__ = all_failures[test] if test in all_failures else "N/A"
UpperCAmelCase__ = failure
break
UpperCAmelCase__ = Message("🤗 Results of the doc tests.", doc_test_results)
message.post()
message.post_reply()
| 362 |
from __future__ import annotations
import typing
from collections import Counter
def _a ( a :int ) -> typing.Counter[int]:
a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a , max_perimeter + 1 ):
a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a ):
a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def _a ( a :int = 1_000 ) -> int:
a = pythagorean_triple(a )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f"""Perimeter {solution()} has maximum solutions""")
| 26 | 0 |
from __future__ import annotations
def _a ( a :float , a :float , a :float ) -> float:
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_interest_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * daily_interest_rate * days_between_payments
def _a ( a :float , a :float , a :float , ) -> float:
if number_of_compounding_periods <= 0:
raise ValueError('''number_of_compounding_periods must be > 0''' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def _a ( a :float , a :float , a :float , ) -> float:
if number_of_years <= 0:
raise ValueError('''number_of_years must be > 0''' )
if nominal_annual_percentage_rate < 0:
raise ValueError('''nominal_annual_percentage_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return compound_interest(
a , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 |
from __future__ import annotations
def _a ( a :dict , a :str ) -> set[str]:
a , a = set(a ), [start]
while stack:
a = stack.pop()
explored.add(a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(a )
return explored
UpperCAmelCase__ = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 26 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase_ ( lowercase , lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : List[str] , *,
__UpperCAmelCase : int = 4 , __UpperCAmelCase : int = 768 , __UpperCAmelCase : int , __UpperCAmelCase : List[Any] , ) ->List[Any]:
"""simple docstring"""
super().__init__()
a = nn.Parameter(torch.zeros(__UpperCAmelCase ) )
# parameters for additional clip time embeddings
a = nn.Linear(__UpperCAmelCase , __UpperCAmelCase )
a = nn.Linear(__UpperCAmelCase , __UpperCAmelCase )
# parameters for encoder hidden states
a = clip_extra_context_tokens
a = nn.Linear(
__UpperCAmelCase , self.clip_extra_context_tokens * cross_attention_dim )
a = nn.Linear(__UpperCAmelCase , __UpperCAmelCase )
a = nn.LayerNorm(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] , *, __UpperCAmelCase : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
a = image_embeddings.shape[0]
a = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
a = classifier_free_guidance_embeddings.expand(
__UpperCAmelCase , -1 )
a = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
a = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
a = self.embedding_proj(__UpperCAmelCase )
a = self.clip_image_embeddings_project_to_time_embeddings(__UpperCAmelCase )
a = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
a = self.clip_extra_context_tokens_proj(__UpperCAmelCase )
a = clip_extra_context_tokens.reshape(__UpperCAmelCase , -1 , self.clip_extra_context_tokens )
a = clip_extra_context_tokens.permute(0 , 2 , 1 )
a = self.encoder_hidden_states_proj(__UpperCAmelCase )
a = self.text_encoder_hidden_states_norm(__UpperCAmelCase )
a = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 364 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase__ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase__ = 10
UpperCAmelCase__ = 256
def _a ( a :List[str] ) -> Optional[MinHash]:
if len(a ) < MIN_NUM_TOKENS:
return None
a = MinHash(num_perm=a )
for token in set(a ):
min_hash.update(token.encode() )
return min_hash
def _a ( a :str ) -> Set[str]:
return {t for t in NON_ALPHA.split(a ) if len(t.strip() ) > 0}
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , *,
__UpperCAmelCase : float = 0.85 , ) ->Dict:
"""simple docstring"""
a = duplication_jaccard_threshold
a = NUM_PERM
a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
a = defaultdict(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : MinHash ) ->None:
"""simple docstring"""
a = self._index.query(__UpperCAmelCase )
if code_key in self._index.keys:
print(F"""Duplicate key {code_key}""" )
return
self._index.insert(__UpperCAmelCase , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase )
def __lowerCAmelCase ( self : Dict ) ->List[List[Dict]]:
"""simple docstring"""
a = []
for base, duplicates in self._duplicate_clusters.items():
a = [base] + list(__UpperCAmelCase )
# reformat the cluster to be a list of dict
a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__UpperCAmelCase )
return duplicate_clusters
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict ) ->None:
"""simple docstring"""
a = self.get_duplicate_clusters()
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def _a ( a :List[Any] ) -> List[Any]:
a , a = element
a = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _a ( a :Type[Dataset] ) -> List[Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(a , max_queue_size=10_000 ) , chunksize=100 , ):
if data is not None:
yield data
def _a ( a :Type[Dataset] , a :float ) -> str:
a = DuplicationIndex(duplication_jaccard_threshold=a )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a ) ) , max_queue_size=100 ) ):
di.add(a , a )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _a ( a :str , a :str ) -> float:
a = get_tokens(a )
a = get_tokens(a )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase__ = None
def _a ( a :Tuple , a :Tuple ) -> Any:
a = []
for elementa in cluster:
a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(a , a ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
a = 1
extremes.append(a )
return extremes
def _a ( a :List[Any] , a :Optional[Any] , a :Union[str, Any] ) -> Optional[int]:
global _shared_dataset
a = dataset
a = []
a = partial(_find_cluster_extremes_shared , jaccard_threshold=a )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
a , a , ) , total=len(a ) , ):
extremes_list.append(a )
return extremes_list
def _a ( a :Type[Dataset] , a :float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
a = make_duplicate_clusters(a , a )
a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
a = {}
a = find_extremes(a , a , a )
for extremes in extremes_clusters:
for element in extremes:
a = element
a = duplicate_indices - set(extreme_dict.keys() )
a = dataset.filter(lambda a , a : idx not in remove_indices , with_indices=a )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
a = extreme_dict[element['''base_index''']]['''copies''']
print(F"""Original dataset size: {len(a )}""" )
print(F"""Number of duplicate clusters: {len(a )}""" )
print(F"""Files in duplicate cluster: {len(a )}""" )
print(F"""Unique files in duplicate cluster: {len(a )}""" )
print(F"""Filtered dataset size: {len(a )}""" )
return ds_filter, duplicate_clusters
| 26 | 0 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase__ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase__ = 10
UpperCAmelCase__ = 256
def _a ( a :List[str] ) -> Optional[MinHash]:
if len(a ) < MIN_NUM_TOKENS:
return None
a = MinHash(num_perm=a )
for token in set(a ):
min_hash.update(token.encode() )
return min_hash
def _a ( a :str ) -> Set[str]:
return {t for t in NON_ALPHA.split(a ) if len(t.strip() ) > 0}
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , *,
__UpperCAmelCase : float = 0.85 , ) ->Dict:
"""simple docstring"""
a = duplication_jaccard_threshold
a = NUM_PERM
a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
a = defaultdict(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : MinHash ) ->None:
"""simple docstring"""
a = self._index.query(__UpperCAmelCase )
if code_key in self._index.keys:
print(F"""Duplicate key {code_key}""" )
return
self._index.insert(__UpperCAmelCase , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase )
def __lowerCAmelCase ( self : Dict ) ->List[List[Dict]]:
"""simple docstring"""
a = []
for base, duplicates in self._duplicate_clusters.items():
a = [base] + list(__UpperCAmelCase )
# reformat the cluster to be a list of dict
a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__UpperCAmelCase )
return duplicate_clusters
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict ) ->None:
"""simple docstring"""
a = self.get_duplicate_clusters()
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def _a ( a :List[Any] ) -> List[Any]:
a , a = element
a = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _a ( a :Type[Dataset] ) -> List[Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(a , max_queue_size=10_000 ) , chunksize=100 , ):
if data is not None:
yield data
def _a ( a :Type[Dataset] , a :float ) -> str:
a = DuplicationIndex(duplication_jaccard_threshold=a )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a ) ) , max_queue_size=100 ) ):
di.add(a , a )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _a ( a :str , a :str ) -> float:
a = get_tokens(a )
a = get_tokens(a )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase__ = None
def _a ( a :Tuple , a :Tuple ) -> Any:
a = []
for elementa in cluster:
a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(a , a ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
a = 1
extremes.append(a )
return extremes
def _a ( a :List[Any] , a :Optional[Any] , a :Union[str, Any] ) -> Optional[int]:
global _shared_dataset
a = dataset
a = []
a = partial(_find_cluster_extremes_shared , jaccard_threshold=a )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
a , a , ) , total=len(a ) , ):
extremes_list.append(a )
return extremes_list
def _a ( a :Type[Dataset] , a :float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
a = make_duplicate_clusters(a , a )
a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
a = {}
a = find_extremes(a , a , a )
for extremes in extremes_clusters:
for element in extremes:
a = element
a = duplicate_indices - set(extreme_dict.keys() )
a = dataset.filter(lambda a , a : idx not in remove_indices , with_indices=a )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
a = extreme_dict[element['''base_index''']]['''copies''']
print(F"""Original dataset size: {len(a )}""" )
print(F"""Number of duplicate clusters: {len(a )}""" )
print(F"""Files in duplicate cluster: {len(a )}""" )
print(F"""Unique files in duplicate cluster: {len(a )}""" )
print(F"""Filtered dataset size: {len(a )}""" )
return ds_filter, duplicate_clusters
| 365 |
from math import ceil, sqrt
def _a ( a :int = 1_000_000 ) -> int:
a = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
a = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 366 |
UpperCAmelCase__ = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 26 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
UpperCAmelCase__ = "bart"
UpperCAmelCase__ = True
@st.cache(allow_output_mutation=a )
def _a ( ) -> Tuple:
if LOAD_DENSE_INDEX:
a = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
a = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
a = qar_model.eval()
else:
a , a = (None, None)
if MODEL_TYPE == "bart":
a = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
a = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
a = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
a = sas_model.eval()
else:
a , a = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Dict:
if LOAD_DENSE_INDEX:
a = faiss.StandardGpuResources()
a = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
a = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
a = faiss.IndexFlatIP(128 )
a = faiss.index_cpu_to_gpu(a , 1 , a )
wikiaab_gpu_index_flat.add(a ) # TODO fix for larger GPU
else:
a , a = (None, None)
a = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Optional[int]:
a = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
a = elia['''train_eli5''']
a = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
a = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(a )
return (elia_train, eli5_train_q_index)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_indexes()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_models()
UpperCAmelCase__ , UpperCAmelCase__ = load_train_data()
def _a ( a :str , a :Tuple=10 ) -> List[str]:
a = embed_questions_for_retrieval([question] , a , a )
a , a = eli5_train_q_index.search(a , a )
a = [elia_train[int(a )] for i in I[0]]
return nn_examples
def _a ( a :str , a :Any="wiki40b" , a :int="dense" , a :Union[str, Any]=10 ) -> List[str]:
if source == "none":
a , a = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
a , a = query_qa_dense_index(
a , a , a , a , a , a )
else:
a , a = query_es_index(
a , a , index_name='''english_wiki40b_snippets_100w''' , n_results=a , )
a = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
a = '''question: {} context: {}'''.format(a , a )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda a : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a : None),
} )
def _a ( a :Tuple , a :int , a :int , a :Dict=64 , a :List[Any]=256 , a :List[Any]=False , a :List[Any]=2 , a :Tuple=0.95 , a :Optional[Any]=0.8 ) -> int:
with torch.no_grad():
a = qa_sas_generate(
a , a , a , num_answers=1 , num_beams=a , min_len=a , max_len=a , do_sample=a , temp=a , top_p=a , top_k=a , max_input_length=1_024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title("Long Form Question Answering with ELI5")
# Start sidebar
UpperCAmelCase__ = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
UpperCAmelCase__ = "\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
UpperCAmelCase__ = "\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)
UpperCAmelCase__ = [
"Answer the question",
"View the retrieved document only",
"View the most similar ELI5 question and answer",
"Show me everything, please!",
]
UpperCAmelCase__ = st.sidebar.checkbox("Demo options")
if demo_options:
UpperCAmelCase__ = st.sidebar.selectbox(
"",
action_list,
index=3,
)
UpperCAmelCase__ = action_list.index(action_st)
UpperCAmelCase__ = st.sidebar.selectbox(
"",
["Show full text of passages", "Show passage section titles"],
index=0,
)
UpperCAmelCase__ = show_type == "Show full text of passages"
else:
UpperCAmelCase__ = 3
UpperCAmelCase__ = True
UpperCAmelCase__ = st.sidebar.checkbox("Retrieval options")
if retrieval_options:
UpperCAmelCase__ = "\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n "
st.sidebar.markdown(retriever_info)
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
UpperCAmelCase__ = "wiki40b"
UpperCAmelCase__ = "dense"
UpperCAmelCase__ = "beam"
UpperCAmelCase__ = 2
UpperCAmelCase__ = 64
UpperCAmelCase__ = 256
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = st.sidebar.checkbox("Generation options")
if generate_options:
UpperCAmelCase__ = "\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n "
st.sidebar.markdown(generate_info)
UpperCAmelCase__ = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
UpperCAmelCase__ = st.sidebar.slider(
"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
UpperCAmelCase__ = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
UpperCAmelCase__ = st.sidebar.slider(
"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
UpperCAmelCase__ = None
# start main text
UpperCAmelCase__ = [
"<MY QUESTION>",
"How do people make chocolate?",
"Why do we get a fever when we are sick?",
"How can different animals perceive different colors?",
"What is natural language processing?",
"What's the best way to treat a sunburn?",
"What exactly are vitamins ?",
"How does nuclear energy provide electricity?",
"What's the difference between viruses and bacteria?",
"Why are flutes classified as woodwinds when most of them are made out of metal ?",
"Why do people like drinking coffee even though it tastes so bad?",
"What happens when wine ages? How does it make the wine taste better?",
"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
"How does New Zealand have so many large bird predators?",
]
UpperCAmelCase__ = st.selectbox(
"What would you like to ask? ---- select <MY QUESTION> to enter a new query",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
UpperCAmelCase__ = st.text_input("Enter your question here:", "")
else:
UpperCAmelCase__ = question_s
if st.button("Show me!"):
if action in [0, 1, 3]:
if index_type == "mixed":
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="dense", n_results=10)
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="sparse", n_results=10)
UpperCAmelCase__ = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
UpperCAmelCase__ = support_list[:10]
UpperCAmelCase__ = "<P> " + " <P> ".join([res[-1] for res in support_list])
else:
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
UpperCAmelCase__ , UpperCAmelCase__ = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == "sampled"),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("### The model generated answer is:")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
for i, res in enumerate(support_list):
UpperCAmelCase__ = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
UpperCAmelCase__ = res[1].strip()
if sec_titles == "":
UpperCAmelCase__ = "[{}]({})".format(res[0], wiki_url)
else:
UpperCAmelCase__ = sec_titles.split(" & ")
UpperCAmelCase__ = " & ".join(
["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
)
st.markdown(
"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
)
if action in [2, 3]:
UpperCAmelCase__ = find_nearest_training(question)
UpperCAmelCase__ = nn_train_list[0]
st.markdown(
"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
)
UpperCAmelCase__ = [
"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
if i == 0 or sc > 2
]
st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
UpperCAmelCase__ = "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 367 |
def _a ( a :list ) -> list:
if len(a ) <= 1:
return lst
a = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a , a = lst[i], lst[i - 1]
i -= 1
if i == 0:
a = 1
return lst
if __name__ == "__main__":
UpperCAmelCase__ = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 26 | 0 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def _a ( a :List[str] , a :Tuple , a :Union[str, Any] , a :Optional[int] ) -> int:
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.""" )
if tokenizer_name is None:
a = TOKENIZER_CLASSES
else:
a = {tokenizer_name: getattr(a , tokenizer_name + '''Fast''' )}
logger.info(F"""Loading tokenizer classes: {tokenizer_names}""" )
for tokenizer_name in tokenizer_names:
a = TOKENIZER_CLASSES[tokenizer_name]
a = True
if checkpoint_name is None:
a = list(tokenizer_class.max_model_input_sizes.keys() )
else:
a = [checkpoint_name]
logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}""" )
for checkpoint in checkpoint_names:
logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}""" )
# Load tokenizer
a = tokenizer_class.from_pretrained(a , force_download=a )
# Save fast tokenizer
logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}""" )
# For organization names we create sub-directories
if "/" in checkpoint:
a , a = checkpoint.split('''/''' )
a = os.path.join(a , a )
elif add_prefix:
a = checkpoint
a = dump_path
else:
a = None
a = dump_path
logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
a = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
a = file_path.split(a )[-1][0]
if next_char == "/":
a = os.path.join(a , a )
a = None
logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )
a = tokenizer.save_pretrained(
a , legacy_format=a , filename_prefix=a )
logger.info(F"""=> File names {file_names}""" )
for file_name in file_names:
if not file_name.endswith('''tokenizer.json''' ):
os.remove(a )
logger.info(F"""=> removing {file_name}""" )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help=(
f"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
"download and convert all the checkpoints from AWS."
),
)
parser.add_argument(
"--checkpoint_name",
default=None,
type=str,
help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
)
parser.add_argument(
"--force_download",
action="store_true",
help="Re-download checkpoints.",
)
UpperCAmelCase__ = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
UpperCAmelCase__ = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def _a ( a :Any ) -> Optional[int]:
a = torch.load(a , map_location='''cpu''' )
return sd
def _a ( a :Tuple , a :Dict , a :Any=rename_keys_prefix ) -> Union[str, Any]:
a = OrderedDict()
a = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
a = key
for name_pair in rename_keys_prefix:
a = new_key.replace(name_pair[0] , name_pair[1] )
a = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
a = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def _a ( a :Optional[int] , a :int ) -> str:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
a = '''pretraining'''
if "vcr" in checkpoint_path:
a = {'''visual_embedding_dim''': 512}
elif "vqa_advanced" in checkpoint_path:
a = {'''visual_embedding_dim''': 2_048}
elif "vqa" in checkpoint_path:
a = {'''visual_embedding_dim''': 2_048}
elif "nlvr" in checkpoint_path:
a = {'''visual_embedding_dim''': 1_024}
else:
raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
a = {'''visual_embedding_dim''': 512}
a = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
a = {'''visual_embedding_dim''': 2_048}
a = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
a = {'''visual_embedding_dim''': 2_048, '''num_labels''': 3_129}
a = '''vqa'''
elif "nlvr" in checkpoint_path:
a = {
'''visual_embedding_dim''': 1_024,
'''num_labels''': 2,
}
a = '''nlvr'''
a = VisualBertConfig(**a )
# Load State Dict
a = load_state_dict(a )
a = get_new_dict(a , a )
if model_type == "pretraining":
a = VisualBertForPreTraining(a )
elif model_type == "vqa":
a = VisualBertForQuestionAnswering(a )
elif model_type == "nlvr":
a = VisualBertForVisualReasoning(a )
elif model_type == "multichoice":
a = VisualBertForMultipleChoice(a )
model.load_state_dict(a )
# Save Checkpoints
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 369 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
UpperCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _a ( a :str ) -> Any:
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
a = model_type_to_module_name(a )
a = importlib.import_module(F""".{module_name}""" , '''transformers.models''' )
try:
return getattr(a , a )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(a , '''__name__''' , a ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
a = importlib.import_module('''transformers''' )
if hasattr(a , a ):
return getattr(a , a )
return None
def _a ( a :Union[str, os.PathLike] , a :Optional[Union[str, os.PathLike]] = None , a :bool = False , a :bool = False , a :Optional[Dict[str, str]] = None , a :Optional[Union[bool, str]] = None , a :Optional[str] = None , a :bool = False , **a :int , ) -> Tuple:
a = get_file_from_repo(
a , a , cache_dir=a , force_download=a , resume_download=a , proxies=a , use_auth_token=a , revision=a , local_files_only=a , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(a , encoding='''utf-8''' ) as reader:
return json.load(a )
class lowercase_ :
'''simple docstring'''
def __init__( self : Tuple ) ->int:
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(__UpperCAmelCase )
def __lowerCAmelCase ( cls : int , __UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : Dict ) ->List[Any]:
"""simple docstring"""
a = kwargs.pop('''config''' , __UpperCAmelCase )
a = kwargs.pop('''trust_remote_code''' , __UpperCAmelCase )
a = True
a , a = FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase , **__UpperCAmelCase )
a = config_dict.get('''feature_extractor_type''' , __UpperCAmelCase )
a = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
a = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = AutoConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
# It could be in `config.feature_extractor_type``
a = getattr(__UpperCAmelCase , '''feature_extractor_type''' , __UpperCAmelCase )
if hasattr(__UpperCAmelCase , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
a = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
a = feature_extractor_class_from_name(__UpperCAmelCase )
a = feature_extractor_auto_map is not None
a = feature_extractor_class is not None or type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
a = resolve_trust_remote_code(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if has_remote_code and trust_remote_code:
a = get_class_from_dynamic_module(
__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
a = kwargs.pop('''code_revision''' , __UpperCAmelCase )
if os.path.isdir(__UpperCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
a = FEATURE_EXTRACTOR_MAPPING[type(__UpperCAmelCase )]
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
raise ValueError(
F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple ) ->Optional[int]:
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(__UpperCAmelCase , __UpperCAmelCase )
| 26 | 0 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : Optional[Any]=3 , __UpperCAmelCase : Optional[Any]=4 , __UpperCAmelCase : List[Any]=2 , __UpperCAmelCase : List[Any]=7 , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : int=True , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Tuple=99 , __UpperCAmelCase : int=36 , __UpperCAmelCase : Dict=2 , __UpperCAmelCase : Dict=4 , __UpperCAmelCase : List[Any]=37 , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : Dict=0.1 , __UpperCAmelCase : Dict=0.1 , __UpperCAmelCase : List[Any]=512 , __UpperCAmelCase : Tuple=16 , __UpperCAmelCase : List[Any]=2 , __UpperCAmelCase : List[Any]=0.02 , __UpperCAmelCase : Tuple=6 , __UpperCAmelCase : Dict=6 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : int=4 , __UpperCAmelCase : int=None , __UpperCAmelCase : List[str]=1_000 , ) ->List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = num_channels
a = image_size
a = patch_size
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = coordinate_size
a = shape_size
a = num_labels
a = num_choices
a = scope
a = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
a = text_seq_length
a = (image_size // patch_size) ** 2 + 1
a = self.text_seq_length + self.image_seq_length
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
a = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
a = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
a = bbox[i, j, 3]
a = bbox[i, j, 1]
a = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
a = bbox[i, j, 2]
a = bbox[i, j, 0]
a = tmp_coordinate
a = tf.constant(__UpperCAmelCase )
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.text_seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
a = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : Dict ) ->Optional[int]:
"""simple docstring"""
a = TFLayoutLMvaModel(config=__UpperCAmelCase )
# text + image
a = model(__UpperCAmelCase , pixel_values=__UpperCAmelCase , training=__UpperCAmelCase )
a = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , training=__UpperCAmelCase , )
a = model(__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
a = model(__UpperCAmelCase , training=__UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
a = model({'''pixel_values''': pixel_values} , training=__UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple ) ->Any:
"""simple docstring"""
a = self.num_labels
a = TFLayoutLMvaForSequenceClassification(config=__UpperCAmelCase )
a = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , training=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : str , __UpperCAmelCase : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
a = self.num_labels
a = TFLayoutLMvaForTokenClassification(config=__UpperCAmelCase )
a = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , training=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : str , __UpperCAmelCase : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any] ) ->str:
"""simple docstring"""
a = 2
a = TFLayoutLMvaForQuestionAnswering(config=__UpperCAmelCase )
a = model(
__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , training=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self : str ) ->Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
((a) , (a) , (a) , (a) , (a) , (a) , (a) , (a)) = config_and_inputs
a = {
'''input_ids''': input_ids,
'''bbox''': bbox,
'''pixel_values''': pixel_values,
'''token_type_ids''': token_type_ids,
'''attention_mask''': input_mask,
}
return config, inputs_dict
@require_tf
class lowercase_ ( lowercase , lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
__snake_case = (
{'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
__snake_case = False
__snake_case = False
__snake_case = False
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : List[Any] ) ->Dict:
"""simple docstring"""
return True
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any]=False ) ->dict:
"""simple docstring"""
a = copy.deepcopy(__UpperCAmelCase )
if model_class in get_values(__UpperCAmelCase ):
a = {
k: tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(__UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
a = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__UpperCAmelCase ):
a = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
a = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__UpperCAmelCase ):
a = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(__UpperCAmelCase ):
a = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
a = TFLayoutLMvaModelTester(self )
a = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Dict ) ->Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__UpperCAmelCase )
if getattr(__UpperCAmelCase , '''hf_compute_loss''' , __UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
a = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
a = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=__UpperCAmelCase )[0]
]
a = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
a = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
a = prepared_for_class.pop('''input_ids''' )
a = model(__UpperCAmelCase , **__UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
a = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
a = prepared_for_class.pop('''input_ids''' )
if "labels" in prepared_for_class:
a = prepared_for_class['''labels'''].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
a = -100
a = tf.convert_to_tensor(__UpperCAmelCase )
a = model(__UpperCAmelCase , **__UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
a = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
a = model(__UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
a = self._prepare_for_class(inputs_dict.copy() , __UpperCAmelCase , return_labels=__UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
a = prepared_for_class.keys() - inputs_dict.keys()
a = inspect.signature(model.call ).parameters
a = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
a = {0: '''input_ids'''}
for label_key in label_keys:
a = signature_names.index(__UpperCAmelCase )
a = label_key
a = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
a = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
a = prepared_for_class[value]
a = tuple(__UpperCAmelCase )
# Send to model
a = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def __lowerCAmelCase ( self : List[Any] ) ->Tuple:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Any ) ->Tuple:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a = type
self.model_tester.create_and_check_model(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[int] ) ->Optional[Any]:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
@slow
def __lowerCAmelCase ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = TFLayoutLMvaModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def _a ( ) -> Optional[int]:
a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __lowerCAmelCase ( self : List[str] ) ->List[str]:
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self : str ) ->List[str]:
"""simple docstring"""
a = TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' )
a = self.default_image_processor
a = prepare_img()
a = image_processor(images=__UpperCAmelCase , return_tensors='''tf''' ).pixel_values
a = tf.constant([[1, 2]] )
a = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
a = model(input_ids=__UpperCAmelCase , bbox=__UpperCAmelCase , pixel_values=__UpperCAmelCase , training=__UpperCAmelCase )
# verify the logits
a = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , __UpperCAmelCase )
a = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , __UpperCAmelCase , atol=1e-4 ) )
| 370 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a = tempfile.mkdtemp()
a = BlipImageProcessor()
a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
a = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
a = InstructBlipProcessor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).tokenizer
def __lowerCAmelCase ( self : int , **__UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).image_processor
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Any ) ->Optional[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).qformer_tokenizer
def __lowerCAmelCase ( self : str ) ->Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
a = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
a = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = self.prepare_image_inputs()
a = image_processor(__UpperCAmelCase , return_tensors='''np''' )
a = processor(images=__UpperCAmelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = processor(text=__UpperCAmelCase )
a = tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
a = qformer_tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key] )
def __lowerCAmelCase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def __lowerCAmelCase ( self : Dict ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a = processor.batch_decode(__UpperCAmelCase )
a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 26 | 0 |
from math import pi, sqrt
def _a ( a :float ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(a ) not in (0, 0.5):
raise NotImplementedError('''num must be an integer or a half-integer''' )
elif num == 0.5:
return sqrt(a )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def _a ( ) -> None:
assert gamma(0.5 ) == sqrt(a )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase__ = 1.0
while num:
UpperCAmelCase__ = float(input("Gamma of: "))
print(f"""gamma({num}) = {gamma(num)}""")
print("\nEnter 0 to exit...")
| 371 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
from __future__ import annotations
def _a ( a :list[int | str] ) -> None:
create_state_space_tree(a , [] , 0 , [0 for i in range(len(a ) )] )
def _a ( a :list[int | str] , a :list[int | str] , a :int , a :list[int] , ) -> None:
if index == len(a ):
print(a )
return
for i in range(len(a ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
a = True
create_state_space_tree(a , a , index + 1 , a )
current_sequence.pop()
a = False
UpperCAmelCase__ = [3, 1, 2, 4]
generate_all_permutations(sequence)
UpperCAmelCase__ = ["A", "B", "C"]
generate_all_permutations(sequence_a)
| 350 |
def _a ( a :int = 600_851_475_143 ) -> int:
try:
a = int(a )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
a = 2
a = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
a = i
while n % i == 0:
a = n // i
i += 1
return int(a )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
import math
def _a ( a :list , a :int = 0 , a :int = 0 ) -> list:
a = end or len(a )
for i in range(a , a ):
a = i
a = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
a = array[temp_index - 1]
temp_index -= 1
a = temp_index_value
return array
def _a ( a :list , a :int , a :int ) -> None: # Max Heap
a = index
a = 2 * index + 1 # Left Node
a = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
a = left_index
if right_index < heap_size and array[largest] < array[right_index]:
a = right_index
if largest != index:
a , a = array[largest], array[index]
heapify(a , a , a )
def _a ( a :list ) -> list:
a = len(a )
for i in range(n // 2 , -1 , -1 ):
heapify(a , a , a )
for i in range(n - 1 , 0 , -1 ):
a , a = array[0], array[i]
heapify(a , 0 , a )
return array
def _a ( a :list , a :int , a :int , a :int ) -> int:
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def _a ( a :list , a :int , a :int , a :int ) -> int:
a = low
a = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
a , a = array[j], array[i]
i += 1
def _a ( a :list ) -> list:
if len(a ) == 0:
return array
a = 2 * math.ceil(math.loga(len(a ) ) )
a = 16
return intro_sort(a , 0 , len(a ) , a , a )
def _a ( a :list , a :int , a :int , a :int , a :int ) -> list:
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(a )
max_depth -= 1
a = median_of_a(a , a , start + ((end - start) // 2) + 1 , end - 1 )
a = partition(a , a , a , a )
intro_sort(a , a , a , a , a )
a = p
return insertion_sort(a , a , a )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase__ = input("Enter numbers separated by a comma : ").strip()
UpperCAmelCase__ = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| 351 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
UpperCAmelCase__ = "bart"
UpperCAmelCase__ = True
@st.cache(allow_output_mutation=a )
def _a ( ) -> Tuple:
if LOAD_DENSE_INDEX:
a = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
a = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
a = qar_model.eval()
else:
a , a = (None, None)
if MODEL_TYPE == "bart":
a = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
a = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
a = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
a = sas_model.eval()
else:
a , a = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Dict:
if LOAD_DENSE_INDEX:
a = faiss.StandardGpuResources()
a = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
a = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
a = faiss.IndexFlatIP(128 )
a = faiss.index_cpu_to_gpu(a , 1 , a )
wikiaab_gpu_index_flat.add(a ) # TODO fix for larger GPU
else:
a , a = (None, None)
a = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Optional[int]:
a = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
a = elia['''train_eli5''']
a = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
a = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(a )
return (elia_train, eli5_train_q_index)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_indexes()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_models()
UpperCAmelCase__ , UpperCAmelCase__ = load_train_data()
def _a ( a :str , a :Tuple=10 ) -> List[str]:
a = embed_questions_for_retrieval([question] , a , a )
a , a = eli5_train_q_index.search(a , a )
a = [elia_train[int(a )] for i in I[0]]
return nn_examples
def _a ( a :str , a :Any="wiki40b" , a :int="dense" , a :Union[str, Any]=10 ) -> List[str]:
if source == "none":
a , a = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
a , a = query_qa_dense_index(
a , a , a , a , a , a )
else:
a , a = query_es_index(
a , a , index_name='''english_wiki40b_snippets_100w''' , n_results=a , )
a = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
a = '''question: {} context: {}'''.format(a , a )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda a : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a : None),
} )
def _a ( a :Tuple , a :int , a :int , a :Dict=64 , a :List[Any]=256 , a :List[Any]=False , a :List[Any]=2 , a :Tuple=0.95 , a :Optional[Any]=0.8 ) -> int:
with torch.no_grad():
a = qa_sas_generate(
a , a , a , num_answers=1 , num_beams=a , min_len=a , max_len=a , do_sample=a , temp=a , top_p=a , top_k=a , max_input_length=1_024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title("Long Form Question Answering with ELI5")
# Start sidebar
UpperCAmelCase__ = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
UpperCAmelCase__ = "\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
UpperCAmelCase__ = "\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)
UpperCAmelCase__ = [
"Answer the question",
"View the retrieved document only",
"View the most similar ELI5 question and answer",
"Show me everything, please!",
]
UpperCAmelCase__ = st.sidebar.checkbox("Demo options")
if demo_options:
UpperCAmelCase__ = st.sidebar.selectbox(
"",
action_list,
index=3,
)
UpperCAmelCase__ = action_list.index(action_st)
UpperCAmelCase__ = st.sidebar.selectbox(
"",
["Show full text of passages", "Show passage section titles"],
index=0,
)
UpperCAmelCase__ = show_type == "Show full text of passages"
else:
UpperCAmelCase__ = 3
UpperCAmelCase__ = True
UpperCAmelCase__ = st.sidebar.checkbox("Retrieval options")
if retrieval_options:
UpperCAmelCase__ = "\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n "
st.sidebar.markdown(retriever_info)
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
UpperCAmelCase__ = "wiki40b"
UpperCAmelCase__ = "dense"
UpperCAmelCase__ = "beam"
UpperCAmelCase__ = 2
UpperCAmelCase__ = 64
UpperCAmelCase__ = 256
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = st.sidebar.checkbox("Generation options")
if generate_options:
UpperCAmelCase__ = "\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n "
st.sidebar.markdown(generate_info)
UpperCAmelCase__ = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
UpperCAmelCase__ = st.sidebar.slider(
"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
UpperCAmelCase__ = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
UpperCAmelCase__ = st.sidebar.slider(
"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
UpperCAmelCase__ = None
# start main text
UpperCAmelCase__ = [
"<MY QUESTION>",
"How do people make chocolate?",
"Why do we get a fever when we are sick?",
"How can different animals perceive different colors?",
"What is natural language processing?",
"What's the best way to treat a sunburn?",
"What exactly are vitamins ?",
"How does nuclear energy provide electricity?",
"What's the difference between viruses and bacteria?",
"Why are flutes classified as woodwinds when most of them are made out of metal ?",
"Why do people like drinking coffee even though it tastes so bad?",
"What happens when wine ages? How does it make the wine taste better?",
"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
"How does New Zealand have so many large bird predators?",
]
UpperCAmelCase__ = st.selectbox(
"What would you like to ask? ---- select <MY QUESTION> to enter a new query",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
UpperCAmelCase__ = st.text_input("Enter your question here:", "")
else:
UpperCAmelCase__ = question_s
if st.button("Show me!"):
if action in [0, 1, 3]:
if index_type == "mixed":
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="dense", n_results=10)
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="sparse", n_results=10)
UpperCAmelCase__ = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
UpperCAmelCase__ = support_list[:10]
UpperCAmelCase__ = "<P> " + " <P> ".join([res[-1] for res in support_list])
else:
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
UpperCAmelCase__ , UpperCAmelCase__ = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == "sampled"),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("### The model generated answer is:")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
for i, res in enumerate(support_list):
UpperCAmelCase__ = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
UpperCAmelCase__ = res[1].strip()
if sec_titles == "":
UpperCAmelCase__ = "[{}]({})".format(res[0], wiki_url)
else:
UpperCAmelCase__ = sec_titles.split(" & ")
UpperCAmelCase__ = " & ".join(
["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
)
st.markdown(
"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
)
if action in [2, 3]:
UpperCAmelCase__ = find_nearest_training(question)
UpperCAmelCase__ = nn_train_list[0]
st.markdown(
"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
)
UpperCAmelCase__ = [
"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
if i == 0 or sc > 2
]
st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
UpperCAmelCase__ = "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 26 | 0 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( enum.Enum ):
'''simple docstring'''
__snake_case = 0
__snake_case = 1
@add_end_docstrings(lowercase )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''generated'''
def __init__( self : int , *__UpperCAmelCase : int , **__UpperCAmelCase : Optional[int] ) ->Optional[int]:
"""simple docstring"""
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : int=None , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : Union[str, Any] , ) ->List[Any]:
"""simple docstring"""
a = {}
if truncation is not None:
a = truncation
a = generate_kwargs
a = {}
if return_tensors is not None and return_type is None:
a = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
a = return_type
if clean_up_tokenization_spaces is not None:
a = clean_up_tokenization_spaces
if stop_sequence is not None:
a = self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
a = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) ->List[Any]:
"""simple docstring"""
return True
def __lowerCAmelCase ( self : Dict , *__UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str ) ->Tuple:
"""simple docstring"""
a = self.model.config.prefix if self.model.config.prefix is not None else ''''''
if isinstance(args[0] , __UpperCAmelCase ):
if self.tokenizer.pad_token_id is None:
raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input''' )
a = ([prefix + arg for arg in args[0]],)
a = True
elif isinstance(args[0] , __UpperCAmelCase ):
a = (prefix + args[0],)
a = False
else:
raise ValueError(
F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
a = self.tokenizer(*__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : Dict , *__UpperCAmelCase : Dict , **__UpperCAmelCase : List[Any] ) ->str:
"""simple docstring"""
a = super().__call__(*__UpperCAmelCase , **__UpperCAmelCase )
if (
isinstance(args[0] , __UpperCAmelCase )
and all(isinstance(__UpperCAmelCase , __UpperCAmelCase ) for el in args[0] )
and all(len(__UpperCAmelCase ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : str=TruncationStrategy.DO_NOT_TRUNCATE , **__UpperCAmelCase : int ) ->List[Any]:
"""simple docstring"""
a = self._parse_and_tokenize(__UpperCAmelCase , truncation=__UpperCAmelCase , **__UpperCAmelCase )
return inputs
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : List[str] , **__UpperCAmelCase : Any ) ->int:
"""simple docstring"""
if self.framework == "pt":
a , a = model_inputs['''input_ids'''].shape
elif self.framework == "tf":
a , a = tf.shape(model_inputs['''input_ids'''] ).numpy()
a = generate_kwargs.get('''min_length''' , self.model.config.min_length )
a = generate_kwargs.get('''max_length''' , self.model.config.max_length )
self.check_inputs(__UpperCAmelCase , generate_kwargs['''min_length'''] , generate_kwargs['''max_length'''] )
a = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
a = output_ids.shape[0]
if self.framework == "pt":
a = output_ids.reshape(__UpperCAmelCase , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
a = tf.reshape(__UpperCAmelCase , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=ReturnType.TEXT , __UpperCAmelCase : Dict=False ) ->Dict:
"""simple docstring"""
a = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
a = {F"""{self.return_name}_token_ids""": output_ids}
elif return_type == ReturnType.TEXT:
a = {
F"""{self.return_name}_text""": self.tokenizer.decode(
__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
}
records.append(__UpperCAmelCase )
return records
@add_end_docstrings(lowercase )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''summary'''
def __call__( self : Dict , *__UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : str ) ->str:
"""simple docstring"""
return super().__call__(*__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) ->bool:
"""simple docstring"""
if max_length < min_length:
logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )
if input_length < max_length:
logger.warning(
F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
'''a summarization task, where outputs shorter than the input are typically wanted, you might '''
F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})""" )
@add_end_docstrings(lowercase )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''translation'''
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int ) ->Optional[int]:
"""simple docstring"""
if input_length > 0.9 * max_length:
logger.warning(
F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
'''increasing your max_length manually, e.g. translator(\'...\', max_length=400)''' )
return True
def __lowerCAmelCase ( self : Dict , *__UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[Any]=TruncationStrategy.DO_NOT_TRUNCATE , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[int]=None ) ->List[Any]:
"""simple docstring"""
if getattr(self.tokenizer , '''_build_translation_inputs''' , __UpperCAmelCase ):
return self.tokenizer._build_translation_inputs(
*__UpperCAmelCase , return_tensors=self.framework , truncation=__UpperCAmelCase , src_lang=__UpperCAmelCase , tgt_lang=__UpperCAmelCase )
else:
return super()._parse_and_tokenize(*__UpperCAmelCase , truncation=__UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict=None , __UpperCAmelCase : List[Any]=None , **__UpperCAmelCase : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
a , a , a = super()._sanitize_parameters(**__UpperCAmelCase )
if src_lang is not None:
a = src_lang
if tgt_lang is not None:
a = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
a = kwargs.get('''task''' , self.task )
a = task.split('''_''' )
if task and len(__UpperCAmelCase ) == 4:
# translation, XX, to YY
a = items[1]
a = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : Tuple , *__UpperCAmelCase : int , **__UpperCAmelCase : int ) ->str:
"""simple docstring"""
return super().__call__(*__UpperCAmelCase , **__UpperCAmelCase )
| 352 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = "▁"
UpperCAmelCase__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = BertGenerationTokenizer
__snake_case = False
__snake_case = True
def __lowerCAmelCase ( self : str ) ->str:
"""simple docstring"""
super().setUp()
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
a = '''<s>'''
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(__UpperCAmelCase ) , 1_002 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def __lowerCAmelCase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
a = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [285, 46, 10, 170, 382] , )
a = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
a = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
a = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def __lowerCAmelCase ( self : Any ) ->str:
"""simple docstring"""
a = '''Hello World!'''
a = [18_536, 2_260, 101]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
a = [
871,
419,
358,
946,
991,
2_521,
452,
358,
1_357,
387,
7_751,
3_536,
112,
985,
456,
126,
865,
938,
5_400,
5_734,
458,
1_368,
467,
786,
2_462,
5_246,
1_159,
633,
865,
4_519,
457,
582,
852,
2_557,
427,
916,
508,
405,
34_324,
497,
391,
408,
11_342,
1_244,
385,
100,
938,
985,
456,
574,
362,
12_597,
3_200,
3_129,
1_172,
]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@require_torch
@slow
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
a = list(self.big_tokenizer.get_vocab().keys() )[:10]
a = ''' '''.join(__UpperCAmelCase )
a = self.big_tokenizer.encode_plus(__UpperCAmelCase , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = BertGenerationConfig()
a = BertGenerationEncoder(__UpperCAmelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__UpperCAmelCase )
model(**__UpperCAmelCase )
@slow
def __lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
a = {'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 26 | 0 |
def _a ( a :int ) -> bool:
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger("transformers.models.speecht5")
def _a ( a :Optional[Any] , a :Tuple , a :Dict ) -> List[str]:
hf_model.apply_weight_norm()
a = checkpoint['''input_conv.weight_g''']
a = checkpoint['''input_conv.weight_v''']
a = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
a = checkpoint[F"""upsamples.{i}.1.weight_g"""]
a = checkpoint[F"""upsamples.{i}.1.weight_v"""]
a = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
a = checkpoint['''output_conv.1.weight_g''']
a = checkpoint['''output_conv.1.weight_v''']
a = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def _a ( a :List[str] , a :Union[str, Any] , a :Dict , a :Dict=None , a :List[Any]=None , ) -> int:
if config_path is not None:
a = SpeechTaHifiGanConfig.from_pretrained(a )
else:
a = SpeechTaHifiGanConfig()
a = SpeechTaHifiGan(a )
a = torch.load(a )
load_weights(orig_checkpoint['''model''']['''generator'''] , a , a )
a = np.load(a )
a = stats[0].reshape(-1 )
a = stats[1].reshape(-1 )
a = torch.from_numpy(a ).float()
a = torch.from_numpy(a ).float()
model.save_pretrained(a )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCAmelCase__ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 26 | 0 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _a ( a :str ) -> Tuple:
return 1 / (1 + np.exp(-z ))
def _a ( a :int , a :Any ) -> Tuple:
return (-y * np.log(a ) - (1 - y) * np.log(1 - h )).mean()
def _a ( a :List[str] , a :Optional[Any] , a :int ) -> List[Any]:
a = np.dot(a , a )
return np.sum(y * scores - np.log(1 + np.exp(a ) ) )
def _a ( a :Union[str, Any] , a :int , a :Optional[Any] , a :Tuple=70_000 ) -> Any:
a = np.zeros(x.shape[1] )
for iterations in range(a ):
a = np.dot(a , a )
a = sigmoid_function(a )
a = np.dot(x.T , h - y ) / y.size
a = theta - alpha * gradient # updating the weights
a = np.dot(a , a )
a = sigmoid_function(a )
a = cost_function(a , a )
if iterations % 100 == 0:
print(F"""loss: {j} \t""" ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
UpperCAmelCase__ = datasets.load_iris()
UpperCAmelCase__ = iris.data[:, :2]
UpperCAmelCase__ = (iris.target != 0) * 1
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = logistic_reg(alpha, x, y, max_iterations=70000)
print("theta: ", theta) # printing the theta i.e our weights vector
def _a ( a :Dict ) -> Tuple:
return sigmoid_function(
np.dot(a , a ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
((UpperCAmelCase__) , (UpperCAmelCase__)) = (x[:, 0].min(), x[:, 0].max())
((UpperCAmelCase__) , (UpperCAmelCase__)) = (x[:, 1].min(), x[:, 1].max())
((UpperCAmelCase__) , (UpperCAmelCase__)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
UpperCAmelCase__ = np.c_[xxa.ravel(), xxa.ravel()]
UpperCAmelCase__ = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
| 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTBigCodeForSequenceClassification",
"GPTBigCodeForTokenClassification",
"GPTBigCodeForCausalLM",
"GPTBigCodeModel",
"GPTBigCodePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
'''simple docstring'''
__snake_case = None
__snake_case = '''utf-8'''
__snake_case = None
__snake_case = None
__snake_case = True # deprecated
__snake_case = None # deprecated
__snake_case = 10 << 20 # 10MB
__snake_case = None
class lowercase_ ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__snake_case = JsonConfig
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
a = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] ) ->Union[str, Any]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
a = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__UpperCAmelCase , (str, list, tuple) ):
a = data_files
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = [files]
a = [dl_manager.iter_files(__UpperCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
a = []
for split_name, files in data_files.items():
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = [files]
a = [dl_manager.iter_files(__UpperCAmelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=__UpperCAmelCase , gen_kwargs={'''files''': files} ) )
return splits
def __lowerCAmelCase ( self : str , __UpperCAmelCase : pa.Table ) ->pa.Table:
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
a = self.config.features.arrow_schema.field(__UpperCAmelCase ).type
a = pa_table.append_column(__UpperCAmelCase , pa.array([None] * len(__UpperCAmelCase ) , type=__UpperCAmelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
a = table_cast(__UpperCAmelCase , self.config.features.arrow_schema )
return pa_table
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] ) ->Union[str, Any]:
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(__UpperCAmelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(__UpperCAmelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
a = json.load(__UpperCAmelCase )
# We keep only the field we are interested in
a = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__UpperCAmelCase , (list, tuple) ):
a = set().union(*[row.keys() for row in dataset] )
a = {col: [row.get(__UpperCAmelCase ) for row in dataset] for col in keys}
else:
a = dataset
a = pa.Table.from_pydict(__UpperCAmelCase )
yield file_idx, self._cast_table(__UpperCAmelCase )
# If the file has one json object per line
else:
with open(__UpperCAmelCase , '''rb''' ) as f:
a = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
a = max(self.config.chunksize // 32 , 16 << 10 )
a = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
a = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__UpperCAmelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
a = batch.decode(self.config.encoding , errors=__UpperCAmelCase ).encode('''utf-8''' )
try:
while True:
try:
a = paj.read_json(
io.BytesIO(__UpperCAmelCase ) , read_options=paj.ReadOptions(block_size=__UpperCAmelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__UpperCAmelCase , pa.ArrowInvalid )
and "straddling" not in str(__UpperCAmelCase )
or block_size > len(__UpperCAmelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F"""Batch of {len(__UpperCAmelCase )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""" )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
__UpperCAmelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
a = json.load(__UpperCAmelCase )
except json.JSONDecodeError:
logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase )}: {e}""" )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__UpperCAmelCase , __UpperCAmelCase ): # list is the only sequence type supported in JSON
try:
a = set().union(*[row.keys() for row in dataset] )
a = {col: [row.get(__UpperCAmelCase ) for row in dataset] for col in keys}
a = pa.Table.from_pydict(__UpperCAmelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase )}: {e}""" )
raise ValueError(F"""Not able to read records in the JSON file at {file}.""" ) from None
yield file_idx, self._cast_table(__UpperCAmelCase )
break
else:
logger.error(F"""Failed to read file '{file}' with error {type(__UpperCAmelCase )}: {e}""" )
raise ValueError(
F"""Not able to read records in the JSON file at {file}. """
F"""You should probably indicate the field of the JSON file containing your records. """
F"""This JSON file contain the following fields: {str(list(dataset.keys() ) )}. """
F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """ ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__UpperCAmelCase )
batch_idx += 1
| 355 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _a ( a :Tuple ) -> int:
a = tmp_path / '''file.csv'''
a = textwrap.dedent(
'''\
header1,header2
1,2
10,20
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :int ) -> List[str]:
a = tmp_path / '''malformed_file.csv'''
a = textwrap.dedent(
'''\
header1,header2
1,2
10,20,
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :Dict , a :int ) -> List[str]:
a = tmp_path / '''csv_with_image.csv'''
a = textwrap.dedent(
F"""\
image
{image_file}
""" )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :List[Any] ) -> Dict:
a = tmp_path / '''csv_with_label.csv'''
a = textwrap.dedent(
'''\
label
good
bad
good
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :Tuple ) -> Any:
a = tmp_path / '''csv_with_int_list.csv'''
a = textwrap.dedent(
'''\
int_list
1 2 3
4 5 6
7 8 9
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
def _a ( a :Dict , a :int , a :Union[str, Any] ) -> List[Any]:
a = Csv()
a = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(a , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(a ) in record.message
for record in caplog.records )
@require_pil
def _a ( a :Dict ) -> Any:
with open(a , encoding='''utf-8''' ) as f:
a = f.read().splitlines()[1]
a = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
a = csv._generate_tables([[csv_file_with_image]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
a = pa_table.to_pydict()['''image''']
assert generated_content == [{"path": image_file, "bytes": None}]
def _a ( a :Any ) -> Tuple:
with open(a , encoding='''utf-8''' ) as f:
a = f.read().splitlines()[1:]
a = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
a = csv._generate_tables([[csv_file_with_label]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
a = pa_table.to_pydict()['''label''']
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(a ) for label in labels]
def _a ( a :Union[str, Any] ) -> Optional[Any]:
a = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda a : [int(a ) for i in x.split()]} )
a = csv._generate_tables([[csv_file_with_int_list]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
a = pa_table.to_pydict()['''int_list''']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 26 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
"--original_config_file",
default=None,
type=str,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--num_in_channels",
default=None,
type=int,
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
)
parser.add_argument(
"--scheduler_type",
default="pndm",
type=str,
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
)
parser.add_argument(
"--pipeline_type",
default=None,
type=str,
help=(
"The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
". If `None` pipeline will be automatically inferred."
),
)
parser.add_argument(
"--image_size",
default=None,
type=int,
help=(
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
" Base. Use 768 for Stable Diffusion v2."
),
)
parser.add_argument(
"--prediction_type",
default=None,
type=str,
help=(
"The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
" Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
),
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--upcast_attention",
action="store_true",
help=(
"Whether the attention computation should always be upcasted. This is necessary when running stable"
" diffusion 2.1."
),
)
parser.add_argument(
"--from_safetensors",
action="store_true",
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
parser.add_argument(
"--stable_unclip",
type=str,
default=None,
required=False,
help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
)
parser.add_argument(
"--stable_unclip_prior",
type=str,
default=None,
required=False,
help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
)
parser.add_argument(
"--clip_stats_path",
type=str,
help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
required=False,
)
parser.add_argument(
"--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
)
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument(
"--vae_path",
type=str,
default=None,
required=False,
help="Set to a path, hub id to an already converted vae to not convert it again.",
)
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 356 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = torch.device("cpu")
def _a ( ) -> Union[str, Any]:
a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
a = Image.open(requests.get(a , stream=a ).raw )
return im
def _a ( a :Dict ) -> Tuple:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02] )
def _a ( a :int , a :Any , a :Union[str, Any] ) -> int:
a = dct.pop(a )
a = val
def _a ( a :Any ) -> Dict:
a = []
for k in state_dict.keys():
a = k
if ".pwconv" in k:
a = k_new.replace('''.pwconv''' , '''.point_wise_conv''' )
if ".dwconv" in k:
a = k_new.replace('''.dwconv''' , '''.depth_wise_conv''' )
if ".Proj." in k:
a = k_new.replace('''.Proj.''' , '''.proj.''' )
if "patch_embed" in k_new:
a = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
a = k_new.split('''.''' )
if ls[2].isdigit():
a = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
a = k_new.replace('''network''' , '''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def _a ( a :List[Any] , a :Tuple , a :List[str] ) -> Union[str, Any]:
a = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
a = 1_000
a = '''huggingface/label-files'''
a = '''imagenet-1k-id2label.json'''
a = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
a = {int(a ): v for k, v in idalabel.items()}
a = idalabel
a = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
a = [3, 3, 6, 4]
a = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
a = [3, 3, 9, 6]
a = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
a = [4, 3, 10, 5]
a = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
a = [4, 4, 12, 6]
a = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
a = torch.hub.load_state_dict_from_url(a , map_location='''cpu''' , check_hash=a )
else:
a = torch.load(a , map_location='''cpu''' )
a = checkpoint
a = create_rename_keys(a )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(a , a , a )
# load HuggingFace model
a = SwiftFormerForImageClassification(a ).eval()
hf_model.load_state_dict(a )
# prepare test inputs
a = prepare_img()
a = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
a = processor(images=a , return_tensors='''pt''' )
# compare outputs from both models
a = get_expected_output(a )
a = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 1_000] )
assert torch.allclose(hf_logits[0, 0:5] , a , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swiftformer_name",
default="swiftformer_xs",
choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
type=str,
help="Name of the SwiftFormer model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="./converted_outputs/",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
UpperCAmelCase__ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 26 | 0 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Dict , __UpperCAmelCase : Distribution , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : int=None , __UpperCAmelCase : Tuple=0 ) ->List[Any]:
"""simple docstring"""
a = 1.0 if scale is None else scale
a = 0.0 if loc is None else loc
super().__init__(__UpperCAmelCase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__UpperCAmelCase )] )
@property
def __lowerCAmelCase ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
return self.base_dist.mean * self.scale + self.loc
@property
def __lowerCAmelCase ( self : Optional[Any] ) ->List[Any]:
"""simple docstring"""
return self.base_dist.variance * self.scale**2
@property
def __lowerCAmelCase ( self : Dict ) ->List[str]:
"""simple docstring"""
return self.variance.sqrt()
class lowercase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : Dict[str, int] , __UpperCAmelCase : Callable[..., Tuple[torch.Tensor]] , **__UpperCAmelCase : Union[str, Any] ) ->None:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
a = args_dim
a = nn.ModuleList([nn.Linear(__UpperCAmelCase , __UpperCAmelCase ) for dim in args_dim.values()] )
a = domain_map
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : torch.Tensor ) ->Tuple[torch.Tensor]:
"""simple docstring"""
a = [proj(__UpperCAmelCase ) for proj in self.proj]
return self.domain_map(*__UpperCAmelCase )
class lowercase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : Tuple ) ->Optional[int]:
"""simple docstring"""
super().__init__()
a = function
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : int , *__UpperCAmelCase : List[str] ) ->Optional[int]:
"""simple docstring"""
return self.function(__UpperCAmelCase , *__UpperCAmelCase )
class lowercase_ :
'''simple docstring'''
__snake_case = 42
__snake_case = 42
__snake_case = 42
def __init__( self : Optional[Any] , __UpperCAmelCase : int = 1 ) ->None:
"""simple docstring"""
a = dim
a = {k: dim * self.args_dim[k] for k in self.args_dim}
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[Any] ) ->List[Any]:
"""simple docstring"""
if self.dim == 1:
return self.distribution_class(*__UpperCAmelCase )
else:
return Independent(self.distribution_class(*__UpperCAmelCase ) , 1 )
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[torch.Tensor] = None , __UpperCAmelCase : Optional[torch.Tensor] = None , ) ->Distribution:
"""simple docstring"""
a = self._base_distribution(__UpperCAmelCase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(__UpperCAmelCase , loc=__UpperCAmelCase , scale=__UpperCAmelCase , event_dim=self.event_dim )
@property
def __lowerCAmelCase ( self : List[Any] ) ->Tuple:
"""simple docstring"""
return () if self.dim == 1 else (self.dim,)
@property
def __lowerCAmelCase ( self : str ) ->int:
"""simple docstring"""
return len(self.event_shape )
@property
def __lowerCAmelCase ( self : Optional[int] ) ->float:
"""simple docstring"""
return 0.0
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : int ) ->nn.Module:
"""simple docstring"""
return ParameterProjection(
in_features=__UpperCAmelCase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def __lowerCAmelCase ( self : List[str] , *__UpperCAmelCase : torch.Tensor ) ->int:
"""simple docstring"""
raise NotImplementedError()
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : torch.Tensor ) ->torch.Tensor:
"""simple docstring"""
return (x + torch.sqrt(torch.square(__UpperCAmelCase ) + 4.0 )) / 2.0
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = {'''df''': 1, '''loc''': 1, '''scale''': 1}
__snake_case = StudentT
@classmethod
def __lowerCAmelCase ( cls : Union[str, Any] , __UpperCAmelCase : torch.Tensor , __UpperCAmelCase : torch.Tensor , __UpperCAmelCase : torch.Tensor ) ->int:
"""simple docstring"""
a = cls.squareplus(__UpperCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
a = 2.0 + cls.squareplus(__UpperCAmelCase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = {'''loc''': 1, '''scale''': 1}
__snake_case = Normal
@classmethod
def __lowerCAmelCase ( cls : Dict , __UpperCAmelCase : torch.Tensor , __UpperCAmelCase : torch.Tensor ) ->Union[str, Any]:
"""simple docstring"""
a = cls.squareplus(__UpperCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = {'''total_count''': 1, '''logits''': 1}
__snake_case = NegativeBinomial
@classmethod
def __lowerCAmelCase ( cls : Optional[int] , __UpperCAmelCase : torch.Tensor , __UpperCAmelCase : torch.Tensor ) ->int:
"""simple docstring"""
a = cls.squareplus(__UpperCAmelCase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : Union[str, Any] ) ->Distribution:
"""simple docstring"""
a , a = distr_args
if self.dim == 1:
return self.distribution_class(total_count=__UpperCAmelCase , logits=__UpperCAmelCase )
else:
return Independent(self.distribution_class(total_count=__UpperCAmelCase , logits=__UpperCAmelCase ) , 1 )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[torch.Tensor] = None , __UpperCAmelCase : Optional[torch.Tensor] = None ) ->Distribution:
"""simple docstring"""
a , a = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 357 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : DDPMScheduler , __UpperCAmelCase : Optional[int] , ) ->List[str]:
"""simple docstring"""
super().__init__()
a = value_function
a = unet
a = scheduler
a = env
a = env.get_dataset()
a = {}
for key in self.data.keys():
try:
a = self.data[key].mean()
except: # noqa: E722
pass
a = {}
for key in self.data.keys():
try:
a = self.data[key].std()
except: # noqa: E722
pass
a = env.observation_space.shape[0]
a = env.action_space.shape[0]
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) ->List[str]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __lowerCAmelCase ( self : int , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
if type(__UpperCAmelCase ) is dict:
return {k: self.to_torch(__UpperCAmelCase ) for k, v in x_in.items()}
elif torch.is_tensor(__UpperCAmelCase ):
return x_in.to(self.unet.device )
return torch.tensor(__UpperCAmelCase , device=self.unet.device )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple ) ->int:
"""simple docstring"""
for key, val in cond.items():
a = val.clone()
return x_in
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = x.shape[0]
a = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a = torch.full((batch_size,) , __UpperCAmelCase , device=self.unet.device , dtype=torch.long )
for _ in range(__UpperCAmelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a = self.value_function(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample
a = torch.autograd.grad([y.sum()] , [x] )[0]
a = self.scheduler._get_variance(__UpperCAmelCase )
a = torch.exp(0.5 * posterior_variance )
a = model_std * grad
a = 0
a = x.detach()
a = x + scale * grad
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.unet(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , predict_epsilon=__UpperCAmelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
return x, y
def __call__( self : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=64 , __UpperCAmelCase : int=32 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : str=0.1 ) ->List[str]:
"""simple docstring"""
a = self.normalize(__UpperCAmelCase , '''observations''' )
a = obs[None].repeat(__UpperCAmelCase , axis=0 )
a = {0: self.to_torch(__UpperCAmelCase )}
a = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a = randn_tensor(__UpperCAmelCase , device=self.unet.device )
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
# run the diffusion process
a , a = self.run_diffusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# sort output trajectories by value
a = y.argsort(0 , descending=__UpperCAmelCase ).squeeze()
a = x[sorted_idx]
a = sorted_values[:, :, : self.action_dim]
a = actions.detach().cpu().numpy()
a = self.de_normalize(__UpperCAmelCase , key='''actions''' )
# select the action with the highest value
if y is not None:
a = 0
else:
# if we didn't run value guiding, select a random action
a = np.random.randint(0 , __UpperCAmelCase )
a = denorm_actions[selected_index, 0]
return denorm_actions
| 26 | 0 |
from __future__ import annotations
import math
def _a ( a :int ) -> list[int]:
if num <= 0:
a = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(a )
a = [True] * (num + 1)
a = []
a = 2
a = int(math.sqrt(a ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(a )
# Set multiples of start be False
for i in range(start * start , num + 1 , a ):
if sieve[i] is True:
a = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(a )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 358 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model"}
UpperCAmelCase__ = {
"vocab_file": {
"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
}
}
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[str]="<s>" , __UpperCAmelCase : int="</s>" , __UpperCAmelCase : Any="<unk>" , __UpperCAmelCase : Optional[Any]="<sep>" , __UpperCAmelCase : int="<pad>" , __UpperCAmelCase : Any="<cls>" , __UpperCAmelCase : List[str]="<mask>" , __UpperCAmelCase : Optional[int]=["<eop>", "<eod>"] , __UpperCAmelCase : Optional[Dict[str, Any]] = None , **__UpperCAmelCase : Union[str, Any] , ) ->None:
"""simple docstring"""
a = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
a = 3
a = do_lower_case
a = remove_space
a = keep_accents
a = vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''' )
a = jieba
a = str.maketrans(''' \n''' , '''\u2582\u2583''' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
return len(self.sp_model )
def __lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
a = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
a = self.__dict__.copy()
a = None
return state
def __setstate__( self : List[str] , __UpperCAmelCase : Optional[int] ) ->str:
"""simple docstring"""
a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] ) ->List[str]:
"""simple docstring"""
if self.remove_space:
a = ''' '''.join(inputs.strip().split() )
else:
a = inputs
a = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
a = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
a = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
a = outputs.lower()
return outputs
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = self.preprocess_text(__UpperCAmelCase )
a = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
a = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
a = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
a = cur_pieces[1:]
else:
a = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCAmelCase )
else:
new_pieces.append(__UpperCAmelCase )
return new_pieces
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Any ) ->Any:
"""simple docstring"""
return self.sp_model.PieceToId(__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Dict ) ->Union[str, Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(__UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase , ''' ''' ).strip()
return out_string
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1, 1]
return ([0] * len(__UpperCAmelCase )) + [1, 1]
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , '''wb''' ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
def __lowerCAmelCase ( self : Any , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = super()._decode(*__UpperCAmelCase , **__UpperCAmelCase )
a = text.replace(''' ''' , '''''' ).replace('''\u2582''' , ''' ''' ).replace('''\u2583''' , '''\n''' )
return text
| 26 | 0 |
"""simple docstring"""
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 359 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _a ( a :Union[str, Any] , a :List[Any] ) -> List[Any]:
a = checkpoint
a = {}
a = vae_state_dict['''encoder.conv_in.weight''']
a = vae_state_dict['''encoder.conv_in.bias''']
a = vae_state_dict['''encoder.conv_out.weight''']
a = vae_state_dict['''encoder.conv_out.bias''']
a = vae_state_dict['''encoder.norm_out.weight''']
a = vae_state_dict['''encoder.norm_out.bias''']
a = vae_state_dict['''decoder.conv_in.weight''']
a = vae_state_dict['''decoder.conv_in.bias''']
a = vae_state_dict['''decoder.conv_out.weight''']
a = vae_state_dict['''decoder.conv_out.bias''']
a = vae_state_dict['''decoder.norm_out.weight''']
a = vae_state_dict['''decoder.norm_out.bias''']
a = vae_state_dict['''quant_conv.weight''']
a = vae_state_dict['''quant_conv.bias''']
a = vae_state_dict['''post_quant_conv.weight''']
a = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(a )
}
# Retrieves the keys for the decoder up blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(a )
}
for i in range(a ):
a = [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]
if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.weight""" )
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.bias""" )
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
for i in range(a ):
a = num_up_blocks - 1 - i
a = [
key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
]
if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.weight"""
]
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.bias"""
]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
return new_checkpoint
def _a ( a :str , a :str , ) -> List[str]:
# Only support V1
a = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
a = io.BytesIO(r.content )
a = OmegaConf.load(a )
a = 512
a = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
a = {}
with safe_open(a , framework='''pt''' , device='''cpu''' ) as f:
for key in f.keys():
a = f.get_tensor(a )
else:
a = torch.load(a , map_location=a )['''state_dict''']
# Convert the VAE model.
a = create_vae_diffusers_config(a , image_size=a )
a = custom_convert_ldm_vae_checkpoint(a , a )
a = AutoencoderKL(**a )
vae.load_state_dict(a )
vae.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
UpperCAmelCase__ = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 26 | 0 |
from __future__ import annotations
def _a ( a :dict , a :str ) -> set[str]:
a , a = set(a ), [start]
while stack:
a = stack.pop()
explored.add(a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(a )
return explored
UpperCAmelCase__ = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 360 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''image_processor''', '''tokenizer''']
__snake_case = '''CLIPImageProcessor'''
__snake_case = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Dict , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
a = kwargs.pop('''feature_extractor''' )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : str ) ->Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
a = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) ->Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def __lowerCAmelCase ( self : int ) ->List[str]:
"""simple docstring"""
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __UpperCAmelCase , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __UpperCAmelCase , )
return self.image_processor
| 26 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class lowercase_ :
'''simple docstring'''
__snake_case = PegasusConfig
__snake_case = {}
__snake_case = '''gelu'''
def __init__( self : Optional[int] , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[str]=13 , __UpperCAmelCase : List[Any]=7 , __UpperCAmelCase : Union[str, Any]=True , __UpperCAmelCase : List[Any]=False , __UpperCAmelCase : str=99 , __UpperCAmelCase : List[Any]=32 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : Any=4 , __UpperCAmelCase : Tuple=37 , __UpperCAmelCase : str=0.1 , __UpperCAmelCase : Optional[Any]=0.1 , __UpperCAmelCase : List[Any]=40 , __UpperCAmelCase : List[str]=2 , __UpperCAmelCase : int=1 , __UpperCAmelCase : List[Any]=0 , ) ->Union[str, Any]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = eos_token_id
a = pad_token_id
a = bos_token_id
def __lowerCAmelCase ( self : int ) ->Union[str, Any]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
a = tf.concat([input_ids, eos_tensor] , axis=1 )
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
a = prepare_pegasus_inputs_dict(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return config, inputs_dict
def __lowerCAmelCase ( self : int , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[int] ) ->List[Any]:
"""simple docstring"""
a = TFPegasusModel(config=__UpperCAmelCase ).get_decoder()
a = inputs_dict['''input_ids''']
a = input_ids[:1, :]
a = inputs_dict['''attention_mask'''][:1, :]
a = inputs_dict['''head_mask''']
a = 1
# first forward pass
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , head_mask=__UpperCAmelCase , use_cache=__UpperCAmelCase )
a , a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
a = ids_tensor((self.batch_size, 3) , config.vocab_size )
a = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
a = tf.concat([input_ids, next_tokens] , axis=-1 )
a = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , past_key_values=__UpperCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
a = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
a = output_from_no_past[:, -3:, random_slice_idx]
a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__UpperCAmelCase , __UpperCAmelCase , rtol=1e-3 )
def _a ( a :Any , a :Optional[Any] , a :int , a :Tuple=None , a :Optional[int]=None , a :List[Any]=None , a :int=None , a :Any=None , ) -> Union[str, Any]:
if attention_mask is None:
a = tf.cast(tf.math.not_equal(a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
a = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
a = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
a = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
a = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class lowercase_ ( lowercase , lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
__snake_case = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
__snake_case = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
__snake_case = True
__snake_case = False
__snake_case = False
def __lowerCAmelCase ( self : List[str] ) ->Union[str, Any]:
"""simple docstring"""
a = TFPegasusModelTester(self )
a = ConfigTester(self , config_class=__UpperCAmelCase )
def __lowerCAmelCase ( self : Any ) ->List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : str ) ->str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
__snake_case = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
__snake_case = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
__snake_case = '''google/pegasus-xsum'''
@cached_property
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
a = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __lowerCAmelCase ( self : Tuple , **__UpperCAmelCase : List[Any] ) ->Dict:
"""simple docstring"""
a = self.translate_src_text(**__UpperCAmelCase )
assert self.expected_text == generated_words
def __lowerCAmelCase ( self : Tuple , **__UpperCAmelCase : Dict ) ->Optional[int]:
"""simple docstring"""
a = self.tokenizer(self.src_text , **__UpperCAmelCase , padding=__UpperCAmelCase , return_tensors='''tf''' )
a = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=__UpperCAmelCase , )
a = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__UpperCAmelCase )
return generated_words
@slow
def __lowerCAmelCase ( self : List[str] ) ->int:
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 361 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 26 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCAmelCase__ = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
"The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
" body.",
"Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
" punishment.",
]
UpperCAmelCase__ = [
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
" Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
" had informed his Lufthansa training school of an episode of severe depression, airline says .",
"Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
" Israel and the United States opposed the move, which could open the door to war crimes investigations against"
" Israelis .",
"Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
" death . Organization claims that governments around the world are using the threat of terrorism to advance"
" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
" sentences up by 28% .",
]
def _a ( ) -> Optional[Any]:
a = calculate_rouge(a , a , bootstrap_aggregation=a , rouge_keys=['''rouge2''', '''rougeL'''] )
assert isinstance(a , a )
a = calculate_rouge(a , a , bootstrap_aggregation=a , rouge_keys=['''rouge2'''] )
assert (
pd.DataFrame(no_aggregation['''rouge2'''] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra['''rouge2'''] ).fmeasure.mean()
)
def _a ( ) -> int:
a = '''rougeLsum'''
a = calculate_rouge(a , a , newline_sep=a , rouge_keys=[k] )[k]
a = calculate_rouge(a , a , newline_sep=a , rouge_keys=[k] )[k]
assert score > score_no_sep
def _a ( ) -> int:
a = ['''rouge1''', '''rouge2''', '''rougeL''']
a = calculate_rouge(a , a , newline_sep=a , rouge_keys=a )
a = calculate_rouge(a , a , newline_sep=a , rouge_keys=a )
assert score_sep == score_no_sep
def _a ( ) -> Union[str, Any]:
a = [
'''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
]
a = [
'''Margot Frank, died in 1945, a month earlier than previously thought.''',
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
''' the final seconds on board Flight 9525.''',
]
assert calculate_rouge(a , a , newline_sep=a ) == calculate_rouge(a , a , newline_sep=a )
def _a ( ) -> str:
a = [
'''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
]
a = [
''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
]
a = calculate_rouge(a , a , rouge_keys=['''rougeLsum'''] , newline_sep=a )['''rougeLsum''']
a = calculate_rouge(a , a , rouge_keys=['''rougeLsum'''] )['''rougeLsum''']
assert new_score > prev_score
def _a ( ) -> int:
a = Path('''examples/seq2seq/test_data/wmt_en_ro''' )
a = calculate_rouge_path(data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) )
assert isinstance(a , a )
a = calculate_rouge_path(
data_dir.joinpath('''test.source''' ) , data_dir.joinpath('''test.target''' ) , bootstrap_aggregation=a )
assert isinstance(a , a )
| 362 |
from __future__ import annotations
import typing
from collections import Counter
def _a ( a :int ) -> typing.Counter[int]:
a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a , max_perimeter + 1 ):
a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a ):
a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def _a ( a :int = 1_000 ) -> int:
a = pythagorean_triple(a )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f"""Perimeter {solution()} has maximum solutions""")
| 26 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''xlm'''
__snake_case = {
'''hidden_size''': '''emb_dim''',
'''num_attention_heads''': '''n_heads''',
'''num_hidden_layers''': '''n_layers''',
'''n_words''': '''vocab_size''', # For backward compatibility
}
def __init__( self : Any , __UpperCAmelCase : Union[str, Any]=30_145 , __UpperCAmelCase : int=2_048 , __UpperCAmelCase : Union[str, Any]=12 , __UpperCAmelCase : List[str]=16 , __UpperCAmelCase : Optional[int]=0.1 , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : List[str]=True , __UpperCAmelCase : Tuple=False , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Any=False , __UpperCAmelCase : List[str]=1 , __UpperCAmelCase : int=True , __UpperCAmelCase : str=512 , __UpperCAmelCase : int=2_048**-0.5 , __UpperCAmelCase : Optional[Any]=1e-1_2 , __UpperCAmelCase : Tuple=0.02 , __UpperCAmelCase : Optional[int]=0 , __UpperCAmelCase : Dict=1 , __UpperCAmelCase : str=2 , __UpperCAmelCase : int=3 , __UpperCAmelCase : Optional[int]=5 , __UpperCAmelCase : int=True , __UpperCAmelCase : str="first" , __UpperCAmelCase : Optional[Any]=True , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : Any=True , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : Any=5 , __UpperCAmelCase : List[str]=5 , __UpperCAmelCase : List[str]=0 , __UpperCAmelCase : Union[str, Any]=0 , __UpperCAmelCase : str=2 , __UpperCAmelCase : Optional[int]=0 , **__UpperCAmelCase : Dict , ) ->int:
"""simple docstring"""
a = vocab_size
a = emb_dim
a = n_layers
a = n_heads
a = dropout
a = attention_dropout
a = gelu_activation
a = sinusoidal_embeddings
a = causal
a = asm
a = n_langs
a = use_lang_emb
a = layer_norm_eps
a = bos_index
a = eos_index
a = pad_index
a = unk_index
a = mask_index
a = is_encoder
a = max_position_embeddings
a = embed_init_std
a = init_std
a = summary_type
a = summary_use_proj
a = summary_activation
a = summary_proj_to_labels
a = summary_first_dropout
a = start_n_top
a = end_n_top
a = mask_token_id
a = lang_id
if "n_words" in kwargs:
a = kwargs['''n_words''']
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
class lowercase_ ( lowercase ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self : List[Any] ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
a = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
a = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 363 |
from __future__ import annotations
def _a ( a :dict , a :str ) -> set[str]:
a , a = set(a ), [start]
while stack:
a = stack.pop()
explored.add(a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(a )
return explored
UpperCAmelCase__ = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 26 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
UpperCAmelCase__ = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
"To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
"To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
"If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
UpperCAmelCase__ = concatenate_datasets
UpperCAmelCase__ = DownloadConfig
UpperCAmelCase__ = DownloadManager
UpperCAmelCase__ = DownloadMode
UpperCAmelCase__ = DownloadConfig
UpperCAmelCase__ = DownloadMode
UpperCAmelCase__ = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 364 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase__ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase__ = 10
UpperCAmelCase__ = 256
def _a ( a :List[str] ) -> Optional[MinHash]:
if len(a ) < MIN_NUM_TOKENS:
return None
a = MinHash(num_perm=a )
for token in set(a ):
min_hash.update(token.encode() )
return min_hash
def _a ( a :str ) -> Set[str]:
return {t for t in NON_ALPHA.split(a ) if len(t.strip() ) > 0}
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , *,
__UpperCAmelCase : float = 0.85 , ) ->Dict:
"""simple docstring"""
a = duplication_jaccard_threshold
a = NUM_PERM
a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
a = defaultdict(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : MinHash ) ->None:
"""simple docstring"""
a = self._index.query(__UpperCAmelCase )
if code_key in self._index.keys:
print(F"""Duplicate key {code_key}""" )
return
self._index.insert(__UpperCAmelCase , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase )
def __lowerCAmelCase ( self : Dict ) ->List[List[Dict]]:
"""simple docstring"""
a = []
for base, duplicates in self._duplicate_clusters.items():
a = [base] + list(__UpperCAmelCase )
# reformat the cluster to be a list of dict
a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__UpperCAmelCase )
return duplicate_clusters
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict ) ->None:
"""simple docstring"""
a = self.get_duplicate_clusters()
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def _a ( a :List[Any] ) -> List[Any]:
a , a = element
a = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _a ( a :Type[Dataset] ) -> List[Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(a , max_queue_size=10_000 ) , chunksize=100 , ):
if data is not None:
yield data
def _a ( a :Type[Dataset] , a :float ) -> str:
a = DuplicationIndex(duplication_jaccard_threshold=a )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a ) ) , max_queue_size=100 ) ):
di.add(a , a )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _a ( a :str , a :str ) -> float:
a = get_tokens(a )
a = get_tokens(a )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase__ = None
def _a ( a :Tuple , a :Tuple ) -> Any:
a = []
for elementa in cluster:
a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(a , a ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
a = 1
extremes.append(a )
return extremes
def _a ( a :List[Any] , a :Optional[Any] , a :Union[str, Any] ) -> Optional[int]:
global _shared_dataset
a = dataset
a = []
a = partial(_find_cluster_extremes_shared , jaccard_threshold=a )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
a , a , ) , total=len(a ) , ):
extremes_list.append(a )
return extremes_list
def _a ( a :Type[Dataset] , a :float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
a = make_duplicate_clusters(a , a )
a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
a = {}
a = find_extremes(a , a , a )
for extremes in extremes_clusters:
for element in extremes:
a = element
a = duplicate_indices - set(extreme_dict.keys() )
a = dataset.filter(lambda a , a : idx not in remove_indices , with_indices=a )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
a = extreme_dict[element['''base_index''']]['''copies''']
print(F"""Original dataset size: {len(a )}""" )
print(F"""Number of duplicate clusters: {len(a )}""" )
print(F"""Files in duplicate cluster: {len(a )}""" )
print(F"""Unique files in duplicate cluster: {len(a )}""" )
print(F"""Filtered dataset size: {len(a )}""" )
return ds_filter, duplicate_clusters
| 26 | 0 |
from math import ceil, sqrt
def _a ( a :int = 1_000_000 ) -> int:
a = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
a = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 365 |
from math import ceil, sqrt
def _a ( a :int = 1_000_000 ) -> int:
a = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
a = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : List[Any] , *__UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : List[Any]=None , **__UpperCAmelCase : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
a = eval_examples
a = post_process_function
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : Optional[Dataset] = None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[List[str]] = None , __UpperCAmelCase : str = "eval" , **__UpperCAmelCase : Any , ) ->Dict[str, float]:
"""simple docstring"""
a = gen_kwargs.copy()
a = (
gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''' ) is not None else self.args.generation_max_length
)
a = (
gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
)
a = gen_kwargs
a = self.eval_dataset if eval_dataset is None else eval_dataset
a = self.get_eval_dataloader(__UpperCAmelCase )
a = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
a = self.compute_metrics
a = None
a = time.time()
a = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
a = eval_loop(
__UpperCAmelCase , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__UpperCAmelCase , metric_key_prefix=__UpperCAmelCase , )
finally:
a = compute_metrics
a = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
__UpperCAmelCase , __UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
a = self.post_process_function(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
a = self.compute_metrics(__UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
a = metrics.pop(__UpperCAmelCase )
metrics.update(output.metrics )
else:
a = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(__UpperCAmelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
a = self.callback_handler.on_evaluate(self.args , self.state , self.control , __UpperCAmelCase )
return metrics
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int=None , __UpperCAmelCase : str = "test" , **__UpperCAmelCase : Any ) ->int:
"""simple docstring"""
a = gen_kwargs.copy()
a = self.get_test_dataloader(__UpperCAmelCase )
# Temporarily disable metric computation, we will do it in the loop here.
a = self.compute_metrics
a = None
a = time.time()
a = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
a = eval_loop(
__UpperCAmelCase , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__UpperCAmelCase , metric_key_prefix=__UpperCAmelCase , )
finally:
a = compute_metrics
a = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
__UpperCAmelCase , __UpperCAmelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
a = self.post_process_function(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , '''predict''' )
a = self.compute_metrics(__UpperCAmelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
a = metrics.pop(__UpperCAmelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__UpperCAmelCase )
| 366 |
UpperCAmelCase__ = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 26 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''gpt_bigcode'''
__snake_case = ['''past_key_values''']
__snake_case = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : List[Any] , __UpperCAmelCase : Union[str, Any]=50_257 , __UpperCAmelCase : Optional[Any]=1_024 , __UpperCAmelCase : int=768 , __UpperCAmelCase : List[str]=12 , __UpperCAmelCase : str=12 , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[Any]="gelu_pytorch_tanh" , __UpperCAmelCase : int=0.1 , __UpperCAmelCase : Optional[int]=0.1 , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : str=1e-5 , __UpperCAmelCase : Union[str, Any]=0.02 , __UpperCAmelCase : int=True , __UpperCAmelCase : Union[str, Any]=True , __UpperCAmelCase : Dict=50_256 , __UpperCAmelCase : Optional[int]=50_256 , __UpperCAmelCase : Union[str, Any]=True , __UpperCAmelCase : int=True , __UpperCAmelCase : int=True , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
a = vocab_size
a = n_positions
a = n_embd
a = n_layer
a = n_head
a = n_inner
a = activation_function
a = resid_pdrop
a = embd_pdrop
a = attn_pdrop
a = layer_norm_epsilon
a = initializer_range
a = scale_attn_weights
a = use_cache
a = attention_softmax_in_fpaa
a = scale_attention_softmax_in_fpaa
a = multi_query
a = bos_token_id
a = eos_token_id
super().__init__(bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
| 367 |
def _a ( a :list ) -> list:
if len(a ) <= 1:
return lst
a = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a , a = lst[i], lst[i - 1]
i -= 1
if i == 0:
a = 1
return lst
if __name__ == "__main__":
UpperCAmelCase__ = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 26 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
UpperCAmelCase__ = None
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
"tokenizer_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
},
}
UpperCAmelCase__ = {
"google/fnet-base": 512,
"google/fnet-large": 512,
}
UpperCAmelCase__ = "▁"
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ['''input_ids''', '''token_type_ids''']
__snake_case = FNetTokenizer
def __init__( self : List[str] , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : Union[str, Any]=False , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Dict="<unk>" , __UpperCAmelCase : Optional[Any]="[SEP]" , __UpperCAmelCase : List[str]="<pad>" , __UpperCAmelCase : Optional[int]="[CLS]" , __UpperCAmelCase : Any="[MASK]" , **__UpperCAmelCase : Tuple , ) ->List[Any]:
"""simple docstring"""
a = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , **__UpperCAmelCase , )
a = do_lower_case
a = remove_space
a = keep_accents
a = vocab_file
a = False if not self.vocab_file else True
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __lowerCAmelCase ( self : str , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ):
copyfile(self.vocab_file , __UpperCAmelCase )
return (out_vocab_file,)
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase__ = "\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)[\"depth\"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline(\"depth-estimation\")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n >>> prompt = \"A robot, 4k photo\"\n >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save(\"robot_cat.png\")\n ```\n"
def _a ( a :Tuple , a :str , a :List[str]=8 ) -> Any:
a = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
a = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Dict , __UpperCAmelCase : UNetaDConditionModel , __UpperCAmelCase : DDPMScheduler , __UpperCAmelCase : VQModel , ) ->Optional[int]:
"""simple docstring"""
super().__init__()
self.register_modules(
unet=__UpperCAmelCase , scheduler=__UpperCAmelCase , movq=__UpperCAmelCase , )
a = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : str ) ->str:
"""simple docstring"""
if latents is None:
a = randn_tensor(__UpperCAmelCase , generator=__UpperCAmelCase , device=__UpperCAmelCase , dtype=__UpperCAmelCase )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
a = latents.to(__UpperCAmelCase )
a = latents * scheduler.init_noise_sigma
return latents
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : List[str]=0 ) ->Dict:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
a = torch.device(F"""cuda:{gpu_id}""" )
a = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : int , __UpperCAmelCase : Union[str, Any]=0 ) ->Dict:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
a = torch.device(F"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=__UpperCAmelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
a = None
for cpu_offloaded_model in [self.unet, self.movq]:
a , a = cpu_offload_with_hook(__UpperCAmelCase , __UpperCAmelCase , prev_module_hook=__UpperCAmelCase )
# We'll offload the last model manually.
a = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowerCAmelCase ( self : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__UpperCAmelCase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __UpperCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __UpperCAmelCase : torch.FloatTensor , __UpperCAmelCase : int = 512 , __UpperCAmelCase : int = 512 , __UpperCAmelCase : int = 100 , __UpperCAmelCase : float = 4.0 , __UpperCAmelCase : int = 1 , __UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __UpperCAmelCase : Optional[torch.FloatTensor] = None , __UpperCAmelCase : Optional[str] = "pil" , __UpperCAmelCase : bool = True , ) ->List[Any]:
"""simple docstring"""
a = self._execution_device
a = guidance_scale > 1.0
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = torch.cat(__UpperCAmelCase , dim=0 )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = torch.cat(__UpperCAmelCase , dim=0 )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = torch.cat(__UpperCAmelCase , dim=0 )
a = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
a = image_embeds.repeat_interleave(__UpperCAmelCase , dim=0 )
a = negative_image_embeds.repeat_interleave(__UpperCAmelCase , dim=0 )
a = hint.repeat_interleave(__UpperCAmelCase , dim=0 )
a = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__UpperCAmelCase )
a = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=__UpperCAmelCase )
self.scheduler.set_timesteps(__UpperCAmelCase , device=__UpperCAmelCase )
a = self.scheduler.timesteps
a = self.movq.config.latent_channels
a , a = downscale_height_and_width(__UpperCAmelCase , __UpperCAmelCase , self.movq_scale_factor )
# create initial latent
a = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__UpperCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a = {'''image_embeds''': image_embeds, '''hint''': hint}
a = self.unet(
sample=__UpperCAmelCase , timestep=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , added_cond_kwargs=__UpperCAmelCase , return_dict=__UpperCAmelCase , )[0]
if do_classifier_free_guidance:
a , a = noise_pred.split(latents.shape[1] , dim=1 )
a , a = noise_pred.chunk(2 )
a , a = variance_pred.chunk(2 )
a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
a = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
a , a = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
a = self.scheduler.step(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase , )[0]
# post-processing
a = self.movq.decode(__UpperCAmelCase , force_not_quantize=__UpperCAmelCase )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
a = image * 0.5 + 0.5
a = image.clamp(0 , 1 )
a = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
a = self.numpy_to_pil(__UpperCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__UpperCAmelCase )
| 369 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
UpperCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _a ( a :str ) -> Any:
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
a = model_type_to_module_name(a )
a = importlib.import_module(F""".{module_name}""" , '''transformers.models''' )
try:
return getattr(a , a )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(a , '''__name__''' , a ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
a = importlib.import_module('''transformers''' )
if hasattr(a , a ):
return getattr(a , a )
return None
def _a ( a :Union[str, os.PathLike] , a :Optional[Union[str, os.PathLike]] = None , a :bool = False , a :bool = False , a :Optional[Dict[str, str]] = None , a :Optional[Union[bool, str]] = None , a :Optional[str] = None , a :bool = False , **a :int , ) -> Tuple:
a = get_file_from_repo(
a , a , cache_dir=a , force_download=a , resume_download=a , proxies=a , use_auth_token=a , revision=a , local_files_only=a , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(a , encoding='''utf-8''' ) as reader:
return json.load(a )
class lowercase_ :
'''simple docstring'''
def __init__( self : Tuple ) ->int:
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(__UpperCAmelCase )
def __lowerCAmelCase ( cls : int , __UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : Dict ) ->List[Any]:
"""simple docstring"""
a = kwargs.pop('''config''' , __UpperCAmelCase )
a = kwargs.pop('''trust_remote_code''' , __UpperCAmelCase )
a = True
a , a = FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase , **__UpperCAmelCase )
a = config_dict.get('''feature_extractor_type''' , __UpperCAmelCase )
a = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
a = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = AutoConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
# It could be in `config.feature_extractor_type``
a = getattr(__UpperCAmelCase , '''feature_extractor_type''' , __UpperCAmelCase )
if hasattr(__UpperCAmelCase , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
a = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
a = feature_extractor_class_from_name(__UpperCAmelCase )
a = feature_extractor_auto_map is not None
a = feature_extractor_class is not None or type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
a = resolve_trust_remote_code(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if has_remote_code and trust_remote_code:
a = get_class_from_dynamic_module(
__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
a = kwargs.pop('''code_revision''' , __UpperCAmelCase )
if os.path.isdir(__UpperCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
a = FEATURE_EXTRACTOR_MAPPING[type(__UpperCAmelCase )]
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
raise ValueError(
F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple ) ->Optional[int]:
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(__UpperCAmelCase , __UpperCAmelCase )
| 26 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = (DPMSolverSDEScheduler,)
__snake_case = 10
def __lowerCAmelCase ( self : List[str] , **__UpperCAmelCase : int ) ->List[Any]:
"""simple docstring"""
a = {
'''num_train_timesteps''': 1_100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__UpperCAmelCase )
return config
def __lowerCAmelCase ( self : Any ) ->List[str]:
"""simple docstring"""
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=__UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__UpperCAmelCase , beta_end=__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple ) ->Tuple:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
a = self.dummy_model()
a = self.dummy_sample_deter * scheduler.init_noise_sigma
a = sample.to(__UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
a = model(__UpperCAmelCase , __UpperCAmelCase )
a = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
a = output.prev_sample
a = torch.sum(torch.abs(__UpperCAmelCase ) )
a = torch.mean(torch.abs(__UpperCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1e-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1e-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1e-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1e-3
def __lowerCAmelCase ( self : Dict ) ->int:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(prediction_type='''v_prediction''' )
a = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
a = self.dummy_model()
a = self.dummy_sample_deter * scheduler.init_noise_sigma
a = sample.to(__UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
a = model(__UpperCAmelCase , __UpperCAmelCase )
a = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
a = output.prev_sample
a = torch.sum(torch.abs(__UpperCAmelCase ) )
a = torch.mean(torch.abs(__UpperCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1e-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1e-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1e-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1e-3
def __lowerCAmelCase ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps , device=__UpperCAmelCase )
a = self.dummy_model()
a = self.dummy_sample_deter.to(__UpperCAmelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
a = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
a = model(__UpperCAmelCase , __UpperCAmelCase )
a = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
a = output.prev_sample
a = torch.sum(torch.abs(__UpperCAmelCase ) )
a = torch.mean(torch.abs(__UpperCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1e-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1e-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1e-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1e-3
def __lowerCAmelCase ( self : int ) ->Union[str, Any]:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__UpperCAmelCase , use_karras_sigmas=__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps , device=__UpperCAmelCase )
a = self.dummy_model()
a = self.dummy_sample_deter.to(__UpperCAmelCase ) * scheduler.init_noise_sigma
a = sample.to(__UpperCAmelCase )
for t in scheduler.timesteps:
a = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
a = model(__UpperCAmelCase , __UpperCAmelCase )
a = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
a = output.prev_sample
a = torch.sum(torch.abs(__UpperCAmelCase ) )
a = torch.mean(torch.abs(__UpperCAmelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1e-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1e-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1e-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1e-2
| 370 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a = tempfile.mkdtemp()
a = BlipImageProcessor()
a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
a = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
a = InstructBlipProcessor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).tokenizer
def __lowerCAmelCase ( self : int , **__UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).image_processor
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Any ) ->Optional[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).qformer_tokenizer
def __lowerCAmelCase ( self : str ) ->Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
a = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
a = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = self.prepare_image_inputs()
a = image_processor(__UpperCAmelCase , return_tensors='''np''' )
a = processor(images=__UpperCAmelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = processor(text=__UpperCAmelCase )
a = tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
a = qformer_tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key] )
def __lowerCAmelCase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def __lowerCAmelCase ( self : Dict ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a = processor.batch_decode(__UpperCAmelCase )
a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 26 | 0 |
import argparse
import json
from tqdm import tqdm
def _a ( ) -> Optional[int]:
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=a , default='''biencoder-nq-dev.json''' , help='''Path to raw DPR training data''' , )
parser.add_argument(
'''--evaluation_set''' , type=a , help='''where to store parsed evaluation_set file''' , )
parser.add_argument(
'''--gold_data_path''' , type=a , help='''where to store parsed gold_data_path file''' , )
a = parser.parse_args()
with open(args.src_path , '''r''' ) as src_file, open(args.evaluation_set , '''w''' ) as eval_file, open(
args.gold_data_path , '''w''' ) as gold_file:
a = json.load(a )
for dpr_record in tqdm(a ):
a = dpr_record['''question''']
a = [context['''title'''] for context in dpr_record['''positive_ctxs''']]
eval_file.write(question + '''\n''' )
gold_file.write('''\t'''.join(a ) + '''\n''' )
if __name__ == "__main__":
main()
| 371 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int]=13 , __UpperCAmelCase : Optional[int]=7 , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : List[str]=True , __UpperCAmelCase : Any=True , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : Union[str, Any]=99 , __UpperCAmelCase : int=64 , __UpperCAmelCase : Any=32 , __UpperCAmelCase : Union[str, Any]=5 , __UpperCAmelCase : Optional[Any]=4 , __UpperCAmelCase : Optional[Any]=37 , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : int=0.1 , __UpperCAmelCase : List[str]=0.1 , __UpperCAmelCase : Union[str, Any]=512 , __UpperCAmelCase : Union[str, Any]=16 , __UpperCAmelCase : Optional[int]=2 , __UpperCAmelCase : List[Any]=0.02 , __UpperCAmelCase : List[Any]=3 , __UpperCAmelCase : Union[str, Any]=4 , __UpperCAmelCase : int=None , ) ->List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def __lowerCAmelCase ( self : Dict ) ->List[str]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
return MegatronBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__UpperCAmelCase , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : Any , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : List[str] ) ->List[Any]:
"""simple docstring"""
a = MegatronBertModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
a = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
a = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[str] ) ->str:
"""simple docstring"""
a = MegatronBertForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , __UpperCAmelCase : List[str] ) ->Optional[int]:
"""simple docstring"""
a = MegatronBertForCausalLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : str , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
a = MegatronBertForNextSentencePrediction(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __lowerCAmelCase ( self : int , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = MegatronBertForPreTraining(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , next_sentence_label=__UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str , __UpperCAmelCase : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
a = MegatronBertForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : int , __UpperCAmelCase : List[str] ) ->Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MegatronBertForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : Any , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple ) ->Dict:
"""simple docstring"""
a = self.num_labels
a = MegatronBertForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str ) ->Optional[int]:
"""simple docstring"""
a = self.num_choices
a = MegatronBertForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self : Dict ) ->Any:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowercase_ ( lowercase , lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
__snake_case = (
{
'''feature-extraction''': MegatronBertModel,
'''fill-mask''': MegatronBertForMaskedLM,
'''question-answering''': MegatronBertForQuestionAnswering,
'''text-classification''': MegatronBertForSequenceClassification,
'''text-generation''': MegatronBertForCausalLM,
'''token-classification''': MegatronBertForTokenClassification,
'''zero-shot''': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case = True
# test_resize_embeddings = False
__snake_case = False
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : List[str]=False ) ->Union[str, Any]:
"""simple docstring"""
a = super()._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase , return_labels=__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__UpperCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = MegatronBertModelTester(self )
a = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def __lowerCAmelCase ( self : List[Any] ) ->Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : List[str] ) ->Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[int] ) ->Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] ) ->Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->List[str]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__UpperCAmelCase )
def _a ( a :Union[str, Any] ) -> int:
return torch.tensor(
a , dtype=torch.long , device=a , )
UpperCAmelCase__ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
@unittest.skip('''Model is not available.''' )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
a = '''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
a = os.path.join(os.environ['''MYDIR'''] , __UpperCAmelCase )
a = MegatronBertModel.from_pretrained(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.half()
a = _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]] )
with torch.no_grad():
a = model(__UpperCAmelCase )[0]
a = torch.Size((1, 9, 1_024) )
self.assertEqual(output.shape , __UpperCAmelCase )
a = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
a = output[0, ii, jj]
a = expected[3 * ii + jj]
a = '''ii={} jj={} a={} b={}'''.format(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
self.assertTrue(math.isclose(__UpperCAmelCase , __UpperCAmelCase , rel_tol=__UpperCAmelCase , abs_tol=__UpperCAmelCase ) , msg=__UpperCAmelCase )
| 350 |
def _a ( a :int = 600_851_475_143 ) -> int:
try:
a = int(a )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
a = 2
a = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
a = i
while n % i == 0:
a = n // i
i += 1
return int(a )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
UpperCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
UpperCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__ = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 351 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
UpperCAmelCase__ = "bart"
UpperCAmelCase__ = True
@st.cache(allow_output_mutation=a )
def _a ( ) -> Tuple:
if LOAD_DENSE_INDEX:
a = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
a = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
a = qar_model.eval()
else:
a , a = (None, None)
if MODEL_TYPE == "bart":
a = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
a = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
a = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
a = sas_model.eval()
else:
a , a = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Dict:
if LOAD_DENSE_INDEX:
a = faiss.StandardGpuResources()
a = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
a = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
a = faiss.IndexFlatIP(128 )
a = faiss.index_cpu_to_gpu(a , 1 , a )
wikiaab_gpu_index_flat.add(a ) # TODO fix for larger GPU
else:
a , a = (None, None)
a = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Optional[int]:
a = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
a = elia['''train_eli5''']
a = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
a = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(a )
return (elia_train, eli5_train_q_index)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_indexes()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_models()
UpperCAmelCase__ , UpperCAmelCase__ = load_train_data()
def _a ( a :str , a :Tuple=10 ) -> List[str]:
a = embed_questions_for_retrieval([question] , a , a )
a , a = eli5_train_q_index.search(a , a )
a = [elia_train[int(a )] for i in I[0]]
return nn_examples
def _a ( a :str , a :Any="wiki40b" , a :int="dense" , a :Union[str, Any]=10 ) -> List[str]:
if source == "none":
a , a = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
a , a = query_qa_dense_index(
a , a , a , a , a , a )
else:
a , a = query_es_index(
a , a , index_name='''english_wiki40b_snippets_100w''' , n_results=a , )
a = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
a = '''question: {} context: {}'''.format(a , a )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda a : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a : None),
} )
def _a ( a :Tuple , a :int , a :int , a :Dict=64 , a :List[Any]=256 , a :List[Any]=False , a :List[Any]=2 , a :Tuple=0.95 , a :Optional[Any]=0.8 ) -> int:
with torch.no_grad():
a = qa_sas_generate(
a , a , a , num_answers=1 , num_beams=a , min_len=a , max_len=a , do_sample=a , temp=a , top_p=a , top_k=a , max_input_length=1_024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title("Long Form Question Answering with ELI5")
# Start sidebar
UpperCAmelCase__ = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
UpperCAmelCase__ = "\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
UpperCAmelCase__ = "\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)
UpperCAmelCase__ = [
"Answer the question",
"View the retrieved document only",
"View the most similar ELI5 question and answer",
"Show me everything, please!",
]
UpperCAmelCase__ = st.sidebar.checkbox("Demo options")
if demo_options:
UpperCAmelCase__ = st.sidebar.selectbox(
"",
action_list,
index=3,
)
UpperCAmelCase__ = action_list.index(action_st)
UpperCAmelCase__ = st.sidebar.selectbox(
"",
["Show full text of passages", "Show passage section titles"],
index=0,
)
UpperCAmelCase__ = show_type == "Show full text of passages"
else:
UpperCAmelCase__ = 3
UpperCAmelCase__ = True
UpperCAmelCase__ = st.sidebar.checkbox("Retrieval options")
if retrieval_options:
UpperCAmelCase__ = "\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n "
st.sidebar.markdown(retriever_info)
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
UpperCAmelCase__ = "wiki40b"
UpperCAmelCase__ = "dense"
UpperCAmelCase__ = "beam"
UpperCAmelCase__ = 2
UpperCAmelCase__ = 64
UpperCAmelCase__ = 256
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = st.sidebar.checkbox("Generation options")
if generate_options:
UpperCAmelCase__ = "\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n "
st.sidebar.markdown(generate_info)
UpperCAmelCase__ = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
UpperCAmelCase__ = st.sidebar.slider(
"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
UpperCAmelCase__ = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
UpperCAmelCase__ = st.sidebar.slider(
"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
UpperCAmelCase__ = None
# start main text
UpperCAmelCase__ = [
"<MY QUESTION>",
"How do people make chocolate?",
"Why do we get a fever when we are sick?",
"How can different animals perceive different colors?",
"What is natural language processing?",
"What's the best way to treat a sunburn?",
"What exactly are vitamins ?",
"How does nuclear energy provide electricity?",
"What's the difference between viruses and bacteria?",
"Why are flutes classified as woodwinds when most of them are made out of metal ?",
"Why do people like drinking coffee even though it tastes so bad?",
"What happens when wine ages? How does it make the wine taste better?",
"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
"How does New Zealand have so many large bird predators?",
]
UpperCAmelCase__ = st.selectbox(
"What would you like to ask? ---- select <MY QUESTION> to enter a new query",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
UpperCAmelCase__ = st.text_input("Enter your question here:", "")
else:
UpperCAmelCase__ = question_s
if st.button("Show me!"):
if action in [0, 1, 3]:
if index_type == "mixed":
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="dense", n_results=10)
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="sparse", n_results=10)
UpperCAmelCase__ = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
UpperCAmelCase__ = support_list[:10]
UpperCAmelCase__ = "<P> " + " <P> ".join([res[-1] for res in support_list])
else:
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
UpperCAmelCase__ , UpperCAmelCase__ = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == "sampled"),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("### The model generated answer is:")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
for i, res in enumerate(support_list):
UpperCAmelCase__ = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
UpperCAmelCase__ = res[1].strip()
if sec_titles == "":
UpperCAmelCase__ = "[{}]({})".format(res[0], wiki_url)
else:
UpperCAmelCase__ = sec_titles.split(" & ")
UpperCAmelCase__ = " & ".join(
["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
)
st.markdown(
"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
)
if action in [2, 3]:
UpperCAmelCase__ = find_nearest_training(question)
UpperCAmelCase__ = nn_train_list[0]
st.markdown(
"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
)
UpperCAmelCase__ = [
"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
if i == 0 or sc > 2
]
st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
UpperCAmelCase__ = "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 26 | 0 |
def _a ( a :float , a :int ) -> float:
if digit_amount > 0:
return round(number - int(a ) , a )
return number - int(a )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 352 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = "▁"
UpperCAmelCase__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = BertGenerationTokenizer
__snake_case = False
__snake_case = True
def __lowerCAmelCase ( self : str ) ->str:
"""simple docstring"""
super().setUp()
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
a = '''<s>'''
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(__UpperCAmelCase ) , 1_002 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def __lowerCAmelCase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
a = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [285, 46, 10, 170, 382] , )
a = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
a = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
a = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def __lowerCAmelCase ( self : Any ) ->str:
"""simple docstring"""
a = '''Hello World!'''
a = [18_536, 2_260, 101]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
a = [
871,
419,
358,
946,
991,
2_521,
452,
358,
1_357,
387,
7_751,
3_536,
112,
985,
456,
126,
865,
938,
5_400,
5_734,
458,
1_368,
467,
786,
2_462,
5_246,
1_159,
633,
865,
4_519,
457,
582,
852,
2_557,
427,
916,
508,
405,
34_324,
497,
391,
408,
11_342,
1_244,
385,
100,
938,
985,
456,
574,
362,
12_597,
3_200,
3_129,
1_172,
]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@require_torch
@slow
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
a = list(self.big_tokenizer.get_vocab().keys() )[:10]
a = ''' '''.join(__UpperCAmelCase )
a = self.big_tokenizer.encode_plus(__UpperCAmelCase , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = BertGenerationConfig()
a = BertGenerationEncoder(__UpperCAmelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__UpperCAmelCase )
model(**__UpperCAmelCase )
@slow
def __lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
a = {'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 26 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
UpperCAmelCase__ = Path(__file__).parent / "model_card_template.md"
UpperCAmelCase__ = uuida().hex
UpperCAmelCase__ = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__ = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__ = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def _a ( a :Union[Dict, str, None] = None ) -> str:
a = F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F"""; torch/{_torch_version}"""
if is_flax_available():
ua += F"""; jax/{_jax_version}"""
ua += F"""; flax/{_flax_version}"""
if is_onnx_available():
ua += F"""; onnxruntime/{_onnxruntime_version}"""
# CI will set this value to True
if os.environ.get('''DIFFUSERS_IS_CI''' , '''''' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(a , a ):
ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items() )
elif isinstance(a , a ):
ua += "; " + user_agent
return ua
def _a ( a :str , a :Optional[str] = None , a :Optional[str] = None ) -> Optional[Any]:
if token is None:
a = HfFolder.get_token()
if organization is None:
a = whoami(a )['''name''']
return F"""{username}/{model_id}"""
else:
return F"""{organization}/{model_id}"""
def _a ( a :Optional[Any] , a :str ) -> str:
if not is_jinja_available():
raise ValueError(
'''Modelcard rendering is based on Jinja templates.'''
''' Please make sure to have `jinja` installed before using `create_model_card`.'''
''' To install it, please run `pip install Jinja2`.''' )
if hasattr(a , '''local_rank''' ) and args.local_rank not in [-1, 0]:
return
a = args.hub_token if hasattr(a , '''hub_token''' ) else None
a = get_full_repo_name(a , token=a )
a = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='''en''' , license='''apache-2.0''' , library_name='''diffusers''' , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=a , model_name=a , repo_name=a , dataset_name=args.dataset_name if hasattr(a , '''dataset_name''' ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(a , '''gradient_accumulation_steps''' ) else None
) , adam_betaa=args.adam_betaa if hasattr(a , '''adam_beta1''' ) else None , adam_betaa=args.adam_betaa if hasattr(a , '''adam_beta2''' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(a , '''adam_weight_decay''' ) else None , adam_epsilon=args.adam_epsilon if hasattr(a , '''adam_epsilon''' ) else None , lr_scheduler=args.lr_scheduler if hasattr(a , '''lr_scheduler''' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(a , '''lr_warmup_steps''' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(a , '''ema_inv_gamma''' ) else None , ema_power=args.ema_power if hasattr(a , '''ema_power''' ) else None , ema_max_decay=args.ema_max_decay if hasattr(a , '''ema_max_decay''' ) else None , mixed_precision=args.mixed_precision , )
a = os.path.join(args.output_dir , '''README.md''' )
model_card.save(a )
def _a ( a :Optional[str] , a :Optional[str] = None ) -> int:
if resolved_file is None or commit_hash is not None:
return commit_hash
a = str(Path(a ).as_posix() )
a = re.search(r'''snapshots/([^/]+)/''' , a )
if search is None:
return None
a = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(a ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCAmelCase__ = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCAmelCase__ = os.path.join(hf_cache_home, "diffusers")
def _a ( a :Optional[str] = None , a :Optional[str] = None ) -> None:
if new_cache_dir is None:
a = DIFFUSERS_CACHE
if old_cache_dir is None:
a = old_diffusers_cache
a = Path(a ).expanduser()
a = Path(a ).expanduser()
for old_blob_path in old_cache_dir.glob('''**/blobs/*''' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
a = new_cache_dir / old_blob_path.relative_to(a )
new_blob_path.parent.mkdir(parents=a , exist_ok=a )
os.replace(a , a )
try:
os.symlink(a , a )
except OSError:
logger.warning(
'''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.''' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
UpperCAmelCase__ = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
UpperCAmelCase__ = 0
else:
with open(cache_version_file) as f:
try:
UpperCAmelCase__ = int(f.read())
except ValueError:
UpperCAmelCase__ = 0
if cache_version < 1:
UpperCAmelCase__ = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
UpperCAmelCase__ = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
"the directory exists and can be written to."
)
def _a ( a :str , a :Optional[str] = None ) -> str:
if variant is not None:
a = weights_name.split('''.''' )
a = splits[:-1] + [variant] + splits[-1:]
a = '''.'''.join(a )
return weights_name
def _a ( a :Dict , *,
a :Tuple , a :Any , a :Any , a :Dict , a :str , a :List[str] , a :List[str] , a :Optional[int] , a :Any , a :List[Any] , a :Optional[Any]=None , ) -> Any:
a = str(a )
if os.path.isfile(a ):
return pretrained_model_name_or_path
elif os.path.isdir(a ):
if os.path.isfile(os.path.join(a , a ) ):
# Load from a PyTorch checkpoint
a = os.path.join(a , a )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(a , a , a ) ):
a = os.path.join(a , a , a )
return model_file
else:
raise EnvironmentError(
F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.""" )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(a ).base_version ) >= version.parse('''0.20.0''' )
):
try:
a = hf_hub_download(
a , filename=_add_variant(a , a ) , cache_dir=a , force_download=a , proxies=a , resume_download=a , local_files_only=a , use_auth_token=a , user_agent=a , subfolder=a , revision=revision or commit_hash , )
warnings.warn(
F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.""" , a , )
return model_file
except: # noqa: E722
warnings.warn(
F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a , a )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a , a )}' so that the correct variant file can be added.""" , a , )
try:
# 2. Load model file as usual
a = hf_hub_download(
a , filename=a , cache_dir=a , force_download=a , proxies=a , resume_download=a , local_files_only=a , use_auth_token=a , user_agent=a , subfolder=a , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
'''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
'''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
'''login`.''' )
except RevisionNotFoundError:
raise EnvironmentError(
F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
'''this model name. Check the model page at '''
F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.""" )
except EntryNotFoundError:
raise EnvironmentError(
F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.""" )
except HTTPError as err:
raise EnvironmentError(
F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}""" )
except ValueError:
raise EnvironmentError(
F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
F""" directory containing a file named {weights_name} or"""
''' \nCheckout your internet connection or see how to run the library in'''
''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.''' )
except EnvironmentError:
raise EnvironmentError(
F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
'''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
F"""containing a file named {weights_name}""" )
| 353 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger("transformers.models.speecht5")
def _a ( a :Optional[Any] , a :Tuple , a :Dict ) -> List[str]:
hf_model.apply_weight_norm()
a = checkpoint['''input_conv.weight_g''']
a = checkpoint['''input_conv.weight_v''']
a = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
a = checkpoint[F"""upsamples.{i}.1.weight_g"""]
a = checkpoint[F"""upsamples.{i}.1.weight_v"""]
a = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
a = checkpoint['''output_conv.1.weight_g''']
a = checkpoint['''output_conv.1.weight_v''']
a = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def _a ( a :List[str] , a :Union[str, Any] , a :Dict , a :Dict=None , a :List[Any]=None , ) -> int:
if config_path is not None:
a = SpeechTaHifiGanConfig.from_pretrained(a )
else:
a = SpeechTaHifiGanConfig()
a = SpeechTaHifiGan(a )
a = torch.load(a )
load_weights(orig_checkpoint['''model''']['''generator'''] , a , a )
a = np.load(a )
a = stats[0].reshape(-1 )
a = stats[1].reshape(-1 )
a = torch.from_numpy(a ).float()
a = torch.from_numpy(a ).float()
model.save_pretrained(a )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCAmelCase__ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 26 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase__ = logging.getLogger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self : str ) ->Any:
"""simple docstring"""
a = False
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[str] ) ->List[str]:
"""simple docstring"""
if not self.initialized:
a = RagRetriever(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , index=__UpperCAmelCase , init_retrieval=__UpperCAmelCase , )
a = True
def __lowerCAmelCase ( self : Any ) ->Tuple:
"""simple docstring"""
self.retriever.index.init_index()
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
a , a = self.retriever._main_retrieve(__UpperCAmelCase , __UpperCAmelCase )
return doc_ids, retrieved_doc_embeds
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict=None ) ->Optional[Any]:
"""simple docstring"""
if index is not None and index.is_initialized() and len(__UpperCAmelCase ) > 0:
raise ValueError(
'''When using Ray for distributed fine-tuning, '''
'''you\'ll need to provide the paths instead, '''
'''as the dataset and the index are loaded '''
'''separately. More info in examples/rag/use_own_knowledge_dataset.py ''' )
super().__init__(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , index=__UpperCAmelCase , init_retrieval=__UpperCAmelCase , )
a = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for worker in self.retrieval_workers
] )
def __lowerCAmelCase ( self : List[Any] ) ->Tuple:
"""simple docstring"""
logger.info('''initializing retrieval''' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple ) ->Dict:
"""simple docstring"""
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
a = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
a , a = ray.get(random_worker.retrieve.remote(__UpperCAmelCase , __UpperCAmelCase ) )
else:
a , a = self._main_retrieve(__UpperCAmelCase , __UpperCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCAmelCase )
@classmethod
def __lowerCAmelCase ( cls : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple=None , **__UpperCAmelCase : Optional[int] ) ->List[Any]:
"""simple docstring"""
return super(__UpperCAmelCase , cls ).get_tokenizers(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
@classmethod
def __lowerCAmelCase ( cls : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : int ) ->str:
"""simple docstring"""
a = kwargs.pop('''config''' , __UpperCAmelCase ) or RagConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
a = RagTokenizer.from_pretrained(__UpperCAmelCase , config=__UpperCAmelCase )
a = rag_tokenizer.question_encoder
a = rag_tokenizer.generator
if indexed_dataset is not None:
a = '''custom'''
a = CustomHFIndex(config.retrieval_vector_size , __UpperCAmelCase )
else:
a = cls._build_index(__UpperCAmelCase )
return cls(
__UpperCAmelCase , question_encoder_tokenizer=__UpperCAmelCase , generator_tokenizer=__UpperCAmelCase , retrieval_workers=__UpperCAmelCase , index=__UpperCAmelCase , )
| 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTBigCodeForSequenceClassification",
"GPTBigCodeForTokenClassification",
"GPTBigCodeForCausalLM",
"GPTBigCodeModel",
"GPTBigCodePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
def _a ( a :int , a :list ) -> List[str]:
_enforce_args(a , a )
if n == 0:
return 0
a = float('''-inf''' )
for i in range(1 , n + 1 ):
a = max(
a , prices[i - 1] + naive_cut_rod_recursive(n - i , a ) )
return max_revue
def _a ( a :int , a :list ) -> int:
_enforce_args(a , a )
a = [float('''-inf''' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(a , a , a )
def _a ( a :int , a :list , a :list ) -> int:
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
a = float('''-inf''' )
for i in range(1 , n + 1 ):
a = max(
a , prices[i - 1] + _top_down_cut_rod_recursive(n - i , a , a ) , )
a = max_revenue
return max_rev[n]
def _a ( a :int , a :list ) -> str:
_enforce_args(a , a )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
a = [float('''-inf''' ) for _ in range(n + 1 )]
a = 0
for i in range(1 , n + 1 ):
a = max_rev[i]
for j in range(1 , i + 1 ):
a = max(a , prices[j - 1] + max_rev[i - j] )
a = max_revenue_i
return max_rev[n]
def _a ( a :int , a :list ) -> List[Any]:
if n < 0:
a = F"""n must be greater than or equal to 0. Got n = {n}"""
raise ValueError(a )
if n > len(a ):
a = (
'''Each integral piece of rod must have a corresponding price. '''
F"""Got n = {n} but length of prices = {len(a )}"""
)
raise ValueError(a )
def _a ( ) -> int:
a = [6, 10, 12, 15, 20, 23]
a = len(a )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
a = 36
a = top_down_cut_rod(a , a )
a = bottom_up_cut_rod(a , a )
a = naive_cut_rod_recursive(a , a )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 355 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _a ( a :Tuple ) -> int:
a = tmp_path / '''file.csv'''
a = textwrap.dedent(
'''\
header1,header2
1,2
10,20
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :int ) -> List[str]:
a = tmp_path / '''malformed_file.csv'''
a = textwrap.dedent(
'''\
header1,header2
1,2
10,20,
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :Dict , a :int ) -> List[str]:
a = tmp_path / '''csv_with_image.csv'''
a = textwrap.dedent(
F"""\
image
{image_file}
""" )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :List[Any] ) -> Dict:
a = tmp_path / '''csv_with_label.csv'''
a = textwrap.dedent(
'''\
label
good
bad
good
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :Tuple ) -> Any:
a = tmp_path / '''csv_with_int_list.csv'''
a = textwrap.dedent(
'''\
int_list
1 2 3
4 5 6
7 8 9
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
def _a ( a :Dict , a :int , a :Union[str, Any] ) -> List[Any]:
a = Csv()
a = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(a , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(a ) in record.message
for record in caplog.records )
@require_pil
def _a ( a :Dict ) -> Any:
with open(a , encoding='''utf-8''' ) as f:
a = f.read().splitlines()[1]
a = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
a = csv._generate_tables([[csv_file_with_image]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
a = pa_table.to_pydict()['''image''']
assert generated_content == [{"path": image_file, "bytes": None}]
def _a ( a :Any ) -> Tuple:
with open(a , encoding='''utf-8''' ) as f:
a = f.read().splitlines()[1:]
a = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
a = csv._generate_tables([[csv_file_with_label]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
a = pa_table.to_pydict()['''label''']
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(a ) for label in labels]
def _a ( a :Union[str, Any] ) -> Optional[Any]:
a = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda a : [int(a ) for i in x.split()]} )
a = csv._generate_tables([[csv_file_with_int_list]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
a = pa_table.to_pydict()['''int_list''']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 26 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''image_processor''', '''tokenizer''']
__snake_case = '''CLIPImageProcessor'''
__snake_case = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Dict , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
a = kwargs.pop('''feature_extractor''' )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : str ) ->Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
a = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) ->Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def __lowerCAmelCase ( self : int ) ->List[str]:
"""simple docstring"""
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __UpperCAmelCase , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __UpperCAmelCase , )
return self.image_processor
| 356 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = torch.device("cpu")
def _a ( ) -> Union[str, Any]:
a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
a = Image.open(requests.get(a , stream=a ).raw )
return im
def _a ( a :Dict ) -> Tuple:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02] )
def _a ( a :int , a :Any , a :Union[str, Any] ) -> int:
a = dct.pop(a )
a = val
def _a ( a :Any ) -> Dict:
a = []
for k in state_dict.keys():
a = k
if ".pwconv" in k:
a = k_new.replace('''.pwconv''' , '''.point_wise_conv''' )
if ".dwconv" in k:
a = k_new.replace('''.dwconv''' , '''.depth_wise_conv''' )
if ".Proj." in k:
a = k_new.replace('''.Proj.''' , '''.proj.''' )
if "patch_embed" in k_new:
a = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
a = k_new.split('''.''' )
if ls[2].isdigit():
a = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
a = k_new.replace('''network''' , '''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def _a ( a :List[Any] , a :Tuple , a :List[str] ) -> Union[str, Any]:
a = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
a = 1_000
a = '''huggingface/label-files'''
a = '''imagenet-1k-id2label.json'''
a = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
a = {int(a ): v for k, v in idalabel.items()}
a = idalabel
a = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
a = [3, 3, 6, 4]
a = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
a = [3, 3, 9, 6]
a = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
a = [4, 3, 10, 5]
a = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
a = [4, 4, 12, 6]
a = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
a = torch.hub.load_state_dict_from_url(a , map_location='''cpu''' , check_hash=a )
else:
a = torch.load(a , map_location='''cpu''' )
a = checkpoint
a = create_rename_keys(a )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(a , a , a )
# load HuggingFace model
a = SwiftFormerForImageClassification(a ).eval()
hf_model.load_state_dict(a )
# prepare test inputs
a = prepare_img()
a = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
a = processor(images=a , return_tensors='''pt''' )
# compare outputs from both models
a = get_expected_output(a )
a = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 1_000] )
assert torch.allclose(hf_logits[0, 0:5] , a , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swiftformer_name",
default="swiftformer_xs",
choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
type=str,
help="Name of the SwiftFormer model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="./converted_outputs/",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
UpperCAmelCase__ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 26 | 0 |
def _a ( a :list ) -> list:
a = len(a )
for i in range(1 , a ):
a = collection[i]
a = 0
a = i - 1
while low <= high:
a = (low + high) // 2
if val < collection[mid]:
a = mid - 1
else:
a = mid + 1
for j in range(a , a , -1 ):
a = collection[j - 1]
a = val
return collection
if __name__ == "__main__":
UpperCAmelCase__ = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 357 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : DDPMScheduler , __UpperCAmelCase : Optional[int] , ) ->List[str]:
"""simple docstring"""
super().__init__()
a = value_function
a = unet
a = scheduler
a = env
a = env.get_dataset()
a = {}
for key in self.data.keys():
try:
a = self.data[key].mean()
except: # noqa: E722
pass
a = {}
for key in self.data.keys():
try:
a = self.data[key].std()
except: # noqa: E722
pass
a = env.observation_space.shape[0]
a = env.action_space.shape[0]
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) ->List[str]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __lowerCAmelCase ( self : int , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
if type(__UpperCAmelCase ) is dict:
return {k: self.to_torch(__UpperCAmelCase ) for k, v in x_in.items()}
elif torch.is_tensor(__UpperCAmelCase ):
return x_in.to(self.unet.device )
return torch.tensor(__UpperCAmelCase , device=self.unet.device )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple ) ->int:
"""simple docstring"""
for key, val in cond.items():
a = val.clone()
return x_in
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = x.shape[0]
a = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a = torch.full((batch_size,) , __UpperCAmelCase , device=self.unet.device , dtype=torch.long )
for _ in range(__UpperCAmelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a = self.value_function(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample
a = torch.autograd.grad([y.sum()] , [x] )[0]
a = self.scheduler._get_variance(__UpperCAmelCase )
a = torch.exp(0.5 * posterior_variance )
a = model_std * grad
a = 0
a = x.detach()
a = x + scale * grad
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.unet(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , predict_epsilon=__UpperCAmelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
return x, y
def __call__( self : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=64 , __UpperCAmelCase : int=32 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : str=0.1 ) ->List[str]:
"""simple docstring"""
a = self.normalize(__UpperCAmelCase , '''observations''' )
a = obs[None].repeat(__UpperCAmelCase , axis=0 )
a = {0: self.to_torch(__UpperCAmelCase )}
a = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a = randn_tensor(__UpperCAmelCase , device=self.unet.device )
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
# run the diffusion process
a , a = self.run_diffusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# sort output trajectories by value
a = y.argsort(0 , descending=__UpperCAmelCase ).squeeze()
a = x[sorted_idx]
a = sorted_values[:, :, : self.action_dim]
a = actions.detach().cpu().numpy()
a = self.de_normalize(__UpperCAmelCase , key='''actions''' )
# select the action with the highest value
if y is not None:
a = 0
else:
# if we didn't run value guiding, select a random action
a = np.random.randint(0 , __UpperCAmelCase )
a = denorm_actions[selected_index, 0]
return denorm_actions
| 26 | 0 |
class lowercase_:
'''simple docstring'''
def __init__( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a = {}
def __lowerCAmelCase ( self : str ) ->None:
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(__UpperCAmelCase , ''' -> ''' , ''' -> '''.join([str(__UpperCAmelCase ) for j in self.vertex[i]] ) )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : int , __UpperCAmelCase : int ) ->None:
"""simple docstring"""
if from_vertex in self.vertex:
self.vertex[from_vertex].append(__UpperCAmelCase )
else:
# else make a new vertex
a = [to_vertex]
def __lowerCAmelCase ( self : Dict ) ->None:
"""simple docstring"""
a = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : int , __UpperCAmelCase : int , __UpperCAmelCase : list ) ->None:
"""simple docstring"""
a = True
print(__UpperCAmelCase , end=''' ''' )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(__UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("DFS:")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 358 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model"}
UpperCAmelCase__ = {
"vocab_file": {
"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
}
}
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[str]="<s>" , __UpperCAmelCase : int="</s>" , __UpperCAmelCase : Any="<unk>" , __UpperCAmelCase : Optional[Any]="<sep>" , __UpperCAmelCase : int="<pad>" , __UpperCAmelCase : Any="<cls>" , __UpperCAmelCase : List[str]="<mask>" , __UpperCAmelCase : Optional[int]=["<eop>", "<eod>"] , __UpperCAmelCase : Optional[Dict[str, Any]] = None , **__UpperCAmelCase : Union[str, Any] , ) ->None:
"""simple docstring"""
a = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
a = 3
a = do_lower_case
a = remove_space
a = keep_accents
a = vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''' )
a = jieba
a = str.maketrans(''' \n''' , '''\u2582\u2583''' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
return len(self.sp_model )
def __lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
a = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
a = self.__dict__.copy()
a = None
return state
def __setstate__( self : List[str] , __UpperCAmelCase : Optional[int] ) ->str:
"""simple docstring"""
a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] ) ->List[str]:
"""simple docstring"""
if self.remove_space:
a = ''' '''.join(inputs.strip().split() )
else:
a = inputs
a = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
a = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
a = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
a = outputs.lower()
return outputs
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = self.preprocess_text(__UpperCAmelCase )
a = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
a = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
a = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
a = cur_pieces[1:]
else:
a = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCAmelCase )
else:
new_pieces.append(__UpperCAmelCase )
return new_pieces
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Any ) ->Any:
"""simple docstring"""
return self.sp_model.PieceToId(__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Dict ) ->Union[str, Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(__UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase , ''' ''' ).strip()
return out_string
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1, 1]
return ([0] * len(__UpperCAmelCase )) + [1, 1]
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , '''wb''' ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
def __lowerCAmelCase ( self : Any , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = super()._decode(*__UpperCAmelCase , **__UpperCAmelCase )
a = text.replace(''' ''' , '''''' ).replace('''\u2582''' , ''' ''' ).replace('''\u2583''' , '''\n''' )
return text
| 26 | 0 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt"}
UpperCAmelCase__ = {
"vocab_file": {
"openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
},
}
UpperCAmelCase__ = {
"openbmb/cpm-ant-10b": 1024,
}
def _a ( a :Dict ) -> List[Any]:
a = collections.OrderedDict()
with open(a , '''r''' , encoding='''utf-8''' ) as reader:
a = reader.readlines()
for index, token in enumerate(a ):
a = token.rstrip('''\n''' )
a = index
return vocab
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict="<unk>" , __UpperCAmelCase : str=200 ) ->Any:
"""simple docstring"""
a = vocab
a = unk_token
a = max_input_chars_per_word
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Optional[Any] ) ->str:
"""simple docstring"""
a = list(__UpperCAmelCase )
if len(__UpperCAmelCase ) > self.max_input_chars_per_word:
return [self.unk_token]
a = 0
a = []
while start < len(__UpperCAmelCase ):
a = len(__UpperCAmelCase )
a = None
while start < end:
a = ''''''.join(chars[start:end] )
if substr in self.vocab:
a = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(__UpperCAmelCase )
a = end
return sub_tokens
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = False
def __init__( self : Tuple , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[int]="<d>" , __UpperCAmelCase : int="</d>" , __UpperCAmelCase : Tuple="<s>" , __UpperCAmelCase : str="</s>" , __UpperCAmelCase : Dict="<pad>" , __UpperCAmelCase : int="<unk>" , __UpperCAmelCase : List[str]="</n>" , __UpperCAmelCase : Optional[int]="</_>" , __UpperCAmelCase : List[str]="left" , **__UpperCAmelCase : Optional[Any] , ) ->List[Any]:
"""simple docstring"""
requires_backends(self , ['''jieba'''] )
super().__init__(
bod_token=__UpperCAmelCase , eod_token=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , line_token=__UpperCAmelCase , space_token=__UpperCAmelCase , padding_side=__UpperCAmelCase , **__UpperCAmelCase , )
a = bod_token
a = eod_token
a = load_vocab(__UpperCAmelCase )
a = self.encoder[space_token]
a = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
a = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __UpperCAmelCase : x[1] ) )
a = {v: k for k, v in self.encoder.items()}
a = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def __lowerCAmelCase ( self : Any ) ->int:
"""simple docstring"""
return self.encoder[self.bod_token]
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Any:
"""simple docstring"""
return self.encoder[self.eod_token]
@property
def __lowerCAmelCase ( self : Any ) ->Tuple:
"""simple docstring"""
return self.encoder["\n"]
@property
def __lowerCAmelCase ( self : Optional[Any] ) ->int:
"""simple docstring"""
return len(self.encoder )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[int] ) ->str:
"""simple docstring"""
a = []
for x in jieba.cut(__UpperCAmelCase , cut_all=__UpperCAmelCase ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(__UpperCAmelCase ) )
return output_tokens
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : List[Any] , **__UpperCAmelCase : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
a = [i for i in token_ids if i >= 0]
a = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[str] ) ->Optional[Any]:
"""simple docstring"""
return token in self.encoder
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[str] ) ->str:
"""simple docstring"""
return "".join(__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : int ) ->Optional[Any]:
"""simple docstring"""
return self.encoder.get(__UpperCAmelCase , self.encoder.get(self.unk_token ) )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Optional[int] ) ->List[str]:
"""simple docstring"""
return self.decoder.get(__UpperCAmelCase , self.unk_token )
def __lowerCAmelCase ( self : str , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if os.path.isdir(__UpperCAmelCase ):
a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
a = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
a = 0
if " " in self.encoder:
a = self.encoder[''' ''']
del self.encoder[" "]
if "\n" in self.encoder:
a = self.encoder['''\n''']
del self.encoder["\n"]
a = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __UpperCAmelCase : x[1] ) )
with open(__UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
''' Please check that the vocabulary is not corrupted!''' )
a = token_index
writer.write(token + '''\n''' )
index += 1
return (vocab_file,)
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : List[int] = None ) ->List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return [1] + ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase ))
return [1] + ([0] * len(__UpperCAmelCase ))
| 359 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _a ( a :Union[str, Any] , a :List[Any] ) -> List[Any]:
a = checkpoint
a = {}
a = vae_state_dict['''encoder.conv_in.weight''']
a = vae_state_dict['''encoder.conv_in.bias''']
a = vae_state_dict['''encoder.conv_out.weight''']
a = vae_state_dict['''encoder.conv_out.bias''']
a = vae_state_dict['''encoder.norm_out.weight''']
a = vae_state_dict['''encoder.norm_out.bias''']
a = vae_state_dict['''decoder.conv_in.weight''']
a = vae_state_dict['''decoder.conv_in.bias''']
a = vae_state_dict['''decoder.conv_out.weight''']
a = vae_state_dict['''decoder.conv_out.bias''']
a = vae_state_dict['''decoder.norm_out.weight''']
a = vae_state_dict['''decoder.norm_out.bias''']
a = vae_state_dict['''quant_conv.weight''']
a = vae_state_dict['''quant_conv.bias''']
a = vae_state_dict['''post_quant_conv.weight''']
a = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(a )
}
# Retrieves the keys for the decoder up blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(a )
}
for i in range(a ):
a = [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]
if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.weight""" )
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.bias""" )
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
for i in range(a ):
a = num_up_blocks - 1 - i
a = [
key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
]
if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.weight"""
]
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.bias"""
]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
return new_checkpoint
def _a ( a :str , a :str , ) -> List[str]:
# Only support V1
a = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
a = io.BytesIO(r.content )
a = OmegaConf.load(a )
a = 512
a = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
a = {}
with safe_open(a , framework='''pt''' , device='''cpu''' ) as f:
for key in f.keys():
a = f.get_tensor(a )
else:
a = torch.load(a , map_location=a )['''state_dict''']
# Convert the VAE model.
a = create_vae_diffusers_config(a , image_size=a )
a = custom_convert_ldm_vae_checkpoint(a , a )
a = AutoencoderKL(**a )
vae.load_state_dict(a )
vae.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
UpperCAmelCase__ = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 26 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 360 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''image_processor''', '''tokenizer''']
__snake_case = '''CLIPImageProcessor'''
__snake_case = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Dict , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
a = kwargs.pop('''feature_extractor''' )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : str ) ->Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
a = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) ->Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def __lowerCAmelCase ( self : int ) ->List[str]:
"""simple docstring"""
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __UpperCAmelCase , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __UpperCAmelCase , )
return self.image_processor
| 26 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : int , __UpperCAmelCase : str , __UpperCAmelCase : Optional[Any]=1 , __UpperCAmelCase : Any=False , **__UpperCAmelCase : str ) ->Optional[int]:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
a = vocab_size
a = d_embed
a = d_proj
a = cutoffs + [vocab_size]
a = [0] + self.cutoffs
a = div_val
a = self.cutoffs[0]
a = len(self.cutoffs ) - 1
a = self.shortlist_size + self.n_clusters
a = keep_order
a = []
a = []
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
if self.n_clusters > 0:
a = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__UpperCAmelCase , name='''cluster_weight''' )
a = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__UpperCAmelCase , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
a = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__UpperCAmelCase , name=F"""out_projs_._{i}""" , )
self.out_projs.append(__UpperCAmelCase )
else:
self.out_projs.append(__UpperCAmelCase )
a = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__UpperCAmelCase , name=F"""out_layers_._{i}_._weight""" , )
a = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__UpperCAmelCase , name=F"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
a , a = self.cutoff_ends[i], self.cutoff_ends[i + 1]
a = self.d_embed // (self.div_val**i)
a = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__UpperCAmelCase , name=F"""out_projs_._{i}""" )
self.out_projs.append(__UpperCAmelCase )
a = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__UpperCAmelCase , name=F"""out_layers_._{i}_._weight""" , )
a = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__UpperCAmelCase , name=F"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
super().build(__UpperCAmelCase )
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple=None ) ->Dict:
"""simple docstring"""
a = x
if proj is not None:
a = tf.einsum('''ibd,ed->ibe''' , __UpperCAmelCase , __UpperCAmelCase )
return tf.einsum('''ibd,nd->ibn''' , __UpperCAmelCase , __UpperCAmelCase ) + b
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : List[Any] , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
a = shape_list(__UpperCAmelCase )
a = tf.range(lp_size[0] , dtype=target.dtype )
a = tf.stack([r, target] , 1 )
return tf.gather_nd(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : Union[str, Any]=False ) ->str:
"""simple docstring"""
a = 0
if self.n_clusters == 0:
a = self._logit(__UpperCAmelCase , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
a = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__UpperCAmelCase , logits=__UpperCAmelCase )
a = tf.nn.log_softmax(__UpperCAmelCase , axis=-1 )
else:
a = shape_list(__UpperCAmelCase )
a = []
a = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
a , a = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
a = (target >= l_idx) & (target < r_idx)
a = tf.where(__UpperCAmelCase )
a = tf.boolean_mask(__UpperCAmelCase , __UpperCAmelCase ) - l_idx
if self.div_val == 1:
a = self.out_layers[0][0][l_idx:r_idx]
a = self.out_layers[0][1][l_idx:r_idx]
else:
a = self.out_layers[i][0]
a = self.out_layers[i][1]
if i == 0:
a = tf.concat([cur_W, self.cluster_weight] , 0 )
a = tf.concat([cur_b, self.cluster_bias] , 0 )
a = self._logit(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , self.out_projs[0] )
a = tf.nn.log_softmax(__UpperCAmelCase )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
a = tf.boolean_mask(__UpperCAmelCase , __UpperCAmelCase )
a = self._gather_logprob(__UpperCAmelCase , __UpperCAmelCase )
else:
a = self._logit(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , self.out_projs[i] )
a = tf.nn.log_softmax(__UpperCAmelCase )
a = self.cutoffs[0] + i - 1 # No probability for the head cluster
a = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__UpperCAmelCase )
if target is not None:
a = tf.boolean_mask(__UpperCAmelCase , __UpperCAmelCase )
a = tf.boolean_mask(__UpperCAmelCase , __UpperCAmelCase )
a = self._gather_logprob(__UpperCAmelCase , __UpperCAmelCase )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__UpperCAmelCase , -cur_logprob , shape_list(__UpperCAmelCase ) )
a = tf.concat(__UpperCAmelCase , axis=-1 )
if target is not None:
if return_mean:
a = tf.reduce_mean(__UpperCAmelCase )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__UpperCAmelCase )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__UpperCAmelCase , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 361 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 26 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCAmelCase__ = "\nimport os\n"
UpperCAmelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCAmelCase__ = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
UpperCAmelCase__ = "\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n"
UpperCAmelCase__ = "\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n"
UpperCAmelCase__ = "\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n"
UpperCAmelCase__ = "\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n"
UpperCAmelCase__ = "\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n"
UpperCAmelCase__ = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n"
UpperCAmelCase__ = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n"
UpperCAmelCase__ = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case''' , a )
def _a ( a :Any , a :Optional[Any] ) -> str:
a = os.path.join(a , '''test_file.py''' )
with open(a , '''w''' ) as _tmp_file:
_tmp_file.write(a )
a = get_imports(a )
assert parsed_imports == ["os"]
| 362 |
from __future__ import annotations
import typing
from collections import Counter
def _a ( a :int ) -> typing.Counter[int]:
a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a , max_perimeter + 1 ):
a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a ):
a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def _a ( a :int = 1_000 ) -> int:
a = pythagorean_triple(a )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f"""Perimeter {solution()} has maximum solutions""")
| 26 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''pixel_values''']
def __init__( self : Optional[int] , __UpperCAmelCase : bool = True , __UpperCAmelCase : Dict[str, int] = None , __UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , __UpperCAmelCase : bool = True , __UpperCAmelCase : Dict[str, int] = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : Union[int, float] = 1 / 255 , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , __UpperCAmelCase : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **__UpperCAmelCase : str , ) ->None:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
a = size if size is not None else {'''shortest_edge''': 224}
a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
a = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
a = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' )
a = do_resize
a = size
a = resample
a = do_center_crop
a = crop_size
a = do_rescale
a = rescale_factor
a = do_normalize
a = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
a = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Dict[str, int] , __UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Tuple , ) ->np.ndarray:
"""simple docstring"""
a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
a = int((256 / 224) * size['''shortest_edge'''] )
a = get_resize_output_image_size(__UpperCAmelCase , size=__UpperCAmelCase , default_to_square=__UpperCAmelCase )
a = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
__UpperCAmelCase , size=(size_dict['''height'''], size_dict['''width''']) , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Dict[str, int] , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Optional[int] , ) ->np.ndarray:
"""simple docstring"""
a = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(__UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Union[int, float] , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Optional[Any] , ) ->np.ndarray:
"""simple docstring"""
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : str , __UpperCAmelCase : np.ndarray , __UpperCAmelCase : Union[float, List[float]] , __UpperCAmelCase : Union[float, List[float]] , __UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **__UpperCAmelCase : Union[str, Any] , ) ->np.ndarray:
"""simple docstring"""
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : ImageInput , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[Dict[str, int]] = None , __UpperCAmelCase : PILImageResampling = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[Dict[str, int]] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[float] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[Union[float, Iterable[float]]] = None , __UpperCAmelCase : Optional[Union[float, Iterable[float]]] = None , __UpperCAmelCase : Optional[TensorType] = None , __UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **__UpperCAmelCase : List[Any] , ) ->BatchFeature:
"""simple docstring"""
a = do_resize if do_resize is not None else self.do_resize
a = resample if resample is not None else self.resample
a = do_center_crop if do_center_crop is not None else self.do_center_crop
a = do_rescale if do_rescale is not None else self.do_rescale
a = rescale_factor if rescale_factor is not None else self.rescale_factor
a = do_normalize if do_normalize is not None else self.do_normalize
a = image_mean if image_mean is not None else self.image_mean
a = image_std if image_std is not None else self.image_std
a = size if size is not None else self.size
a = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
a = crop_size if crop_size is not None else self.crop_size
a = get_size_dict(__UpperCAmelCase , param_name='''crop_size''' )
a = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
a = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
a = [self.resize(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for image in images]
if do_center_crop:
a = [self.center_crop(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
if do_rescale:
a = [self.rescale(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
if do_normalize:
a = [self.normalize(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for image in images]
a = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
a = {'''pixel_values''': images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 363 |
from __future__ import annotations
def _a ( a :dict , a :str ) -> set[str]:
a , a = set(a ), [start]
while stack:
a = stack.pop()
explored.add(a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(a )
return explored
UpperCAmelCase__ = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 26 | 0 |
from math import sqrt
def _a ( a :int ) -> int:
a = 0
for i in range(1 , int(sqrt(a ) + 1 ) ):
if n % i == 0 and i != sqrt(a ):
total += i + n // i
elif i == sqrt(a ):
total += i
return total - n
def _a ( a :int = 10_000 ) -> int:
a = sum(
i
for i in range(1 , a )
if sum_of_divisors(sum_of_divisors(a ) ) == i and sum_of_divisors(a ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 364 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase__ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase__ = 10
UpperCAmelCase__ = 256
def _a ( a :List[str] ) -> Optional[MinHash]:
if len(a ) < MIN_NUM_TOKENS:
return None
a = MinHash(num_perm=a )
for token in set(a ):
min_hash.update(token.encode() )
return min_hash
def _a ( a :str ) -> Set[str]:
return {t for t in NON_ALPHA.split(a ) if len(t.strip() ) > 0}
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , *,
__UpperCAmelCase : float = 0.85 , ) ->Dict:
"""simple docstring"""
a = duplication_jaccard_threshold
a = NUM_PERM
a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
a = defaultdict(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : MinHash ) ->None:
"""simple docstring"""
a = self._index.query(__UpperCAmelCase )
if code_key in self._index.keys:
print(F"""Duplicate key {code_key}""" )
return
self._index.insert(__UpperCAmelCase , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase )
def __lowerCAmelCase ( self : Dict ) ->List[List[Dict]]:
"""simple docstring"""
a = []
for base, duplicates in self._duplicate_clusters.items():
a = [base] + list(__UpperCAmelCase )
# reformat the cluster to be a list of dict
a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__UpperCAmelCase )
return duplicate_clusters
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict ) ->None:
"""simple docstring"""
a = self.get_duplicate_clusters()
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def _a ( a :List[Any] ) -> List[Any]:
a , a = element
a = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _a ( a :Type[Dataset] ) -> List[Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(a , max_queue_size=10_000 ) , chunksize=100 , ):
if data is not None:
yield data
def _a ( a :Type[Dataset] , a :float ) -> str:
a = DuplicationIndex(duplication_jaccard_threshold=a )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a ) ) , max_queue_size=100 ) ):
di.add(a , a )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _a ( a :str , a :str ) -> float:
a = get_tokens(a )
a = get_tokens(a )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase__ = None
def _a ( a :Tuple , a :Tuple ) -> Any:
a = []
for elementa in cluster:
a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(a , a ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
a = 1
extremes.append(a )
return extremes
def _a ( a :List[Any] , a :Optional[Any] , a :Union[str, Any] ) -> Optional[int]:
global _shared_dataset
a = dataset
a = []
a = partial(_find_cluster_extremes_shared , jaccard_threshold=a )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
a , a , ) , total=len(a ) , ):
extremes_list.append(a )
return extremes_list
def _a ( a :Type[Dataset] , a :float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
a = make_duplicate_clusters(a , a )
a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
a = {}
a = find_extremes(a , a , a )
for extremes in extremes_clusters:
for element in extremes:
a = element
a = duplicate_indices - set(extreme_dict.keys() )
a = dataset.filter(lambda a , a : idx not in remove_indices , with_indices=a )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
a = extreme_dict[element['''base_index''']]['''copies''']
print(F"""Original dataset size: {len(a )}""" )
print(F"""Number of duplicate clusters: {len(a )}""" )
print(F"""Files in duplicate cluster: {len(a )}""" )
print(F"""Unique files in duplicate cluster: {len(a )}""" )
print(F"""Filtered dataset size: {len(a )}""" )
return ds_filter, duplicate_clusters
| 26 | 0 |
def _a ( a :int ) -> bool:
if not isinstance(a , a ):
a = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a )
if number < 0:
return False
a = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 365 |
from math import ceil, sqrt
def _a ( a :int = 1_000_000 ) -> int:
a = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
a = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 366 |
UpperCAmelCase__ = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 26 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''time_series_transformer'''
__snake_case = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : str , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : str = "student_t" , __UpperCAmelCase : str = "nll" , __UpperCAmelCase : int = 1 , __UpperCAmelCase : List[int] = [1, 2, 3, 4, 5, 6, 7] , __UpperCAmelCase : Optional[Union[str, bool]] = "mean" , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 , __UpperCAmelCase : int = 0 , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : int = 32 , __UpperCAmelCase : int = 32 , __UpperCAmelCase : int = 2 , __UpperCAmelCase : int = 2 , __UpperCAmelCase : int = 2 , __UpperCAmelCase : int = 2 , __UpperCAmelCase : bool = True , __UpperCAmelCase : str = "gelu" , __UpperCAmelCase : int = 64 , __UpperCAmelCase : float = 0.1 , __UpperCAmelCase : float = 0.1 , __UpperCAmelCase : float = 0.1 , __UpperCAmelCase : float = 0.1 , __UpperCAmelCase : float = 0.1 , __UpperCAmelCase : int = 100 , __UpperCAmelCase : float = 0.02 , __UpperCAmelCase : List[Any]=True , **__UpperCAmelCase : Dict , ) ->int:
"""simple docstring"""
a = prediction_length
a = context_length or prediction_length
a = distribution_output
a = loss
a = input_size
a = num_time_features
a = lags_sequence
a = scaling
a = num_dynamic_real_features
a = num_static_real_features
a = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(__UpperCAmelCase ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
a = cardinality
else:
a = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(__UpperCAmelCase ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
a = embedding_dimension
else:
a = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
a = num_parallel_samples
# Transformer architecture configuration
a = input_size * len(__UpperCAmelCase ) + self._number_of_features
a = d_model
a = encoder_attention_heads
a = decoder_attention_heads
a = encoder_ffn_dim
a = decoder_ffn_dim
a = encoder_layers
a = decoder_layers
a = dropout
a = attention_dropout
a = activation_dropout
a = encoder_layerdrop
a = decoder_layerdrop
a = activation_function
a = init_std
a = use_cache
super().__init__(is_encoder_decoder=__UpperCAmelCase , **__UpperCAmelCase )
@property
def __lowerCAmelCase ( self : Tuple ) ->int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 367 |
def _a ( a :list ) -> list:
if len(a ) <= 1:
return lst
a = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a , a = lst[i], lst[i - 1]
i -= 1
if i == 0:
a = 1
return lst
if __name__ == "__main__":
UpperCAmelCase__ = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 26 | 0 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
UpperCAmelCase__ = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class lowercase_ :
'''simple docstring'''
def __init__( self : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Any=16 , __UpperCAmelCase : Any=13 , __UpperCAmelCase : Any=7 , __UpperCAmelCase : Optional[Any]=14 , __UpperCAmelCase : Union[str, Any]=10 , __UpperCAmelCase : Tuple=19 , __UpperCAmelCase : Any=5 , __UpperCAmelCase : Optional[int]=4 , __UpperCAmelCase : int=True , __UpperCAmelCase : List[str]=16 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : Any=4 , __UpperCAmelCase : Tuple=4 , __UpperCAmelCase : Any="gelu" , __UpperCAmelCase : Optional[Any]=0.1 , __UpperCAmelCase : Tuple=0.1 , __UpperCAmelCase : Optional[int]=[1, 2, 3, 4, 5] , __UpperCAmelCase : List[Any]=25 , __UpperCAmelCase : Union[str, Any]=5 , ) ->Optional[Any]:
"""simple docstring"""
a = d_model
a = parent
a = batch_size
a = prediction_length
a = context_length
a = cardinality
a = num_time_features
a = lags_sequence
a = embedding_dimension
a = is_training
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = context_length
a = prediction_length + label_length
a = label_length
a = moving_average
a = autocorrelation_factor
def __lowerCAmelCase ( self : List[Any] ) ->Optional[Any]:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def __lowerCAmelCase ( self : str , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
a = config.context_length + max(config.lags_sequence )
a = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
a = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
a = floats_tensor([self.batch_size, _past_length] )
a = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
a = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
a = floats_tensor([self.batch_size, config.prediction_length] )
a = {
'''past_values''': past_values,
'''static_categorical_features''': static_categorical_features,
'''past_time_features''': past_time_features,
'''past_observed_mask''': past_observed_mask,
'''future_time_features''': future_time_features,
'''future_values''': future_values,
}
return inputs_dict
def __lowerCAmelCase ( self : int ) ->int:
"""simple docstring"""
a = self.get_config()
a = self.prepare_autoformer_inputs_dict(__UpperCAmelCase )
return config, inputs_dict
def __lowerCAmelCase ( self : str ) ->List[str]:
"""simple docstring"""
a , a = self.prepare_config_and_inputs()
return config, inputs_dict
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple ) ->Union[str, Any]:
"""simple docstring"""
a = AutoformerModel(config=__UpperCAmelCase ).to(__UpperCAmelCase ).eval()
a = model(**__UpperCAmelCase )
a = outputs.encoder_last_hidden_state
a = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
a = model.get_encoder()
encoder.save_pretrained(__UpperCAmelCase )
a = AutoformerEncoder.from_pretrained(__UpperCAmelCase ).to(__UpperCAmelCase )
a , a , a , a , a = model.create_network_inputs(**__UpperCAmelCase )
a , a = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
a = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
a = encoder(inputs_embeds=__UpperCAmelCase )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
a = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
a = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
a = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
a = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
a = model.get_decoder()
decoder.save_pretrained(__UpperCAmelCase )
a = AutoformerDecoder.from_pretrained(__UpperCAmelCase ).to(__UpperCAmelCase )
a = decoder(
trend=__UpperCAmelCase , inputs_embeds=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase_ ( lowercase , lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__snake_case = (AutoformerForPrediction,) if is_torch_available() else ()
__snake_case = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = AutoformerModelTester(self )
a = ConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
a = model_class(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCAmelCase )
a , a = model_class.from_pretrained(__UpperCAmelCase , output_loading_info=__UpperCAmelCase )
self.assertEqual(info['''missing_keys'''] , [] )
def __lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__UpperCAmelCase )
@unittest.skip(reason='''Model has no tokens embeddings''' )
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Optional[int] ) ->Dict:
"""simple docstring"""
a = inspect.signature(getattr(__UpperCAmelCase , '''forward''' ) )
# The main input is the name of the argument after `self`
a = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , __UpperCAmelCase )
def __lowerCAmelCase ( self : Dict ) ->int:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__UpperCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = [
'''past_values''',
'''past_time_features''',
'''past_observed_mask''',
'''static_categorical_features''',
'''static_real_features''',
'''future_values''',
'''future_time_features''',
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('''future_observed_mask''' )
expected_arg_names.extend(
[
'''decoder_attention_mask''',
'''head_mask''',
'''decoder_head_mask''',
'''cross_attn_head_mask''',
'''encoder_outputs''',
'''past_key_values''',
'''output_hidden_states''',
'''output_attentions''',
'''use_cache''',
'''return_dict''',
] )
self.assertListEqual(arg_names[: len(__UpperCAmelCase )] , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = True
a = getattr(self.model_tester , '''seq_length''' , __UpperCAmelCase )
a = getattr(self.model_tester , '''decoder_seq_length''' , __UpperCAmelCase )
a = getattr(self.model_tester , '''encoder_seq_length''' , __UpperCAmelCase )
a = getattr(self.model_tester , '''d_model''' , __UpperCAmelCase )
a = getattr(self.model_tester , '''num_attention_heads''' , __UpperCAmelCase )
a = d_model // num_attention_heads
for model_class in self.all_model_classes:
a = True
a = False
a = True
a = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
a = True
a = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
a = outputs.encoder_attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
a = len(__UpperCAmelCase )
a = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
# decoder attentions
a = outputs.decoder_attentions
self.assertIsInstance(__UpperCAmelCase , (list, tuple) )
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
a = outputs.cross_attentions
self.assertIsInstance(__UpperCAmelCase , (list, tuple) )
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
a = True
a = True
a = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 2 , len(__UpperCAmelCase ) )
a = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def __lowerCAmelCase ( self : Any ) ->List[str]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def _a ( a :List[str]="train-batch.pt" ) -> List[Any]:
a = hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''' , filename=a , repo_type='''dataset''' )
a = torch.load(a , map_location=a )
return batch
@require_torch
@slow
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) ->Any:
"""simple docstring"""
a = AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(__UpperCAmelCase )
a = prepare_batch()
with torch.no_grad():
a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , future_values=batch['''future_values'''] , future_time_features=batch['''future_time_features'''] , )[0]
a = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
a = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=__UpperCAmelCase )
self.assertTrue(torch.allclose(output[0, :3, :3] , __UpperCAmelCase , atol=__UpperCAmelCase ) )
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(__UpperCAmelCase )
a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
a = model(
past_values=batch['''past_values'''] , past_time_features=batch['''past_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , static_categorical_features=batch['''static_categorical_features'''] , ).encoder_last_hidden_state
a = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , __UpperCAmelCase )
a = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=__UpperCAmelCase )
self.assertTrue(torch.allclose(output[0, :3, :3] , __UpperCAmelCase , atol=__UpperCAmelCase ) )
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
a = AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''' ).to(__UpperCAmelCase )
a = prepare_batch('''val-batch.pt''' )
with torch.no_grad():
a = model.generate(
static_categorical_features=batch['''static_categorical_features'''] , past_time_features=batch['''past_time_features'''] , past_values=batch['''past_values'''] , future_time_features=batch['''future_time_features'''] , past_observed_mask=batch['''past_observed_mask'''] , )
a = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , __UpperCAmelCase )
a = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=__UpperCAmelCase )
a = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __UpperCAmelCase , rtol=1e-1 ) )
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple=7 , __UpperCAmelCase : str=3 , __UpperCAmelCase : Union[str, Any]=30 , __UpperCAmelCase : Optional[Any]=400 , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : int=None , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Optional[int]=1 / 255 , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Any=[0.5, 0.5, 0.5] , __UpperCAmelCase : Optional[int]=[0.5, 0.5, 0.5] , __UpperCAmelCase : Any=True , ) ->Dict:
"""simple docstring"""
a = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
a = parent
a = batch_size
a = num_channels
a = min_resolution
a = max_resolution
a = do_resize
a = size
a = do_rescale
a = rescale_factor
a = do_normalize
a = image_mean
a = image_std
a = do_pad
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def __lowerCAmelCase ( self : int , __UpperCAmelCase : Any , __UpperCAmelCase : Any=False ) ->int:
"""simple docstring"""
if not batched:
a = image_inputs[0]
if isinstance(__UpperCAmelCase , Image.Image ):
a , a = image.size
else:
a , a = image.shape[1], image.shape[2]
if w < h:
a = int(self.size['''shortest_edge'''] * h / w )
a = self.size['''shortest_edge''']
elif w > h:
a = self.size['''shortest_edge''']
a = int(self.size['''shortest_edge'''] * w / h )
else:
a = self.size['''shortest_edge''']
a = self.size['''shortest_edge''']
else:
a = []
for image in image_inputs:
a , a = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
a = max(__UpperCAmelCase , key=lambda __UpperCAmelCase : item[0] )[0]
a = max(__UpperCAmelCase , key=lambda __UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = DetrImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a = DetrImageProcessingTester(self )
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_std''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_rescale''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''rescale_factor''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''size''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_pad''' ) )
def __lowerCAmelCase ( self : Tuple ) ->Dict:
"""simple docstring"""
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1_333} )
self.assertEqual(image_processor.do_pad , __UpperCAmelCase )
a = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__UpperCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : List[Any] ) ->Any:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a , a = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self : Union[str, Any] ) ->List[str]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowerCAmelCase ( self : str ) ->List[Any]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
a , a = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __lowerCAmelCase ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
a = json.loads(f.read() )
a = {'''image_id''': 39_769, '''annotations''': target}
# encode them
a = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
a = image_processing(images=__UpperCAmelCase , annotations=__UpperCAmelCase , return_tensors='''pt''' )
# verify pixel values
a = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['''pixel_values'''].shape , __UpperCAmelCase )
a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __UpperCAmelCase , atol=1e-4 ) )
# verify area
a = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __UpperCAmelCase ) )
# verify boxes
a = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __UpperCAmelCase )
a = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __UpperCAmelCase , atol=1e-3 ) )
# verify image_id
a = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __UpperCAmelCase ) )
# verify is_crowd
a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __UpperCAmelCase ) )
# verify class_labels
a = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __UpperCAmelCase ) )
# verify orig_size
a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __UpperCAmelCase ) )
# verify size
a = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __UpperCAmelCase ) )
@slow
def __lowerCAmelCase ( self : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
a = json.loads(f.read() )
a = {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}
a = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
a = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
a = image_processing(images=__UpperCAmelCase , annotations=__UpperCAmelCase , masks_path=__UpperCAmelCase , return_tensors='''pt''' )
# verify pixel values
a = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['''pixel_values'''].shape , __UpperCAmelCase )
a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __UpperCAmelCase , atol=1e-4 ) )
# verify area
a = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __UpperCAmelCase ) )
# verify boxes
a = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __UpperCAmelCase )
a = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __UpperCAmelCase , atol=1e-3 ) )
# verify image_id
a = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __UpperCAmelCase ) )
# verify is_crowd
a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __UpperCAmelCase ) )
# verify class_labels
a = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __UpperCAmelCase ) )
# verify masks
a = 822_873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __UpperCAmelCase )
# verify orig_size
a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __UpperCAmelCase ) )
# verify size
a = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __UpperCAmelCase ) )
| 369 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
UpperCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _a ( a :str ) -> Any:
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
a = model_type_to_module_name(a )
a = importlib.import_module(F""".{module_name}""" , '''transformers.models''' )
try:
return getattr(a , a )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(a , '''__name__''' , a ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
a = importlib.import_module('''transformers''' )
if hasattr(a , a ):
return getattr(a , a )
return None
def _a ( a :Union[str, os.PathLike] , a :Optional[Union[str, os.PathLike]] = None , a :bool = False , a :bool = False , a :Optional[Dict[str, str]] = None , a :Optional[Union[bool, str]] = None , a :Optional[str] = None , a :bool = False , **a :int , ) -> Tuple:
a = get_file_from_repo(
a , a , cache_dir=a , force_download=a , resume_download=a , proxies=a , use_auth_token=a , revision=a , local_files_only=a , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(a , encoding='''utf-8''' ) as reader:
return json.load(a )
class lowercase_ :
'''simple docstring'''
def __init__( self : Tuple ) ->int:
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(__UpperCAmelCase )
def __lowerCAmelCase ( cls : int , __UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : Dict ) ->List[Any]:
"""simple docstring"""
a = kwargs.pop('''config''' , __UpperCAmelCase )
a = kwargs.pop('''trust_remote_code''' , __UpperCAmelCase )
a = True
a , a = FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase , **__UpperCAmelCase )
a = config_dict.get('''feature_extractor_type''' , __UpperCAmelCase )
a = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
a = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = AutoConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
# It could be in `config.feature_extractor_type``
a = getattr(__UpperCAmelCase , '''feature_extractor_type''' , __UpperCAmelCase )
if hasattr(__UpperCAmelCase , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
a = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
a = feature_extractor_class_from_name(__UpperCAmelCase )
a = feature_extractor_auto_map is not None
a = feature_extractor_class is not None or type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
a = resolve_trust_remote_code(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if has_remote_code and trust_remote_code:
a = get_class_from_dynamic_module(
__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
a = kwargs.pop('''code_revision''' , __UpperCAmelCase )
if os.path.isdir(__UpperCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
a = FEATURE_EXTRACTOR_MAPPING[type(__UpperCAmelCase )]
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
raise ValueError(
F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple ) ->Optional[int]:
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(__UpperCAmelCase , __UpperCAmelCase )
| 26 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = '''levit'''
def __init__( self : str , __UpperCAmelCase : Dict=224 , __UpperCAmelCase : int=3 , __UpperCAmelCase : Dict=3 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : Dict=1 , __UpperCAmelCase : Optional[Any]=16 , __UpperCAmelCase : Optional[int]=[128, 256, 384] , __UpperCAmelCase : Optional[int]=[4, 8, 12] , __UpperCAmelCase : Dict=[4, 4, 4] , __UpperCAmelCase : Dict=[16, 16, 16] , __UpperCAmelCase : Optional[int]=0 , __UpperCAmelCase : Optional[int]=[2, 2, 2] , __UpperCAmelCase : Optional[int]=[2, 2, 2] , __UpperCAmelCase : Dict=0.02 , **__UpperCAmelCase : Dict , ) ->int:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
a = image_size
a = num_channels
a = kernel_size
a = stride
a = padding
a = hidden_sizes
a = num_attention_heads
a = depths
a = key_dim
a = drop_path_rate
a = patch_size
a = attention_ratio
a = mlp_ratio
a = initializer_range
a = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = version.parse('''1.11''' )
@property
def __lowerCAmelCase ( self : str ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCAmelCase ( self : int ) ->float:
"""simple docstring"""
return 1e-4
| 370 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a = tempfile.mkdtemp()
a = BlipImageProcessor()
a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
a = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
a = InstructBlipProcessor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).tokenizer
def __lowerCAmelCase ( self : int , **__UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).image_processor
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Any ) ->Optional[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).qformer_tokenizer
def __lowerCAmelCase ( self : str ) ->Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
a = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
a = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = self.prepare_image_inputs()
a = image_processor(__UpperCAmelCase , return_tensors='''np''' )
a = processor(images=__UpperCAmelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = processor(text=__UpperCAmelCase )
a = tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
a = qformer_tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key] )
def __lowerCAmelCase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def __lowerCAmelCase ( self : Dict ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a = processor.batch_decode(__UpperCAmelCase )
a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 26 | 0 |
from __future__ import annotations
from collections.abc import Callable
def _a ( a :Callable[[int | float], int | float] , a :int | float , a :int | float , a :int = 100 , ) -> float:
a = x_start
a = fnc(a )
a = 0.0
for _ in range(a ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
a = (x_end - x_start) / steps + xa
a = fnc(a )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
a = xa
a = fxa
return area
if __name__ == "__main__":
def _a ( a :int ) -> Tuple:
return x**3 + x**2
print("f(x) = x^3 + x^2")
print("The area between the curve, x = -5, x = 5 and the x axis is:")
UpperCAmelCase__ = 10
while i <= 100000:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 10
| 371 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model"}
UpperCAmelCase__ = {
"vocab_file": {
"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
}
}
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[str]="<s>" , __UpperCAmelCase : int="</s>" , __UpperCAmelCase : Any="<unk>" , __UpperCAmelCase : Optional[Any]="<sep>" , __UpperCAmelCase : int="<pad>" , __UpperCAmelCase : Any="<cls>" , __UpperCAmelCase : List[str]="<mask>" , __UpperCAmelCase : Optional[int]=["<eop>", "<eod>"] , __UpperCAmelCase : Optional[Dict[str, Any]] = None , **__UpperCAmelCase : Union[str, Any] , ) ->None:
"""simple docstring"""
a = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
a = 3
a = do_lower_case
a = remove_space
a = keep_accents
a = vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''' )
a = jieba
a = str.maketrans(''' \n''' , '''\u2582\u2583''' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
return len(self.sp_model )
def __lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
a = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
a = self.__dict__.copy()
a = None
return state
def __setstate__( self : List[str] , __UpperCAmelCase : Optional[int] ) ->str:
"""simple docstring"""
a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] ) ->List[str]:
"""simple docstring"""
if self.remove_space:
a = ''' '''.join(inputs.strip().split() )
else:
a = inputs
a = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
a = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
a = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
a = outputs.lower()
return outputs
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = self.preprocess_text(__UpperCAmelCase )
a = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
a = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
a = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
a = cur_pieces[1:]
else:
a = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCAmelCase )
else:
new_pieces.append(__UpperCAmelCase )
return new_pieces
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Any ) ->Any:
"""simple docstring"""
return self.sp_model.PieceToId(__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Dict ) ->Union[str, Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(__UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase , ''' ''' ).strip()
return out_string
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1, 1]
return ([0] * len(__UpperCAmelCase )) + [1, 1]
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , '''wb''' ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
def __lowerCAmelCase ( self : Any , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = super()._decode(*__UpperCAmelCase , **__UpperCAmelCase )
a = text.replace(''' ''' , '''''' ).replace('''\u2582''' , ''' ''' ).replace('''\u2583''' , '''\n''' )
return text
| 350 |
def _a ( a :int = 600_851_475_143 ) -> int:
try:
a = int(a )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
a = 2
a = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
a = i
while n % i == 0:
a = n // i
i += 1
return int(a )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class lowercase_ ( lowercase , unittest.TestCase ):
__snake_case = VideoToVideoSDPipeline
__snake_case = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''} ) - {'''image''', '''width''', '''height'''}
__snake_case = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''} ) - {'''image'''}
__snake_case = PipelineTesterMixin.required_optional_params - {'''latents'''}
__snake_case = False
# No `output_type`.
__snake_case = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def __lowerCAmelCase ( self : Tuple ) ->Dict:
"""simple docstring"""
torch.manual_seed(0 )
a = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''') , up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''') , cross_attention_dim=32 , attention_head_dim=4 , )
a = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__UpperCAmelCase , set_alpha_to_one=__UpperCAmelCase , )
torch.manual_seed(0 )
a = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act='''gelu''' , projection_dim=512 , )
a = CLIPTextModel(__UpperCAmelCase )
a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
}
return components
def __lowerCAmelCase ( self : str , __UpperCAmelCase : int , __UpperCAmelCase : Optional[Any]=0 ) ->Optional[Any]:
"""simple docstring"""
a = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
if str(__UpperCAmelCase ).startswith('''mps''' ):
a = torch.manual_seed(__UpperCAmelCase )
else:
a = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''video''': video,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''pt''',
}
return inputs
def __lowerCAmelCase ( self : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = VideoToVideoSDPipeline(**__UpperCAmelCase )
a = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_dummy_inputs(__UpperCAmelCase )
a = '''np'''
a = sd_pipe(**__UpperCAmelCase ).frames
a = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
a = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __lowerCAmelCase ( self : List[Any] ) ->Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCAmelCase , expected_max_diff=5e-3 )
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def __lowerCAmelCase ( self : List[str] ) ->Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
def __lowerCAmelCase ( self : List[str] ) ->Tuple:
"""simple docstring"""
pass
@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
def __lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Dict ) ->str:
"""simple docstring"""
return super().test_progress_bar()
@slow
@skip_mps
class lowercase_ ( unittest.TestCase ):
def __lowerCAmelCase ( self : Union[str, Any] ) ->str:
"""simple docstring"""
a = VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL''' , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
a = torch.Generator(device='''cpu''' ).manual_seed(0 )
a = torch.randn((1, 10, 3, 1_024, 576) , generator=__UpperCAmelCase )
a = video.to('''cuda''' )
a = '''Spiderman is surfing'''
a = pipe(__UpperCAmelCase , video=__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=3 , output_type='''pt''' ).frames
a = np.array([-1.0458984, -1.1279297, -0.9663086, -0.91503906, -0.75097656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1e-2
| 351 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
UpperCAmelCase__ = "bart"
UpperCAmelCase__ = True
@st.cache(allow_output_mutation=a )
def _a ( ) -> Tuple:
if LOAD_DENSE_INDEX:
a = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
a = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
a = qar_model.eval()
else:
a , a = (None, None)
if MODEL_TYPE == "bart":
a = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
a = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
a = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
a = sas_model.eval()
else:
a , a = make_qa_sas_model(
model_name='''t5-small''' , from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''' , device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Dict:
if LOAD_DENSE_INDEX:
a = faiss.StandardGpuResources()
a = datasets.load_dataset(path='''wiki_snippets''' , name='''wiki40b_en_100_0''' )['''train''']
a = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''' , dtype='''float32''' , mode='''r''' , shape=(wikiaab_passages.num_rows, 128) , )
a = faiss.IndexFlatIP(128 )
a = faiss.index_cpu_to_gpu(a , 1 , a )
wikiaab_gpu_index_flat.add(a ) # TODO fix for larger GPU
else:
a , a = (None, None)
a = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=a )
def _a ( ) -> Optional[int]:
a = datasets.load_dataset('''eli5''' , name='''LFQA_reddit''' )
a = elia['''train_eli5''']
a = np.memmap(
'''eli5_questions_reps.dat''' , dtype='''float32''' , mode='''r''' , shape=(elia_train.num_rows, 128) )
a = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(a )
return (elia_train, eli5_train_q_index)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_indexes()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = load_models()
UpperCAmelCase__ , UpperCAmelCase__ = load_train_data()
def _a ( a :str , a :Tuple=10 ) -> List[str]:
a = embed_questions_for_retrieval([question] , a , a )
a , a = eli5_train_q_index.search(a , a )
a = [elia_train[int(a )] for i in I[0]]
return nn_examples
def _a ( a :str , a :Any="wiki40b" , a :int="dense" , a :Union[str, Any]=10 ) -> List[str]:
if source == "none":
a , a = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
a , a = query_qa_dense_index(
a , a , a , a , a , a )
else:
a , a = query_es_index(
a , a , index_name='''english_wiki40b_snippets_100w''' , n_results=a , )
a = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
a = '''question: {} context: {}'''.format(a , a )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda a : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a : None),
} )
def _a ( a :Tuple , a :int , a :int , a :Dict=64 , a :List[Any]=256 , a :List[Any]=False , a :List[Any]=2 , a :Tuple=0.95 , a :Optional[Any]=0.8 ) -> int:
with torch.no_grad():
a = qa_sas_generate(
a , a , a , num_answers=1 , num_beams=a , min_len=a , max_len=a , do_sample=a , temp=a , top_p=a , top_k=a , max_input_length=1_024 , device='''cuda:0''' , )[0]
return (answer, support_list)
st.title("Long Form Question Answering with ELI5")
# Start sidebar
UpperCAmelCase__ = "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
UpperCAmelCase__ = "\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class=\"img-container\"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
UpperCAmelCase__ = "\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)
UpperCAmelCase__ = [
"Answer the question",
"View the retrieved document only",
"View the most similar ELI5 question and answer",
"Show me everything, please!",
]
UpperCAmelCase__ = st.sidebar.checkbox("Demo options")
if demo_options:
UpperCAmelCase__ = st.sidebar.selectbox(
"",
action_list,
index=3,
)
UpperCAmelCase__ = action_list.index(action_st)
UpperCAmelCase__ = st.sidebar.selectbox(
"",
["Show full text of passages", "Show passage section titles"],
index=0,
)
UpperCAmelCase__ = show_type == "Show full text of passages"
else:
UpperCAmelCase__ = 3
UpperCAmelCase__ = True
UpperCAmelCase__ = st.sidebar.checkbox("Retrieval options")
if retrieval_options:
UpperCAmelCase__ = "\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n "
st.sidebar.markdown(retriever_info)
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
UpperCAmelCase__ = st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
UpperCAmelCase__ = "wiki40b"
UpperCAmelCase__ = "dense"
UpperCAmelCase__ = "beam"
UpperCAmelCase__ = 2
UpperCAmelCase__ = 64
UpperCAmelCase__ = 256
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = st.sidebar.checkbox("Generation options")
if generate_options:
UpperCAmelCase__ = "\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder's output probabilities.\n "
st.sidebar.markdown(generate_info)
UpperCAmelCase__ = st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
UpperCAmelCase__ = st.sidebar.slider(
"Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
UpperCAmelCase__ = st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
UpperCAmelCase__ = st.sidebar.slider(
"Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
UpperCAmelCase__ = st.sidebar.slider(
"Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
UpperCAmelCase__ = None
# start main text
UpperCAmelCase__ = [
"<MY QUESTION>",
"How do people make chocolate?",
"Why do we get a fever when we are sick?",
"How can different animals perceive different colors?",
"What is natural language processing?",
"What's the best way to treat a sunburn?",
"What exactly are vitamins ?",
"How does nuclear energy provide electricity?",
"What's the difference between viruses and bacteria?",
"Why are flutes classified as woodwinds when most of them are made out of metal ?",
"Why do people like drinking coffee even though it tastes so bad?",
"What happens when wine ages? How does it make the wine taste better?",
"If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
"How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
"How does New Zealand have so many large bird predators?",
]
UpperCAmelCase__ = st.selectbox(
"What would you like to ask? ---- select <MY QUESTION> to enter a new query",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
UpperCAmelCase__ = st.text_input("Enter your question here:", "")
else:
UpperCAmelCase__ = question_s
if st.button("Show me!"):
if action in [0, 1, 3]:
if index_type == "mixed":
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="dense", n_results=10)
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method="sparse", n_results=10)
UpperCAmelCase__ = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
UpperCAmelCase__ = support_list[:10]
UpperCAmelCase__ = "<P> " + " <P> ".join([res[-1] for res in support_list])
else:
UpperCAmelCase__ , UpperCAmelCase__ = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
UpperCAmelCase__ , UpperCAmelCase__ = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == "sampled"),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("### The model generated answer is:")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
for i, res in enumerate(support_list):
UpperCAmelCase__ = "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
UpperCAmelCase__ = res[1].strip()
if sec_titles == "":
UpperCAmelCase__ = "[{}]({})".format(res[0], wiki_url)
else:
UpperCAmelCase__ = sec_titles.split(" & ")
UpperCAmelCase__ = " & ".join(
["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
)
st.markdown(
"{0:02d} - **Article**: {1:<18} <br> _Section_: {2}".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
)
if action in [2, 3]:
UpperCAmelCase__ = find_nearest_training(question)
UpperCAmelCase__ = nn_train_list[0]
st.markdown(
"--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
)
UpperCAmelCase__ = [
"{}. {}".format(i + 1, " \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
if i == 0 or sc > 2
]
st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))
UpperCAmelCase__ = "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 26 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : Union[str, "sqlalchemy.sql.Selectable"] , __UpperCAmelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __UpperCAmelCase : Optional[Features] = None , __UpperCAmelCase : str = None , __UpperCAmelCase : bool = False , **__UpperCAmelCase : str , ) ->Any:
"""simple docstring"""
super().__init__(features=__UpperCAmelCase , cache_dir=__UpperCAmelCase , keep_in_memory=__UpperCAmelCase , **__UpperCAmelCase )
a = Sql(
cache_dir=__UpperCAmelCase , features=__UpperCAmelCase , sql=__UpperCAmelCase , con=__UpperCAmelCase , **__UpperCAmelCase , )
def __lowerCAmelCase ( self : Tuple ) ->Tuple:
"""simple docstring"""
a = None
a = None
a = None
a = None
self.builder.download_and_prepare(
download_config=__UpperCAmelCase , download_mode=__UpperCAmelCase , verification_mode=__UpperCAmelCase , base_path=__UpperCAmelCase , )
# Build dataset for splits
a = self.builder.as_dataset(
split='''train''' , verification_mode=__UpperCAmelCase , in_memory=self.keep_in_memory )
return dataset
class lowercase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : Dataset , __UpperCAmelCase : str , __UpperCAmelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[int] = None , **__UpperCAmelCase : Any , ) ->str:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )
a = dataset
a = name
a = con
a = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
a = num_proc
a = to_sql_kwargs
def __lowerCAmelCase ( self : Optional[Any] ) ->int:
"""simple docstring"""
a = self.to_sql_kwargs.pop('''sql''' , __UpperCAmelCase )
a = self.to_sql_kwargs.pop('''con''' , __UpperCAmelCase )
a = self.to_sql_kwargs.pop('''index''' , __UpperCAmelCase )
a = self._write(index=__UpperCAmelCase , **self.to_sql_kwargs )
return written
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple ) ->List[Any]:
"""simple docstring"""
a , a , a = args
a = {**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
a = query_table(
table=self.dataset.data , key=slice(__UpperCAmelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
a = batch.to_pandas()
a = df.to_sql(self.name , self.con , index=__UpperCAmelCase , **__UpperCAmelCase )
return num_rows or len(__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[Any] , **__UpperCAmelCase : List[str] ) ->int:
"""simple docstring"""
a = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
a , a = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , __UpperCAmelCase , __UpperCAmelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += num_rows
return written
| 352 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = "▁"
UpperCAmelCase__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = BertGenerationTokenizer
__snake_case = False
__snake_case = True
def __lowerCAmelCase ( self : str ) ->str:
"""simple docstring"""
super().setUp()
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
a = '''<s>'''
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(__UpperCAmelCase ) , 1_002 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def __lowerCAmelCase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
a = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [285, 46, 10, 170, 382] , )
a = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
a = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
a = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def __lowerCAmelCase ( self : Any ) ->str:
"""simple docstring"""
a = '''Hello World!'''
a = [18_536, 2_260, 101]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
a = [
871,
419,
358,
946,
991,
2_521,
452,
358,
1_357,
387,
7_751,
3_536,
112,
985,
456,
126,
865,
938,
5_400,
5_734,
458,
1_368,
467,
786,
2_462,
5_246,
1_159,
633,
865,
4_519,
457,
582,
852,
2_557,
427,
916,
508,
405,
34_324,
497,
391,
408,
11_342,
1_244,
385,
100,
938,
985,
456,
574,
362,
12_597,
3_200,
3_129,
1_172,
]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@require_torch
@slow
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
a = list(self.big_tokenizer.get_vocab().keys() )[:10]
a = ''' '''.join(__UpperCAmelCase )
a = self.big_tokenizer.encode_plus(__UpperCAmelCase , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = BertGenerationConfig()
a = BertGenerationEncoder(__UpperCAmelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__UpperCAmelCase )
model(**__UpperCAmelCase )
@slow
def __lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
a = {'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 26 | 0 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : str , *__UpperCAmelCase : Union[str, Any] , **__UpperCAmelCase : Tuple ) ->None:
"""simple docstring"""
warnings.warn(
'''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use MobileViTImageProcessor instead.''' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 353 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger("transformers.models.speecht5")
def _a ( a :Optional[Any] , a :Tuple , a :Dict ) -> List[str]:
hf_model.apply_weight_norm()
a = checkpoint['''input_conv.weight_g''']
a = checkpoint['''input_conv.weight_v''']
a = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
a = checkpoint[F"""upsamples.{i}.1.weight_g"""]
a = checkpoint[F"""upsamples.{i}.1.weight_v"""]
a = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
a = checkpoint['''output_conv.1.weight_g''']
a = checkpoint['''output_conv.1.weight_v''']
a = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def _a ( a :List[str] , a :Union[str, Any] , a :Dict , a :Dict=None , a :List[Any]=None , ) -> int:
if config_path is not None:
a = SpeechTaHifiGanConfig.from_pretrained(a )
else:
a = SpeechTaHifiGanConfig()
a = SpeechTaHifiGan(a )
a = torch.load(a )
load_weights(orig_checkpoint['''model''']['''generator'''] , a , a )
a = np.load(a )
a = stats[0].reshape(-1 )
a = stats[1].reshape(-1 )
a = torch.from_numpy(a ).float()
a = torch.from_numpy(a ).float()
model.save_pretrained(a )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCAmelCase__ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 26 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowercase_ ( lowercase , lowercase , lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = StableDiffusionInpaintPipeline
__snake_case = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
__snake_case = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__snake_case = frozenset([] )
def __lowerCAmelCase ( self : Dict ) ->Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
a = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__UpperCAmelCase , )
a = PNDMScheduler(skip_prk_steps=__UpperCAmelCase )
torch.manual_seed(0 )
a = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act='''gelu''' , projection_dim=512 , )
a = CLIPTextModel(__UpperCAmelCase )
a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Dict=0 ) ->List[Any]:
"""simple docstring"""
a = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
a = image.cpu().permute(0 , 2 , 3 , 1 )[0]
a = Image.fromarray(np.uinta(__UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
a = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
if str(__UpperCAmelCase ).startswith('''mps''' ):
a = torch.manual_seed(__UpperCAmelCase )
else:
a = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
a = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def __lowerCAmelCase ( self : Any ) ->Any:
"""simple docstring"""
a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionInpaintPipeline(**__UpperCAmelCase )
a = sd_pipe.to(__UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_dummy_inputs(__UpperCAmelCase )
a = sd_pipe(**__UpperCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self : Union[str, Any] ) ->List[str]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : str ) ->str:
"""simple docstring"""
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
a = '''stabilityai/stable-diffusion-2-inpainting'''
a = StableDiffusionInpaintPipeline.from_pretrained(__UpperCAmelCase , safety_checker=__UpperCAmelCase )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
a = '''Face of a yellow cat, high resolution, sitting on a park bench'''
a = torch.manual_seed(0 )
a = pipe(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , mask_image=__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''np''' , )
a = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def __lowerCAmelCase ( self : str ) ->Dict:
"""simple docstring"""
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
a = '''stabilityai/stable-diffusion-2-inpainting'''
a = StableDiffusionInpaintPipeline.from_pretrained(
__UpperCAmelCase , torch_dtype=torch.floataa , safety_checker=__UpperCAmelCase , )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
a = '''Face of a yellow cat, high resolution, sitting on a park bench'''
a = torch.manual_seed(0 )
a = pipe(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , mask_image=__UpperCAmelCase , generator=__UpperCAmelCase , output_type='''np''' , )
a = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def __lowerCAmelCase ( self : Union[str, Any] ) ->int:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
a = '''stabilityai/stable-diffusion-2-inpainting'''
a = PNDMScheduler.from_pretrained(__UpperCAmelCase , subfolder='''scheduler''' )
a = StableDiffusionInpaintPipeline.from_pretrained(
__UpperCAmelCase , safety_checker=__UpperCAmelCase , scheduler=__UpperCAmelCase , torch_dtype=torch.floataa , )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
a = '''Face of a yellow cat, high resolution, sitting on a park bench'''
a = torch.manual_seed(0 )
a = pipe(
prompt=__UpperCAmelCase , image=__UpperCAmelCase , mask_image=__UpperCAmelCase , generator=__UpperCAmelCase , num_inference_steps=2 , output_type='''np''' , )
a = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTBigCodeForSequenceClassification",
"GPTBigCodeForTokenClassification",
"GPTBigCodeForCausalLM",
"GPTBigCodeModel",
"GPTBigCodePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
import unittest
import numpy as np
def _a ( a :np.ndarray , a :np.ndarray , a :np.ndarray , a :np.ndarray | None = None , ) -> np.ndarray:
a = np.shape(a )
a = np.shape(a )
a = np.shape(a )
if shape_a[0] != shape_b[0]:
a = (
'''Expected the same number of rows for A and B. '''
F"""Instead found A of size {shape_a} and B of size {shape_b}"""
)
raise ValueError(a )
if shape_b[1] != shape_c[1]:
a = (
'''Expected the same number of columns for B and C. '''
F"""Instead found B of size {shape_b} and C of size {shape_c}"""
)
raise ValueError(a )
a = pseudo_inv
if a_inv is None:
try:
a = np.linalg.inv(a )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Dict ) ->None:
"""simple docstring"""
a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
a = np.array([[0, 3], [3, 0], [2, 3]] )
a = np.array([[2, 1], [6, 3]] )
a = schur_complement(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
a = np.block([[a, b], [b.T, c]] )
a = np.linalg.det(__UpperCAmelCase )
a = np.linalg.det(__UpperCAmelCase )
a = np.linalg.det(__UpperCAmelCase )
self.assertAlmostEqual(__UpperCAmelCase , det_a * det_s )
def __lowerCAmelCase ( self : Union[str, Any] ) ->None:
"""simple docstring"""
a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
a = np.array([[0, 3], [3, 0], [2, 3]] )
a = np.array([[2, 1], [6, 3]] )
with self.assertRaises(__UpperCAmelCase ):
schur_complement(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Any ) ->None:
"""simple docstring"""
a = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
a = np.array([[0, 3], [3, 0], [2, 3]] )
a = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(__UpperCAmelCase ):
schur_complement(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 355 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _a ( a :Tuple ) -> int:
a = tmp_path / '''file.csv'''
a = textwrap.dedent(
'''\
header1,header2
1,2
10,20
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :int ) -> List[str]:
a = tmp_path / '''malformed_file.csv'''
a = textwrap.dedent(
'''\
header1,header2
1,2
10,20,
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :Dict , a :int ) -> List[str]:
a = tmp_path / '''csv_with_image.csv'''
a = textwrap.dedent(
F"""\
image
{image_file}
""" )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :List[Any] ) -> Dict:
a = tmp_path / '''csv_with_label.csv'''
a = textwrap.dedent(
'''\
label
good
bad
good
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
@pytest.fixture
def _a ( a :Tuple ) -> Any:
a = tmp_path / '''csv_with_int_list.csv'''
a = textwrap.dedent(
'''\
int_list
1 2 3
4 5 6
7 8 9
''' )
with open(a , '''w''' ) as f:
f.write(a )
return str(a )
def _a ( a :Dict , a :int , a :Union[str, Any] ) -> List[Any]:
a = Csv()
a = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(a , match='''Error tokenizing data''' ):
for _ in generator:
pass
assert any(
record.levelname == '''ERROR'''
and '''Failed to read file''' in record.message
and os.path.basename(a ) in record.message
for record in caplog.records )
@require_pil
def _a ( a :Dict ) -> Any:
with open(a , encoding='''utf-8''' ) as f:
a = f.read().splitlines()[1]
a = Csv(encoding='''utf-8''' , features=Features({'''image''': Image()} ) )
a = csv._generate_tables([[csv_file_with_image]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''image''' ).type == Image()()
a = pa_table.to_pydict()['''image''']
assert generated_content == [{"path": image_file, "bytes": None}]
def _a ( a :Any ) -> Tuple:
with open(a , encoding='''utf-8''' ) as f:
a = f.read().splitlines()[1:]
a = Csv(encoding='''utf-8''' , features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
a = csv._generate_tables([[csv_file_with_label]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
a = pa_table.to_pydict()['''label''']
assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(a ) for label in labels]
def _a ( a :Union[str, Any] ) -> Optional[Any]:
a = Csv(encoding='''utf-8''' , sep=''',''' , converters={'''int_list''': lambda a : [int(a ) for i in x.split()]} )
a = csv._generate_tables([[csv_file_with_int_list]] )
a = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
a = pa_table.to_pydict()['''int_list''']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 26 | 0 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = ConsistencyModelPipeline
__snake_case = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
__snake_case = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
__snake_case = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''output_type''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
@property
def __lowerCAmelCase ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
a = UNetaDModel.from_pretrained(
'''diffusers/consistency-models-test''' , subfolder='''test_unet''' , )
return unet
@property
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = UNetaDModel.from_pretrained(
'''diffusers/consistency-models-test''' , subfolder='''test_unet_class_cond''' , )
return unet
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : Optional[Any]=False ) ->List[Any]:
"""simple docstring"""
if class_cond:
a = self.dummy_cond_unet
else:
a = self.dummy_uncond_unet
# Default to CM multistep sampler
a = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
a = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def __lowerCAmelCase ( self : str , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any]=0 ) ->Union[str, Any]:
"""simple docstring"""
if str(__UpperCAmelCase ).startswith('''mps''' ):
a = torch.manual_seed(__UpperCAmelCase )
else:
a = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
a = {
'''batch_size''': 1,
'''num_inference_steps''': None,
'''timesteps''': [22, 0],
'''generator''': generator,
'''output_type''': '''np''',
}
return inputs
def __lowerCAmelCase ( self : Tuple ) ->Any:
"""simple docstring"""
a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = ConsistencyModelPipeline(**__UpperCAmelCase )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_dummy_inputs(__UpperCAmelCase )
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 32, 32, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components(class_cond=__UpperCAmelCase )
a = ConsistencyModelPipeline(**__UpperCAmelCase )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_dummy_inputs(__UpperCAmelCase )
a = 0
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 32, 32, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = ConsistencyModelPipeline(**__UpperCAmelCase )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_dummy_inputs(__UpperCAmelCase )
a = 1
a = None
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 32, 32, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCAmelCase ( self : str ) ->int:
"""simple docstring"""
a = '''cpu''' # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components(class_cond=__UpperCAmelCase )
a = ConsistencyModelPipeline(**__UpperCAmelCase )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_dummy_inputs(__UpperCAmelCase )
a = 1
a = None
a = 0
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 32, 32, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Union[str, Any] ) ->Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Dict=0 , __UpperCAmelCase : Union[str, Any]=False , __UpperCAmelCase : Dict="cpu" , __UpperCAmelCase : str=torch.floataa , __UpperCAmelCase : List[Any]=(1, 3, 64, 64) ) ->List[str]:
"""simple docstring"""
a = torch.manual_seed(__UpperCAmelCase )
a = {
'''num_inference_steps''': None,
'''timesteps''': [22, 0],
'''class_labels''': 0,
'''generator''': generator,
'''output_type''': '''np''',
}
if get_fixed_latents:
a = self.get_fixed_latents(seed=__UpperCAmelCase , device=__UpperCAmelCase , dtype=__UpperCAmelCase , shape=__UpperCAmelCase )
a = latents
return inputs
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[str]=0 , __UpperCAmelCase : List[str]="cpu" , __UpperCAmelCase : Optional[int]=torch.floataa , __UpperCAmelCase : Tuple=(1, 3, 64, 64) ) ->List[Any]:
"""simple docstring"""
if type(__UpperCAmelCase ) == str:
a = torch.device(__UpperCAmelCase )
a = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
a = randn_tensor(__UpperCAmelCase , generator=__UpperCAmelCase , device=__UpperCAmelCase , dtype=__UpperCAmelCase )
return latents
def __lowerCAmelCase ( self : Dict ) ->Any:
"""simple docstring"""
a = UNetaDModel.from_pretrained('''diffusers/consistency_models''' , subfolder='''diffusers_cd_imagenet64_l2''' )
a = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
a = ConsistencyModelPipeline(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase )
pipe.to(torch_device=__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_inputs()
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 64, 64, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def __lowerCAmelCase ( self : Dict ) ->List[Any]:
"""simple docstring"""
a = UNetaDModel.from_pretrained('''diffusers/consistency_models''' , subfolder='''diffusers_cd_imagenet64_l2''' )
a = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
a = ConsistencyModelPipeline(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase )
pipe.to(torch_device=__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_inputs()
a = 1
a = None
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 64, 64, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
@require_torch_a
def __lowerCAmelCase ( self : Dict ) ->Dict:
"""simple docstring"""
a = UNetaDModel.from_pretrained('''diffusers/consistency_models''' , subfolder='''diffusers_cd_imagenet64_l2''' )
a = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
a = ConsistencyModelPipeline(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase )
pipe.to(torch_device=__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_inputs(get_fixed_latents=__UpperCAmelCase , device=__UpperCAmelCase )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=__UpperCAmelCase , enable_math=__UpperCAmelCase , enable_mem_efficient=__UpperCAmelCase ):
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 64, 64, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@require_torch_a
def __lowerCAmelCase ( self : Optional[Any] ) ->int:
"""simple docstring"""
a = UNetaDModel.from_pretrained('''diffusers/consistency_models''' , subfolder='''diffusers_cd_imagenet64_l2''' )
a = CMStochasticIterativeScheduler(
num_train_timesteps=40 , sigma_min=0.002 , sigma_max=80.0 , )
a = ConsistencyModelPipeline(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase )
pipe.to(torch_device=__UpperCAmelCase , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = self.get_inputs(get_fixed_latents=__UpperCAmelCase , device=__UpperCAmelCase )
a = 1
a = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=__UpperCAmelCase , enable_math=__UpperCAmelCase , enable_mem_efficient=__UpperCAmelCase ):
a = pipe(**__UpperCAmelCase ).images
assert image.shape == (1, 64, 64, 3)
a = image[0, -3:, -3:, -1]
a = np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 356 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = torch.device("cpu")
def _a ( ) -> Union[str, Any]:
a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
a = Image.open(requests.get(a , stream=a ).raw )
return im
def _a ( a :Dict ) -> Tuple:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02] )
def _a ( a :int , a :Any , a :Union[str, Any] ) -> int:
a = dct.pop(a )
a = val
def _a ( a :Any ) -> Dict:
a = []
for k in state_dict.keys():
a = k
if ".pwconv" in k:
a = k_new.replace('''.pwconv''' , '''.point_wise_conv''' )
if ".dwconv" in k:
a = k_new.replace('''.dwconv''' , '''.depth_wise_conv''' )
if ".Proj." in k:
a = k_new.replace('''.Proj.''' , '''.proj.''' )
if "patch_embed" in k_new:
a = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
a = k_new.split('''.''' )
if ls[2].isdigit():
a = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
a = k_new.replace('''network''' , '''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def _a ( a :List[Any] , a :Tuple , a :List[str] ) -> Union[str, Any]:
a = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
a = 1_000
a = '''huggingface/label-files'''
a = '''imagenet-1k-id2label.json'''
a = json.load(open(hf_hub_download(a , a , repo_type='''dataset''' ) , '''r''' ) )
a = {int(a ): v for k, v in idalabel.items()}
a = idalabel
a = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
a = [3, 3, 6, 4]
a = [48, 56, 112, 220]
elif swiftformer_name == "swiftformer_s":
a = [3, 3, 9, 6]
a = [48, 64, 168, 224]
elif swiftformer_name == "swiftformer_l1":
a = [4, 3, 10, 5]
a = [48, 96, 192, 384]
elif swiftformer_name == "swiftformer_l3":
a = [4, 4, 12, 6]
a = [64, 128, 320, 512]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
a = torch.hub.load_state_dict_from_url(a , map_location='''cpu''' , check_hash=a )
else:
a = torch.load(a , map_location='''cpu''' )
a = checkpoint
a = create_rename_keys(a )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(a , a , a )
# load HuggingFace model
a = SwiftFormerForImageClassification(a ).eval()
hf_model.load_state_dict(a )
# prepare test inputs
a = prepare_img()
a = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
a = processor(images=a , return_tensors='''pt''' )
# compare outputs from both models
a = get_expected_output(a )
a = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 1_000] )
assert torch.allclose(hf_logits[0, 0:5] , a , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swiftformer_name",
default="swiftformer_xs",
choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
type=str,
help="Name of the SwiftFormer model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="./converted_outputs/",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
UpperCAmelCase__ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 26 | 0 |
from math import asin, atan, cos, radians, sin, sqrt, tan
UpperCAmelCase__ = 6378137.0
UpperCAmelCase__ = 6356752.314245
UpperCAmelCase__ = 6378137
def _a ( a :float , a :float , a :float , a :float ) -> float:
a = (AXIS_A - AXIS_B) / AXIS_A
a = atan((1 - flattening) * tan(radians(a ) ) )
a = atan((1 - flattening) * tan(radians(a ) ) )
a = radians(a )
a = radians(a )
# Equation
a = sin((phi_a - phi_a) / 2 )
a = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
a = sqrt(sin_sq_phi + (cos(a ) * cos(a ) * sin_sq_lambda) )
return 2 * RADIUS * asin(a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : UNetaDModel , __UpperCAmelCase : DDPMScheduler , __UpperCAmelCase : Optional[int] , ) ->List[str]:
"""simple docstring"""
super().__init__()
a = value_function
a = unet
a = scheduler
a = env
a = env.get_dataset()
a = {}
for key in self.data.keys():
try:
a = self.data[key].mean()
except: # noqa: E722
pass
a = {}
for key in self.data.keys():
try:
a = self.data[key].std()
except: # noqa: E722
pass
a = env.observation_space.shape[0]
a = env.action_space.shape[0]
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[int] ) ->Dict:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict ) ->List[str]:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def __lowerCAmelCase ( self : int , __UpperCAmelCase : int ) ->List[str]:
"""simple docstring"""
if type(__UpperCAmelCase ) is dict:
return {k: self.to_torch(__UpperCAmelCase ) for k, v in x_in.items()}
elif torch.is_tensor(__UpperCAmelCase ):
return x_in.to(self.unet.device )
return torch.tensor(__UpperCAmelCase , device=self.unet.device )
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Tuple ) ->int:
"""simple docstring"""
for key, val in cond.items():
a = val.clone()
return x_in
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = x.shape[0]
a = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a = torch.full((batch_size,) , __UpperCAmelCase , device=self.unet.device , dtype=torch.long )
for _ in range(__UpperCAmelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a = self.value_function(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample
a = torch.autograd.grad([y.sum()] , [x] )[0]
a = self.scheduler._get_variance(__UpperCAmelCase )
a = torch.exp(0.5 * posterior_variance )
a = model_std * grad
a = 0
a = x.detach()
a = x + scale * grad
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.unet(x.permute(0 , 2 , 1 ) , __UpperCAmelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , predict_epsilon=__UpperCAmelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
return x, y
def __call__( self : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[int]=64 , __UpperCAmelCase : int=32 , __UpperCAmelCase : Optional[Any]=2 , __UpperCAmelCase : str=0.1 ) ->List[str]:
"""simple docstring"""
a = self.normalize(__UpperCAmelCase , '''observations''' )
a = obs[None].repeat(__UpperCAmelCase , axis=0 )
a = {0: self.to_torch(__UpperCAmelCase )}
a = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a = randn_tensor(__UpperCAmelCase , device=self.unet.device )
a = self.reset_xa(__UpperCAmelCase , __UpperCAmelCase , self.action_dim )
a = self.to_torch(__UpperCAmelCase )
# run the diffusion process
a , a = self.run_diffusion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# sort output trajectories by value
a = y.argsort(0 , descending=__UpperCAmelCase ).squeeze()
a = x[sorted_idx]
a = sorted_values[:, :, : self.action_dim]
a = actions.detach().cpu().numpy()
a = self.de_normalize(__UpperCAmelCase , key='''actions''' )
# select the action with the highest value
if y is not None:
a = 0
else:
# if we didn't run value guiding, select a random action
a = np.random.randint(0 , __UpperCAmelCase )
a = denorm_actions[selected_index, 0]
return denorm_actions
| 26 | 0 |
import torch
def _a ( ) -> str:
if torch.cuda.is_available():
a = torch.cuda.device_count()
else:
a = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 358 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model"}
UpperCAmelCase__ = {
"vocab_file": {
"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
}
}
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : List[str]="<s>" , __UpperCAmelCase : int="</s>" , __UpperCAmelCase : Any="<unk>" , __UpperCAmelCase : Optional[Any]="<sep>" , __UpperCAmelCase : int="<pad>" , __UpperCAmelCase : Any="<cls>" , __UpperCAmelCase : List[str]="<mask>" , __UpperCAmelCase : Optional[int]=["<eop>", "<eod>"] , __UpperCAmelCase : Optional[Dict[str, Any]] = None , **__UpperCAmelCase : Union[str, Any] , ) ->None:
"""simple docstring"""
a = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , additional_special_tokens=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
a = 3
a = do_lower_case
a = remove_space
a = keep_accents
a = vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__UpperCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''' )
a = jieba
a = str.maketrans(''' \n''' , '''\u2582\u2583''' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
return len(self.sp_model )
def __lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
a = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
a = self.__dict__.copy()
a = None
return state
def __setstate__( self : List[str] , __UpperCAmelCase : Optional[int] ) ->str:
"""simple docstring"""
a = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : List[str] ) ->List[str]:
"""simple docstring"""
if self.remove_space:
a = ''' '''.join(inputs.strip().split() )
else:
a = inputs
a = outputs.replace('''``''' , '''"''' ).replace('''\'\'''' , '''"''' )
if not self.keep_accents:
a = unicodedata.normalize('''NFKD''' , __UpperCAmelCase )
a = ''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase )] )
if self.do_lower_case:
a = outputs.lower()
return outputs
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = self.preprocess_text(__UpperCAmelCase )
a = self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
a = []
for piece in pieces:
if len(__UpperCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
a = self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
a = cur_pieces[1:]
else:
a = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__UpperCAmelCase )
else:
new_pieces.append(__UpperCAmelCase )
return new_pieces
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Any ) ->Any:
"""simple docstring"""
return self.sp_model.PieceToId(__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Dict ) ->Union[str, Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(__UpperCAmelCase )
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
a = ''''''.join(__UpperCAmelCase ).replace(__UpperCAmelCase , ''' ''' ).strip()
return out_string
def __lowerCAmelCase ( self : Tuple , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) ->List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is not None:
return ([0] * len(__UpperCAmelCase )) + [1] + ([0] * len(__UpperCAmelCase )) + [1, 1]
return ([0] * len(__UpperCAmelCase )) + [1, 1]
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowerCAmelCase ( self : Dict , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__UpperCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , '''wb''' ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (out_vocab_file,)
def __lowerCAmelCase ( self : Any , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Optional[Any] ) ->Tuple:
"""simple docstring"""
a = super()._decode(*__UpperCAmelCase , **__UpperCAmelCase )
a = text.replace(''' ''' , '''''' ).replace('''\u2582''' , ''' ''' ).replace('''\u2583''' , '''\n''' )
return text
| 26 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = ShapEImgaImgPipeline
__snake_case = ['''image''']
__snake_case = ['''image''']
__snake_case = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
__snake_case = False
@property
def __lowerCAmelCase ( self : Optional[int] ) ->Dict:
"""simple docstring"""
return 32
@property
def __lowerCAmelCase ( self : Optional[Any] ) ->List[Any]:
"""simple docstring"""
return 32
@property
def __lowerCAmelCase ( self : List[Any] ) ->int:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self : int ) ->Any:
"""simple docstring"""
return 8
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->int:
"""simple docstring"""
torch.manual_seed(0 )
a = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
a = CLIPVisionModel(__UpperCAmelCase )
return model
@property
def __lowerCAmelCase ( self : List[str] ) ->Tuple:
"""simple docstring"""
a = CLIPImageProcessor(
crop_size=224 , do_center_crop=__UpperCAmelCase , do_normalize=__UpperCAmelCase , do_resize=__UpperCAmelCase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=224 , )
return image_processor
@property
def __lowerCAmelCase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
a = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
a = PriorTransformer(**__UpperCAmelCase )
return model
@property
def __lowerCAmelCase ( self : Tuple ) ->int:
"""simple docstring"""
torch.manual_seed(0 )
a = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
a = ShapERenderer(**__UpperCAmelCase )
return model
def __lowerCAmelCase ( self : int ) ->Optional[Any]:
"""simple docstring"""
a = self.dummy_prior
a = self.dummy_image_encoder
a = self.dummy_image_processor
a = self.dummy_renderer
a = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1_024 , prediction_type='''sample''' , use_karras_sigmas=__UpperCAmelCase , clip_sample=__UpperCAmelCase , clip_sample_range=1.0 , )
a = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __lowerCAmelCase ( self : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Dict=0 ) ->Union[str, Any]:
"""simple docstring"""
a = floats_tensor((1, 3, 64, 64) , rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )
if str(__UpperCAmelCase ).startswith('''mps''' ):
a = torch.manual_seed(__UpperCAmelCase )
else:
a = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
a = {
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __lowerCAmelCase ( self : List[Any] ) ->Optional[Any]:
"""simple docstring"""
a = '''cpu'''
a = self.get_dummy_components()
a = self.pipeline_class(**__UpperCAmelCase )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = pipe(**self.get_dummy_inputs(__UpperCAmelCase ) )
a = output.images[0]
a = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
a = np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __lowerCAmelCase ( self : Tuple ) ->List[str]:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowerCAmelCase ( self : Any ) ->Tuple:
"""simple docstring"""
a = torch_device == '''cpu'''
a = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__UpperCAmelCase , relax_max_difference=__UpperCAmelCase , )
def __lowerCAmelCase ( self : Any ) ->List[Any]:
"""simple docstring"""
a = self.get_dummy_components()
a = self.pipeline_class(**__UpperCAmelCase )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = 1
a = 2
a = self.get_dummy_inputs(__UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
a = batch_size * [inputs[key]]
a = pipe(**__UpperCAmelCase , num_images_per_prompt=__UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[Any] ) ->Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
a = ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
a = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
a = torch.Generator(device=__UpperCAmelCase ).manual_seed(0 )
a = pipe(
__UpperCAmelCase , generator=__UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__UpperCAmelCase , __UpperCAmelCase )
| 359 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def _a ( a :Union[str, Any] , a :List[Any] ) -> List[Any]:
a = checkpoint
a = {}
a = vae_state_dict['''encoder.conv_in.weight''']
a = vae_state_dict['''encoder.conv_in.bias''']
a = vae_state_dict['''encoder.conv_out.weight''']
a = vae_state_dict['''encoder.conv_out.bias''']
a = vae_state_dict['''encoder.norm_out.weight''']
a = vae_state_dict['''encoder.norm_out.bias''']
a = vae_state_dict['''decoder.conv_in.weight''']
a = vae_state_dict['''decoder.conv_in.bias''']
a = vae_state_dict['''decoder.conv_out.weight''']
a = vae_state_dict['''decoder.conv_out.bias''']
a = vae_state_dict['''decoder.norm_out.weight''']
a = vae_state_dict['''decoder.norm_out.bias''']
a = vae_state_dict['''quant_conv.weight''']
a = vae_state_dict['''quant_conv.bias''']
a = vae_state_dict['''post_quant_conv.weight''']
a = vae_state_dict['''post_quant_conv.bias''']
# Retrieves the keys for the encoder down blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(a )
}
# Retrieves the keys for the decoder up blocks only
a = len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
a = {
layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(a )
}
for i in range(a ):
a = [key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]
if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.weight""" )
a = vae_state_dict.pop(
F"""encoder.down.{i}.downsample.conv.bias""" )
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
for i in range(a ):
a = num_up_blocks - 1 - i
a = [
key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
]
if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.weight"""
]
a = vae_state_dict[
F"""decoder.up.{block_id}.upsample.conv.bias"""
]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.block''' in key]
a = 2
for i in range(1 , num_mid_res_blocks + 1 ):
a = [key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]
a = renew_vae_resnet_paths(a )
a = {'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
a = [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
a = renew_vae_attention_paths(a )
a = {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
return new_checkpoint
def _a ( a :str , a :str , ) -> List[str]:
# Only support V1
a = requests.get(
''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
a = io.BytesIO(r.content )
a = OmegaConf.load(a )
a = 512
a = '''cuda''' if torch.cuda.is_available() else '''cpu'''
if checkpoint_path.endswith('''safetensors''' ):
from safetensors import safe_open
a = {}
with safe_open(a , framework='''pt''' , device='''cpu''' ) as f:
for key in f.keys():
a = f.get_tensor(a )
else:
a = torch.load(a , map_location=a )['''state_dict''']
# Convert the VAE model.
a = create_vae_diffusers_config(a , image_size=a )
a = custom_convert_ldm_vae_checkpoint(a , a )
a = AutoencoderKL(**a )
vae.load_state_dict(a )
vae.save_pretrained(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
UpperCAmelCase__ = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 26 | 0 |
def _a ( a :str ) -> list:
if n_term == "":
return []
a = []
for temp in range(int(a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
return series
if __name__ == "__main__":
UpperCAmelCase__ = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| 360 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ['''image_processor''', '''tokenizer''']
__snake_case = '''CLIPImageProcessor'''
__snake_case = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Dict , __UpperCAmelCase : str=None , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
a = kwargs.pop('''feature_extractor''' )
a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self : List[str] , __UpperCAmelCase : Any=None , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Any=None , **__UpperCAmelCase : str ) ->Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
a = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def __lowerCAmelCase ( self : Tuple , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) ->Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def __lowerCAmelCase ( self : int ) ->List[str]:
"""simple docstring"""
a = self.tokenizer.model_input_names
a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __UpperCAmelCase , )
return self.image_processor_class
@property
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __UpperCAmelCase , )
return self.image_processor
| 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeTop2Router",
"NllbMoeSparseMLP",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 361 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 26 | 0 |
import random
def _a ( a :int ) -> bool:
a = num - 1
a = 0
while s % 2 == 0:
a = s // 2
t += 1
for _ in range(5 ):
a = random.randrange(2 , num - 1 )
a = pow(a , a , a )
if v != 1:
a = 0
while v != (num - 1):
if i == t - 1:
return False
else:
a = i + 1
a = (v**2) % num
return True
def _a ( a :int ) -> bool:
if num < 2:
return False
a = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(a )
def _a ( a :int = 1_024 ) -> int:
while True:
a = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(a ):
return num
if __name__ == "__main__":
UpperCAmelCase__ = generate_large_prime()
print(("Prime number:", num))
print(("is_prime_low_num:", is_prime_low_num(num)))
| 362 |
from __future__ import annotations
import typing
from collections import Counter
def _a ( a :int ) -> typing.Counter[int]:
a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a , max_perimeter + 1 ):
a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a ):
a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def _a ( a :int = 1_000 ) -> int:
a = pythagorean_triple(a )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f"""Perimeter {solution()} has maximum solutions""")
| 26 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig",
],
"processing_x_clip": ["XCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"XCLIPModel",
"XCLIPPreTrainedModel",
"XCLIPTextModel",
"XCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 363 |
from __future__ import annotations
def _a ( a :dict , a :str ) -> set[str]:
a , a = set(a ), [start]
while stack:
a = stack.pop()
explored.add(a )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(a )
return explored
UpperCAmelCase__ = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 26 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 364 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase__ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase__ = 10
UpperCAmelCase__ = 256
def _a ( a :List[str] ) -> Optional[MinHash]:
if len(a ) < MIN_NUM_TOKENS:
return None
a = MinHash(num_perm=a )
for token in set(a ):
min_hash.update(token.encode() )
return min_hash
def _a ( a :str ) -> Set[str]:
return {t for t in NON_ALPHA.split(a ) if len(t.strip() ) > 0}
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , *,
__UpperCAmelCase : float = 0.85 , ) ->Dict:
"""simple docstring"""
a = duplication_jaccard_threshold
a = NUM_PERM
a = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
a = defaultdict(__UpperCAmelCase )
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : MinHash ) ->None:
"""simple docstring"""
a = self._index.query(__UpperCAmelCase )
if code_key in self._index.keys:
print(F"""Duplicate key {code_key}""" )
return
self._index.insert(__UpperCAmelCase , __UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase )
def __lowerCAmelCase ( self : Dict ) ->List[List[Dict]]:
"""simple docstring"""
a = []
for base, duplicates in self._duplicate_clusters.items():
a = [base] + list(__UpperCAmelCase )
# reformat the cluster to be a list of dict
a = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(__UpperCAmelCase )
return duplicate_clusters
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Dict ) ->None:
"""simple docstring"""
a = self.get_duplicate_clusters()
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def _a ( a :List[Any] ) -> List[Any]:
a , a = element
a = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def _a ( a :Type[Dataset] ) -> List[Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(a , max_queue_size=10_000 ) , chunksize=100 , ):
if data is not None:
yield data
def _a ( a :Type[Dataset] , a :float ) -> str:
a = DuplicationIndex(duplication_jaccard_threshold=a )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a ) ) , max_queue_size=100 ) ):
di.add(a , a )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def _a ( a :str , a :str ) -> float:
a = get_tokens(a )
a = get_tokens(a )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase__ = None
def _a ( a :Tuple , a :Tuple ) -> Any:
a = []
for elementa in cluster:
a = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
a = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(a , a ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
a = 1
extremes.append(a )
return extremes
def _a ( a :List[Any] , a :Optional[Any] , a :Union[str, Any] ) -> Optional[int]:
global _shared_dataset
a = dataset
a = []
a = partial(_find_cluster_extremes_shared , jaccard_threshold=a )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
a , a , ) , total=len(a ) , ):
extremes_list.append(a )
return extremes_list
def _a ( a :Type[Dataset] , a :float = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
a = make_duplicate_clusters(a , a )
a = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
a = {}
a = find_extremes(a , a , a )
for extremes in extremes_clusters:
for element in extremes:
a = element
a = duplicate_indices - set(extreme_dict.keys() )
a = dataset.filter(lambda a , a : idx not in remove_indices , with_indices=a )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
a = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
a = extreme_dict[element['''base_index''']]['''copies''']
print(F"""Original dataset size: {len(a )}""" )
print(F"""Number of duplicate clusters: {len(a )}""" )
print(F"""Files in duplicate cluster: {len(a )}""" )
print(F"""Unique files in duplicate cluster: {len(a )}""" )
print(F"""Filtered dataset size: {len(a )}""" )
return ds_filter, duplicate_clusters
| 26 | 0 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = "▁"
UpperCAmelCase__ = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = BertGenerationTokenizer
__snake_case = False
__snake_case = True
def __lowerCAmelCase ( self : str ) ->str:
"""simple docstring"""
super().setUp()
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
a = '''<s>'''
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<pad>''' )
self.assertEqual(len(__UpperCAmelCase ) , 1_002 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_000 )
def __lowerCAmelCase ( self : Tuple ) ->Optional[int]:
"""simple docstring"""
a = BertGenerationTokenizer(__UpperCAmelCase , keep_accents=__UpperCAmelCase )
a = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__UpperCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , [285, 46, 10, 170, 382] , )
a = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
a = tokenizer.convert_tokens_to_ids(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
a = tokenizer.convert_ids_to_tokens(__UpperCAmelCase )
self.assertListEqual(
__UpperCAmelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def __lowerCAmelCase ( self : Any ) ->str:
"""simple docstring"""
a = '''Hello World!'''
a = [18_536, 2_260, 101]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@slow
def __lowerCAmelCase ( self : List[Any] ) ->str:
"""simple docstring"""
a = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
a = [
871,
419,
358,
946,
991,
2_521,
452,
358,
1_357,
387,
7_751,
3_536,
112,
985,
456,
126,
865,
938,
5_400,
5_734,
458,
1_368,
467,
786,
2_462,
5_246,
1_159,
633,
865,
4_519,
457,
582,
852,
2_557,
427,
916,
508,
405,
34_324,
497,
391,
408,
11_342,
1_244,
385,
100,
938,
985,
456,
574,
362,
12_597,
3_200,
3_129,
1_172,
]
self.assertListEqual(__UpperCAmelCase , self.big_tokenizer.encode(__UpperCAmelCase ) )
@require_torch
@slow
def __lowerCAmelCase ( self : Any ) ->Dict:
"""simple docstring"""
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
a = list(self.big_tokenizer.get_vocab().keys() )[:10]
a = ''' '''.join(__UpperCAmelCase )
a = self.big_tokenizer.encode_plus(__UpperCAmelCase , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=__UpperCAmelCase )
a = BertGenerationConfig()
a = BertGenerationEncoder(__UpperCAmelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__UpperCAmelCase )
model(**__UpperCAmelCase )
@slow
def __lowerCAmelCase ( self : str ) ->Optional[Any]:
"""simple docstring"""
a = {'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' , revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' , )
| 365 |
from math import ceil, sqrt
def _a ( a :int = 1_000_000 ) -> int:
a = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
a = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict=7 , __UpperCAmelCase : Optional[Any]=3 , __UpperCAmelCase : Union[str, Any]=18 , __UpperCAmelCase : Any=30 , __UpperCAmelCase : Optional[int]=400 , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Tuple=None , __UpperCAmelCase : str=True , __UpperCAmelCase : Any=[0.5, 0.5, 0.5] , __UpperCAmelCase : Union[str, Any]=[0.5, 0.5, 0.5] , ) ->int:
"""simple docstring"""
a = size if size is not None else {'''shortest_edge''': 18}
a = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
a = parent
a = batch_size
a = num_channels
a = image_size
a = min_resolution
a = max_resolution
a = do_resize
a = size
a = do_center_crop
a = crop_size
a = do_normalize
a = image_mean
a = image_std
def __lowerCAmelCase ( self : Any ) ->Optional[Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowercase_ ( lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = LevitImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = LevitImageProcessingTester(self )
@property
def __lowerCAmelCase ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : Optional[Any] ) ->Optional[int]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''image_std''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__UpperCAmelCase , '''size''' ) )
def __lowerCAmelCase ( self : List[str] ) ->Union[str, Any]:
"""simple docstring"""
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def __lowerCAmelCase ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
pass
def __lowerCAmelCase ( self : Dict ) ->Tuple:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __lowerCAmelCase ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
a = image_processing(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 366 |
UpperCAmelCase__ = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 26 | 0 |
import enum
import shutil
import sys
UpperCAmelCase__ , UpperCAmelCase__ = shutil.get_terminal_size()
UpperCAmelCase__ = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class lowercase_ ( enum.Enum ):
'''simple docstring'''
__snake_case = 0
__snake_case = 1
def _a ( a :Dict , a :Any="" ) -> str:
sys.stdout.write(str(a ) + end )
sys.stdout.flush()
def _a ( a :str , a :List[Any] , a :str="" ) -> Union[str, Any]:
forceWrite(F"""\u001b[{color}m{content}\u001b[0m""" , a )
def _a ( ) -> Union[str, Any]:
forceWrite('''\r''' )
def _a ( a :int , a :str ) -> List[Any]:
forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" )
def _a ( ) -> Any:
forceWrite(''' ''' * TERMINAL_WIDTH )
reset_cursor()
def _a ( ) -> int:
reset_cursor()
forceWrite('''-''' * TERMINAL_WIDTH )
| 367 |
def _a ( a :list ) -> list:
if len(a ) <= 1:
return lst
a = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a , a = lst[i], lst[i - 1]
i -= 1
if i == 0:
a = 1
return lst
if __name__ == "__main__":
UpperCAmelCase__ = input("Enter numbers separated by a comma:\n").strip()
UpperCAmelCase__ = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 26 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase__ = "\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e-img2img\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n ```\n"
@dataclass
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = 42
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : Dict , __UpperCAmelCase : PriorTransformer , __UpperCAmelCase : CLIPVisionModel , __UpperCAmelCase : CLIPImageProcessor , __UpperCAmelCase : HeunDiscreteScheduler , __UpperCAmelCase : ShapERenderer , ) ->List[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(
prior=__UpperCAmelCase , image_encoder=__UpperCAmelCase , image_processor=__UpperCAmelCase , scheduler=__UpperCAmelCase , renderer=__UpperCAmelCase , )
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] ) ->int:
"""simple docstring"""
if latents is None:
a = randn_tensor(__UpperCAmelCase , generator=__UpperCAmelCase , device=__UpperCAmelCase , dtype=__UpperCAmelCase )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
a = latents.to(__UpperCAmelCase )
a = latents * scheduler.init_noise_sigma
return latents
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : List[str]=0 ) ->Tuple:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
a = torch.device(F"""cuda:{gpu_id}""" )
a = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__UpperCAmelCase , __UpperCAmelCase )
@property
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder , '''_hf_hook''' ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(__UpperCAmelCase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : Dict , ) ->str:
"""simple docstring"""
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(image[0] , torch.Tensor ):
a = torch.cat(__UpperCAmelCase , axis=0 ) if image[0].ndim == 4 else torch.stack(__UpperCAmelCase , axis=0 )
if not isinstance(__UpperCAmelCase , torch.Tensor ):
a = self.image_processor(__UpperCAmelCase , return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )
a = image.to(dtype=self.image_encoder.dtype , device=__UpperCAmelCase )
a = self.image_encoder(__UpperCAmelCase )['''last_hidden_state''']
a = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
a = image_embeds.repeat_interleave(__UpperCAmelCase , dim=0 )
if do_classifier_free_guidance:
a = torch.zeros_like(__UpperCAmelCase )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
a = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(__UpperCAmelCase )
def __call__( self : Optional[Any] , __UpperCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , __UpperCAmelCase : int = 1 , __UpperCAmelCase : int = 25 , __UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __UpperCAmelCase : Optional[torch.FloatTensor] = None , __UpperCAmelCase : float = 4.0 , __UpperCAmelCase : int = 64 , __UpperCAmelCase : Optional[str] = "pil" , __UpperCAmelCase : bool = True , ) ->Dict:
"""simple docstring"""
if isinstance(__UpperCAmelCase , PIL.Image.Image ):
a = 1
elif isinstance(__UpperCAmelCase , torch.Tensor ):
a = image.shape[0]
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
a = len(__UpperCAmelCase )
else:
raise ValueError(
F"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__UpperCAmelCase )}""" )
a = self._execution_device
a = batch_size * num_images_per_prompt
a = guidance_scale > 1.0
a = self._encode_image(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# prior
self.scheduler.set_timesteps(__UpperCAmelCase , device=__UpperCAmelCase )
a = self.scheduler.timesteps
a = self.prior.config.num_embeddings
a = self.prior.config.embedding_dim
a = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
a = latents.reshape(latents.shape[0] , __UpperCAmelCase , __UpperCAmelCase )
for i, t in enumerate(self.progress_bar(__UpperCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a = self.scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
a = self.prior(
__UpperCAmelCase , timestep=__UpperCAmelCase , proj_embedding=__UpperCAmelCase , ).predicted_image_embedding
# remove the variance
a , a = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
a , a = noise_pred.chunk(2 )
a = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
a = self.scheduler.step(
__UpperCAmelCase , timestep=__UpperCAmelCase , sample=__UpperCAmelCase , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=__UpperCAmelCase )
a = []
for i, latent in enumerate(__UpperCAmelCase ):
print()
a = self.renderer.decode(
latent[None, :] , __UpperCAmelCase , size=__UpperCAmelCase , ray_batch_size=4_096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(__UpperCAmelCase )
a = torch.stack(__UpperCAmelCase )
if output_type not in ["np", "pil"]:
raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}""" )
a = images.cpu().numpy()
if output_type == "pil":
a = [self.numpy_to_pil(__UpperCAmelCase ) for image in images]
# Offload last model to CPU
if hasattr(self , '''final_offload_hook''' ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=__UpperCAmelCase )
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26 | 0 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def _a ( *a :List[str] ) -> List[Any]:
with open(a , '''r''' ) as fh:
fcntl.flock(a , fcntl.LOCK_EX )
try:
print(*a )
finally:
fcntl.flock(a , fcntl.LOCK_UN )
UpperCAmelCase__ = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
UpperCAmelCase__ = torch.device("cuda", local_rank)
UpperCAmelCase__ = socket.gethostname()
UpperCAmelCase__ = f"""[{hostname}-{local_rank}]"""
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
UpperCAmelCase__ = dist.get_rank()
UpperCAmelCase__ = dist.get_world_size()
printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")
dist.barrier()
if rank == 0:
printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")
except Exception:
printflock(f"""{gpu} is broken""")
raise
| 369 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
UpperCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _a ( a :str ) -> Any:
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
a = model_type_to_module_name(a )
a = importlib.import_module(F""".{module_name}""" , '''transformers.models''' )
try:
return getattr(a , a )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(a , '''__name__''' , a ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
a = importlib.import_module('''transformers''' )
if hasattr(a , a ):
return getattr(a , a )
return None
def _a ( a :Union[str, os.PathLike] , a :Optional[Union[str, os.PathLike]] = None , a :bool = False , a :bool = False , a :Optional[Dict[str, str]] = None , a :Optional[Union[bool, str]] = None , a :Optional[str] = None , a :bool = False , **a :int , ) -> Tuple:
a = get_file_from_repo(
a , a , cache_dir=a , force_download=a , resume_download=a , proxies=a , use_auth_token=a , revision=a , local_files_only=a , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(a , encoding='''utf-8''' ) as reader:
return json.load(a )
class lowercase_ :
'''simple docstring'''
def __init__( self : Tuple ) ->int:
"""simple docstring"""
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(__UpperCAmelCase )
def __lowerCAmelCase ( cls : int , __UpperCAmelCase : Optional[Any] , **__UpperCAmelCase : Dict ) ->List[Any]:
"""simple docstring"""
a = kwargs.pop('''config''' , __UpperCAmelCase )
a = kwargs.pop('''trust_remote_code''' , __UpperCAmelCase )
a = True
a , a = FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase , **__UpperCAmelCase )
a = config_dict.get('''feature_extractor_type''' , __UpperCAmelCase )
a = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
a = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
a = AutoConfig.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
# It could be in `config.feature_extractor_type``
a = getattr(__UpperCAmelCase , '''feature_extractor_type''' , __UpperCAmelCase )
if hasattr(__UpperCAmelCase , '''auto_map''' ) and "AutoFeatureExtractor" in config.auto_map:
a = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
a = feature_extractor_class_from_name(__UpperCAmelCase )
a = feature_extractor_auto_map is not None
a = feature_extractor_class is not None or type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING
a = resolve_trust_remote_code(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
if has_remote_code and trust_remote_code:
a = get_class_from_dynamic_module(
__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
a = kwargs.pop('''code_revision''' , __UpperCAmelCase )
if os.path.isdir(__UpperCAmelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__UpperCAmelCase ) in FEATURE_EXTRACTOR_MAPPING:
a = FEATURE_EXTRACTOR_MAPPING[type(__UpperCAmelCase )]
return feature_extractor_class.from_dict(__UpperCAmelCase , **__UpperCAmelCase )
raise ValueError(
F"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
F"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def __lowerCAmelCase ( __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple ) ->Optional[int]:
"""simple docstring"""
FEATURE_EXTRACTOR_MAPPING.register(__UpperCAmelCase , __UpperCAmelCase )
| 26 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=__UpperCAmelCase ).to(__UpperCAmelCase )
a = AutoTokenizer.from_pretrained('''google/mt5-small''' )
a = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
a = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
a = model(input_ids.to(__UpperCAmelCase ) , labels=labels.to(__UpperCAmelCase ) ).loss
a = -(labels.shape[-1] * loss.item())
a = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 370 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Optional[int] ) ->Tuple:
"""simple docstring"""
a = tempfile.mkdtemp()
a = BlipImageProcessor()
a = GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''' )
a = BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
a = InstructBlipProcessor(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Tuple ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).tokenizer
def __lowerCAmelCase ( self : int , **__UpperCAmelCase : str ) ->List[str]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).image_processor
def __lowerCAmelCase ( self : Optional[Any] , **__UpperCAmelCase : Any ) ->Optional[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ).qformer_tokenizer
def __lowerCAmelCase ( self : str ) ->Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Optional[int] ) ->str:
"""simple docstring"""
a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a = [Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
a = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
a = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
a = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 )
a = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __UpperCAmelCase )
self.assertIsInstance(processor.qformer_tokenizer , __UpperCAmelCase )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = self.prepare_image_inputs()
a = image_processor(__UpperCAmelCase , return_tensors='''np''' )
a = processor(images=__UpperCAmelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowerCAmelCase ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = processor(text=__UpperCAmelCase )
a = tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
a = qformer_tokenizer(__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor['''qformer_''' + key] )
def __lowerCAmelCase ( self : Dict ) ->Optional[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
# test if it raises when no input is passed
with pytest.raises(__UpperCAmelCase ):
processor()
def __lowerCAmelCase ( self : Dict ) ->List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a = processor.batch_decode(__UpperCAmelCase )
a = tokenizer.batch_decode(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] ) ->str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = self.get_qformer_tokenizer()
a = InstructBlipProcessor(
tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase , qformer_tokenizer=__UpperCAmelCase )
a = '''lower newer'''
a = self.prepare_image_inputs()
a = processor(text=__UpperCAmelCase , images=__UpperCAmelCase )
self.assertListEqual(
list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values'''] , )
| 26 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowercase_ ( lowercase , lowercase ):
'''simple docstring'''
__snake_case = 1
@register_to_config
def __init__( self : Any , __UpperCAmelCase : Union[str, Any]=2_000 , __UpperCAmelCase : int=0.1 , __UpperCAmelCase : Union[str, Any]=20 , __UpperCAmelCase : Any=1e-3 ) ->Dict:
"""simple docstring"""
a = None
a = None
a = None
def __lowerCAmelCase ( self : Any , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Union[str, torch.device] = None ) ->Any:
"""simple docstring"""
a = torch.linspace(1 , self.config.sampling_eps , __UpperCAmelCase , device=__UpperCAmelCase )
def __lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[str]=None ) ->Union[str, Any]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
a = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
a = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
a = std.flatten()
while len(std.shape ) < len(score.shape ):
a = std.unsqueeze(-1 )
a = -score / std
# compute
a = -1.0 / len(self.timesteps )
a = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
a = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
a = beta_t.unsqueeze(-1 )
a = -0.5 * beta_t * x
a = torch.sqrt(__UpperCAmelCase )
a = drift - diffusion**2 * score
a = x + drift * dt
# add noise
a = randn_tensor(x.shape , layout=x.layout , generator=__UpperCAmelCase , device=x.device , dtype=x.dtype )
a = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
return self.config.num_train_timesteps
| 371 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 26 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Optional[Any] = StableDiffusionInpaintPipeline
lowercase : int = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
lowercase : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Any = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : int = frozenset([] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCamelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
UpperCamelCase : List[Any] = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
UpperCamelCase : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : List[str] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" ).resize((64, 64) )
UpperCamelCase : Union[str, Any] = Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : int = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : int = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": init_image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Optional[Any] = StableDiffusionInpaintPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = sd_pipe.to(SCREAMING_SNAKE_CASE_ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = sd_pipe(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase : Any = np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def a_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self ):
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : str = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench.npy""" )
UpperCamelCase : Optional[int] = """stabilityai/stable-diffusion-2-inpainting"""
UpperCamelCase : str = StableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
UpperCamelCase : Any = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = torch.manual_seed(0 )
UpperCamelCase : Any = pipe(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , )
UpperCamelCase : Tuple = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def a_ ( self ):
UpperCamelCase : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )
UpperCamelCase : Dict = """stabilityai/stable-diffusion-2-inpainting"""
UpperCamelCase : Union[str, Any] = StableDiffusionInpaintPipeline.from_pretrained(
SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa , safety_checker=SCREAMING_SNAKE_CASE_ , )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing()
UpperCamelCase : str = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = torch.manual_seed(0 )
UpperCamelCase : List[Any] = pipe(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , )
UpperCamelCase : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def a_ ( self ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCamelCase : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : str = """stabilityai/stable-diffusion-2-inpainting"""
UpperCamelCase : Dict = PNDMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_ , subfolder="""scheduler""" )
UpperCamelCase : Any = StableDiffusionInpaintPipeline.from_pretrained(
SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa , )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
UpperCamelCase : Dict = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : Optional[Any] = torch.manual_seed(0 )
UpperCamelCase : str = pipe(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , output_type="""np""" , )
UpperCamelCase : Tuple = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 27 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
UpperCamelCase : int = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = """sshleifer/tiny-gpt2"""
UpperCamelCase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : int = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Dict = """sgugger/tiny-distilbert-classification"""
UpperCamelCase : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , only_pretrain_model=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Optional[Any] = """sshleifer/tiny-gpt2"""
UpperCamelCase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : List[Any] = """sshleifer/tiny-gpt2"""
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Any = """sshleifer/tiny-gpt2"""
UpperCamelCase : List[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Any = """sshleifer/tiny-gpt2"""
UpperCamelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a_ ( self ):
UpperCamelCase : str = """sshleifer/tiny-gpt2"""
UpperCamelCase : Dict = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a_ ( self ):
UpperCamelCase : Tuple = """patrickvonplaten/t5-tiny-random"""
UpperCamelCase : Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , configs=[config] )
UpperCamelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , """Cannot do xla on CPU.""" )
def a_ ( self ):
UpperCamelCase : Tuple = """sshleifer/tiny-gpt2"""
UpperCamelCase : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[str] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : str = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , save_to_csv=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """inf_time.csv""" ) , inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """inf_mem.csv""" ) , env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """env.csv""" ) , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
benchmark.run()
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """env.csv""" ) ).exists() )
def a_ ( self ):
UpperCamelCase : List[str] = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_ ):
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """sequential""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """cumulative""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """current""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(SCREAMING_SNAKE_CASE_ , """log.txt""" ) , log_print=SCREAMING_SNAKE_CASE_ , trace_memory_line_by_line=SCREAMING_SNAKE_CASE_ , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[str] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """log.txt""" ) ).exists() )
| 27 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27 | 1 |
"""simple docstring"""
from typing import Any
def A_ ( snake_case_ : list ,snake_case_ : list ,snake_case_ : dict ,snake_case_ : dict ,snake_case_ : dict ,):
'''simple docstring'''
_validation(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,)
# Creates data structures and fill initial step
UpperCamelCase : dict = {}
UpperCamelCase : dict = {}
for state in states_space:
UpperCamelCase : int = observations_space[0]
UpperCamelCase : Optional[Any] = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
UpperCamelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 ,len(snake_case_ ) ):
UpperCamelCase : Optional[Any] = observations_space[o]
UpperCamelCase : Dict = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
UpperCamelCase : Union[str, Any] = """"""
UpperCamelCase : Any = -1
for k_state in states_space:
UpperCamelCase : Dict = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
UpperCamelCase : Optional[Any] = probability
UpperCamelCase : Dict = k_state
# Update probabilities and pointers dicts
UpperCamelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
UpperCamelCase : List[Any] = arg_max
# The final observation
UpperCamelCase : Union[str, Any] = observations_space[len(snake_case_ ) - 1]
# argmax for given final observation
UpperCamelCase : List[str] = """"""
UpperCamelCase : List[Any] = -1
for k_state in states_space:
UpperCamelCase : Optional[int] = probabilities[(k_state, final_observation)]
if probability > max_probability:
UpperCamelCase : int = probability
UpperCamelCase : List[Any] = k_state
UpperCamelCase : Union[str, Any] = arg_max
# Process pointers backwards
UpperCamelCase : Optional[int] = last_state
UpperCamelCase : List[str] = []
for o in range(len(snake_case_ ) - 1 ,-1 ,-1 ):
result.append(snake_case_ )
UpperCamelCase : Union[str, Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def A_ ( snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,):
'''simple docstring'''
_validate_not_empty(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,)
_validate_lists(snake_case_ ,snake_case_ )
_validate_dicts(
snake_case_ ,snake_case_ ,snake_case_ )
def A_ ( snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,):
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("""There's an empty parameter""" )
def A_ ( snake_case_ : Any ,snake_case_ : Any ):
'''simple docstring'''
_validate_list(snake_case_ ,"""observations_space""" )
_validate_list(snake_case_ ,"""states_space""" )
def A_ ( snake_case_ : Any ,snake_case_ : str ):
'''simple docstring'''
if not isinstance(_object ,snake_case_ ):
UpperCamelCase : Any = f'{var_name} must be a list'
raise ValueError(snake_case_ )
else:
for x in _object:
if not isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : List[Any] = f'{var_name} must be a list of strings'
raise ValueError(snake_case_ )
def A_ ( snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Any ,):
'''simple docstring'''
_validate_dict(snake_case_ ,"""initial_probabilities""" ,snake_case_ )
_validate_nested_dict(snake_case_ ,"""transition_probabilities""" )
_validate_nested_dict(snake_case_ ,"""emission_probabilities""" )
def A_ ( snake_case_ : Any ,snake_case_ : str ):
'''simple docstring'''
_validate_dict(_object ,snake_case_ ,snake_case_ )
for x in _object.values():
_validate_dict(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
def A_ ( snake_case_ : Any ,snake_case_ : str ,snake_case_ : type ,snake_case_ : bool = False ):
'''simple docstring'''
if not isinstance(_object ,snake_case_ ):
UpperCamelCase : str = f'{var_name} must be a dict'
raise ValueError(snake_case_ )
if not all(isinstance(snake_case_ ,snake_case_ ) for x in _object ):
UpperCamelCase : Dict = f'{var_name} all keys must be strings'
raise ValueError(snake_case_ )
if not all(isinstance(snake_case_ ,snake_case_ ) for x in _object.values() ):
UpperCamelCase : str = """nested dictionary """ if nested else """"""
UpperCamelCase : Dict = f'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(snake_case_ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 27 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A : int = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class lowerCamelCase :
lowercase : Optional[str] = field(
default='cifar10' , metadata={'help': 'Name of a dataset from the datasets package'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'The column name of the images in the files.'} )
lowercase : Optional[str] = field(default=_UpperCAmelCase , metadata={'help': 'A folder containing the training data.'} )
lowercase : Optional[str] = field(default=_UpperCAmelCase , metadata={'help': 'A folder containing the validation data.'} )
lowercase : Optional[float] = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'} )
lowercase : Optional[int] = field(
default=_UpperCAmelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowercase : Optional[int] = field(
default=_UpperCAmelCase , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def a_ ( self ):
UpperCamelCase : List[str] = {}
if self.train_dir is not None:
UpperCamelCase : List[str] = self.train_dir
if self.validation_dir is not None:
UpperCamelCase : int = self.validation_dir
UpperCamelCase : List[Any] = data_files if data_files else None
@dataclass
class lowerCamelCase :
lowercase : str = field(
default=_UpperCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
lowercase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowercase : str = field(default=_UpperCAmelCase , metadata={'help': 'Name or path of preprocessor config.'} )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
lowercase : float = field(
default=0.75 , metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )
@dataclass
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : float = field(
default=1E-3 , metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Any = torch.stack([example["""pixel_values"""] for example in examples] )
return {"pixel_values": pixel_values}
def A_ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[int] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_mae""" ,snake_case_ ,snake_case_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,datefmt="""%m/%d/%Y %H:%M:%S""" ,handlers=[logging.StreamHandler(sys.stdout )] ,)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCamelCase : List[Any] = training_args.get_process_log_level()
logger.setLevel(snake_case_ )
transformers.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
UpperCamelCase : int = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase : Optional[int] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Initialize our dataset.
UpperCamelCase : Union[str, Any] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,data_files=data_args.data_files ,cache_dir=model_args.cache_dir ,use_auth_token=True if model_args.use_auth_token else None ,)
# If we don't have a validation split, split off a percentage of train as validation.
UpperCamelCase : Dict = None if """validation""" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split ,snake_case_ ) and data_args.train_val_split > 0.0:
UpperCamelCase : Any = ds["""train"""].train_test_split(data_args.train_val_split )
UpperCamelCase : int = split["""train"""]
UpperCamelCase : str = split["""test"""]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase : List[Any] = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase : Tuple = ViTMAEConfig.from_pretrained(model_args.config_name ,**snake_case_ )
elif model_args.model_name_or_path:
UpperCamelCase : Union[str, Any] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,**snake_case_ )
else:
UpperCamelCase : Any = ViTMAEConfig()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(f'Overriding config: {model_args.config_overrides}' )
config.update_from_string(model_args.config_overrides )
logger.info(f'New config: {config}' )
# adapt config
config.update(
{
"""mask_ratio""": model_args.mask_ratio,
"""norm_pix_loss""": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
UpperCamelCase : List[str] = ViTImageProcessor.from_pretrained(model_args.image_processor_name ,**snake_case_ )
elif model_args.model_name_or_path:
UpperCamelCase : Tuple = ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,**snake_case_ )
else:
UpperCamelCase : Optional[Any] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
UpperCamelCase : Union[str, Any] = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) ,config=snake_case_ ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase : str = ViTMAEForPreTraining(snake_case_ )
if training_args.do_train:
UpperCamelCase : List[Any] = ds["""train"""].column_names
else:
UpperCamelCase : Optional[Any] = ds["""validation"""].column_names
if data_args.image_column_name is not None:
UpperCamelCase : List[Any] = data_args.image_column_name
elif "image" in column_names:
UpperCamelCase : int = """image"""
elif "img" in column_names:
UpperCamelCase : Optional[int] = """img"""
else:
UpperCamelCase : List[str] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
UpperCamelCase : Optional[Any] = image_processor.size["""shortest_edge"""]
else:
UpperCamelCase : List[str] = (image_processor.size["""height"""], image_processor.size["""width"""])
UpperCamelCase : List[str] = Compose(
[
Lambda(lambda snake_case_ : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
RandomResizedCrop(snake_case_ ,scale=(0.2, 1.0) ,interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean ,std=image_processor.image_std ),
] )
def preprocess_images(snake_case_ : Optional[int] ):
UpperCamelCase : Union[str, Any] = [transforms(snake_case_ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("""--do_train requires a train dataset""" )
if data_args.max_train_samples is not None:
UpperCamelCase : int = ds["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(snake_case_ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("""--do_eval requires a validation dataset""" )
if data_args.max_eval_samples is not None:
UpperCamelCase : Union[str, Any] = (
ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(snake_case_ )
# Compute absolute learning rate
UpperCamelCase : List[Any] = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
UpperCamelCase : int = training_args.base_learning_rate * total_train_batch_size / 2_5_6
# Initialize our trainer
UpperCamelCase : int = Trainer(
model=snake_case_ ,args=snake_case_ ,train_dataset=ds["""train"""] if training_args.do_train else None ,eval_dataset=ds["""validation"""] if training_args.do_eval else None ,tokenizer=snake_case_ ,data_collator=snake_case_ ,)
# Training
if training_args.do_train:
UpperCamelCase : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
UpperCamelCase : Optional[int] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCamelCase : int = last_checkpoint
UpperCamelCase : str = trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model()
trainer.log_metrics("""train""" ,train_result.metrics )
trainer.save_metrics("""train""" ,train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCamelCase : str = trainer.evaluate()
trainer.log_metrics("""eval""" ,snake_case_ )
trainer.save_metrics("""eval""" ,snake_case_ )
# Write model card and (optionally) push to hub
UpperCamelCase : List[Any] = {
"""tasks""": """masked-auto-encoding""",
"""dataset""": data_args.dataset_name,
"""tags""": ["""masked-auto-encoding"""],
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case_ )
else:
trainer.create_model_card(**snake_case_ )
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 27 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 27 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "AAPL" ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
UpperCamelCase : Tuple = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Any = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" ,class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')
| 27 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : List[Any] = RoCBertTokenizer
lowercase : Tuple = None
lowercase : Tuple = False
lowercase : Optional[Any] = True
lowercase : Dict = filter_non_english
def a_ ( self ):
super().setUp()
UpperCamelCase : Tuple = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
UpperCamelCase : int = {}
UpperCamelCase : Optional[Any] = {}
for i, value in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = i
UpperCamelCase : List[str] = i
UpperCamelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCamelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_shape_file"""] )
UpperCamelCase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_pronunciation_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
with open(self.word_shape_file , """w""" , encoding="""utf-8""" ) as word_shape_writer:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ )
with open(self.word_pronunciation_file , """w""" , encoding="""utf-8""" ) as word_pronunciation_writer:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ensure_ascii=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
UpperCamelCase : List[Any] = tokenizer.tokenize("""你好[SEP]你是谁""" )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE_ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE_ ) , [5, 6, 2, 5, 7, 8] )
def a_ ( self ):
UpperCamelCase : int = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def a_ ( self ):
UpperCamelCase : str = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : List[Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def a_ ( self ):
UpperCamelCase : Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : List[Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : List[Any] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : Optional[int] = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : Tuple = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def a_ ( self ):
UpperCamelCase : Optional[int] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
UpperCamelCase : str = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = i
UpperCamelCase : Tuple = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def a_ ( self ):
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def a_ ( self ):
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def a_ ( self ):
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def a_ ( self ):
UpperCamelCase : List[str] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
if self.test_rust_tokenizer:
UpperCamelCase : Any = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
def a_ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase : int = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
UpperCamelCase : List[str] = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , return_offsets_mapping=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_ , """do_lower_case""" ) else False
UpperCamelCase : List[Any] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def a_ ( self ):
UpperCamelCase : Dict = ["""的""", """人""", """有"""]
UpperCamelCase : Tuple = """""".join(SCREAMING_SNAKE_CASE_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase : Optional[Any] = True
UpperCamelCase : str = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = False
UpperCamelCase : Dict = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCamelCase : List[str] = [
f'##{token}' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE_ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
UpperCamelCase : Tuple = tokenizer.encode("""你好""" , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenizer.encode("""你是谁""" , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_ )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
UpperCamelCase : str = """你好，你是谁"""
UpperCamelCase : int = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer.prepare_for_model(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = tokenizer.encode_plus(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 27 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Tuple = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
'''BridgeTower/bridgetower-base-itm-mlm''': (
'''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
),
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[Any] = 'bridgetower_vision_model'
def __init__( self , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=288 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=1e-05 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = hidden_size
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Union[str, Any] = num_channels
UpperCamelCase : Tuple = patch_size
UpperCamelCase : Any = image_size
UpperCamelCase : Union[str, Any] = initializer_factor
UpperCamelCase : Optional[int] = layer_norm_eps
UpperCamelCase : Dict = stop_gradient
UpperCamelCase : List[str] = share_layernorm
UpperCamelCase : Dict = remove_last_layer
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase , UpperCamelCase : int = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config_dict.get("""model_type""" ) == "bridgetower":
UpperCamelCase : List[str] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : int = 'bridgetower_text_model'
def __init__( self , SCREAMING_SNAKE_CASE_=5_0265 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=514 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=1e-05 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_="absolute" , SCREAMING_SNAKE_CASE_=True , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Dict = num_hidden_layers
UpperCamelCase : Union[str, Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_act
UpperCamelCase : List[Any] = initializer_factor
UpperCamelCase : Tuple = intermediate_size
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : Tuple = attention_probs_dropout_prob
UpperCamelCase : Optional[Any] = max_position_embeddings
UpperCamelCase : List[Any] = type_vocab_size
UpperCamelCase : Dict = layer_norm_eps
UpperCamelCase : int = position_embedding_type
UpperCamelCase : Optional[Any] = use_cache
UpperCamelCase : Union[str, Any] = pad_token_id
UpperCamelCase : Tuple = bos_token_id
UpperCamelCase : List[Any] = eos_token_id
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase , UpperCamelCase : List[Any] = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if config_dict.get("""model_type""" ) == "bridgetower":
UpperCamelCase : Optional[Any] = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = 'bridgetower'
def __init__( self , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=1e-05 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_="add" , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
# TODO: remove this once the Hub files are updated.
UpperCamelCase : List[str] = kwargs.pop("""text_config_dict""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = kwargs.pop("""vision_config_dict""" , SCREAMING_SNAKE_CASE_ )
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = share_cross_modal_transformer_layers
UpperCamelCase : str = hidden_act
UpperCamelCase : Any = hidden_size
UpperCamelCase : Optional[Any] = initializer_factor
UpperCamelCase : List[Any] = layer_norm_eps
UpperCamelCase : Optional[Any] = share_link_tower_layers
UpperCamelCase : Optional[int] = link_tower_type
UpperCamelCase : Union[str, Any] = num_attention_heads
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Union[str, Any] = tie_word_embeddings
UpperCamelCase : List[str] = init_layernorm_from_vision_encoder
if text_config is None:
UpperCamelCase : Any = {}
logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.""" )
if vision_config is None:
UpperCamelCase : List[Any] = {}
logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.""" )
UpperCamelCase : Optional[Any] = BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE_ )
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = copy.deepcopy(self.__dict__ )
UpperCamelCase : str = self.text_config.to_dict()
UpperCamelCase : int = self.vision_config.to_dict()
UpperCamelCase : List[str] = self.__class__.model_type
return output
| 27 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__A : Union[str, Any] = logging.get_logger(__name__)
__A : List[str] = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
__A : Optional[int] = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Optional[int] ,snake_case_ : Optional[int] ,snake_case_ : str ,snake_case_ : Optional[Any] ,snake_case_ : Optional[Any] ):
'''simple docstring'''
for attribute in key.split(""".""" ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
UpperCamelCase : Union[str, Any] = """lm_head"""
UpperCamelCase : Union[str, Any] = getattr(snake_case_ ,snake_case_ )
if weight_type is not None:
UpperCamelCase : List[str] = getattr(snake_case_ ,snake_case_ ).shape
else:
UpperCamelCase : Union[str, Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
UpperCamelCase : Dict = value
elif weight_type == "weight_g":
UpperCamelCase : List[Any] = value
elif weight_type == "weight_v":
UpperCamelCase : Tuple = value
elif weight_type == "bias":
UpperCamelCase : Union[str, Any] = value
else:
UpperCamelCase : int = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A_ ( snake_case_ : List[str] ,snake_case_ : Optional[int] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : List[Any] = fairseq_model.state_dict()
UpperCamelCase : str = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
UpperCamelCase : Any = False
if "conv_layers" in name:
load_conv_layer(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,hf_model.config.feat_extract_norm == """group""" ,)
UpperCamelCase : int = True
else:
for key, mapped_key in MAPPING.items():
UpperCamelCase : Optional[int] = """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
UpperCamelCase : Optional[int] = True
if "*" in mapped_key:
UpperCamelCase : List[Any] = name.split(snake_case_ )[0].split(""".""" )[-2]
UpperCamelCase : Optional[Any] = mapped_key.replace("""*""" ,snake_case_ )
if "weight_g" in name:
UpperCamelCase : Optional[int] = """weight_g"""
elif "weight_v" in name:
UpperCamelCase : Optional[Any] = """weight_v"""
elif "bias" in name:
UpperCamelCase : List[Any] = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCamelCase : Optional[int] = """weight"""
else:
UpperCamelCase : Optional[int] = None
set_recursively(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
continue
if not is_used:
unused_weights.append(snake_case_ )
logger.warning(f'Unused weights: {unused_weights}' )
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Optional[Any] ,snake_case_ : List[str] ,snake_case_ : Tuple ,snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Tuple = full_name.split("""conv_layers.""" )[-1]
UpperCamelCase : List[str] = name.split(""".""" )
UpperCamelCase : Dict = int(items[0] )
UpperCamelCase : str = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
UpperCamelCase : Dict = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
UpperCamelCase : Dict = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
UpperCamelCase : Dict = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
UpperCamelCase : Optional[Any] = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(snake_case_ )
@torch.no_grad()
def A_ ( snake_case_ : Optional[int] ,snake_case_ : List[str] ,snake_case_ : Tuple=None ,snake_case_ : List[str]=None ,snake_case_ : Union[str, Any]=True ):
'''simple docstring'''
if config_path is not None:
UpperCamelCase : Optional[int] = UniSpeechConfig.from_pretrained(snake_case_ )
else:
UpperCamelCase : Tuple = UniSpeechConfig()
if is_finetuned:
if dict_path:
UpperCamelCase : Tuple = Dictionary.load_from_json(snake_case_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCamelCase : Union[str, Any] = target_dict.pad_index
UpperCamelCase : List[Any] = target_dict.bos_index
UpperCamelCase : str = target_dict.eos_index
UpperCamelCase : Tuple = len(target_dict.symbols )
UpperCamelCase : Union[str, Any] = os.path.join(snake_case_ ,"""vocab.json""" )
if not os.path.isdir(snake_case_ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case_ ) )
return
os.makedirs(snake_case_ ,exist_ok=snake_case_ )
UpperCamelCase : str = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCamelCase : int = 4_2
UpperCamelCase : str = 4_3
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as vocab_handle:
json.dump(snake_case_ ,snake_case_ )
UpperCamelCase : Dict = WavaVecaPhonemeCTCTokenizer(
snake_case_ ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token="""|""" ,do_lower_case=snake_case_ ,)
UpperCamelCase : List[Any] = True if config.feat_extract_norm == """layer""" else False
UpperCamelCase : Optional[int] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_6_0_0_0 ,padding_value=0 ,do_normalize=snake_case_ ,return_attention_mask=snake_case_ ,)
UpperCamelCase : Any = WavaVecaProcessor(feature_extractor=snake_case_ ,tokenizer=snake_case_ )
processor.save_pretrained(snake_case_ )
UpperCamelCase : Optional[int] = UniSpeechForCTC(snake_case_ )
else:
UpperCamelCase : Any = UniSpeechForPreTraining(snake_case_ )
if is_finetuned:
UpperCamelCase , UpperCamelCase , UpperCamelCase : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] ), """w2v_path""": checkpoint_path} )
else:
UpperCamelCase , UpperCamelCase , UpperCamelCase : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
UpperCamelCase : Optional[Any] = model[0].eval()
recursively_load_weights(snake_case_ ,snake_case_ ,snake_case_ )
hf_unispeech.save_pretrained(snake_case_ )
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__A : Any = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 27 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27 | 1 |
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class lowerCamelCase ( _UpperCAmelCase ):
def a_ ( self ):
UpperCamelCase : str = tempfile.mkdtemp()
UpperCamelCase : List[Any] = 8
# DPR tok
UpperCamelCase : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
UpperCamelCase : Any = os.path.join(self.tmpdirname , """dpr_tokenizer""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = os.path.join(SCREAMING_SNAKE_CASE_ , DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
# BART tok
UpperCamelCase : List[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
UpperCamelCase : str = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
UpperCamelCase : Dict = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
UpperCamelCase : str = {"""unk_token""": """<unk>"""}
UpperCamelCase : List[str] = os.path.join(self.tmpdirname , """bart_tokenizer""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = os.path.join(SCREAMING_SNAKE_CASE_ , BART_VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCamelCase : List[Any] = os.path.join(SCREAMING_SNAKE_CASE_ , BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def a_ ( self ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , """dpr_tokenizer""" ) )
def a_ ( self ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , """bart_tokenizer""" ) )
def a_ ( self ):
shutil.rmtree(self.tmpdirname )
def a_ ( self ):
UpperCamelCase : int = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("""embeddings""" , string_factory="""Flat""" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_dataset()
UpperCamelCase : str = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
UpperCamelCase : Any = dataset
UpperCamelCase : Tuple = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = self.get_dummy_dataset()
UpperCamelCase : List[str] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="""custom""" , )
if from_disk:
UpperCamelCase : Optional[Any] = os.path.join(self.tmpdirname , """dataset""" )
UpperCamelCase : Union[str, Any] = os.path.join(self.tmpdirname , """index.faiss""" )
dataset.get_index("""embeddings""" ).save(os.path.join(self.tmpdirname , """index.faiss""" ) )
dataset.drop_index("""embeddings""" )
dataset.save_to_disk(os.path.join(self.tmpdirname , """dataset""" ) )
del dataset
UpperCamelCase : int = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
UpperCamelCase : str = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , SCREAMING_SNAKE_CASE_ ) , )
return retriever
def a_ ( self ):
UpperCamelCase : int = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("""embeddings""" , string_factory="""Flat""" , metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCamelCase : Optional[Any] = os.path.join(self.tmpdirname , """hf_bert_base.hnswSQ8_correct_phi_128.c_index""" )
dataset.save_faiss_index("""embeddings""" , index_file_name + """.index.dpr""" )
pickle.dump(dataset["""id"""] , open(index_file_name + """.index_meta.dpr""" , """wb""" ) )
UpperCamelCase : List[Any] = os.path.join(self.tmpdirname , """psgs_w100.tsv.pkl""" )
UpperCamelCase : Dict = {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
pickle.dump(SCREAMING_SNAKE_CASE_ , open(SCREAMING_SNAKE_CASE_ , """wb""" ) )
UpperCamelCase : Optional[Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="""legacy""" , index_path=self.tmpdirname , )
UpperCamelCase : Optional[int] = RagRetriever(
SCREAMING_SNAKE_CASE_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def a_ ( self ):
UpperCamelCase : Any = 1
UpperCamelCase : List[Any] = self.get_dummy_canonical_hf_index_retriever()
UpperCamelCase : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase , UpperCamelCase , UpperCamelCase : Tuple = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
UpperCamelCase : Dict = self.get_dummy_dataset()
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase : Optional[Any] = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
def a_ ( self ):
UpperCamelCase : Optional[int] = 1
UpperCamelCase : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase , UpperCamelCase , UpperCamelCase : Tuple = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def a_ ( self ):
UpperCamelCase : Tuple = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase : str = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = 1
UpperCamelCase : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase , UpperCamelCase , UpperCamelCase : str = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]["""id"""][0] , """1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] , """0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def a_ ( self ):
UpperCamelCase : Tuple = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase : List[Any] = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
def a_ ( self ):
UpperCamelCase : Optional[Any] = 1
UpperCamelCase : List[str] = self.get_dummy_legacy_index_retriever()
UpperCamelCase : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase , UpperCamelCase , UpperCamelCase : Union[str, Any] = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["""text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""text"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(doc_dicts[0]["""text"""][0] , """bar""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""text"""][0] , """foo""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase : Optional[Any] = retriever.retrieve(SCREAMING_SNAKE_CASE_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def a_ ( self ):
import torch
UpperCamelCase : str = 1
UpperCamelCase : List[Any] = self.get_dummy_canonical_hf_index_retriever()
UpperCamelCase : Tuple = [[5, 7], [10, 11]]
UpperCamelCase : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase : Any = retriever(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = (
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
UpperCamelCase : Dict = retriever(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Union[str, Any] = ( # noqa: F841
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
out["""doc_ids"""],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def a_ ( self ):
UpperCamelCase : Tuple = self.get_dpr_ctx_encoder_tokenizer()
UpperCamelCase : Optional[int] = 1
UpperCamelCase : int = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = [[5, 7], [10, 11]]
UpperCamelCase : List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase : Any = retriever(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
len(SCREAMING_SNAKE_CASE_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""") ) , SCREAMING_SNAKE_CASE_ ) # check for doc token related keys in dictionary.
| 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : Tuple = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
UpperCamelCase : Dict = get_activation("""gelu""" )
self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_ ) , torch_builtin(SCREAMING_SNAKE_CASE_ ) ) )
self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_ ) , gelu_new(SCREAMING_SNAKE_CASE_ ) ) )
def a_ ( self ):
UpperCamelCase : Optional[Any] = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
UpperCamelCase : Union[str, Any] = get_activation("""gelu""" )
UpperCamelCase : List[Any] = get_activation("""gelu_10""" )
UpperCamelCase : Optional[Any] = torch_builtin(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = geluaa(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(SCREAMING_SNAKE_CASE_ ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def a_ ( self ):
get_activation("""gelu""" )
get_activation("""gelu_10""" )
get_activation("""gelu_fast""" )
get_activation("""gelu_new""" )
get_activation("""gelu_python""" )
get_activation("""gelu_pytorch_tanh""" )
get_activation("""linear""" )
get_activation("""mish""" )
get_activation("""quick_gelu""" )
get_activation("""relu""" )
get_activation("""sigmoid""" )
get_activation("""silu""" )
get_activation("""swish""" )
get_activation("""tanh""" )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
get_activation("""bogus""" )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
get_activation(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = get_activation("""gelu""" )
UpperCamelCase : Union[str, Any] = 1
UpperCamelCase : int = get_activation("""gelu""" )
self.assertEqual(acta.a , 1 )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = acta.a
| 27 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Optional[Any] = [], []
while len(snake_case_ ) > 1:
UpperCamelCase , UpperCamelCase : Optional[Any] = min(snake_case_ ), max(snake_case_ )
start.append(snake_case_ )
end.append(snake_case_ )
collection.remove(snake_case_ )
collection.remove(snake_case_ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
__A : Union[str, Any] = input('''Enter numbers separated by a comma:\n''').strip()
__A : Union[str, Any] = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : Dict = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 1 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ ):
super().__init__()
UpperCamelCase : Optional[int] = nn.ModuleList(SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = True , ):
for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.nets ) ):
UpperCamelCase , UpperCamelCase : str = controlnet(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
# merge samples
if i == 0:
UpperCamelCase , UpperCamelCase : List[str] = down_samples, mid_sample
else:
UpperCamelCase : int = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , ):
UpperCamelCase : Dict = 0
UpperCamelCase : Any = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
SCREAMING_SNAKE_CASE_ , is_main_process=SCREAMING_SNAKE_CASE_ , save_function=SCREAMING_SNAKE_CASE_ , safe_serialization=SCREAMING_SNAKE_CASE_ , variant=SCREAMING_SNAKE_CASE_ , )
idx += 1
UpperCamelCase : str = model_path_to_save + f'_{idx}'
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = 0
UpperCamelCase : Optional[int] = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
UpperCamelCase : Tuple = pretrained_model_path
while os.path.isdir(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
controlnets.append(SCREAMING_SNAKE_CASE_ )
idx += 1
UpperCamelCase : Tuple = pretrained_model_path + f'_{idx}'
logger.info(f'{len(SCREAMING_SNAKE_CASE_ )} controlnets loaded from {pretrained_model_path}.' )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
raise ValueError(
f'No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE_ )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(SCREAMING_SNAKE_CASE_ )
| 27 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase : List[Any] = """"""
UpperCamelCase : Optional[int] = """"""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(snake_case_ ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
UpperCamelCase , UpperCamelCase : Optional[Any] = 0, 0
# length[i] shows the length of palindromic substring with center i
UpperCamelCase : Optional[int] = [1 for i in range(len(snake_case_ ) )]
# for each character in new_string find corresponding palindromic string
UpperCamelCase : List[Any] = 0
for j in range(len(snake_case_ ) ):
UpperCamelCase : List[Any] = 1 if j > r else min(length[l + r - j] // 2 ,r - j + 1 )
while (
j - k >= 0
and j + k < len(snake_case_ )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
UpperCamelCase : Dict = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
UpperCamelCase : int = j - k + 1 # noqa: E741
UpperCamelCase : str = j + k - 1
# update max_length and start position
if max_length < length[j]:
UpperCamelCase : Tuple = length[j]
UpperCamelCase : List[Any] = j
# create that string
UpperCamelCase : int = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 1 |
"""simple docstring"""
from math import ceil
def A_ ( snake_case_ : List[str] ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = list(range(0 ,snake_case_ ) )
UpperCamelCase : Optional[Any] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCamelCase : str = []
for i in device_map_blocks:
if device_map_blocks.count(snake_case_ ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(snake_case_ )
# Missing blocks
UpperCamelCase : Union[str, Any] = [i for i in blocks if i not in device_map_blocks]
UpperCamelCase : Optional[int] = [i for i in device_map_blocks if i not in blocks]
if len(snake_case_ ) != 0:
raise ValueError(
"""Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
""" These attention blocks were specified more than once: """ + str(snake_case_ ) )
if len(snake_case_ ) != 0:
raise ValueError(
"""There are attention blocks for this model that are not specified in the device_map. Add these attention """
"""blocks to a device on the device_map: """ + str(snake_case_ ) )
if len(snake_case_ ) != 0:
raise ValueError(
"""The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
+ str(snake_case_ ) )
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : str = list(range(snake_case_ ) )
UpperCamelCase : List[Any] = int(ceil(n_layers / len(snake_case_ ) ) )
UpperCamelCase : List[Any] = [layers[i : i + n_blocks] for i in range(0 ,snake_case_ ,snake_case_ )]
return dict(zip(snake_case_ ,snake_case_ ) )
| 27 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27 | 1 |
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