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import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "char"
snake_case_ = "bpe"
snake_case_ = "wp"
SCREAMING_SNAKE_CASE :Optional[int] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = ["image_processor", "char_tokenizer"]
snake_case_ = "ViTImageProcessor"
snake_case_ = "MgpstrTokenizer"
def __init__( self : Tuple ,A : Optional[Any]=None ,A : Optional[Any]=None ,**A : Optional[Any] ):
__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." ,A ,)
__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`." )
__A = tokenizer
__A = AutoTokenizer.from_pretrained("gpt2" )
__A = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(A ,A )
def __call__( self : Optional[Any] ,A : Tuple=None ,A : List[str]=None ,A : Optional[Any]=None ,**A : Union[str, Any] ):
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
__A = self.image_processor(A ,return_tensors=A ,**A )
if text is not None:
__A = self.char_tokenizer(A ,return_tensors=A ,**A )
if text is None:
return inputs
elif images is None:
return encodings
else:
__A = encodings["input_ids"]
return inputs
def UpperCamelCase_ ( self : Union[str, Any] ,A : Optional[Any] ):
__A , __A , __A = sequences
__A = char_preds.size(0 )
__A , __A = self._decode_helper(A ,"char" )
__A , __A = self._decode_helper(A ,"bpe" )
__A , __A = self._decode_helper(A ,"wp" )
__A = []
__A = []
for i in range(A ):
__A = [char_scores[i], bpe_scores[i], wp_scores[i]]
__A = [char_strs[i], bpe_strs[i], wp_strs[i]]
__A = scores.index(max(A ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
__A = {}
__A = final_strs
__A = final_scores
__A = char_strs
__A = bpe_strs
__A = wp_strs
return out
def UpperCamelCase_ ( self : Tuple ,A : str ,A : Tuple ):
if format == DecodeType.CHARACTER:
__A = self.char_decode
__A = 1
__A = "[s]"
elif format == DecodeType.BPE:
__A = self.bpe_decode
__A = 2
__A = "#"
elif format == DecodeType.WORDPIECE:
__A = self.wp_decode
__A = 1_02
__A = "[SEP]"
else:
raise ValueError(f'''Format {format} is not supported.''' )
__A , __A = [], []
__A = pred_logits.size(0 )
__A = pred_logits.size(1 )
__A , __A = pred_logits.topk(1 ,dim=-1 ,largest=A ,sorted=A )
__A = preds_index.view(-1 ,A )[:, 1:]
__A = decoder(A )
__A , __A = torch.nn.functional.softmax(A ,dim=2 ).max(dim=2 )
__A = preds_max_prob[:, 1:]
for index in range(A ):
__A = preds_str[index].find(A )
__A = preds_str[index][:pred_eos]
__A = preds_index[index].cpu().tolist()
__A = pred_index.index(A ) if eos_token in pred_index else -1
__A = preds_max_prob[index][: pred_eos_index + 1]
__A = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A )
conf_scores.append(A )
return dec_strs, conf_scores
def UpperCamelCase_ ( self : Tuple ,A : Optional[int] ):
__A = [seq.replace(" " ,"" ) for seq in self.char_tokenizer.batch_decode(A )]
return decode_strs
def UpperCamelCase_ ( self : List[Any] ,A : Union[str, Any] ):
return self.bpe_tokenizer.batch_decode(A )
def UpperCamelCase_ ( self : Tuple ,A : List[str] ):
__A = [seq.replace(" " ,"" ) for seq in self.wp_tokenizer.batch_decode(A )]
return decode_strs
| 15 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__A = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE :List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 15 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCAmelCase ( a_ , a_=0.999 , a_="cosine" , ) -> Optional[Any]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(a_ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a_ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
__A = []
for i in range(a_ ):
__A = i / num_diffusion_timesteps
__A = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(a_ ) / alpha_bar_fn(a_ ) , a_ ) )
return torch.tensor(a_ , dtype=torch.floataa )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = [e.name for e in KarrasDiffusionSchedulers]
snake_case_ = 2
@register_to_config
def __init__( self : Optional[Any] ,A : int = 10_00 ,A : float = 0.0_00_85 ,A : float = 0.0_12 ,A : str = "linear" ,A : Optional[Union[np.ndarray, List[float]]] = None ,A : str = "epsilon" ,A : str = "linspace" ,A : int = 0 ,):
if trained_betas is not None:
__A = torch.tensor(A ,dtype=torch.floataa )
elif beta_schedule == "linear":
__A = torch.linspace(A ,A ,A ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__A = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,A ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__A = betas_for_alpha_bar(A )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
__A = 1.0 - self.betas
__A = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(A ,A ,A )
def UpperCamelCase_ ( self : Dict ,A : List[str] ,A : List[str]=None ):
if schedule_timesteps is None:
__A = self.timesteps
__A = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
__A = 1 if len(A ) > 1 else 0
else:
__A = timestep.cpu().item() if torch.is_tensor(A ) else timestep
__A = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCamelCase_ ( self : Any ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : Union[float, torch.FloatTensor] ,):
__A = self.index_for_timestep(A )
if self.state_in_first_order:
__A = self.sigmas[step_index]
else:
__A = self.sigmas_interpol[step_index]
__A = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCamelCase_ ( self : Optional[Any] ,A : int ,A : Union[str, torch.device] = None ,A : Optional[int] = None ,):
__A = num_inference_steps
__A = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
__A = np.linspace(0 ,num_train_timesteps - 1 ,A ,dtype=A )[::-1].copy()
elif self.config.timestep_spacing == "leading":
__A = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__A = (np.arange(0 ,A ) * step_ratio).round()[::-1].copy().astype(A )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
__A = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__A = (np.arange(A ,0 ,-step_ratio )).round().copy().astype(A )
timesteps -= 1
else:
raise ValueError(
f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )
__A = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
__A = torch.from_numpy(np.log(A ) ).to(A )
__A = np.interp(A ,np.arange(0 ,len(A ) ) ,A )
__A = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
__A = torch.from_numpy(A ).to(device=A )
# interpolate sigmas
__A = sigmas.log().lerp(sigmas.roll(1 ).log() ,0.5 ).exp()
__A = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
__A = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(A ).startswith("mps" ):
# mps does not support float64
__A = torch.from_numpy(A ).to(A ,dtype=torch.floataa )
else:
__A = torch.from_numpy(A ).to(A )
# interpolate timesteps
__A = self.sigma_to_t(A ).to(A ,dtype=timesteps.dtype )
__A = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,dim=-1 ).flatten()
__A = torch.cat([timesteps[:1], interleaved_timesteps] )
__A = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
__A = defaultdict(A )
def UpperCamelCase_ ( self : Any ,A : Any ):
# get log sigma
__A = sigma.log()
# get distribution
__A = log_sigma - self.log_sigmas[:, None]
# get sigmas range
__A = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
__A = low_idx + 1
__A = self.log_sigmas[low_idx]
__A = self.log_sigmas[high_idx]
# interpolate sigmas
__A = (low - log_sigma) / (low - high)
__A = w.clamp(0 ,1 )
# transform interpolation to time range
__A = (1 - w) * low_idx + w * high_idx
__A = t.view(sigma.shape )
return t
@property
def UpperCamelCase_ ( self : Dict ):
return self.sample is None
def UpperCamelCase_ ( self : Optional[Any] ,A : Union[torch.FloatTensor, np.ndarray] ,A : Union[float, torch.FloatTensor] ,A : Union[torch.FloatTensor, np.ndarray] ,A : bool = True ,):
__A = self.index_for_timestep(A )
# advance index counter by 1
__A = timestep.cpu().item() if torch.is_tensor(A ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
__A = self.sigmas[step_index]
__A = self.sigmas_interpol[step_index + 1]
__A = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
__A = self.sigmas[step_index - 1]
__A = self.sigmas_interpol[step_index]
__A = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
__A = 0
__A = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
__A = sigma_hat if self.state_in_first_order else sigma_interpol
__A = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
__A = sigma_hat if self.state_in_first_order else sigma_interpol
__A = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("prediction_type not implemented yet: sample" )
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
__A = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
__A = sigma_interpol - sigma_hat
# store for 2nd order step
__A = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
__A = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
__A = sigma_next - sigma_hat
__A = self.sample
__A = None
__A = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def UpperCamelCase_ ( self : Any ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__A = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(A ):
# mps does not support float64
__A = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
__A = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
__A = self.timesteps.to(original_samples.device )
__A = timesteps.to(original_samples.device )
__A = [self.index_for_timestep(A ,A ) for t in timesteps]
__A = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
__A = sigma.unsqueeze(-1 )
__A = original_samples + noise * sigma
return noisy_samples
def __len__( self : str ):
return self.config.num_train_timesteps
| 15 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
__A = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__A = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
__A = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
__A = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
__A = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
__A = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
__A = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
__A = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
__A = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
__A = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
__A = key.replace("image_encoder.module" , "flava.image_model" )
__A = key.replace("text_encoder.module" , "flava.text_model" )
__A = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
__A = key.replace("mm_encoder.module" , "flava.multimodal_model" )
__A = key.replace("text_projection" , "flava.text_projection" )
__A = key.replace("image_projection" , "flava.image_projection" )
__A = value.float()
for key, value in codebook_state_dict.items():
__A = value
return upgrade
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__A = FlavaConfig.from_pretrained(a_ )
else:
__A = FlavaConfig()
__A = FlavaForPreTraining(a_ ).eval()
__A = convert_dalle_checkpoint(a_ , a_ , save_checkpoint=a_ )
if os.path.exists(a_ ):
__A = torch.load(a_ , map_location="cpu" )
else:
__A = torch.hub.load_state_dict_from_url(a_ , map_location="cpu" )
__A = upgrade_state_dict(a_ , a_ )
hf_model.load_state_dict(a_ )
__A = hf_model.state_dict()
__A = count_parameters(a_ )
__A = count_parameters(a_ ) + count_parameters(a_ )
assert torch.allclose(a_ , a_ , atol=1E-3 )
hf_model.save_pretrained(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 15 | 1 |
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
_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 UpperCAmelCase ( a_ , a_ ) -> Union[str, Any]:
"""simple docstring"""
_enforce_args(a_ , a_ )
__A = [float("-inf" ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(a_ , a_ , a_ )
def UpperCAmelCase ( a_ , a_ , a_ ) -> str:
"""simple docstring"""
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 UpperCAmelCase ( a_ , a_ ) -> List[str]:
"""simple docstring"""
_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 UpperCAmelCase ( a_ , a_ ) -> int:
"""simple docstring"""
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 UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
__A = [6, 1_0, 1_2, 1_5, 2_0, 2_3]
__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 = 3_6
__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()
| 15 |
import os
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 logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE :Tuple = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
SCREAMING_SNAKE_CASE :List[Any] = {
'camembert-base': 512,
}
SCREAMING_SNAKE_CASE :List[str] = '▁'
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[Any] ,A : List[str] ,A : List[Any]="<s>" ,A : Tuple="</s>" ,A : Any="</s>" ,A : Optional[Any]="<s>" ,A : Tuple="<unk>" ,A : str="<pad>" ,A : int="<mask>" ,A : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] ,A : Optional[Dict[str, Any]] = None ,**A : Optional[Any] ,):
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,sep_token=A ,cls_token=A ,pad_token=A ,mask_token=A ,additional_special_tokens=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
__A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__A = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
__A = len(self.fairseq_tokens_to_ids )
__A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
__A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self : int ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__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 + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase_ ( self : Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self : int ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Dict ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase_ ( self : Optional[Any] ,A : Dict ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self : Dict ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Any ):
__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 UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 15 | 1 |
def UpperCAmelCase ( a_ = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
__A = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , a_ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 15 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , a_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__A , __A = arr[k - 1], arr[i]
else: # k is odd
__A , __A = arr[k - 1], arr[0]
generate(k - 1 , a_ )
generate(len(a_ ) , a_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :Dict = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 15 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = '▁'
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'vocab_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
),
},
'spm_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
)
},
}
SCREAMING_SNAKE_CASE :Optional[Any] = {
'facebook/s2t-small-librispeech-asr': 1024,
}
SCREAMING_SNAKE_CASE :Optional[Any] = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']
SCREAMING_SNAKE_CASE :Union[str, Any] = {'mustc': MUSTC_LANGS}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = MAX_MODEL_INPUT_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = []
def __init__( self : Optional[Any] ,A : List[str] ,A : str ,A : Tuple="<s>" ,A : Dict="</s>" ,A : List[str]="<pad>" ,A : Tuple="<unk>" ,A : Dict=False ,A : Tuple=False ,A : str=None ,A : List[str]=None ,A : Optional[Dict[str, Any]] = None ,**A : str ,):
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,do_upper_case=A ,do_lower_case=A ,tgt_lang=A ,lang_codes=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = do_upper_case
__A = do_lower_case
__A = load_json(A )
__A = {v: k for k, v in self.encoder.items()}
__A = spm_file
__A = load_spm(A ,self.sp_model_kwargs )
if lang_codes is not None:
__A = lang_codes
__A = LANGUAGES[lang_codes]
__A = [f'''<lang:{lang}>''' for lang in self.langs]
__A = {lang: self.sp_model.PieceToId(f'''<lang:{lang}>''' ) for lang in self.langs}
__A = self.lang_tokens
__A = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
__A = {}
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return len(self.encoder )
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
return self._tgt_lang
@tgt_lang.setter
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, Any] ):
__A = new_tgt_lang
self.set_tgt_lang_special_tokens(A )
def UpperCamelCase_ ( self : Optional[int] ,A : str ):
__A = self.lang_code_to_id[tgt_lang]
__A = [lang_code_id]
def UpperCamelCase_ ( self : str ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : Optional[int] ,A : Optional[int] ):
return self.encoder.get(A ,self.encoder[self.unk_token] )
def UpperCamelCase_ ( self : str ,A : int ):
return self.decoder.get(A ,self.unk_token )
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[str] ):
__A = []
__A = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
__A = self.sp_model.decode(A )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
__A = []
else:
current_sub_tokens.append(A )
__A = self.sp_model.decode(A )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def UpperCamelCase_ ( self : List[str] ,A : List[str] ,A : Tuple=None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCamelCase_ ( self : Tuple ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
__A = [1] * len(self.prefix_tokens )
__A = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(A )) + suffix_ones
return prefix_ones + ([0] * len(A )) + ([0] * len(A )) + suffix_ones
def UpperCamelCase_ ( self : Optional[int] ):
__A = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Tuple ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Dict ):
__A = d
# for backward compatibility
if not hasattr(self ,"sp_model_kwargs" ):
__A = {}
__A = load_spm(self.spm_file ,self.sp_model_kwargs )
def UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
__A = Path(A )
assert save_dir.is_dir(), f'''{save_directory} should be a directory'''
__A = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
__A = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
)
save_json(self.encoder ,A )
if os.path.abspath(self.spm_file ) != os.path.abspath(A ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file ,A )
elif not os.path.isfile(self.spm_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (str(A ), str(A ))
def UpperCAmelCase ( a_ , a_ ) -> sentencepiece.SentencePieceProcessor:
"""simple docstring"""
__A = sentencepiece.SentencePieceProcessor(**a_ )
spm.Load(str(a_ ) )
return spm
def UpperCAmelCase ( a_ ) -> Union[Dict, List]:
"""simple docstring"""
with open(a_ , "r" ) as f:
return json.load(a_ )
def UpperCAmelCase ( a_ , a_ ) -> None:
"""simple docstring"""
with open(a_ , "w" ) as f:
json.dump(a_ , a_ , indent=2 )
| 15 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
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__":
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :List[Any] = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 15 | 1 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : int ,A : int ):
__A = n
__A = [None] * self.n
__A = 0 # index of the first element
__A = 0
__A = 0
def __len__( self : Tuple ):
return self.size
def UpperCamelCase_ ( self : int ):
return self.size == 0
def UpperCamelCase_ ( self : str ):
return False if self.is_empty() else self.array[self.front]
def UpperCamelCase_ ( self : Union[str, Any] ,A : Dict ):
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
__A = data
__A = (self.rear + 1) % self.n
self.size += 1
return self
def UpperCamelCase_ ( self : Union[str, Any] ):
if self.size == 0:
raise Exception("UNDERFLOW" )
__A = self.array[self.front]
__A = None
__A = (self.front + 1) % self.n
self.size -= 1
return temp
| 15 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 2
@register_to_config
def __init__( self : str ,A : float = 0.02 ,A : float = 1_00 ,A : float = 1.0_07 ,A : float = 80 ,A : float = 0.05 ,A : float = 50 ,):
# standard deviation of the initial noise distribution
__A = sigma_max
# setable values
__A = None
__A = None
__A = None # sigma(t_i)
def UpperCamelCase_ ( self : str ,A : torch.FloatTensor ,A : Optional[int] = None ):
return sample
def UpperCamelCase_ ( self : Dict ,A : int ,A : Union[str, torch.device] = None ):
__A = num_inference_steps
__A = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__A = torch.from_numpy(A ).to(A )
__A = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__A = torch.tensor(A ,dtype=torch.floataa ,device=A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : float ,A : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
__A = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__A = 0
# sample eps ~ N(0, S_noise^2 * I)
__A = self.config.s_noise * randn_tensor(sample.shape ,generator=A ).to(sample.device )
__A = sigma + gamma * sigma
__A = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase_ ( self : Dict ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_hat + sigma_hat * model_output
__A = (sample_hat - pred_original_sample) / sigma_hat
__A = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : Optional[int] ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_prev + sigma_prev * model_output
__A = (sample_prev - pred_original_sample) / sigma_prev
__A = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : str ):
raise NotImplementedError()
| 15 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE :Dict = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
SCREAMING_SNAKE_CASE :Dict = reader.read()
SCREAMING_SNAKE_CASE :List[str] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
else:
SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
SCREAMING_SNAKE_CASE :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
SCREAMING_SNAKE_CASE :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
SCREAMING_SNAKE_CASE :Optional[Any] = config[key]
del config[key]
SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
SCREAMING_SNAKE_CASE :Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
SCREAMING_SNAKE_CASE :List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
SCREAMING_SNAKE_CASE :List[Any] = param_value
SCREAMING_SNAKE_CASE :str = True
if not has_changed:
SCREAMING_SNAKE_CASE :List[str] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 15 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE :Union[str, Any] = get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = "dummy_data"
snake_case_ = "datasets"
snake_case_ = False
def __init__( self : Optional[int] ,A : str ,A : str ,A : Union[Version, str] ,A : Optional[str] = None ,A : bool = False ,A : bool = True ,A : Optional[List[Callable]] = None ,):
__A = 0
__A = dataset_name
__A = cache_dir
__A = use_local_dummy_data
__A = config
# download_callbacks take a single url as input
__A = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__A = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__A = str(A )
# to be downloaded
__A = None
__A = None
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
if self._dummy_file is None:
__A = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase_ ( self : Optional[Any] ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def UpperCamelCase_ ( self : List[Any] ):
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def UpperCamelCase_ ( self : Tuple ):
__A = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__A = cached_path(
A ,cache_dir=self.cache_dir ,extract_compressed_file=A ,force_extract=A )
return os.path.join(A ,self.dummy_file_name )
@property
def UpperCamelCase_ ( self : str ):
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def UpperCamelCase_ ( self : Any ):
if self._bucket_url is None:
__A = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def UpperCamelCase_ ( self : Tuple ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def UpperCamelCase_ ( self : List[str] ,A : List[Any] ,*A : Dict ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__A = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__A = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A ,A ):
return self.create_dummy_data_dict(A ,A )
elif isinstance(A ,(list, tuple) ):
return self.create_dummy_data_list(A ,A )
else:
return self.create_dummy_data_single(A ,A )
def UpperCamelCase_ ( self : str ,A : List[Any] ,*A : List[Any] ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : List[str] ,A : List[str] ,A : Tuple ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : Any ,A : Any ,*A : Optional[Any] ,**A : List[str] ):
return path
def UpperCamelCase_ ( self : str ):
return {}
def UpperCamelCase_ ( self : int ,A : int ,A : Tuple ):
__A = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A ,A ):
for single_url in single_urls:
download_callback(A )
else:
__A = single_urls
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A ,A ):
__A = [os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) ) for x in single_urls]
else:
__A = single_urls
__A = os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) )
__A = value
# make sure that values are unique
if all(isinstance(A ,A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__A = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,A : str ):
__A = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__A = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,A ) ) for url in data_url )
__A = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__A = [data_url[0]] * len(A )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(A )
return dummy_data_list
def UpperCamelCase_ ( self : str ,A : List[Any] ,A : Optional[Any] ):
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(A ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : Dict ):
pass
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ):
def _iter_archive_members(A : Optional[Any] ):
# this preserves the order of the members inside the ZIP archive
__A = Path(self.dummy_file ).parent
__A = path.relative_to(A )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__A = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A )
__A = Path(A )
__A = _iter_archive_members(A ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(A ).as_posix(), file_path.open("rb" )
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
if not isinstance(A ,A ):
__A = [paths]
for path in paths:
if os.path.isfile(A ):
if os.path.basename(A ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A ):
if os.path.basename(A ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(A ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(A ,A )
| 15 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> float:
"""simple docstring"""
__A = np.array([[1, item, train_mtch[i]] for i, item in enumerate(a_ )] )
__A = np.array(a_ )
__A = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , a_ ) ) , x.transpose() ) , a_ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCAmelCase ( a_ , a_ , a_ ) -> float:
"""simple docstring"""
__A = (1, 2, 1)
__A = (1, 1, 0, 7)
__A = SARIMAX(
a_ , exog=a_ , order=a_ , seasonal_order=a_ )
__A = model.fit(disp=a_ , maxiter=6_0_0 , method="nm" )
__A = model_fit.predict(1 , len(a_ ) , exog=[test_match] )
return result[0]
def UpperCAmelCase ( a_ , a_ , a_ ) -> float:
"""simple docstring"""
__A = SVR(kernel="rbf" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(a_ , a_ )
__A = regressor.predict(a_ )
return y_pred[0]
def UpperCAmelCase ( a_ ) -> float:
"""simple docstring"""
train_user.sort()
__A = np.percentile(a_ , 2_5 )
__A = np.percentile(a_ , 7_5 )
__A = qa - qa
__A = qa - (iqr * 0.1)
return low_lim
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = 0
__A = 0
for i in list_vote:
if i > actual_result:
__A = not_safe + 1
else:
if abs(abs(a_ ) - abs(a_ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
SCREAMING_SNAKE_CASE :List[str] = [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
SCREAMING_SNAKE_CASE :Optional[int] = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
SCREAMING_SNAKE_CASE :Union[str, Any] = Normalizer().fit_transform(data_input_df.values)
# split data
SCREAMING_SNAKE_CASE :int = normalize_df[:, 2].tolist()
SCREAMING_SNAKE_CASE :List[str] = normalize_df[:, 0].tolist()
SCREAMING_SNAKE_CASE :List[Any] = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
SCREAMING_SNAKE_CASE :Dict = normalize_df[:, [1, 2]].tolist()
SCREAMING_SNAKE_CASE :str = x[: len(x) - 1]
SCREAMING_SNAKE_CASE :Any = x[len(x) - 1 :]
# for linear regression & sarimax
SCREAMING_SNAKE_CASE :List[Any] = total_date[: len(total_date) - 1]
SCREAMING_SNAKE_CASE :Tuple = total_user[: len(total_user) - 1]
SCREAMING_SNAKE_CASE :Tuple = total_match[: len(total_match) - 1]
SCREAMING_SNAKE_CASE :int = total_date[len(total_date) - 1 :]
SCREAMING_SNAKE_CASE :Tuple = total_user[len(total_user) - 1 :]
SCREAMING_SNAKE_CASE :int = total_match[len(total_match) - 1 :]
# voting system with forecasting
SCREAMING_SNAKE_CASE :str = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
SCREAMING_SNAKE_CASE :Dict = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 15 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[str] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
SCREAMING_SNAKE_CASE :Optional[int] = NewType('DataClass', Any)
SCREAMING_SNAKE_CASE :int = NewType('DataClassType', Any)
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
if isinstance(a_ , a_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def UpperCAmelCase ( a_ ) -> Callable[[str], Any]:
"""simple docstring"""
__A = {str(a_ ): choice for choice in choices}
return lambda a_ : str_to_choice.get(a_ , a_ )
def UpperCAmelCase ( *,
a_ = None , a_ = None , a_ = dataclasses.MISSING , a_ = dataclasses.MISSING , a_ = None , **a_ , ) -> dataclasses.Field:
"""simple docstring"""
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__A = {}
if aliases is not None:
__A = aliases
if help is not None:
__A = help
return dataclasses.field(metadata=a_ , default=a_ , default_factory=a_ , **a_ )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
def __init__( self : Union[str, Any] ,A : Union[DataClassType, Iterable[DataClassType]] ,**A : List[Any] ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__A = ArgumentDefaultsHelpFormatter
super().__init__(**A )
if dataclasses.is_dataclass(A ):
__A = [dataclass_types]
__A = list(A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(A )
@staticmethod
def UpperCamelCase_ ( A : ArgumentParser ,A : dataclasses.Field ):
__A = f'''--{field.name}'''
__A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type ,A ):
raise RuntimeError(
"Unresolved type detected, which should have been done with the help of "
"`typing.get_type_hints` method by default" )
__A = kwargs.pop("aliases" ,[] )
if isinstance(A ,A ):
__A = [aliases]
__A = getattr(field.type ,"__origin__" ,field.type )
if origin_type is Union or (hasattr(A ,"UnionType" ) and isinstance(A ,types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(A ) not in field.type.__args__
):
raise ValueError(
"Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
" the argument parser only supports one type per argument."
f''' Problem encountered in field \'{field.name}\'.''' )
if type(A ) not in field.type.__args__:
# filter `str` in Union
__A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__A = getattr(field.type ,"__origin__" ,field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__A = (
field.type.__args__[0] if isinstance(A ,field.type.__args__[1] ) else field.type.__args__[1]
)
__A = getattr(field.type ,"__origin__" ,field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__A = {}
if origin_type is Literal or (isinstance(field.type ,A ) and issubclass(field.type ,A )):
if origin_type is Literal:
__A = field.type.__args__
else:
__A = [x.value for x in field.type]
__A = make_choice_type_function(kwargs["choices"] )
if field.default is not dataclasses.MISSING:
__A = field.default
else:
__A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__A = copy(A )
# Hack because type=bool in argparse does not behave as we want.
__A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__A = default
# This tells argparse we accept 0 or 1 value after --field_name
__A = "?"
# This is the value that will get picked if we do --field_name (without value)
__A = True
elif isclass(A ) and issubclass(A ,A ):
__A = field.type.__args__[0]
__A = "+"
if field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
elif field.default is dataclasses.MISSING:
__A = True
else:
__A = field.type
if field.default is not dataclasses.MISSING:
__A = field.default
elif field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
else:
__A = True
parser.add_argument(A ,*A ,**A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__A = False
parser.add_argument(f'''--no_{field.name}''' ,action="store_false" ,dest=field.name ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : DataClassType ):
if hasattr(A ,"_argument_group_name" ):
__A = self.add_argument_group(dtype._argument_group_name )
else:
__A = self
try:
__A = get_type_hints(A )
except NameError:
raise RuntimeError(
f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
"removing line of `from __future__ import annotations` which opts in Postponed "
"Evaluation of Annotations (PEP 563)" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A ):
__A = ".".join(map(A ,sys.version_info[:3] ) )
raise RuntimeError(
f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
"line of `from __future__ import annotations` which opts in union types as "
"`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
"support Python versions that lower than 3.10, you need to use "
"`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
"`X | None`." ) from ex
raise
for field in dataclasses.fields(A ):
if not field.init:
continue
__A = type_hints[field.name]
self._parse_dataclass_field(A ,A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[Any]=None ,A : List[Any]=False ,A : Optional[Any]=True ,A : Union[str, Any]=None ,A : Union[str, Any]=None ,):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
__A = []
if args_filename:
args_files.append(Path(A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__A = ArgumentParser()
args_file_parser.add_argument(A ,type=A ,action="append" )
# Use only remaining args for further parsing (remove the args_file_flag)
__A , __A = args_file_parser.parse_known_args(args=A )
__A = vars(A ).get(args_file_flag.lstrip("-" ) ,A )
if cmd_args_file_paths:
args_files.extend([Path(A ) for p in cmd_args_file_paths] )
__A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__A = file_args + args if args is not None else file_args + sys.argv[1:]
__A , __A = self.parse_known_args(args=A )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in vars(A ).items() if k in keys}
for k in keys:
delattr(A ,A )
__A = dtype(**A )
outputs.append(A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def UpperCamelCase_ ( self : Dict ,A : Dict[str, Any] ,A : bool = False ):
__A = set(args.keys() )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
__A = dtype(**A )
outputs.append(A )
if not allow_extra_keys and unused_keys:
raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(A )}''' )
return tuple(A )
def UpperCamelCase_ ( self : List[str] ,A : str ,A : bool = False ):
with open(Path(A ) ,encoding="utf-8" ) as open_json_file:
__A = json.loads(open_json_file.read() )
__A = self.parse_dict(A ,allow_extra_keys=A )
return tuple(A )
def UpperCamelCase_ ( self : int ,A : str ,A : bool = False ):
__A = self.parse_dict(yaml.safe_load(Path(A ).read_text() ) ,allow_extra_keys=A )
return tuple(A )
| 15 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
SCREAMING_SNAKE_CASE :List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
SCREAMING_SNAKE_CASE :str = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Tuple:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
__A = new_id
# turn into Numpy arrays
__A = np.array(a_ )
__A = np.array(a_ )
if reduce_labels:
__A = 2_5_5
__A = label - 1
__A = 2_5_5
__A = label != ignore_index
__A = np.not_equal(a_ , a_ )
__A = pred_label[mask]
__A = np.array(a_ )[mask]
__A = pred_label[pred_label == label]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Union[str, Any]:
"""simple docstring"""
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a_ , a_ ):
__A , __A , __A , __A = intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = None , a_ = False , ) -> str:
"""simple docstring"""
__A , __A , __A , __A = total_intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
# compute metrics
__A = {}
__A = total_area_intersect.sum() / total_area_label.sum()
__A = total_area_intersect / total_area_union
__A = total_area_intersect / total_area_label
__A = np.nanmean(a_ )
__A = np.nanmean(a_ )
__A = all_acc
__A = iou
__A = acc
if nan_to_num is not None:
__A = {metric: np.nan_to_num(a_ , nan=a_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) ,reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] ,)
def UpperCamelCase_ ( self : int ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : bool ,A : Optional[int] = None ,A : Optional[Dict[int, int]] = None ,A : bool = False ,):
__A = mean_iou(
results=A ,gt_seg_maps=A ,num_labels=A ,ignore_index=A ,nan_to_num=A ,label_map=A ,reduce_labels=A ,)
return iou_result
| 15 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE :Dict = TypeVar('T')
class UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : List[str] ,A : bool = True ):
__A = {} # dictionary of lists
__A = directed
def UpperCamelCase_ ( self : str ,A : T ,A : T ):
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(A )
self.adj_list[destination_vertex].append(A )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(A )
__A = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(A )
__A = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
__A = [destination_vertex]
__A = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(A )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(A )
__A = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
__A = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
__A = [destination_vertex]
__A = []
return self
def __repr__( self : Union[str, Any] ):
return pformat(self.adj_list )
| 15 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[str] = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Dict = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE :Optional[Any] = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[int] ,A : Optional[Any] ,A : Optional[int]=False ,A : int=False ,A : Union[str, Any]=False ,A : int=None ,A : Optional[Any]=None ,A : Union[str, Any]=None ,A : Optional[Any]=None ,A : Optional[Dict[str, Any]] = None ,**A : Tuple ,):
__A = {} if sp_model_kwargs is None else sp_model_kwargs
__A = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
__A = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__A = "<|endoftext|>" if eos_token is None else eos_token
__A = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__A = unk_token if pad_token is None else pad_token
__A = eos_token if bos_token is None else bos_token
else:
__A = "<pad>" if pad_token is None else pad_token
__A = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=A ,remove_space=A ,keep_accents=A ,bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = do_lower_case
__A = remove_space
__A = keep_accents
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
__A = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__A = re.compile(
f'''[{''.join(map(A ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(1_27 ,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Optional[Any] ,A : Union[str, Any] ):
__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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase_ ( self : List[str] ):
return len(self.sp_model )
def UpperCamelCase_ ( self : int ,A : str ):
__A = self.non_printing_characters_re.sub("" ,A )
# Normalize whitespaces
__A = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
__A = unicodedata.normalize("NFC" ,A )
return text
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,**A : Optional[int] ):
__A = self.preprocess_text(A )
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : int ):
return self.sp_model.IdToPiece(A )
@staticmethod
def UpperCamelCase_ ( A : str ):
return out_string
def UpperCamelCase_ ( self : str ,A : List[str] ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string
def UpperCamelCase_ ( self : str ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : List[str] ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, List[str]] ,A : Union[str, bool] = False ):
if isinstance(A ,A ):
__A = self.preprocess_text(A )
__A = self.sp_model.encode(A )
else:
__A = [self.preprocess_text(A ) for t in text]
__A = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
__A = torch.tensor(A )
return token_ids
def UpperCamelCase_ ( self : List[Any] ,A : Union[int, List[int]] ):
return self.sp_model.decode(A )
def UpperCamelCase_ ( self : List[str] ,A : "Conversation" ):
__A = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__A = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 15 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
SCREAMING_SNAKE_CASE :Dict = pytest.mark.integration
@require_faiss
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Tuple ):
__A = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A ) for x in np.arange(30 ).tolist()]} )
return dset
def UpperCamelCase_ ( self : Optional[Any] ):
import faiss
__A = self._create_dummy_dataset()
__A = dset.map(
lambda A ,A : {"vecs": i * np.ones(5 ,dtype=np.floataa )} ,with_indices=A ,keep_in_memory=A )
__A = dset.add_faiss_index("vecs" ,batch_size=1_00 ,metric_type=faiss.METRIC_INNER_PRODUCT )
__A , __A = dset.get_nearest_examples("vecs" ,np.ones(5 ,dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] ,"my_name-train_29" )
dset.drop_index("vecs" )
def UpperCamelCase_ ( self : Tuple ):
import faiss
__A = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 ,1 ) ,index_name="vecs" ,batch_size=1_00 ,metric_type=faiss.METRIC_INNER_PRODUCT ,)
__A , __A = dset.get_nearest_examples("vecs" ,np.ones(5 ,dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] ,"my_name-train_29" )
def UpperCamelCase_ ( self : List[str] ):
import faiss
__A = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 ,1 ) ,index_name="vecs" ,metric_type=faiss.METRIC_INNER_PRODUCT ,)
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A ) as tmp_file:
dset.save_faiss_index("vecs" ,tmp_file.name )
dset.load_faiss_index("vecs2" ,tmp_file.name )
os.unlink(tmp_file.name )
__A , __A = dset.get_nearest_examples("vecs2" ,np.ones(5 ,dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] ,"my_name-train_29" )
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 ,1 ) ,index_name="vecs" )
dset.drop_index("vecs" )
self.assertRaises(A ,partial(dset.get_nearest_examples ,"vecs2" ,np.ones(5 ,dtype=np.floataa ) ) )
def UpperCamelCase_ ( self : Dict ):
from elasticsearch import Elasticsearch
__A = self._create_dummy_dataset()
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
__A = {"acknowledged": True}
mocked_bulk.return_value([(True, None)] * 30 )
__A = {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
__A = Elasticsearch()
dset.add_elasticsearch_index("filename" ,es_client=A )
__A , __A = dset.get_nearest_examples("filename" ,"my_name-train_29" )
self.assertEqual(examples["filename"][0] ,"my_name-train_29" )
@require_faiss
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Optional[int] ):
import faiss
__A = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal ,5 )
index.add_vectors(np.zeros((5, 5) ,dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal ,10 )
# single query
__A = np.zeros(5 ,dtype=np.floataa )
__A = 1
__A , __A = index.search(A )
self.assertRaises(A ,index.search ,query.reshape(-1 ,1 ) )
self.assertGreater(scores[0] ,0 )
self.assertEqual(indices[0] ,1 )
# batched queries
__A = np.eye(5 ,dtype=np.floataa )[::-1]
__A , __A = index.search_batch(A )
self.assertRaises(A ,index.search_batch ,queries[0] )
__A = [scores[0] for scores in total_scores]
__A = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A ) ,0 )
self.assertListEqual([4, 3, 2, 1, 0] ,A )
def UpperCamelCase_ ( self : Tuple ):
import faiss
__A = FaissIndex(string_factory="Flat" )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index ,faiss.IndexFlat )
__A = FaissIndex(string_factory="LSH" )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index ,faiss.IndexLSH )
with self.assertRaises(A ):
__A = FaissIndex(string_factory="Flat" ,custom_index=faiss.IndexFlat(5 ) )
def UpperCamelCase_ ( self : Any ):
import faiss
__A = faiss.IndexFlat(5 )
__A = FaissIndex(custom_index=A )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index ,faiss.IndexFlat )
def UpperCamelCase_ ( self : Union[str, Any] ):
import faiss
__A = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A ) as tmp_file:
index.save(tmp_file.name )
__A = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__A = np.zeros(5 ,dtype=np.floataa )
__A = 1
__A , __A = index.search(A )
self.assertGreater(scores[0] ,0 )
self.assertEqual(indices[0] ,1 )
@require_faiss
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
import faiss
__A = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
__A = "index.faiss"
__A = F'''mock://{index_name}'''
index.save(a_ , storage_options=mockfs.storage_options )
__A = FaissIndex.load(a_ , storage_options=mockfs.storage_options )
__A = np.zeros(5 , dtype=np.floataa )
__A = 1
__A , __A = index.search(a_ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : Dict ):
from elasticsearch import Elasticsearch
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
__A = Elasticsearch()
__A = {"acknowledged": True}
__A = ElasticSearchIndex(es_client=A )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["foo", "bar", "foobar"] )
# single query
__A = "foo"
__A = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
__A , __A = index.search(A )
self.assertEqual(scores[0] ,1 )
self.assertEqual(indices[0] ,0 )
# single query with timeout
__A = "foo"
__A = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
__A , __A = index.search(A ,request_timeout=30 )
self.assertEqual(scores[0] ,1 )
self.assertEqual(indices[0] ,0 )
# batched queries
__A = ["foo", "bar", "foobar"]
__A = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
__A , __A = index.search_batch(A )
__A = [scores[0] for scores in total_scores]
__A = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A ) ,0 )
self.assertListEqual([1, 1, 1] ,A )
# batched queries with timeout
__A = ["foo", "bar", "foobar"]
__A = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
__A , __A = index.search_batch(A ,request_timeout=30 )
__A = [scores[0] for scores in total_scores]
__A = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A ) ,0 )
self.assertListEqual([1, 1, 1] ,A )
| 15 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
__A = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__A = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
__A = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
__A = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
__A = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
__A = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
__A = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
__A = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
__A = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
__A = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
__A = key.replace("image_encoder.module" , "flava.image_model" )
__A = key.replace("text_encoder.module" , "flava.text_model" )
__A = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
__A = key.replace("mm_encoder.module" , "flava.multimodal_model" )
__A = key.replace("text_projection" , "flava.text_projection" )
__A = key.replace("image_projection" , "flava.image_projection" )
__A = value.float()
for key, value in codebook_state_dict.items():
__A = value
return upgrade
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__A = FlavaConfig.from_pretrained(a_ )
else:
__A = FlavaConfig()
__A = FlavaForPreTraining(a_ ).eval()
__A = convert_dalle_checkpoint(a_ , a_ , save_checkpoint=a_ )
if os.path.exists(a_ ):
__A = torch.load(a_ , map_location="cpu" )
else:
__A = torch.hub.load_state_dict_from_url(a_ , map_location="cpu" )
__A = upgrade_state_dict(a_ , a_ )
hf_model.load_state_dict(a_ )
__A = hf_model.state_dict()
__A = count_parameters(a_ )
__A = count_parameters(a_ ) + count_parameters(a_ )
assert torch.allclose(a_ , a_ , atol=1E-3 )
hf_model.save_pretrained(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 15 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE :str = 'RegNetConfig'
# Base docstring
SCREAMING_SNAKE_CASE :List[str] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 1088, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE :Optional[int] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Any = 'tabby, tabby cat'
SCREAMING_SNAKE_CASE :Optional[int] = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : int ,A : int = 3 ,A : int = 1 ,A : int = 1 ,A : Optional[str] = "relu" ,**A : Dict ,):
super().__init__(**A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__A = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=A ,strides=A ,padding="VALID" ,groups=A ,use_bias=A ,name="convolution" ,)
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
__A = ACTaFN[activation] if activation is not None else tf.identity
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
__A = self.convolution(self.padding(A ) )
__A = self.normalization(A )
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : RegNetConfig ,**A : str ):
super().__init__(**A )
__A = config.num_channels
__A = TFRegNetConvLayer(
out_channels=config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act ,name="embedder" ,)
def UpperCamelCase_ ( self : Tuple ,A : Optional[Any] ):
__A = shape_list(A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__A = tf.transpose(A ,perm=(0, 2, 3, 1) )
__A = self.embedder(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : int ,A : int = 2 ,**A : Tuple ):
super().__init__(**A )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=1 ,strides=A ,use_bias=A ,name="convolution" )
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
def UpperCamelCase_ ( self : Union[str, Any] ,A : tf.Tensor ,A : bool = False ):
return self.normalization(self.convolution(A ) ,training=A )
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Dict ,A : int ,A : int ,**A : str ):
super().__init__(**A )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
__A = [
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="relu" ,name="attention.0" ),
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="sigmoid" ,name="attention.2" ),
]
def UpperCamelCase_ ( self : Dict ,A : List[Any] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__A = self.pooler(A )
for layer_module in self.attention:
__A = layer_module(A )
__A = hidden_state * pooled
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : Optional[int] ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.2" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : int ,A : Optional[int] ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : str ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetSELayer(A ,reduced_channels=int(round(in_channels / 4 ) ) ,name="layer.2" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.3" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : Dict ,A : Any ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 2 ,A : int = 2 ,**A : Optional[int] ):
super().__init__(**A )
__A = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
__A = [
# downsampling is done in the first layer with stride of 2
layer(A ,A ,A ,stride=A ,name="layers.0" ),
*[layer(A ,A ,A ,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def UpperCamelCase_ ( self : Any ,A : List[str] ):
for layer_module in self.layers:
__A = layer_module(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any ,A : RegNetConfig ,**A : List[str] ):
super().__init__(**A )
__A = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
A ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,name="stages.0" ,) )
__A = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(A ,config.depths[1:] ) ):
self.stages.append(TFRegNetStage(A ,A ,A ,depth=A ,name=f'''stages.{i+1}''' ) )
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor ,A : bool = False ,A : bool = True ):
__A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__A = hidden_states + (hidden_state,)
__A = stage_module(A )
if output_hidden_states:
__A = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=A ,hidden_states=A )
@keras_serializable
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
snake_case_ = RegNetConfig
def __init__( self : int ,A : Optional[int] ,**A : Dict ):
super().__init__(**A )
__A = config
__A = TFRegNetEmbeddings(A ,name="embedder" )
__A = TFRegNetEncoder(A ,name="encoder" )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
@unpack_inputs
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : bool = False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.embedder(A ,training=A )
__A = self.encoder(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = encoder_outputs[0]
__A = self.pooler(A )
# Change to NCHW output format have uniformity in the modules
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__A = tuple([tf.transpose(A ,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A ,pooler_output=A ,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = RegNetConfig
snake_case_ = "regnet"
snake_case_ = "pixel_values"
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) ,dtype=tf.floataa )}
SCREAMING_SNAKE_CASE :Dict = R'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE :Dict = R'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,*A : List[Any] ,**A : str ):
super().__init__(A ,*A ,**A )
__A = TFRegNetMainLayer(A ,name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=A ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : int=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
pixel_values=A ,output_hidden_states=A ,return_dict=A ,training=A ,)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state ,pooler_output=outputs.pooler_output ,hidden_states=outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : RegNetConfig ,*A : str ,**A : Tuple ):
super().__init__(A ,*A ,**A )
__A = config.num_labels
__A = TFRegNetMainLayer(A ,name="regnet" )
# classification head
__A = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels ,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=A ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor = None ,A : tf.Tensor = None ,A : bool = None ,A : bool = None ,A : Union[str, Any]=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = outputs.pooler_output if return_dict else outputs[1]
__A = self.classifier[0](A )
__A = self.classifier[1](A )
__A = None if labels is None else self.hf_compute_loss(labels=A ,logits=A )
if not return_dict:
__A = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=A ,logits=A ,hidden_states=outputs.hidden_states )
| 15 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[Any] = {
'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "owlvit_text_model"
def __init__( self : Dict ,A : Any=4_94_08 ,A : Optional[Any]=5_12 ,A : Optional[Any]=20_48 ,A : int=12 ,A : List[Any]=8 ,A : str=16 ,A : Dict="quick_gelu" ,A : str=1E-5 ,A : Dict=0.0 ,A : str=0.02 ,A : List[Any]=1.0 ,A : List[Any]=0 ,A : int=4_94_06 ,A : List[Any]=4_94_07 ,**A : List[str] ,):
super().__init__(pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,**A )
__A = vocab_size
__A = hidden_size
__A = intermediate_size
__A = num_hidden_layers
__A = num_attention_heads
__A = max_position_embeddings
__A = hidden_act
__A = layer_norm_eps
__A = attention_dropout
__A = initializer_range
__A = initializer_factor
@classmethod
def UpperCamelCase_ ( cls : List[str] ,A : Union[str, os.PathLike] ,**A : Optional[Any] ):
cls._set_token_in_kwargs(A )
__A , __A = cls.get_config_dict(A ,**A )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
__A = 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(A ,**A )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "owlvit_vision_model"
def __init__( self : List[Any] ,A : str=7_68 ,A : Tuple=30_72 ,A : List[str]=12 ,A : str=12 ,A : List[str]=3 ,A : List[str]=7_68 ,A : Optional[Any]=32 ,A : Optional[int]="quick_gelu" ,A : List[str]=1E-5 ,A : Optional[int]=0.0 ,A : Union[str, Any]=0.02 ,A : Optional[Any]=1.0 ,**A : Union[str, Any] ,):
super().__init__(**A )
__A = hidden_size
__A = intermediate_size
__A = num_hidden_layers
__A = num_attention_heads
__A = num_channels
__A = image_size
__A = patch_size
__A = hidden_act
__A = layer_norm_eps
__A = attention_dropout
__A = initializer_range
__A = initializer_factor
@classmethod
def UpperCamelCase_ ( cls : Union[str, Any] ,A : Union[str, os.PathLike] ,**A : List[Any] ):
cls._set_token_in_kwargs(A )
__A , __A = cls.get_config_dict(A ,**A )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
__A = config_dict["vision_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(A ,**A )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "owlvit"
snake_case_ = True
def __init__( self : Any ,A : List[Any]=None ,A : Tuple=None ,A : Optional[Any]=5_12 ,A : Tuple=2.65_92 ,A : Dict=True ,**A : Optional[Any] ,):
super().__init__(**A )
if text_config is None:
__A = {}
logger.info("text_config is None. Initializing the OwlViTTextConfig with default values." )
if vision_config is None:
__A = {}
logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values." )
__A = OwlViTTextConfig(**A )
__A = OwlViTVisionConfig(**A )
__A = projection_dim
__A = logit_scale_init_value
__A = return_dict
__A = 1.0
@classmethod
def UpperCamelCase_ ( cls : List[Any] ,A : Union[str, os.PathLike] ,**A : List[Any] ):
cls._set_token_in_kwargs(A )
__A , __A = cls.get_config_dict(A ,**A )
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(A ,**A )
@classmethod
def UpperCamelCase_ ( cls : Dict ,A : Dict ,A : Dict ,**A : Any ):
__A = {}
__A = text_config
__A = vision_config
return cls.from_dict(A ,**A )
def UpperCamelCase_ ( self : Any ):
__A = copy.deepcopy(self.__dict__ )
__A = self.text_config.to_dict()
__A = self.vision_config.to_dict()
__A = self.__class__.model_type
return output
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def UpperCamelCase_ ( self : Dict ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("attention_mask", {0: "batch", 1: "sequence"}),
] )
@property
def UpperCamelCase_ ( self : List[str] ):
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
] )
@property
def UpperCamelCase_ ( self : Dict ):
return 1E-4
def UpperCamelCase_ ( self : Union[str, Any] ,A : "ProcessorMixin" ,A : int = -1 ,A : int = -1 ,A : Optional["TensorType"] = None ,):
__A = super().generate_dummy_inputs(
processor.tokenizer ,batch_size=A ,seq_length=A ,framework=A )
__A = super().generate_dummy_inputs(
processor.image_processor ,batch_size=A ,framework=A )
return {**text_input_dict, **image_input_dict}
@property
def UpperCamelCase_ ( self : Any ):
return 14
| 15 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ ) -> None: # Max Heap
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) == 0:
return array
__A = 2 * math.ceil(math.loga(len(a_ ) ) )
__A = 1_6
return intro_sort(a_ , 0 , len(a_ ) , a_ , a_ )
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> list:
"""simple docstring"""
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()
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma : ').strip()
SCREAMING_SNAKE_CASE :str = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 15 | 1 |
from math import ceil
def UpperCAmelCase ( a_ = 1_0_0_1 ) -> int:
"""simple docstring"""
__A = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__A = 2 * i + 1
__A = 2 * i
__A = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
SCREAMING_SNAKE_CASE :Tuple = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number')
| 15 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
SCREAMING_SNAKE_CASE :Optional[int] = NewType('DataClass', Any)
SCREAMING_SNAKE_CASE :int = NewType('DataClassType', Any)
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
if isinstance(a_ , a_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def UpperCAmelCase ( a_ ) -> Callable[[str], Any]:
"""simple docstring"""
__A = {str(a_ ): choice for choice in choices}
return lambda a_ : str_to_choice.get(a_ , a_ )
def UpperCAmelCase ( *,
a_ = None , a_ = None , a_ = dataclasses.MISSING , a_ = dataclasses.MISSING , a_ = None , **a_ , ) -> dataclasses.Field:
"""simple docstring"""
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__A = {}
if aliases is not None:
__A = aliases
if help is not None:
__A = help
return dataclasses.field(metadata=a_ , default=a_ , default_factory=a_ , **a_ )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
def __init__( self : Union[str, Any] ,A : Union[DataClassType, Iterable[DataClassType]] ,**A : List[Any] ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__A = ArgumentDefaultsHelpFormatter
super().__init__(**A )
if dataclasses.is_dataclass(A ):
__A = [dataclass_types]
__A = list(A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(A )
@staticmethod
def UpperCamelCase_ ( A : ArgumentParser ,A : dataclasses.Field ):
__A = f'''--{field.name}'''
__A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type ,A ):
raise RuntimeError(
"Unresolved type detected, which should have been done with the help of "
"`typing.get_type_hints` method by default" )
__A = kwargs.pop("aliases" ,[] )
if isinstance(A ,A ):
__A = [aliases]
__A = getattr(field.type ,"__origin__" ,field.type )
if origin_type is Union or (hasattr(A ,"UnionType" ) and isinstance(A ,types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(A ) not in field.type.__args__
):
raise ValueError(
"Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
" the argument parser only supports one type per argument."
f''' Problem encountered in field \'{field.name}\'.''' )
if type(A ) not in field.type.__args__:
# filter `str` in Union
__A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__A = getattr(field.type ,"__origin__" ,field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__A = (
field.type.__args__[0] if isinstance(A ,field.type.__args__[1] ) else field.type.__args__[1]
)
__A = getattr(field.type ,"__origin__" ,field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__A = {}
if origin_type is Literal or (isinstance(field.type ,A ) and issubclass(field.type ,A )):
if origin_type is Literal:
__A = field.type.__args__
else:
__A = [x.value for x in field.type]
__A = make_choice_type_function(kwargs["choices"] )
if field.default is not dataclasses.MISSING:
__A = field.default
else:
__A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__A = copy(A )
# Hack because type=bool in argparse does not behave as we want.
__A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__A = default
# This tells argparse we accept 0 or 1 value after --field_name
__A = "?"
# This is the value that will get picked if we do --field_name (without value)
__A = True
elif isclass(A ) and issubclass(A ,A ):
__A = field.type.__args__[0]
__A = "+"
if field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
elif field.default is dataclasses.MISSING:
__A = True
else:
__A = field.type
if field.default is not dataclasses.MISSING:
__A = field.default
elif field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
else:
__A = True
parser.add_argument(A ,*A ,**A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__A = False
parser.add_argument(f'''--no_{field.name}''' ,action="store_false" ,dest=field.name ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : DataClassType ):
if hasattr(A ,"_argument_group_name" ):
__A = self.add_argument_group(dtype._argument_group_name )
else:
__A = self
try:
__A = get_type_hints(A )
except NameError:
raise RuntimeError(
f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
"removing line of `from __future__ import annotations` which opts in Postponed "
"Evaluation of Annotations (PEP 563)" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A ):
__A = ".".join(map(A ,sys.version_info[:3] ) )
raise RuntimeError(
f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
"line of `from __future__ import annotations` which opts in union types as "
"`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
"support Python versions that lower than 3.10, you need to use "
"`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
"`X | None`." ) from ex
raise
for field in dataclasses.fields(A ):
if not field.init:
continue
__A = type_hints[field.name]
self._parse_dataclass_field(A ,A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[Any]=None ,A : List[Any]=False ,A : Optional[Any]=True ,A : Union[str, Any]=None ,A : Union[str, Any]=None ,):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
__A = []
if args_filename:
args_files.append(Path(A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__A = ArgumentParser()
args_file_parser.add_argument(A ,type=A ,action="append" )
# Use only remaining args for further parsing (remove the args_file_flag)
__A , __A = args_file_parser.parse_known_args(args=A )
__A = vars(A ).get(args_file_flag.lstrip("-" ) ,A )
if cmd_args_file_paths:
args_files.extend([Path(A ) for p in cmd_args_file_paths] )
__A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__A = file_args + args if args is not None else file_args + sys.argv[1:]
__A , __A = self.parse_known_args(args=A )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in vars(A ).items() if k in keys}
for k in keys:
delattr(A ,A )
__A = dtype(**A )
outputs.append(A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def UpperCamelCase_ ( self : Dict ,A : Dict[str, Any] ,A : bool = False ):
__A = set(args.keys() )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
__A = dtype(**A )
outputs.append(A )
if not allow_extra_keys and unused_keys:
raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(A )}''' )
return tuple(A )
def UpperCamelCase_ ( self : List[str] ,A : str ,A : bool = False ):
with open(Path(A ) ,encoding="utf-8" ) as open_json_file:
__A = json.loads(open_json_file.read() )
__A = self.parse_dict(A ,allow_extra_keys=A )
return tuple(A )
def UpperCamelCase_ ( self : int ,A : str ,A : bool = False ):
__A = self.parse_dict(yaml.safe_load(Path(A ).read_text() ) ,allow_extra_keys=A )
return tuple(A )
| 15 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] ,A : List[str] ,A : List[Any]=3 ,A : Any=32 ,A : Optional[int]=3 ,A : Optional[int]=10 ,A : Optional[Any]=[8, 16, 32, 64] ,A : Optional[Any]=[1, 1, 2, 1] ,A : Any=True ,A : str=True ,A : Any="relu" ,A : Dict=3 ,A : Optional[Any]=None ,A : Dict=["stage2", "stage3", "stage4"] ,A : List[str]=[2, 3, 4] ,A : Union[str, Any]=1 ,):
__A = parent
__A = batch_size
__A = image_size
__A = num_channels
__A = embeddings_size
__A = hidden_sizes
__A = depths
__A = is_training
__A = use_labels
__A = hidden_act
__A = num_labels
__A = scope
__A = len(A )
__A = out_features
__A = out_indices
__A = num_groups
def UpperCamelCase_ ( self : Any ):
__A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size] ,self.num_labels )
__A = self.get_config()
return config, pixel_values, labels
def UpperCamelCase_ ( self : Tuple ):
return BitConfig(
num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,out_features=self.out_features ,out_indices=self.out_indices ,num_groups=self.num_groups ,)
def UpperCamelCase_ ( self : Dict ,A : int ,A : Dict ,A : List[Any] ):
__A = BitModel(config=A )
model.to(A )
model.eval()
__A = model(A )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[Any] ,A : List[str] ,A : Tuple ):
__A = self.num_labels
__A = BitForImageClassification(A )
model.to(A )
model.eval()
__A = model(A ,labels=A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ,A : List[str] ,A : List[str] ):
__A = BitBackbone(config=A )
model.to(A )
model.eval()
__A = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,len(config.out_features ) )
self.parent.assertListEqual(model.channels ,config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__A = None
__A = BitBackbone(config=A )
model.to(A )
model.eval()
__A = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,1 )
self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]] )
def UpperCamelCase_ ( self : Optional[int] ):
__A = self.prepare_config_and_inputs()
__A , __A , __A = config_and_inputs
__A = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
snake_case_ = (
{"feature-extraction": BitModel, "image-classification": BitForImageClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : Optional[int] ):
__A = BitModelTester(self )
__A = ConfigTester(self ,config_class=A ,has_text_modality=A )
def UpperCamelCase_ ( self : Optional[int] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase_ ( self : Optional[int] ):
return
@unittest.skip(reason="Bit does not output attentions" )
def UpperCamelCase_ ( self : Any ):
pass
@unittest.skip(reason="Bit does not use inputs_embeds" )
def UpperCamelCase_ ( self : List[Any] ):
pass
@unittest.skip(reason="Bit does not support input and output embeddings" )
def UpperCamelCase_ ( self : List[str] ):
pass
def UpperCamelCase_ ( self : Union[str, Any] ):
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(A )
__A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A = [*signature.parameters.keys()]
__A = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,A )
def UpperCamelCase_ ( self : Tuple ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self : Optional[Any] ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A )
def UpperCamelCase_ ( self : List[str] ):
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(config=A )
for name, module in model.named_modules():
if isinstance(A ,(nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,)
self.assertTrue(
torch.all(module.bias == 0 ) ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' ,)
def UpperCamelCase_ ( self : Tuple ):
def check_hidden_states_output(A : Any ,A : List[str] ,A : Tuple ):
__A = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
__A = model(**self._prepare_for_class(A ,A ) )
__A = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__A = self.model_tester.num_stages
self.assertEqual(len(A ) ,expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
__A , __A = self.model_tester.prepare_config_and_inputs_for_common()
__A = ["preactivation", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
__A = layer_type
__A = True
check_hidden_states_output(A ,A ,A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A = True
check_hidden_states_output(A ,A ,A )
@unittest.skip(reason="Bit does not use feedforward chunking" )
def UpperCamelCase_ ( self : Optional[int] ):
pass
def UpperCamelCase_ ( self : int ):
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def UpperCamelCase_ ( self : List[str] ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = BitModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCAmelCase ( ) -> int:
"""simple docstring"""
__A = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self : List[str] ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def UpperCamelCase_ ( self : Any ):
__A = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A )
__A = self.default_image_processor
__A = prepare_img()
__A = image_processor(images=A ,return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
__A = model(**A )
# verify the logits
__A = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape ,A )
__A = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,A ,atol=1E-4 ) )
@require_torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (BitBackbone,) if is_torch_available() else ()
snake_case_ = BitConfig
snake_case_ = False
def UpperCamelCase_ ( self : Optional[int] ):
__A = BitModelTester(self )
| 15 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
return False
__A = 0
__A = 0
__A = 1
# Calculating the hash of pattern and substring of text
for i in range(a_ ):
__A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
__A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
__A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
__A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = "abc1abc12"
__A = "alskfjaldsabc1abc1abc12k23adsfabcabc"
__A = "alskfjaldsk23adsfabcabc"
assert rabin_karp(a_ , a_ ) and not rabin_karp(a_ , a_ )
# Test 2)
__A = "ABABX"
__A = "ABABZABABYABABX"
assert rabin_karp(a_ , a_ )
# Test 3)
__A = "AAAB"
__A = "ABAAAAAB"
assert rabin_karp(a_ , a_ )
# Test 4)
__A = "abcdabcy"
__A = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(a_ , a_ )
# Test 5)
__A = "Lü"
__A = "Lüsai"
assert rabin_karp(a_ , a_ )
__A = "Lue"
assert not rabin_karp(a_ , a_ )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 15 | 1 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE :Dict = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
SCREAMING_SNAKE_CASE :Dict = reader.read()
SCREAMING_SNAKE_CASE :List[str] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
else:
SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
SCREAMING_SNAKE_CASE :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
SCREAMING_SNAKE_CASE :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
SCREAMING_SNAKE_CASE :Optional[Any] = config[key]
del config[key]
SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
SCREAMING_SNAKE_CASE :Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
SCREAMING_SNAKE_CASE :List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
SCREAMING_SNAKE_CASE :List[Any] = param_value
SCREAMING_SNAKE_CASE :str = True
if not has_changed:
SCREAMING_SNAKE_CASE :List[str] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 15 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
SCREAMING_SNAKE_CASE :str = '\\n\n'
SCREAMING_SNAKE_CASE :List[str] = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
SCREAMING_SNAKE_CASE :Dict = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : Union[str, Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"input_texts": datasets.Value("string" ),
} ) ,reference_urls=["https://huggingface.co/docs/transformers/perplexity"] ,)
def UpperCamelCase_ ( self : Optional[int] ,A : Optional[Any] ,A : int ,A : int = 16 ,A : bool = True ,A : Optional[int]=None ):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__A = "cuda"
else:
__A = "cuda" if torch.cuda.is_available() else "cpu"
__A = AutoModelForCausalLM.from_pretrained(A )
__A = model.to(A )
__A = AutoTokenizer.from_pretrained(A )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__A = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(A ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__A = model.config.max_length - 1
else:
__A = model.config.max_length
__A = tokenizer(
A ,add_special_tokens=A ,padding=A ,truncation=A ,max_length=A ,return_tensors="pt" ,return_attention_mask=A ,).to(A )
__A = encodings["input_ids"]
__A = encodings["attention_mask"]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) ,1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) ,2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__A = []
__A = CrossEntropyLoss(reduction="none" )
for start_index in logging.tqdm(range(0 ,len(A ) ,A ) ):
__A = min(start_index + batch_size ,len(A ) )
__A = encoded_texts[start_index:end_index]
__A = attn_masks[start_index:end_index]
if add_start_token:
__A = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(A )
__A = torch.cat([bos_tokens_tensor, encoded_batch] ,dim=1 )
__A = torch.cat(
[torch.ones(bos_tokens_tensor.size() ,dtype=torch.intaa ).to(A ), attn_mask] ,dim=1 )
__A = encoded_batch
with torch.no_grad():
__A = model(A ,attention_mask=A ).logits
__A = out_logits[..., :-1, :].contiguous()
__A = labels[..., 1:].contiguous()
__A = attn_mask[..., 1:].contiguous()
__A = torch.expa(
(loss_fct(shift_logits.transpose(1 ,2 ) ,A ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(A )}
| 15 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
__A = date_parser.parse(a_ )
__A = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__A = start
__A = end
__A = duration_in_min
return job_info
def UpperCAmelCase ( a_ , a_=None ) -> str:
"""simple docstring"""
__A = None
if token is not None:
__A = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
__A = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__A = requests.get(a_ , headers=a_ ).json()
__A = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
__A = math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )
for i in range(a_ ):
__A = requests.get(url + F'''&page={i + 2}''' , headers=a_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
SCREAMING_SNAKE_CASE :Union[str, Any] = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE :Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f'''{k}: {v["duration"]}''')
| 15 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,A : List[str] ,A : Tuple=13 ,A : List[Any]=7 ,A : Optional[int]=True ,A : Tuple=True ,A : List[Any]=True ,A : Optional[int]=True ,A : str=99 ,A : Tuple=32 ,A : List[str]=5 ,A : Dict=4 ,A : int=37 ,A : Optional[int]="gelu" ,A : Optional[int]=0.1 ,A : Any=0.1 ,A : int=5_12 ,A : List[str]=16 ,A : List[str]=2 ,A : int=0.02 ,A : Union[str, Any]=4 ,):
__A = parent
__A = batch_size
__A = seq_length
__A = is_training
__A = use_attention_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 = num_choices
def UpperCamelCase_ ( self : Tuple ):
__A = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
__A = None
if self.use_attention_mask:
__A = random_attention_mask([self.batch_size, self.seq_length] )
__A = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def UpperCamelCase_ ( self : Optional[int] ):
__A = self.prepare_config_and_inputs()
__A , __A , __A = config_and_inputs
__A = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCamelCase_ ( self : int ):
__A = FlaxDistilBertModelTester(self )
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
for model_class_name in self.all_model_classes:
__A = model_class_name.from_pretrained("distilbert-base-uncased" )
__A = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase_ ( self : str ):
__A = FlaxDistilBertModel.from_pretrained("distilbert-base-uncased" )
__A = np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
__A = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__A = model(A ,attention_mask=A )[0]
__A = (1, 11, 7_68)
self.assertEqual(output.shape ,A )
__A = np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1E-4 ) )
| 15 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.model_name_or_path.rstrip("/" )
__A = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
__A = torch.load(os.path.join(a_ , "pytorch_model.bin" ) )
__A = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
__A = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__A = MagnitudeBinarizer.apply(inputs=a_ , threshold=a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = TopKBinarizer.apply(a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = ThresholdBinarizer.apply(a_ , a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A , __A = -0.1, 1.1
__A = torch.sigmoid(a_ )
__A = s * (r - l) + l
__A = s_bar.clamp(min=0.0 , max=1.0 )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
__A = os.path.join(
os.path.dirname(a_ ) , F'''bertarized_{os.path.basename(a_ )}''' )
if not os.path.isdir(a_ ):
shutil.copytree(a_ , a_ )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(a_ , os.path.join(a_ , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
SCREAMING_SNAKE_CASE :str = parser.parse_args()
main(args)
| 15 | 1 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_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 UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = MobileBertTokenizer
snake_case_ = MobileBertTokenizerFast
snake_case_ = True
snake_case_ = True
snake_case_ = filter_non_english
snake_case_ = "google/mobilebert-uncased"
def UpperCamelCase_ ( self : Optional[Any] ):
super().setUp()
__A = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
__A = os.path.join(self.tmpdirname ,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] ) )
__A = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase_ ( self : Tuple ,A : List[str] ):
__A = "UNwant\u00E9d,running"
__A = "unwanted, running"
return input_text, output_text
def UpperCamelCase_ ( self : str ):
__A = self.tokenizer_class(self.vocab_file )
__A = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(A ,["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,[9, 6, 7, 12, 10, 11] )
def UpperCamelCase_ ( self : List[Any] ):
if not self.test_rust_tokenizer:
return
__A = self.get_tokenizer()
__A = self.get_rust_tokenizer()
__A = "UNwant\u00E9d,running"
__A = tokenizer.tokenize(A )
__A = rust_tokenizer.tokenize(A )
self.assertListEqual(A ,A )
__A = tokenizer.encode(A ,add_special_tokens=A )
__A = rust_tokenizer.encode(A ,add_special_tokens=A )
self.assertListEqual(A ,A )
__A = self.get_rust_tokenizer()
__A = tokenizer.encode(A )
__A = rust_tokenizer.encode(A )
self.assertListEqual(A ,A )
# With lower casing
__A = self.get_tokenizer(do_lower_case=A )
__A = self.get_rust_tokenizer(do_lower_case=A )
__A = "UNwant\u00E9d,running"
__A = tokenizer.tokenize(A )
__A = rust_tokenizer.tokenize(A )
self.assertListEqual(A ,A )
__A = tokenizer.encode(A ,add_special_tokens=A )
__A = rust_tokenizer.encode(A ,add_special_tokens=A )
self.assertListEqual(A ,A )
__A = self.get_rust_tokenizer()
__A = tokenizer.encode(A )
__A = rust_tokenizer.encode(A )
self.assertListEqual(A ,A )
def UpperCamelCase_ ( self : List[str] ):
__A = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) ,["ah", "\u535A", "\u63A8", "zz"] )
def UpperCamelCase_ ( self : int ):
__A = BasicTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) ,["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) ,["hello"] )
def UpperCamelCase_ ( self : Tuple ):
__A = BasicTokenizer(do_lower_case=A ,strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) ,["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) ,["h\u00E9llo"] )
def UpperCamelCase_ ( self : List[Any] ):
__A = BasicTokenizer(do_lower_case=A ,strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) ,["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) ,["hello"] )
def UpperCamelCase_ ( self : str ):
__A = BasicTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) ,["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) ,["hello"] )
def UpperCamelCase_ ( self : List[str] ):
__A = BasicTokenizer(do_lower_case=A )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) ,["HeLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCamelCase_ ( self : Tuple ):
__A = BasicTokenizer(do_lower_case=A ,strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) ,["HäLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = BasicTokenizer(do_lower_case=A ,strip_accents=A )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) ,["HaLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCamelCase_ ( self : str ):
__A = BasicTokenizer(do_lower_case=A ,never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) ,["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
__A = {}
for i, token in enumerate(A ):
__A = i
__A = WordpieceTokenizer(vocab=A ,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 UpperCamelCase_ ( self : Union[str, Any] ):
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 UpperCamelCase_ ( self : Union[str, Any] ):
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 UpperCamelCase_ ( self : Optional[int] ):
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 UpperCamelCase_ ( self : List[Any] ):
__A = self.get_tokenizer()
__A = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A ) for t in ["Test", "\xad", "test"]] ,[["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A ) for t in ["Test", "\xad", "test"]] ,[["[UNK]"], [], ["[UNK]"]] )
@slow
def UpperCamelCase_ ( self : Any ):
__A = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
__A = tokenizer.encode("sequence builders" ,add_special_tokens=A )
__A = tokenizer.encode("multi-sequence build" ,add_special_tokens=A )
__A = tokenizer.build_inputs_with_special_tokens(A )
__A = tokenizer.build_inputs_with_special_tokens(A ,A )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase_ ( self : str ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__A = self.rust_tokenizer_class.from_pretrained(A ,**A )
__A = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
__A = tokenizer_r.encode_plus(
A ,return_attention_mask=A ,return_token_type_ids=A ,return_offsets_mapping=A ,add_special_tokens=A ,)
__A = tokenizer_r.do_lower_case if hasattr(A ,"do_lower_case" ) else False
__A = (
[
((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 UpperCamelCase_ ( self : Optional[int] ):
__A = ["的", "人", "有"]
__A = "".join(A )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__A = True
__A = self.tokenizer_class.from_pretrained(A ,**A )
__A = self.rust_tokenizer_class.from_pretrained(A ,**A )
__A = tokenizer_p.encode(A ,add_special_tokens=A )
__A = tokenizer_r.encode(A ,add_special_tokens=A )
__A = tokenizer_r.convert_ids_to_tokens(A )
__A = tokenizer_p.convert_ids_to_tokens(A )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A ,A )
self.assertListEqual(A ,A )
__A = False
__A = self.rust_tokenizer_class.from_pretrained(A ,**A )
__A = self.tokenizer_class.from_pretrained(A ,**A )
__A = tokenizer_r.encode(A ,add_special_tokens=A )
__A = tokenizer_p.encode(A ,add_special_tokens=A )
__A = tokenizer_r.convert_ids_to_tokens(A )
__A = tokenizer_p.convert_ids_to_tokens(A )
# it is expected that only the first Chinese character is not preceded by "##".
__A = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(A )
]
self.assertListEqual(A ,A )
self.assertListEqual(A ,A )
| 15 |
import os
import re
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 logging
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :int = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
SCREAMING_SNAKE_CASE :int = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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_ = []
def __init__( self : Any ,A : List[str] ,A : str="<unk>" ,A : int="<s>" ,A : Union[str, Any]="</s>" ,A : List[str]="<pad>" ,A : int="[SEP]" ,A : Optional[Any]="[MASK]" ,A : Tuple="[CLS]" ,A : Optional[Dict[str, Any]] = None ,**A : Any ,):
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else bos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else eos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else unk_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else pad_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else cls_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sep_token=A ,mask_token=A ,cls_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def UpperCamelCase_ ( self : List[str] ):
return self.sp_model.get_piece_size()
def UpperCamelCase_ ( self : Optional[Any] ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : str ,A : Optional[Any] ):
__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 UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Tuple ):
return self.sp_model.piece_to_id(A )
def UpperCamelCase_ ( self : List[Any] ,A : Tuple ):
__A = self.sp_model.IdToPiece(A )
return token
def UpperCamelCase_ ( self : List[Any] ,A : int ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def UpperCamelCase_ ( self : Tuple ,A : List[int] ,A : bool = False ,A : bool = None ,A : bool = True ,**A : Union[str, Any] ,):
__A = kwargs.pop("use_source_tokenizer" ,A )
__A = self.convert_ids_to_tokens(A ,skip_special_tokens=A )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
__A = []
__A = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
__A = []
sub_texts.append(A )
else:
current_sub_text.append(A )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
__A = re.sub(R" (\[(MASK|SEP)\])" ,R"\1" ," ".join(A ) )
else:
__A = "".join(A )
__A = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__A = self.clean_up_tokenization(A )
return clean_text
else:
return text
def UpperCamelCase_ ( self : str ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Optional[int] ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Any ,A : List[int] ,A : Optional[List[int]] = None ):
__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]
| 15 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE :List[str] = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
@dataclass
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "train"
snake_case_ = "dev"
snake_case_ = "test"
class UpperCAmelCase :
'''simple docstring'''
@staticmethod
def UpperCamelCase_ ( A : Optional[int] ,A : Union[Split, str] ):
raise NotImplementedError
@staticmethod
def UpperCamelCase_ ( A : str ):
raise NotImplementedError
@staticmethod
def UpperCamelCase_ ( A : List[InputExample] ,A : List[str] ,A : int ,A : PreTrainedTokenizer ,A : Tuple=False ,A : Optional[int]="[CLS]" ,A : Union[str, Any]=1 ,A : Optional[int]="[SEP]" ,A : List[Any]=False ,A : str=False ,A : List[str]=0 ,A : int=0 ,A : Tuple=-1_00 ,A : str=0 ,A : Any=True ,):
__A = {label: i for i, label in enumerate(A )}
__A = []
for ex_index, example in enumerate(A ):
if ex_index % 1_00_00 == 0:
logger.info("Writing example %d of %d" ,A ,len(A ) )
__A = []
__A = []
for word, label in zip(example.words ,example.labels ):
__A = tokenizer.tokenize(A )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(A ) > 0:
tokens.extend(A )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
__A = tokenizer.num_special_tokens_to_add()
if len(A ) > max_seq_length - special_tokens_count:
__A = tokens[: (max_seq_length - special_tokens_count)]
__A = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
__A = [sequence_a_segment_id] * len(A )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
__A = [cls_token] + tokens
__A = [pad_token_label_id] + label_ids
__A = [cls_token_segment_id] + segment_ids
__A = tokenizer.convert_tokens_to_ids(A )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
__A = [1 if mask_padding_with_zero else 0] * len(A )
# Zero-pad up to the sequence length.
__A = max_seq_length - len(A )
if pad_on_left:
__A = ([pad_token] * padding_length) + input_ids
__A = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
__A = ([pad_token_segment_id] * padding_length) + segment_ids
__A = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(A ) == max_seq_length
assert len(A ) == max_seq_length
assert len(A ) == max_seq_length
assert len(A ) == max_seq_length
if ex_index < 5:
logger.info("*** Example ***" )
logger.info("guid: %s" ,example.guid )
logger.info("tokens: %s" ," ".join([str(A ) for x in tokens] ) )
logger.info("input_ids: %s" ," ".join([str(A ) for x in input_ids] ) )
logger.info("input_mask: %s" ," ".join([str(A ) for x in input_mask] ) )
logger.info("segment_ids: %s" ," ".join([str(A ) for x in segment_ids] ) )
logger.info("label_ids: %s" ," ".join([str(A ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
__A = None
features.append(
InputFeatures(
input_ids=A ,attention_mask=A ,token_type_ids=A ,label_ids=A ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = nn.CrossEntropyLoss().ignore_index
def __init__( self : Optional[Any] ,A : TokenClassificationTask ,A : str ,A : PreTrainedTokenizer ,A : List[str] ,A : str ,A : Optional[int] = None ,A : int=False ,A : Split = Split.train ,):
# Load data features from cache or dataset file
__A = os.path.join(
A ,"cached_{}_{}_{}".format(mode.value ,tokenizer.__class__.__name__ ,str(A ) ) ,)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__A = cached_features_file + ".lock"
with FileLock(A ):
if os.path.exists(A ) and not overwrite_cache:
logger.info(f'''Loading features from cached file {cached_features_file}''' )
__A = torch.load(A )
else:
logger.info(f'''Creating features from dataset file at {data_dir}''' )
__A = token_classification_task.read_examples_from_file(A ,A )
# TODO clean up all this to leverage built-in features of tokenizers
__A = token_classification_task.convert_examples_to_features(
A ,A ,A ,A ,cls_token_at_end=bool(model_type in ["xlnet"] ) ,cls_token=tokenizer.cls_token ,cls_token_segment_id=2 if model_type in ["xlnet"] else 0 ,sep_token=tokenizer.sep_token ,sep_token_extra=A ,pad_on_left=bool(tokenizer.padding_side == "left" ) ,pad_token=tokenizer.pad_token_id ,pad_token_segment_id=tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
logger.info(f'''Saving features into cached file {cached_features_file}''' )
torch.save(self.features ,A )
def __len__( self : str ):
return len(self.features )
def __getitem__( self : Optional[Any] ,A : Union[str, Any] ):
return self.features[i]
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = 42
snake_case_ = -100
def __init__( self : Optional[Any] ,A : TokenClassificationTask ,A : str ,A : PreTrainedTokenizer ,A : List[str] ,A : str ,A : Optional[int] = None ,A : List[Any]=False ,A : Split = Split.train ,):
__A = token_classification_task.read_examples_from_file(A ,A )
# TODO clean up all this to leverage built-in features of tokenizers
__A = token_classification_task.convert_examples_to_features(
A ,A ,A ,A ,cls_token_at_end=bool(model_type in ["xlnet"] ) ,cls_token=tokenizer.cls_token ,cls_token_segment_id=2 if model_type in ["xlnet"] else 0 ,sep_token=tokenizer.sep_token ,sep_token_extra=A ,pad_on_left=bool(tokenizer.padding_side == "left" ) ,pad_token=tokenizer.pad_token_id ,pad_token_segment_id=tokenizer.pad_token_type_id ,pad_token_label_id=self.pad_token_label_id ,)
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
__A = tf.data.Dataset.from_generator(
A ,({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa) ,(
{"input_ids": tf.TensorShape([None] ), "attention_mask": tf.TensorShape([None] )},
tf.TensorShape([None] ),
) ,)
else:
__A = tf.data.Dataset.from_generator(
A ,({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa) ,(
{
"input_ids": tf.TensorShape([None] ),
"attention_mask": tf.TensorShape([None] ),
"token_type_ids": tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) ,)
def UpperCamelCase_ ( self : int ):
__A = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self : Any ):
return len(self.features )
def __getitem__( self : List[Any] ,A : Optional[Any] ):
return self.features[i]
| 15 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
SCREAMING_SNAKE_CASE :int = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = (images / 2 + 0.5).clamp(0 , 1 )
__A = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A = numpy_to_pil(a_ )
return images
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if images.ndim == 3:
__A = images[None, ...]
__A = (images * 2_5_5).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
__A = [Image.fromarray(a_ ) for image in images]
return pil_images
| 15 | 1 |
from __future__ import annotations
from collections import namedtuple
def _a ( a :float , a :float , a :float ) -> tuple:
a = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError('''Only one argument must be 0''' )
elif power < 0:
raise ValueError(
'''Power cannot be negative in any electrical/electronics system''' )
elif voltage == 0:
return result('''voltage''' , power / current )
elif current == 0:
return result('''current''' , power / voltage )
elif power == 0:
return result('''power''' , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "yolos"
def __init__( self : Any ,A : Optional[Any]=7_68 ,A : Dict=12 ,A : Any=12 ,A : str=30_72 ,A : Any="gelu" ,A : str=0.0 ,A : List[str]=0.0 ,A : Dict=0.02 ,A : int=1E-12 ,A : Tuple=[5_12, 8_64] ,A : List[Any]=16 ,A : str=3 ,A : str=True ,A : Any=1_00 ,A : Dict=True ,A : Dict=False ,A : Tuple=1 ,A : Union[str, Any]=5 ,A : Optional[Any]=2 ,A : Union[str, Any]=5 ,A : int=2 ,A : int=0.1 ,**A : List[str] ,):
super().__init__(**A )
__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 = initializer_range
__A = layer_norm_eps
__A = image_size
__A = patch_size
__A = num_channels
__A = qkv_bias
__A = num_detection_tokens
__A = use_mid_position_embeddings
__A = auxiliary_loss
# Hungarian matcher
__A = class_cost
__A = bbox_cost
__A = giou_cost
# Loss coefficients
__A = bbox_loss_coefficient
__A = giou_loss_coefficient
__A = eos_coefficient
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = version.parse("1.11" )
@property
def UpperCamelCase_ ( self : str ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase_ ( self : List[Any] ):
return 1E-4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 12
| 15 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __A ( unittest.TestCase ):
a__ : Union[str, Any] = StableDiffusionLDMaDPipeline
a__ : str = TEXT_TO_IMAGE_PARAMS
a__ : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
a__ : str = TEXT_TO_IMAGE_IMAGE_PARAMS
def _lowercase (self : int ):
torch.manual_seed(0 )
UpperCAmelCase_ = 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 , )
UpperCAmelCase_ = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , clip_sample=__a , set_alpha_to_one=__a , )
torch.manual_seed(0 )
UpperCAmelCase_ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCAmelCase_ = 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 , )
UpperCAmelCase_ = CLIPTextModel(__a )
UpperCAmelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCAmelCase_ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _lowercase (self : Any , __a : str , __a : Any=0 ):
if str(__a ).startswith("mps" ):
UpperCAmelCase_ = torch.manual_seed(__a )
else:
UpperCAmelCase_ = torch.Generator(device=__a ).manual_seed(__a )
UpperCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _lowercase (self : List[str] ):
UpperCAmelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = StableDiffusionLDMaDPipeline(**__a )
UpperCAmelCase_ = ldmad_pipe.to(__a )
ldmad_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs(__a )
UpperCAmelCase_ = ldmad_pipe(**__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = rgb[0, -3:, -3:, -1]
UpperCAmelCase_ = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
UpperCAmelCase_ = np.array(
[0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62] )
UpperCAmelCase_ = np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2
def _lowercase (self : Optional[int] ):
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = StableDiffusionLDMaDPipeline(**__a )
UpperCAmelCase_ = ldmad_pipe.to(__a )
ldmad_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs(__a )
UpperCAmelCase_ = 3 * [inputs["prompt"]]
# forward
UpperCAmelCase_ = ldmad_pipe(**__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = rgb_slice_a[0, -3:, -3:, -1]
UpperCAmelCase_ = depth_slice_a[0, -3:, -1]
UpperCAmelCase_ = self.get_dummy_inputs(__a )
UpperCAmelCase_ = 3 * [inputs.pop("prompt" )]
UpperCAmelCase_ = ldmad_pipe.tokenizer(
__a , padding="max_length" , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=__a , return_tensors="pt" , )
UpperCAmelCase_ = text_inputs["input_ids"].to(__a )
UpperCAmelCase_ = ldmad_pipe.text_encoder(__a )[0]
UpperCAmelCase_ = prompt_embeds
# forward
UpperCAmelCase_ = ldmad_pipe(**__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = rgb_slice_a[0, -3:, -3:, -1]
UpperCAmelCase_ = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = PNDMScheduler(skip_prk_steps=__a )
UpperCAmelCase_ = StableDiffusionLDMaDPipeline(**__a )
UpperCAmelCase_ = ldmad_pipe.to(__a )
ldmad_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_dummy_inputs(__a )
UpperCAmelCase_ = "french fries"
UpperCAmelCase_ = ldmad_pipe(**__a , negative_prompt=__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = rgb[0, -3:, -3:, -1]
UpperCAmelCase_ = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
UpperCAmelCase_ = np.array(
[0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17] )
UpperCAmelCase_ = np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def _lowercase (self : str ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase (self : Union[str, Any] , __a : List[Any] , __a : int="cpu" , __a : Dict=torch.floataa , __a : Dict=0 ):
UpperCAmelCase_ = torch.Generator(device=__a ).manual_seed(__a )
UpperCAmelCase_ = np.random.RandomState(__a ).standard_normal((1, 4, 64, 64) )
UpperCAmelCase_ = torch.from_numpy(__a ).to(device=__a , dtype=__a )
UpperCAmelCase_ = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def _lowercase (self : Tuple ):
UpperCAmelCase_ = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
UpperCAmelCase_ = ldmad_pipe.to(__a )
ldmad_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_inputs(__a )
UpperCAmelCase_ = ldmad_pipe(**__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = rgb[0, -3:, -3:, -1].flatten()
UpperCAmelCase_ = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
UpperCAmelCase_ = np.array(
[0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06] )
UpperCAmelCase_ = np.array(
[0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3
@nightly
@require_torch_gpu
class __A ( unittest.TestCase ):
def _lowercase (self : List[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase (self : int , __a : Union[str, Any] , __a : List[str]="cpu" , __a : List[str]=torch.floataa , __a : Tuple=0 ):
UpperCAmelCase_ = torch.Generator(device=__a ).manual_seed(__a )
UpperCAmelCase_ = np.random.RandomState(__a ).standard_normal((1, 4, 64, 64) )
UpperCAmelCase_ = torch.from_numpy(__a ).to(device=__a , dtype=__a )
UpperCAmelCase_ = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def _lowercase (self : Optional[int] ):
UpperCAmelCase_ = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(__a )
ldmad_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_inputs(__a )
UpperCAmelCase_ = ldmad_pipe(**__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = 0.49_55_86
UpperCAmelCase_ = 0.33_79_55_15
UpperCAmelCase_ = 1_12.4_85_18
UpperCAmelCase_ = 98.48_97_46
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
assert np.abs(expected_depth_std - depth.std() ) < 1E-3
def _lowercase (self : Any ):
UpperCAmelCase_ = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(__a )
ldmad_pipe.set_progress_bar_config(disable=__a )
UpperCAmelCase_ = self.get_inputs(__a )
UpperCAmelCase_ = ldmad_pipe(**__a )
UpperCAmelCase_ , UpperCAmelCase_ = output.rgb, output.depth
UpperCAmelCase_ = 0.4_19_41_27
UpperCAmelCase_ = 0.35_37_55_86
UpperCAmelCase_ = 0.5_63_85_02
UpperCAmelCase_ = 0.34_68_61_03
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
assert np.abs(expected_depth_std - depth.std() ) < 1E-3
| 1 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
lowerCamelCase : Tuple = 'http://www.mocksite.com/file1.txt'
lowerCamelCase : Union[str, Any] = '"text": ["foo", "foo"]'
lowerCamelCase : Dict = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class __lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = 200
lowerCAmelCase__ : str = {"""Content-Length""": """100"""}
lowerCAmelCase__ : Optional[Any] = {}
def UpperCamelCase__ (self : Tuple , **UpperCamelCase : int ):
'''simple docstring'''
return [bytes(UpperCamelCase , '''utf-8''' )]
def _SCREAMING_SNAKE_CASE (*A , **A ) -> str:
"""simple docstring"""
return MockResponse()
@pytest.mark.parametrize('''urls_type''' , [str, list, dict] )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> List[str]:
"""simple docstring"""
import requests
monkeypatch.setattr(A , '''request''' , A )
lowercase__ = URL
if issubclass(A , A ):
lowercase__ = url
elif issubclass(A , A ):
lowercase__ = [url]
elif issubclass(A , A ):
lowercase__ = {'''train''': url}
lowercase__ = '''dummy'''
lowercase__ = '''downloads'''
lowercase__ = tmp_path
lowercase__ = DownloadConfig(
cache_dir=os.path.join(A , A ) , use_etag=A , )
lowercase__ = DownloadManager(dataset_name=A , download_config=A )
lowercase__ = dl_manager.download(A )
lowercase__ = urls
for downloaded_paths in [downloaded_paths]:
if isinstance(A , A ):
lowercase__ = [downloaded_paths]
lowercase__ = [urls]
elif isinstance(A , A ):
assert "train" in downloaded_paths.keys()
lowercase__ = downloaded_paths.values()
lowercase__ = urls.values()
assert downloaded_paths
for downloaded_path, input_url in zip(A , A ):
assert downloaded_path == dl_manager.downloaded_paths[input_url]
lowercase__ = Path(A )
lowercase__ = downloaded_path.parts
assert parts[-1] == HASH
assert parts[-2] == cache_subdir
assert downloaded_path.exists()
lowercase__ = downloaded_path.read_text()
assert content == CONTENT
lowercase__ = downloaded_path.with_suffix('''.json''' )
assert metadata_downloaded_path.exists()
lowercase__ = json.loads(metadata_downloaded_path.read_text() )
assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type''' , [str, list, dict] )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> List[str]:
"""simple docstring"""
lowercase__ = str(A )
if issubclass(A , A ):
lowercase__ = filename
elif issubclass(A , A ):
lowercase__ = [filename]
elif issubclass(A , A ):
lowercase__ = {'''train''': filename}
lowercase__ = '''dummy'''
lowercase__ = xz_file.parent
lowercase__ = '''extracted'''
lowercase__ = DownloadConfig(
cache_dir=A , use_etag=A , )
lowercase__ = DownloadManager(dataset_name=A , download_config=A )
lowercase__ = dl_manager.extract(A )
lowercase__ = paths
for extracted_paths in [extracted_paths]:
if isinstance(A , A ):
lowercase__ = [extracted_paths]
lowercase__ = [paths]
elif isinstance(A , A ):
assert "train" in extracted_paths.keys()
lowercase__ = extracted_paths.values()
lowercase__ = paths.values()
assert extracted_paths
for extracted_path, input_path in zip(A , A ):
assert extracted_path == dl_manager.extracted_paths[input_path]
lowercase__ = Path(A )
lowercase__ = extracted_path.parts
assert parts[-1] == hash_url_to_filename(A , etag=A )
assert parts[-2] == extracted_subdir
assert extracted_path.exists()
lowercase__ = extracted_path.read_text()
lowercase__ = text_file.read_text()
assert extracted_file_content == expected_file_content
def _SCREAMING_SNAKE_CASE (A , A ) -> Any:
"""simple docstring"""
assert path.endswith('''.jsonl''' )
for num_items, line in enumerate(A , start=1 ):
lowercase__ = json.loads(line.decode('''utf-8''' ) )
assert item.keys() == {"col_1", "col_2", "col_3"}
assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl''' , ['''tar_jsonl_path''', '''zip_jsonl_path'''] )
def _SCREAMING_SNAKE_CASE (A , A ) -> Any:
"""simple docstring"""
lowercase__ = request.getfixturevalue(A )
lowercase__ = DownloadManager()
for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(A ) , start=1 ):
_test_jsonl(A , A )
assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl''' , ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''] )
def _SCREAMING_SNAKE_CASE (A , A ) -> int:
"""simple docstring"""
lowercase__ = request.getfixturevalue(A )
lowercase__ = DownloadManager()
for num_tar, (path, file) in enumerate(dl_manager.iter_archive(A ) , start=1 ):
for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(A ) , start=1 ):
_test_jsonl(A , A )
assert num_tar == 1
assert num_jsonl == 2
def _SCREAMING_SNAKE_CASE (A ) -> Tuple:
"""simple docstring"""
lowercase__ = DownloadManager()
for num_file, file in enumerate(dl_manager.iter_files(A ) , start=1 ):
assert os.path.basename(A ) == ("test.txt" if num_file == 1 else "train.txt")
assert num_file == 2
| 2 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__A = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE :List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 15 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : int = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( __snake_case ):
__magic_name__ = '''falcon'''
__magic_name__ = ['''past_key_values''']
def __init__( self , SCREAMING_SNAKE_CASE=65024 , SCREAMING_SNAKE_CASE=4544 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=71 , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=11 , SCREAMING_SNAKE_CASE=11 , **SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
A : Tuple = vocab_size
# Backward compatibility with n_embed kwarg
A : Dict = kwargs.pop('''n_embed''' , SCREAMING_SNAKE_CASE )
A : int = hidden_size if n_embed is None else n_embed
A : Dict = num_hidden_layers
A : List[Any] = num_attention_heads
A : Optional[Any] = layer_norm_epsilon
A : List[Any] = initializer_range
A : str = use_cache
A : Optional[int] = hidden_dropout
A : Union[str, Any] = attention_dropout
A : Dict = bos_token_id
A : List[Any] = eos_token_id
A : List[str] = num_attention_heads if num_kv_heads is None else num_kv_heads
A : int = alibi
A : List[str] = new_decoder_architecture
A : Union[str, Any] = multi_query # Ignored when new_decoder_architecture is True
A : int = parallel_attn
A : Union[str, Any] = bias
super().__init__(bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
@property
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 3 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
__A = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__A = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
__A = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
__A = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
__A = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
__A = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
__A = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
__A = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
__A = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
__A = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
__A = key.replace("image_encoder.module" , "flava.image_model" )
__A = key.replace("text_encoder.module" , "flava.text_model" )
__A = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
__A = key.replace("mm_encoder.module" , "flava.multimodal_model" )
__A = key.replace("text_projection" , "flava.text_projection" )
__A = key.replace("image_projection" , "flava.image_projection" )
__A = value.float()
for key, value in codebook_state_dict.items():
__A = value
return upgrade
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__A = FlavaConfig.from_pretrained(a_ )
else:
__A = FlavaConfig()
__A = FlavaForPreTraining(a_ ).eval()
__A = convert_dalle_checkpoint(a_ , a_ , save_checkpoint=a_ )
if os.path.exists(a_ ):
__A = torch.load(a_ , map_location="cpu" )
else:
__A = torch.hub.load_state_dict_from_url(a_ , map_location="cpu" )
__A = upgrade_state_dict(a_ , a_ )
hf_model.load_state_dict(a_ )
__A = hf_model.state_dict()
__A = count_parameters(a_ )
__A = count_parameters(a_ ) + count_parameters(a_ )
assert torch.allclose(a_ , a_ , atol=1E-3 )
hf_model.save_pretrained(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 15 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
__snake_case =get_logger()
__snake_case =None
class UpperCAmelCase_ ( TensorFormatter[Mapping, '''jax.Array''', Mapping] ):
def __init__( self : Optional[int] , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : List[Any]=None , **UpperCAmelCase__ : Dict ) -> int:
super().__init__(features=UpperCAmelCase__ )
import jax
from jaxlib.xla_client import Device
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError(
F'''Expected {device} to be a `str` not {type(UpperCAmelCase__ )}, as `jaxlib.xla_extension.Device` '''
'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
'the device with `str()` to get its string identifier that will be internally mapped '
'to the actual `jaxlib.xla_extension.Device`.' )
lowerCAmelCase = device if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
lowerCAmelCase = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F'''Device with string identifier {self.device} not listed among the available '''
F'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
F'''device: {str(jax.devices()[0] )}.''' )
lowerCAmelCase = str(jax.devices()[0] )
lowerCAmelCase = jnp_array_kwargs
@staticmethod
def __UpperCAmelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(UpperCAmelCase__ ): device for device in jax.devices()}
def __UpperCAmelCase ( self : Tuple , UpperCAmelCase__ : Optional[Any] ) -> List[Any]:
import jax
import jax.numpy as jnp
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and column:
if all(
isinstance(UpperCAmelCase__ , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(UpperCAmelCase__ , axis=0 )
return column
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Union[str, Any] ) -> List[Any]:
import jax
import jax.numpy as jnp
if isinstance(UpperCAmelCase__ , (str, bytes, type(UpperCAmelCase__ )) ):
return value
elif isinstance(UpperCAmelCase__ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowerCAmelCase = {}
if isinstance(UpperCAmelCase__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
lowerCAmelCase = {'dtype': jnp.intaa}
else:
lowerCAmelCase = {'dtype': jnp.intaa}
elif isinstance(UpperCAmelCase__ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowerCAmelCase = {'dtype': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowerCAmelCase = np.asarray(UpperCAmelCase__ )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
lowerCAmelCase = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(UpperCAmelCase__ , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Optional[int] ) -> str:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(UpperCAmelCase__ , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(UpperCAmelCase__ , '__array__' ) and not isinstance(UpperCAmelCase__ , jax.Array ):
lowerCAmelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(UpperCAmelCase__ , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(UpperCAmelCase__ ) for substruct in data_struct] )
elif isinstance(UpperCAmelCase__ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(UpperCAmelCase__ ) for substruct in data_struct] )
return self._tensorize(UpperCAmelCase__ )
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : dict ) -> str:
return map_nested(self._recursive_tensorize , UpperCAmelCase__ , map_list=UpperCAmelCase__ )
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : pa.Table ) -> Mapping:
lowerCAmelCase = self.numpy_arrow_extractor().extract_row(UpperCAmelCase__ )
lowerCAmelCase = self.python_features_decoder.decode_row(UpperCAmelCase__ )
return self.recursive_tensorize(UpperCAmelCase__ )
def __UpperCAmelCase ( self : str , UpperCAmelCase__ : pa.Table ) -> "jax.Array":
lowerCAmelCase = self.numpy_arrow_extractor().extract_column(UpperCAmelCase__ )
lowerCAmelCase = self.python_features_decoder.decode_column(UpperCAmelCase__ , pa_table.column_names[0] )
lowerCAmelCase = self.recursive_tensorize(UpperCAmelCase__ )
lowerCAmelCase = self._consolidate(UpperCAmelCase__ )
return column
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : pa.Table ) -> Mapping:
lowerCAmelCase = self.numpy_arrow_extractor().extract_batch(UpperCAmelCase__ )
lowerCAmelCase = self.python_features_decoder.decode_batch(UpperCAmelCase__ )
lowerCAmelCase = self.recursive_tensorize(UpperCAmelCase__ )
for column_name in batch:
lowerCAmelCase = self._consolidate(batch[column_name] )
return batch
| 4 |
import os
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 logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE :Tuple = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
SCREAMING_SNAKE_CASE :List[Any] = {
'camembert-base': 512,
}
SCREAMING_SNAKE_CASE :List[str] = '▁'
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[Any] ,A : List[str] ,A : List[Any]="<s>" ,A : Tuple="</s>" ,A : Any="</s>" ,A : Optional[Any]="<s>" ,A : Tuple="<unk>" ,A : str="<pad>" ,A : int="<mask>" ,A : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] ,A : Optional[Dict[str, Any]] = None ,**A : Optional[Any] ,):
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,sep_token=A ,cls_token=A ,pad_token=A ,mask_token=A ,additional_special_tokens=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
__A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__A = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
__A = len(self.fairseq_tokens_to_ids )
__A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
__A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self : int ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__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 + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase_ ( self : Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self : int ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Dict ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase_ ( self : Optional[Any] ,A : Dict ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self : Dict ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Any ):
__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 UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 15 | 0 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
UpperCAmelCase__ = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class lowerCamelCase__ ( unittest.TestCase):
@classmethod
def __A (cls ) -> List[str]:
_lowercase =TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def __A (cls ) -> Dict:
try:
delete_repo(token=cls._token , repo_id='''test-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-config-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-config''' )
except HTTPError:
pass
def __A (self ) -> Dict:
_lowercase =BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('''test-config''' , use_auth_token=self._token )
_lowercase =BertConfig.from_pretrained(f"{USER}/test-config" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='''test-config''' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
_lowercase =BertConfig.from_pretrained(f"{USER}/test-config" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def __A (self ) -> Optional[Any]:
_lowercase =BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('''valid_org/test-config-org''' , use_auth_token=self._token )
_lowercase =BertConfig.from_pretrained('''valid_org/test-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='''valid_org/test-config-org''' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
_lowercase =BertConfig.from_pretrained('''valid_org/test-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def __A (self ) -> str:
CustomConfig.register_for_auto_class()
_lowercase =CustomConfig(attribute=4_2 )
config.push_to_hub('''test-dynamic-config''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'''AutoConfig''': '''custom_configuration.CustomConfig'''} )
_lowercase =AutoConfig.from_pretrained(f"{USER}/test-dynamic-config" , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , '''CustomConfig''' )
self.assertEqual(new_config.attribute , 4_2 )
class lowerCamelCase__ ( unittest.TestCase):
def __A (self ) -> Any:
_lowercase =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowercase =c.n_embd + 1 # int
_lowercase =c.resid_pdrop + 1.0 # float
_lowercase =not c.scale_attn_weights # bool
_lowercase =c.summary_type + '''foo''' # str
c.update_from_string(
f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}" )
self.assertEqual(UpperCAmelCase , c.n_embd , '''mismatch for key: n_embd''' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , '''mismatch for key: resid_pdrop''' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , '''mismatch for key: scale_attn_weights''' )
self.assertEqual(UpperCAmelCase , c.summary_type , '''mismatch for key: summary_type''' )
def __A (self ) -> Union[str, Any]:
_lowercase =PretrainedConfig()
_lowercase =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['''is_encoder_decoder''', '''_name_or_path''', '''_commit_hash''', '''transformers_version'''] )
_lowercase =[key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'''The following keys are set with the default values in'''
''' `test_configuration_common.config_common_kwargs` pick another value for them:'''
f" {', '.join(UpperCAmelCase )}." )
def __A (self ) -> Optional[int]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowercase =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' )
_lowercase =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' , subfolder='''bert''' )
self.assertIsNotNone(UpperCAmelCase )
def __A (self ) -> List[str]:
# A mock response for an HTTP head request to emulate server down
_lowercase =mock.Mock()
_lowercase =5_0_0
_lowercase ={}
_lowercase =HTTPError
_lowercase ={}
# Download this model to make sure it's in the cache.
_lowercase =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=UpperCAmelCase ) as mock_head:
_lowercase =BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# This check we did call the fake head request
mock_head.assert_called()
def __A (self ) -> Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
_lowercase =BertConfig.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json''' )
def __A (self ) -> Any:
_lowercase =AutoConfig.from_pretrained('''bert-base-cased''' )
_lowercase =['''config.4.0.0.json''']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
_lowercase =2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , '''config.4.0.0.json''' ) , '''w''' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowercase =AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowercase =['''config.42.0.0.json''']
_lowercase =7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , '''config.4.0.0.json''' ) , os.path.join(UpperCAmelCase , '''config.42.0.0.json''' ) )
_lowercase =AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def __A (self ) -> List[Any]:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowercase ='''hf-internal-testing/test-two-configs'''
import transformers as new_transformers
_lowercase ='''v4.0.0'''
_lowercase , _lowercase =new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowercase ='''v3.0.0'''
_lowercase =old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 5 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , a_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__A , __A = arr[k - 1], arr[i]
else: # k is odd
__A , __A = arr[k - 1], arr[0]
generate(k - 1 , a_ )
generate(len(a_ ) , a_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :Dict = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 15 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class __A( a ):
snake_case_ = (CMStochasticIterativeScheduler,)
snake_case_ = 1_0
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> List[str]:
'''simple docstring'''
__a = {
'''num_train_timesteps''': 201,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
config.update(**_snake_case )
return config
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = 10
__a = self.get_scheduler_config()
__a = self.scheduler_classes[0](**_snake_case )
scheduler.set_timesteps(_snake_case )
__a = scheduler.timesteps[0]
__a = scheduler.timesteps[1]
__a = self.dummy_sample
__a = 0.1 * sample
__a = scheduler.step(_snake_case , _snake_case , _snake_case ).prev_sample
__a = scheduler.step(_snake_case , _snake_case , _snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
__a = 1
scheduler.set_timesteps(_snake_case )
__a = scheduler.timesteps
__a = torch.manual_seed(0 )
__a = self.dummy_model()
__a = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(_snake_case ):
# 1. scale model input
__a = scheduler.scale_model_input(_snake_case , _snake_case )
# 2. predict noise residual
__a = model(_snake_case , _snake_case )
# 3. predict previous sample x_t-1
__a = scheduler.step(_snake_case , _snake_case , _snake_case , generator=_snake_case ).prev_sample
__a = pred_prev_sample
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
assert abs(result_sum.item() - 192.7614 ) < 1E-2
assert abs(result_mean.item() - 0.2510 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
__a = [106, 0]
scheduler.set_timesteps(timesteps=_snake_case )
__a = scheduler.timesteps
__a = torch.manual_seed(0 )
__a = self.dummy_model()
__a = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
__a = scheduler.scale_model_input(_snake_case , _snake_case )
# 2. predict noise residual
__a = model(_snake_case , _snake_case )
# 3. predict previous sample x_t-1
__a = scheduler.step(_snake_case , _snake_case , _snake_case , generator=_snake_case ).prev_sample
__a = pred_prev_sample
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
assert abs(result_sum.item() - 347.6357 ) < 1E-2
assert abs(result_mean.item() - 0.4527 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
__a = [39, 30, 12, 15, 0]
with self.assertRaises(_snake_case , msg='''`timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
__a = [39, 30, 12, 1, 0]
__a = len(_snake_case )
with self.assertRaises(_snake_case , msg='''Can only pass one of `num_inference_steps` or `timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=_snake_case , timesteps=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
__a = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_snake_case , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=_snake_case ) | 6 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
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__":
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :List[Any] = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 15 | 0 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
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.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class A ( _UpperCAmelCase ):
"""simple docstring"""
def snake_case__ ( self : List[Any] )-> int:
'''simple docstring'''
A__ = tempfile.mkdtemp()
A__ = 8
# DPR tok
A__ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
A__ = os.path.join(self.tmpdirname,'dpr_tokenizer' )
os.makedirs(lowercase_,exist_ok=lowercase_ )
A__ = os.path.join(lowercase_,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
A__ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
A__ = dict(zip(lowercase_,range(len(lowercase_ ) ) ) )
A__ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
A__ = {'unk_token': '<unk>'}
A__ = os.path.join(self.tmpdirname,'bart_tokenizer' )
os.makedirs(lowercase_,exist_ok=lowercase_ )
A__ = os.path.join(lowercase_,BART_VOCAB_FILES_NAMES['vocab_file'] )
A__ = os.path.join(lowercase_,BART_VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file,'w',encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase_ ) + '\n' )
with open(self.merges_file,'w',encoding='utf-8' ) as fp:
fp.write('\n'.join(lowercase_ ) )
def snake_case__ ( self : List[str] )-> DPRQuestionEncoderTokenizer:
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,'dpr_tokenizer' ) )
def snake_case__ ( self : List[Any] )-> BartTokenizer:
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,'bart_tokenizer' ) )
def snake_case__ ( self : List[str] )-> List[str]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def snake_case__ ( self : str )-> Any:
'''simple docstring'''
A__ = os.path.join(self.tmpdirname,'rag_tokenizer' )
A__ = RagConfig(question_encoder=DPRConfig().to_dict(),generator=BartConfig().to_dict() )
A__ = RagTokenizer(question_encoder=self.get_dpr_tokenizer(),generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(lowercase_ )
rag_tokenizer.save_pretrained(lowercase_ )
A__ = RagTokenizer.from_pretrained(lowercase_,config=lowercase_ )
self.assertIsInstance(new_rag_tokenizer.question_encoder,lowercase_ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab(),rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator,lowercase_ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab(),rag_tokenizer.generator.get_vocab() )
@slow
def snake_case__ ( self : Optional[int] )-> Union[str, Any]:
'''simple docstring'''
A__ = RagTokenizer.from_pretrained('facebook/rag-token-nq' )
A__ = [
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
A__ = tokenizer(lowercase_ )
self.assertIsNotNone(lowercase_ )
@slow
def snake_case__ ( self : Optional[Any] )-> Dict:
'''simple docstring'''
A__ = RagTokenizer.from_pretrained('facebook/rag-sequence-nq' )
A__ = [
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
A__ = tokenizer(lowercase_ )
self.assertIsNotNone(lowercase_ )
| 7 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 2
@register_to_config
def __init__( self : str ,A : float = 0.02 ,A : float = 1_00 ,A : float = 1.0_07 ,A : float = 80 ,A : float = 0.05 ,A : float = 50 ,):
# standard deviation of the initial noise distribution
__A = sigma_max
# setable values
__A = None
__A = None
__A = None # sigma(t_i)
def UpperCamelCase_ ( self : str ,A : torch.FloatTensor ,A : Optional[int] = None ):
return sample
def UpperCamelCase_ ( self : Dict ,A : int ,A : Union[str, torch.device] = None ):
__A = num_inference_steps
__A = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__A = torch.from_numpy(A ).to(A )
__A = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__A = torch.tensor(A ,dtype=torch.floataa ,device=A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : float ,A : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
__A = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__A = 0
# sample eps ~ N(0, S_noise^2 * I)
__A = self.config.s_noise * randn_tensor(sample.shape ,generator=A ).to(sample.device )
__A = sigma + gamma * sigma
__A = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase_ ( self : Dict ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_hat + sigma_hat * model_output
__A = (sample_hat - pred_original_sample) / sigma_hat
__A = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : Optional[int] ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_prev + sigma_prev * model_output
__A = (sample_prev - pred_original_sample) / sigma_prev
__A = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : str ):
raise NotImplementedError()
| 15 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class snake_case_ ( __A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = ReformerTokenizer
SCREAMING_SNAKE_CASE : int = ReformerTokenizerFast
SCREAMING_SNAKE_CASE : int = True
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : str = True
def snake_case__( self : Dict ) ->List[str]:
super().setUp()
snake_case_ = ReformerTokenizer(_UpperCamelCase , keep_accents=_UpperCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__( self : Optional[Any] ) ->int:
snake_case_ = '''<s>'''
snake_case_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase ) , _UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase ) , _UpperCamelCase )
def snake_case__( self : Optional[Any] ) ->Dict:
snake_case_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_UpperCamelCase ) , 1_0_0_0 )
def snake_case__( self : Union[str, Any] ) ->Dict:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0 )
def snake_case__( self : List[Any] ) ->str:
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer()
snake_case_ = '''I was born in 92000, and this is falsé.'''
snake_case_ = tokenizer.tokenize(_UpperCamelCase )
snake_case_ = rust_tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
snake_case_ = tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
snake_case_ = rust_tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
snake_case_ = self.get_rust_tokenizer()
snake_case_ = tokenizer.encode(_UpperCamelCase )
snake_case_ = rust_tokenizer.encode(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
def snake_case__( self : Optional[int] , _UpperCamelCase : Tuple=1_5 ) ->Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(_UpperCamelCase , **_UpperCamelCase )
# Simple input
snake_case_ = '''This is a simple input'''
snake_case_ = ['''This is a simple input 1''', '''This is a simple input 2''']
snake_case_ = ('''This is a simple input''', '''This is a pair''')
snake_case_ = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
self.assertRaises(_UpperCamelCase , tokenizer_r.encode , _UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' )
# Simple input
self.assertRaises(_UpperCamelCase , tokenizer_r.encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' )
# Simple input
self.assertRaises(
_UpperCamelCase , tokenizer_r.batch_encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' , )
# Pair input
self.assertRaises(_UpperCamelCase , tokenizer_r.encode , _UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' )
# Pair input
self.assertRaises(_UpperCamelCase , tokenizer_r.encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' )
# Pair input
self.assertRaises(
_UpperCamelCase , tokenizer_r.batch_encode_plus , _UpperCamelCase , max_length=_UpperCamelCase , padding='''max_length''' , )
def snake_case__( self : Dict ) ->int:
pass
def snake_case__( self : Dict ) ->str:
snake_case_ = ReformerTokenizer(_UpperCamelCase , keep_accents=_UpperCamelCase )
snake_case_ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_UpperCamelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] , )
snake_case_ = 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''',
'''.''',
] , )
snake_case_ = tokenizer.convert_tokens_to_ids(_UpperCamelCase )
self.assertListEqual(
_UpperCamelCase , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] , )
snake_case_ = 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 snake_case__( self : Optional[int] ) ->Tuple:
return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment''' )
@slow
def snake_case__( self : Optional[Any] ) ->int:
snake_case_ = '''Hello World!'''
snake_case_ = [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]
self.assertListEqual(_UpperCamelCase , self.big_tokenizer.encode(_UpperCamelCase ) )
@slow
def snake_case__( self : str ) ->str:
snake_case_ = (
'''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'''
)
snake_case_ = [
1_0_8,
2_6_5,
2_4,
1_1_1,
4,
2_5_8,
1_5_6,
3_5,
2_8,
2_7_5,
3,
2_5_9,
2_9_7,
2_6_0,
8_4,
4,
3_5,
1_1_0,
4_4,
8,
2_5_9,
9_1,
2_6_8,
2_1,
1_1,
2_0_9,
2_7_4,
1_0_9,
2_6_6,
2_7_7,
1_1_7,
8_6,
9_3,
3_1_5,
2_5_8,
2_7_8,
2_5_8,
2_7_7,
2_5_8,
0,
2_5_8,
2_8_8,
2_5_8,
3_1_9,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
2_8_7,
2_5_8,
3_1_5,
2_5_8,
2_8_9,
2_5_8,
2_7_8,
9_9,
2_6_9,
2_6_6,
2_6_2,
8,
2_5_9,
2_4_1,
4,
2_1_7,
2_3_0,
2_6_8,
2_6_6,
5_5,
1_6_8,
1_0_6,
7_5,
1_9_3,
2_6_6,
2_2_3,
2_7,
4_9,
2_6,
2_8_2,
2_5,
2_6_4,
2_9_9,
1_9,
2_6,
0,
2_5_8,
2_7_7,
1_1_7,
8_6,
9_3,
1_7_6,
1_8_3,
2_7_0,
1_1,
2_6_2,
4_2,
6_1,
2_6_5,
]
self.assertListEqual(_UpperCamelCase , self.big_tokenizer.encode(_UpperCamelCase ) )
@require_torch
@slow
def snake_case__( self : List[str] ) ->List[str]:
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
snake_case_ = list(self.big_tokenizer.get_vocab().keys() )[:1_0]
snake_case_ = ''' '''.join(_UpperCamelCase )
snake_case_ = self.big_tokenizer.encode_plus(_UpperCamelCase , return_tensors='''pt''' )
snake_case_ = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors='''pt''' )
snake_case_ = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
snake_case_ = encoded_sequence['''input_ids'''].shape
snake_case_ = ReformerModel(_UpperCamelCase )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_UpperCamelCase )
model(**_UpperCamelCase )
@slow
def snake_case__( self : List[Any] ) ->Dict:
# fmt: off
snake_case_ = {'''input_ids''': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '''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]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
snake_case_ = [
'''This is a very simple sentence.''',
'''The quick brown fox jumps over the lazy dog.''',
]
self.tokenizer_integration_test_util(
expected_encoding=_UpperCamelCase , model_name='''google/reformer-crime-and-punishment''' , revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a''' , padding=_UpperCamelCase , sequences=_UpperCamelCase , ) | 8 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE :Union[str, Any] = get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = "dummy_data"
snake_case_ = "datasets"
snake_case_ = False
def __init__( self : Optional[int] ,A : str ,A : str ,A : Union[Version, str] ,A : Optional[str] = None ,A : bool = False ,A : bool = True ,A : Optional[List[Callable]] = None ,):
__A = 0
__A = dataset_name
__A = cache_dir
__A = use_local_dummy_data
__A = config
# download_callbacks take a single url as input
__A = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__A = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__A = str(A )
# to be downloaded
__A = None
__A = None
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
if self._dummy_file is None:
__A = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase_ ( self : Optional[Any] ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def UpperCamelCase_ ( self : List[Any] ):
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def UpperCamelCase_ ( self : Tuple ):
__A = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__A = cached_path(
A ,cache_dir=self.cache_dir ,extract_compressed_file=A ,force_extract=A )
return os.path.join(A ,self.dummy_file_name )
@property
def UpperCamelCase_ ( self : str ):
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def UpperCamelCase_ ( self : Any ):
if self._bucket_url is None:
__A = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def UpperCamelCase_ ( self : Tuple ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def UpperCamelCase_ ( self : List[str] ,A : List[Any] ,*A : Dict ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__A = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__A = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A ,A ):
return self.create_dummy_data_dict(A ,A )
elif isinstance(A ,(list, tuple) ):
return self.create_dummy_data_list(A ,A )
else:
return self.create_dummy_data_single(A ,A )
def UpperCamelCase_ ( self : str ,A : List[Any] ,*A : List[Any] ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : List[str] ,A : List[str] ,A : Tuple ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : Any ,A : Any ,*A : Optional[Any] ,**A : List[str] ):
return path
def UpperCamelCase_ ( self : str ):
return {}
def UpperCamelCase_ ( self : int ,A : int ,A : Tuple ):
__A = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A ,A ):
for single_url in single_urls:
download_callback(A )
else:
__A = single_urls
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A ,A ):
__A = [os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) ) for x in single_urls]
else:
__A = single_urls
__A = os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) )
__A = value
# make sure that values are unique
if all(isinstance(A ,A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__A = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,A : str ):
__A = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__A = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,A ) ) for url in data_url )
__A = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__A = [data_url[0]] * len(A )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(A )
return dummy_data_list
def UpperCamelCase_ ( self : str ,A : List[Any] ,A : Optional[Any] ):
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(A ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : Dict ):
pass
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ):
def _iter_archive_members(A : Optional[Any] ):
# this preserves the order of the members inside the ZIP archive
__A = Path(self.dummy_file ).parent
__A = path.relative_to(A )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__A = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A )
__A = Path(A )
__A = _iter_archive_members(A ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(A ).as_posix(), file_path.open("rb" )
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
if not isinstance(A ,A ):
__A = [paths]
for path in paths:
if os.path.isfile(A ):
if os.path.basename(A ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A ):
if os.path.basename(A ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(A ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(A ,A )
| 15 | 0 |
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = set()
# edges = list of graph's edges
__SCREAMING_SNAKE_CASE : List[str] = get_edges(lowercase__ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edges.pop()
chosen_vertices.add(lowercase__ )
chosen_vertices.add(lowercase__ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowercase__ )
return chosen_vertices
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 9 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[str] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__A = {
"facebook/maskformer-swin-base-ade": (
"https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
__A = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "maskformer"
lowercase_ = {"hidden_size": "mask_feature_size"}
lowercase_ = ["resnet", "swin"]
lowercase_ = ["detr"]
def __init__(self : Optional[int] , UpperCAmelCase_ : int = 256 , UpperCAmelCase_ : int = 256 , UpperCAmelCase_ : float = 0.1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[Dict] = None , UpperCAmelCase_ : Optional[Dict] = None , UpperCAmelCase_ : float = 0.02 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : float = 1.0 , UpperCAmelCase_ : float = 20.0 , UpperCAmelCase_ : Optional[bool] = None , **UpperCAmelCase_ : Optional[Any] , ) ->str:
'''simple docstring'''
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowerCamelCase__: Any =SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
lowerCamelCase__: List[str] =backbone_config.pop("model_type")
lowerCamelCase__: int =CONFIG_MAPPING[backbone_model_type]
lowerCamelCase__: List[Any] =config_class.from_dict(UpperCAmelCase_)
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
F"""Supported model types: {",".join(self.backbones_supported)}""")
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowerCamelCase__: Tuple =DetrConfig()
else:
# verify that the decoder is supported
lowerCamelCase__: Any =(
decoder_config.pop("model_type") if isinstance(UpperCAmelCase_ , UpperCAmelCase_) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F"""Transformer Decoder {decoder_type} not supported, please use one of"""
F""" {",".join(self.decoders_supported)}""")
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
lowerCamelCase__: Union[str, Any] =CONFIG_MAPPING[decoder_type]
lowerCamelCase__: List[Any] =config_class.from_dict(UpperCAmelCase_)
lowerCamelCase__: List[str] =backbone_config
lowerCamelCase__: Dict =decoder_config
# main feature dimension for the model
lowerCamelCase__: Any =fpn_feature_size
lowerCamelCase__: Optional[int] =mask_feature_size
# initializer
lowerCamelCase__: Union[str, Any] =init_std
lowerCamelCase__: str =init_xavier_std
# Hungarian matcher && loss
lowerCamelCase__: Tuple =cross_entropy_weight
lowerCamelCase__: Union[str, Any] =dice_weight
lowerCamelCase__: List[str] =mask_weight
lowerCamelCase__: List[str] =use_auxiliary_loss
lowerCamelCase__: List[str] =no_object_weight
lowerCamelCase__: Dict =output_auxiliary_logits
lowerCamelCase__: Any =self.decoder_config.encoder_attention_heads
lowerCamelCase__: List[Any] =self.decoder_config.num_hidden_layers
super().__init__(**UpperCAmelCase_)
@classmethod
def SCREAMING_SNAKE_CASE_ (cls : List[str] , UpperCAmelCase_ : PretrainedConfig , UpperCAmelCase_ : PretrainedConfig , **UpperCAmelCase_ : List[str]) ->int:
'''simple docstring'''
return cls(
backbone_config=UpperCAmelCase_ , decoder_config=UpperCAmelCase_ , **UpperCAmelCase_ , )
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Dict[str, any]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =copy.deepcopy(self.__dict__)
lowerCamelCase__: Any =self.backbone_config.to_dict()
lowerCamelCase__: Any =self.decoder_config.to_dict()
lowerCamelCase__: List[str] =self.__class__.model_type
return output
| 10 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
SCREAMING_SNAKE_CASE :List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
SCREAMING_SNAKE_CASE :str = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Tuple:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
__A = new_id
# turn into Numpy arrays
__A = np.array(a_ )
__A = np.array(a_ )
if reduce_labels:
__A = 2_5_5
__A = label - 1
__A = 2_5_5
__A = label != ignore_index
__A = np.not_equal(a_ , a_ )
__A = pred_label[mask]
__A = np.array(a_ )[mask]
__A = pred_label[pred_label == label]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Union[str, Any]:
"""simple docstring"""
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a_ , a_ ):
__A , __A , __A , __A = intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = None , a_ = False , ) -> str:
"""simple docstring"""
__A , __A , __A , __A = total_intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
# compute metrics
__A = {}
__A = total_area_intersect.sum() / total_area_label.sum()
__A = total_area_intersect / total_area_union
__A = total_area_intersect / total_area_label
__A = np.nanmean(a_ )
__A = np.nanmean(a_ )
__A = all_acc
__A = iou
__A = acc
if nan_to_num is not None:
__A = {metric: np.nan_to_num(a_ , nan=a_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) ,reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] ,)
def UpperCamelCase_ ( self : int ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : bool ,A : Optional[int] = None ,A : Optional[Dict[int, int]] = None ,A : bool = False ,):
__A = mean_iou(
results=A ,gt_seg_maps=A ,num_labels=A ,ignore_index=A ,nan_to_num=A ,label_map=A ,reduce_labels=A ,)
return iou_result
| 15 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCAmelCase (UpperCamelCase__ : Optional[int] ):
_A : List[Any] = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def _UpperCAmelCase (UpperCamelCase__ : Union[str, Any] ):
_A , _A : List[str] = emb.weight.shape
_A : Union[str, Any] = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
_A : Dict = emb.weight.data
return lin_layer
def _UpperCAmelCase (UpperCamelCase__ : int ):
_A : Tuple = torch.load(UpperCamelCase__ , map_location="cpu" )
_A : int = mam_aaa["args"] or mam_aaa["cfg"]["model"]
_A : int = mam_aaa["model"]
remove_ignore_keys_(UpperCamelCase__ )
_A : Union[str, Any] = state_dict["encoder.embed_tokens.weight"].shape[0]
_A : List[str] = MaMaaaConfig(
vocab_size=UpperCamelCase__ , max_position_embeddings=1024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , )
_A : Optional[Any] = state_dict["decoder.embed_tokens.weight"]
_A : int = MaMaaaForConditionalGeneration(UpperCamelCase__ )
model.model.load_state_dict(UpperCamelCase__ , strict=UpperCamelCase__ )
_A : Any = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 11 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[str] = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Dict = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE :Optional[Any] = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[int] ,A : Optional[Any] ,A : Optional[int]=False ,A : int=False ,A : Union[str, Any]=False ,A : int=None ,A : Optional[Any]=None ,A : Union[str, Any]=None ,A : Optional[Any]=None ,A : Optional[Dict[str, Any]] = None ,**A : Tuple ,):
__A = {} if sp_model_kwargs is None else sp_model_kwargs
__A = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
__A = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__A = "<|endoftext|>" if eos_token is None else eos_token
__A = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__A = unk_token if pad_token is None else pad_token
__A = eos_token if bos_token is None else bos_token
else:
__A = "<pad>" if pad_token is None else pad_token
__A = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=A ,remove_space=A ,keep_accents=A ,bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = do_lower_case
__A = remove_space
__A = keep_accents
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
__A = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__A = re.compile(
f'''[{''.join(map(A ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(1_27 ,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Optional[Any] ,A : Union[str, Any] ):
__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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase_ ( self : List[str] ):
return len(self.sp_model )
def UpperCamelCase_ ( self : int ,A : str ):
__A = self.non_printing_characters_re.sub("" ,A )
# Normalize whitespaces
__A = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
__A = unicodedata.normalize("NFC" ,A )
return text
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,**A : Optional[int] ):
__A = self.preprocess_text(A )
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : int ):
return self.sp_model.IdToPiece(A )
@staticmethod
def UpperCamelCase_ ( A : str ):
return out_string
def UpperCamelCase_ ( self : str ,A : List[str] ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string
def UpperCamelCase_ ( self : str ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : List[str] ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, List[str]] ,A : Union[str, bool] = False ):
if isinstance(A ,A ):
__A = self.preprocess_text(A )
__A = self.sp_model.encode(A )
else:
__A = [self.preprocess_text(A ) for t in text]
__A = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
__A = torch.tensor(A )
return token_ids
def UpperCamelCase_ ( self : List[Any] ,A : Union[int, List[int]] ):
return self.sp_model.decode(A )
def UpperCamelCase_ ( self : List[str] ,A : "Conversation" ):
__A = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__A = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 15 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed
UpperCAmelCase_ = logging.getLogger(__name__)
def lowerCamelCase__ ( A__ : Union[str, Any]=2 , A__ : List[Any]=3 , A__ : Optional[int]=16 , A__ : int = 10 , A__ : int = 2 ):
'''simple docstring'''
def get_dataset(A__ : Union[str, Any] ):
__lowerCamelCase = torch.randn(batch_size * n_batches , 1 )
return TensorDataset(A__ , a * x + b + 0.1 * torch.randn(batch_size * n_batches , 1 ) )
__lowerCamelCase = get_dataset(A__ )
__lowerCamelCase = get_dataset(A__ )
__lowerCamelCase = DataLoader(A__ , shuffle=A__ , batch_size=A__ , num_workers=4 )
__lowerCamelCase = DataLoader(A__ , shuffle=A__ , batch_size=A__ , num_workers=4 )
return (train_dataloader, valid_dataloader)
def lowerCamelCase__ ( A__ : List[str] , A__ : List[str] , A__ : Optional[Any] , A__ : Tuple , A__ : Dict , A__ : Union[str, Any]=None ):
'''simple docstring'''
__lowerCamelCase = []
for epoch in range(A__ ):
# Train quickly
model.train()
for batch in dataloader:
__lowerCamelCase, __lowerCamelCase = batch
__lowerCamelCase = model(A__ )
__lowerCamelCase = torch.nn.functional.mse_loss(A__ , A__ )
accelerator.backward(A__ )
optimizer.step()
optimizer.zero_grad()
rands.append(random.random() ) # Introduce some randomness
if scheduler is not None:
scheduler.step()
return rands
class lowerCamelCase__( nn.Module):
def __init__( self: Dict ):
super().__init__()
__lowerCamelCase = nn.Parameter(torch.randn(1 ) )
__lowerCamelCase = nn.Parameter(torch.randn(1 ) )
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Union[str, Any] ):
return x * self.a + self.b
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ ( self: int ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
__lowerCamelCase, __lowerCamelCase = dummy_dataloaders()
__lowerCamelCase = ProjectConfiguration(total_limit=1 , project_dir=UpperCamelCase_ , automatic_checkpoint_naming=UpperCamelCase_ )
# Train baseline
__lowerCamelCase = Accelerator(project_config=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save initial
accelerator.save_state()
# Save second state
accelerator.save_state()
self.assertEqual(len(os.listdir(accelerator.project_dir ) ) , 1 )
def lowerCAmelCase__ ( self: Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
__lowerCamelCase, __lowerCamelCase = dummy_dataloaders()
# Train baseline
__lowerCamelCase = Accelerator()
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save initial
__lowerCamelCase = os.path.join(UpperCamelCase_ , """initial""" )
accelerator.save_state(UpperCamelCase_ )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
__lowerCamelCase = train(3 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
# Train partially
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
__lowerCamelCase, __lowerCamelCase = dummy_dataloaders()
__lowerCamelCase = Accelerator()
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
accelerator.load_state(UpperCamelCase_ )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
__lowerCamelCase = train(2 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save everything
__lowerCamelCase = os.path.join(UpperCamelCase_ , """checkpoint""" )
accelerator.save_state(UpperCamelCase_ )
# Load everything back in and make sure all states work
accelerator.load_state(UpperCamelCase_ )
test_rands += train(1 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
__lowerCamelCase, __lowerCamelCase = dummy_dataloaders()
__lowerCamelCase = ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_ )
# Train baseline
__lowerCamelCase = Accelerator(project_dir=UpperCamelCase_ , project_config=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save initial
accelerator.save_state()
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
__lowerCamelCase = train(3 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
# Train partially
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
__lowerCamelCase, __lowerCamelCase = dummy_dataloaders()
__lowerCamelCase = ProjectConfiguration(iteration=1 , automatic_checkpoint_naming=UpperCamelCase_ )
__lowerCamelCase = Accelerator(project_dir=UpperCamelCase_ , project_config=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
accelerator.load_state(os.path.join(UpperCamelCase_ , """checkpoints""" , """checkpoint_0""" ) )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
__lowerCamelCase = train(2 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save everything
accelerator.save_state()
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(UpperCamelCase_ , """checkpoints""" , """checkpoint_1""" ) )
test_rands += train(1 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
((__lowerCamelCase), (__lowerCamelCase)) = model.a.item(), model.b.item()
__lowerCamelCase = optimizer.state_dict()
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = torch.tensor([1, 2, 3] )
__lowerCamelCase = torch.tensor([2, 3, 4] )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(net.parameters() )
__lowerCamelCase = Accelerator()
with self.assertRaises(UpperCamelCase_ ) as ve:
accelerator.register_for_checkpointing(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__lowerCamelCase = str(ve.exception )
self.assertTrue("""Item at index 0""" in message )
self.assertTrue("""Item at index 1""" in message )
self.assertFalse("""Item at index 2""" in message )
self.assertFalse("""Item at index 3""" in message )
def lowerCAmelCase__ ( self: Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = torch.optim.Adam(params=model.parameters() , lr=1E-3 )
__lowerCamelCase = torch.optim.lr_scheduler.StepLR(UpperCamelCase_ , step_size=1 , gamma=0.99 )
__lowerCamelCase, __lowerCamelCase = dummy_dataloaders()
__lowerCamelCase = ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_ )
# Train baseline
__lowerCamelCase = Accelerator(project_dir=UpperCamelCase_ , project_config=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase = accelerator.prepare(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Save initial
accelerator.save_state()
__lowerCamelCase = scheduler.state_dict()
train(3 , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.assertNotEqual(UpperCamelCase_ , scheduler.state_dict() )
# Load everything back in and make sure all states work
accelerator.load_state(os.path.join(UpperCamelCase_ , """checkpoints""" , """checkpoint_0""" ) )
self.assertEqual(UpperCamelCase_ , scheduler.state_dict() )
def lowerCAmelCase__ ( self: Dict ):
with tempfile.TemporaryDirectory() as tmpdir:
set_seed(42 )
__lowerCamelCase = DummyModel()
__lowerCamelCase = ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_ , total_limit=2 )
# Train baseline
__lowerCamelCase = Accelerator(project_dir=UpperCamelCase_ , project_config=UpperCamelCase_ )
__lowerCamelCase = accelerator.prepare(UpperCamelCase_ )
# Save 3 states:
for _ in range(11 ):
accelerator.save_state()
self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase_ , """checkpoints""" , """checkpoint_0""" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_ , """checkpoints""" , """checkpoint_9""" ) ) )
self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_ , """checkpoints""" , """checkpoint_10""" ) ) )
@require_cuda
def lowerCAmelCase__ ( self: Optional[Any] ):
__lowerCamelCase = ["""torchrun""", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
execute_subprocess_async(UpperCamelCase_ , env=os.environ.copy() )
if __name__ == "__main__":
UpperCAmelCase_ = '/tmp/accelerate/state_checkpointing'
UpperCAmelCase_ = DummyModel()
UpperCAmelCase_ = torch.optim.Adam(params=model.parameters(), lr=1E-3)
UpperCAmelCase_ = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
UpperCAmelCase_ , UpperCAmelCase_ = dummy_dataloaders()
UpperCAmelCase_ = ProjectConfiguration(automatic_checkpoint_naming=True)
# Train baseline
UpperCAmelCase_ = Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
if accelerator.process_index == 0:
if os.path.exists(savedir):
shutil.rmtree(savedir)
os.makedirs(savedir)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(
model, optimizer, train_dataloader, valid_dataloader, scheduler
)
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(model, optimizer)
train(3, model, train_dataloader, optimizer, accelerator, scheduler)
# Check that the intial optimizer is loaded on the GPU
for group in optimizer.param_groups:
UpperCAmelCase_ = group['params'][0].device
break
assert param_device.type == accelerator.device.type
UpperCAmelCase_ = model.cpu()
accelerator.wait_for_everyone()
accelerator.save_state()
accelerator.wait_for_everyone()
# Check CPU state
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
for group in optimizer.param_groups:
UpperCAmelCase_ = group['params'][0].device
break
assert (
param_device.type == torch.device('cpu').type
), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"
# Check device state
model.to(accelerator.device)
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
for group in optimizer.param_groups:
UpperCAmelCase_ = group['params'][0].device
break
assert (
param_device.type == accelerator.device.type
), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"
# Check error
with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
shutil.rmtree(savedir)
accelerator.wait_for_everyone()
| 12 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : List[str]
_UpperCAmelCase : Optional[str] = None
# Automatically constructed
_UpperCAmelCase : ClassVar[str] = "dict"
_UpperCAmelCase : ClassVar[Any] = None
_UpperCAmelCase : str = field(default='''Translation''' , init=UpperCAmelCase_ , repr=UpperCAmelCase_ )
def __call__( self : str):
return pa.struct({lang: pa.string() for lang in sorted(self.languages)})
def _SCREAMING_SNAKE_CASE ( self : Tuple):
from .features import Value
return {k: Value("string") for k in sorted(self.languages)}
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : Optional[List] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[str] = None
# Automatically constructed
_UpperCAmelCase : ClassVar[str] = "dict"
_UpperCAmelCase : ClassVar[Any] = None
_UpperCAmelCase : str = field(default='''TranslationVariableLanguages''' , init=UpperCAmelCase_ , repr=UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: List[str] = sorted(set(self.languages)) if self.languages else None
SCREAMING_SNAKE_CASE_: Tuple = len(self.languages) if self.languages else None
def __call__( self : Any):
return pa.struct({"language": pa.list_(pa.string()), "translation": pa.list_(pa.string())})
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Optional[Any] = set(self.languages)
if self.languages and set(lowerCAmelCase__) - lang_set:
raise ValueError(
F"Some languages in example ({', '.join(sorted(set(lowerCAmelCase__) - lang_set))}) are not in valid set ({', '.join(lowerCAmelCase__)}).")
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
SCREAMING_SNAKE_CASE_: Tuple = []
for lang, text in translation_dict.items():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__):
translation_tuples.append((lang, text))
else:
translation_tuples.extend([(lang, el) for el in text])
# Ensure translations are in ascending order by language code.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] = zip(*sorted(lowerCAmelCase__))
return {"language": languages, "translation": translations}
def _SCREAMING_SNAKE_CASE ( self : List[str]):
from .features import Sequence, Value
return {
"language": Sequence(Value("string")),
"translation": Sequence(Value("string")),
}
| 13 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE :str = 'RegNetConfig'
# Base docstring
SCREAMING_SNAKE_CASE :List[str] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 1088, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE :Optional[int] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Any = 'tabby, tabby cat'
SCREAMING_SNAKE_CASE :Optional[int] = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : int ,A : int = 3 ,A : int = 1 ,A : int = 1 ,A : Optional[str] = "relu" ,**A : Dict ,):
super().__init__(**A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__A = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=A ,strides=A ,padding="VALID" ,groups=A ,use_bias=A ,name="convolution" ,)
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
__A = ACTaFN[activation] if activation is not None else tf.identity
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
__A = self.convolution(self.padding(A ) )
__A = self.normalization(A )
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : RegNetConfig ,**A : str ):
super().__init__(**A )
__A = config.num_channels
__A = TFRegNetConvLayer(
out_channels=config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act ,name="embedder" ,)
def UpperCamelCase_ ( self : Tuple ,A : Optional[Any] ):
__A = shape_list(A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__A = tf.transpose(A ,perm=(0, 2, 3, 1) )
__A = self.embedder(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : int ,A : int = 2 ,**A : Tuple ):
super().__init__(**A )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=1 ,strides=A ,use_bias=A ,name="convolution" )
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
def UpperCamelCase_ ( self : Union[str, Any] ,A : tf.Tensor ,A : bool = False ):
return self.normalization(self.convolution(A ) ,training=A )
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Dict ,A : int ,A : int ,**A : str ):
super().__init__(**A )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
__A = [
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="relu" ,name="attention.0" ),
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="sigmoid" ,name="attention.2" ),
]
def UpperCamelCase_ ( self : Dict ,A : List[Any] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__A = self.pooler(A )
for layer_module in self.attention:
__A = layer_module(A )
__A = hidden_state * pooled
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : Optional[int] ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.2" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : int ,A : Optional[int] ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : str ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetSELayer(A ,reduced_channels=int(round(in_channels / 4 ) ) ,name="layer.2" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.3" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : Dict ,A : Any ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 2 ,A : int = 2 ,**A : Optional[int] ):
super().__init__(**A )
__A = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
__A = [
# downsampling is done in the first layer with stride of 2
layer(A ,A ,A ,stride=A ,name="layers.0" ),
*[layer(A ,A ,A ,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def UpperCamelCase_ ( self : Any ,A : List[str] ):
for layer_module in self.layers:
__A = layer_module(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any ,A : RegNetConfig ,**A : List[str] ):
super().__init__(**A )
__A = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
A ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,name="stages.0" ,) )
__A = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(A ,config.depths[1:] ) ):
self.stages.append(TFRegNetStage(A ,A ,A ,depth=A ,name=f'''stages.{i+1}''' ) )
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor ,A : bool = False ,A : bool = True ):
__A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__A = hidden_states + (hidden_state,)
__A = stage_module(A )
if output_hidden_states:
__A = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=A ,hidden_states=A )
@keras_serializable
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
snake_case_ = RegNetConfig
def __init__( self : int ,A : Optional[int] ,**A : Dict ):
super().__init__(**A )
__A = config
__A = TFRegNetEmbeddings(A ,name="embedder" )
__A = TFRegNetEncoder(A ,name="encoder" )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
@unpack_inputs
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : bool = False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.embedder(A ,training=A )
__A = self.encoder(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = encoder_outputs[0]
__A = self.pooler(A )
# Change to NCHW output format have uniformity in the modules
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__A = tuple([tf.transpose(A ,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A ,pooler_output=A ,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = RegNetConfig
snake_case_ = "regnet"
snake_case_ = "pixel_values"
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) ,dtype=tf.floataa )}
SCREAMING_SNAKE_CASE :Dict = R'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE :Dict = R'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,*A : List[Any] ,**A : str ):
super().__init__(A ,*A ,**A )
__A = TFRegNetMainLayer(A ,name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=A ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : int=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
pixel_values=A ,output_hidden_states=A ,return_dict=A ,training=A ,)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state ,pooler_output=outputs.pooler_output ,hidden_states=outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : RegNetConfig ,*A : str ,**A : Tuple ):
super().__init__(A ,*A ,**A )
__A = config.num_labels
__A = TFRegNetMainLayer(A ,name="regnet" )
# classification head
__A = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels ,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=A ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor = None ,A : tf.Tensor = None ,A : bool = None ,A : bool = None ,A : Union[str, Any]=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = outputs.pooler_output if return_dict else outputs[1]
__A = self.classifier[0](A )
__A = self.classifier[1](A )
__A = None if labels is None else self.hf_compute_loss(labels=A ,logits=A )
if not return_dict:
__A = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=A ,logits=A ,hidden_states=outputs.hidden_states )
| 15 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = args.threshold
A__ = args.model_name_or_path.rstrip('''/''' )
A__ = args.target_model_path
print(f"""Load fine-pruned model from {model_name_or_path}""" )
A__ = torch.load(os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
A__ = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
A__ = tensor
print(f"""Copied layer {name}""" )
elif "classifier" in name or "qa_output" in name:
A__ = tensor
print(f"""Copied layer {name}""" )
elif "bias" in name:
A__ = tensor
print(f"""Copied layer {name}""" )
else:
if pruning_method == "magnitude":
A__ = MagnitudeBinarizer.apply(inputs=lowercase_ , threshold=lowercase_ )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
A__ = name[:-6]
A__ = model[f"""{prefix_}mask_scores"""]
A__ = TopKBinarizer.apply(lowercase_ , lowercase_ )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
A__ = name[:-6]
A__ = model[f"""{prefix_}mask_scores"""]
A__ = ThresholdBinarizer.apply(lowercase_ , lowercase_ , lowercase_ )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
A__ = name[:-6]
A__ = model[f"""{prefix_}mask_scores"""]
A__ , A__ = -0.1, 1.1
A__ = torch.sigmoid(lowercase_ )
A__ = s * (r - l) + l
A__ = s_bar.clamp(min=0.0 , max=1.0 )
A__ = tensor * mask
print(f"""Pruned layer {name}""" )
else:
raise ValueError('''Unknown pruning method''' )
if target_model_path is None:
A__ = os.path.join(
os.path.dirname(lowercase_ ) , f"""bertarized_{os.path.basename(lowercase_ )}""" )
if not os.path.isdir(lowercase_ ):
shutil.copytree(lowercase_ , lowercase_ )
print(f"""\nCreated folder {target_model_path}""" )
torch.save(lowercase_ , os.path.join(lowercase_ , '''pytorch_model.bin''' ) )
print('''\nPruned model saved! See you later!''' )
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--pruning_method""",
choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
type=str,
required=True,
help=(
"""Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
""" sigmoied_threshold = Soft movement pruning)"""
),
)
parser.add_argument(
"""--threshold""",
type=float,
required=False,
help=(
"""For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
"""For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
"""Not needed for `l0`"""
),
)
parser.add_argument(
"""--model_name_or_path""",
type=str,
required=True,
help="""Folder containing the model that was previously fine-pruned""",
)
parser.add_argument(
"""--target_model_path""",
default=None,
type=str,
required=False,
help="""Folder containing the model that was previously fine-pruned""",
)
_lowerCamelCase : int = parser.parse_args()
main(args)
| 14 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ ) -> None: # Max Heap
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) == 0:
return array
__A = 2 * math.ceil(math.loga(len(a_ ) ) )
__A = 1_6
return intro_sort(a_ , 0 , len(a_ ) , a_ , a_ )
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> list:
"""simple docstring"""
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()
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma : ').strip()
SCREAMING_SNAKE_CASE :str = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 15 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
lowerCAmelCase_ = random.Random()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase=1.0 , __lowerCamelCase=None , __lowerCamelCase=None ) -> int:
if rng is None:
lowercase__ : List[Any] = global_rng
lowercase__ : Union[str, Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Tuple ,_snake_case : Dict ,_snake_case : Optional[Any]=7 ,_snake_case : int=400 ,_snake_case : Dict=2_000 ,_snake_case : Dict=24 ,_snake_case : Any=24 ,_snake_case : str=0.0 ,_snake_case : Optional[Any]=16_000 ,_snake_case : List[str]=True ,_snake_case : Dict=True ,) -> Dict:
"""simple docstring"""
lowercase__ : str = parent
lowercase__ : Union[str, Any] = batch_size
lowercase__ : Optional[Any] = min_seq_length
lowercase__ : int = max_seq_length
lowercase__ : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowercase__ : Tuple = feature_size
lowercase__ : str = num_mel_bins
lowercase__ : List[Any] = padding_value
lowercase__ : Tuple = sampling_rate
lowercase__ : List[Any] = return_attention_mask
lowercase__ : Dict = do_normalize
def UpperCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase ( self : Any ,_snake_case : Any=False ,_snake_case : int=False ) -> Tuple:
"""simple docstring"""
def _flatten(_snake_case : Optional[Any] ):
return list(itertools.chain(*_snake_case ) )
if equal_length:
lowercase__ : Union[str, Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowercase__ : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
lowercase__ : Optional[Any] = [np.asarray(_snake_case ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __A ( A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Tuple = SpeechaTextFeatureExtractor if is_speech_available() else None
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
lowercase__ : Any = SpeechaTextFeatureExtractionTester(self )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : List[str] ) -> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(_snake_case ,axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(_snake_case ,axis=0 ) - 1 ) < 1e-3 ) )
def UpperCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowercase__ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
lowercase__ : Any = [np.asarray(_snake_case ) for speech_input in speech_inputs]
# Test feature size
lowercase__ : List[str] = feature_extractor(_snake_case ,padding=_snake_case ,return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowercase__ : Dict = feature_extractor(speech_inputs[0] ,return_tensors='''np''' ).input_features
lowercase__ : str = feature_extractor(np_speech_inputs[0] ,return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(_snake_case ,_snake_case ,atol=1e-3 ) )
# Test batched
lowercase__ : Optional[int] = feature_extractor(_snake_case ,return_tensors='''np''' ).input_features
lowercase__ : Union[str, Any] = feature_extractor(_snake_case ,return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(_snake_case ,_snake_case ):
self.assertTrue(np.allclose(_snake_case ,_snake_case ,atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowercase__ : Any = [floats_list((1, x) )[0] for x in (800, 800, 800)]
lowercase__ : Dict = np.asarray(_snake_case )
lowercase__ : str = feature_extractor(_snake_case ,return_tensors='''np''' ).input_features
lowercase__ : Tuple = feature_extractor(_snake_case ,return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(_snake_case ,_snake_case ):
self.assertTrue(np.allclose(_snake_case ,_snake_case ,atol=1e-3 ) )
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
lowercase__ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : str = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
lowercase__ : str = ['''longest''', '''max_length''', '''do_not_pad''']
lowercase__ : Union[str, Any] = [None, 16, None]
for max_length, padding in zip(_snake_case ,_snake_case ):
lowercase__ : Union[str, Any] = feature_extractor(
_snake_case ,padding=_snake_case ,max_length=_snake_case ,return_attention_mask=_snake_case )
lowercase__ : Any = inputs.input_features
lowercase__ : Any = inputs.attention_mask
lowercase__ : Optional[Any] = [np.sum(_snake_case ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
lowercase__ : List[Any] = ['''longest''', '''max_length''', '''do_not_pad''']
lowercase__ : Optional[int] = [None, 16, None]
for max_length, padding in zip(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = feature_extractor(
_snake_case ,max_length=_snake_case ,padding=_snake_case ,return_tensors='''np''' ,return_attention_mask=_snake_case )
lowercase__ : Any = inputs.input_features
lowercase__ : Any = inputs.attention_mask
lowercase__ : Tuple = [np.sum(_snake_case ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowercase__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : Any = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
lowercase__ : List[Any] = feature_extractor(
_snake_case ,padding='''max_length''' ,max_length=4 ,truncation=_snake_case ,return_tensors='''np''' ,return_attention_mask=_snake_case ,)
lowercase__ : Optional[int] = inputs.input_features
lowercase__ : Optional[Any] = inputs.attention_mask
lowercase__ : str = np.sum(attention_mask == 1 ,axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def UpperCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : int = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
lowercase__ : str = feature_extractor(
_snake_case ,padding='''longest''' ,max_length=4 ,truncation=_snake_case ,return_tensors='''np''' ,return_attention_mask=_snake_case ,)
lowercase__ : Optional[Any] = inputs.input_features
lowercase__ : List[str] = inputs.attention_mask
lowercase__ : int = np.sum(attention_mask == 1 ,axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape ,(3, 4, 24) )
lowercase__ : Dict = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
lowercase__ : List[Any] = feature_extractor(
_snake_case ,padding='''longest''' ,max_length=16 ,truncation=_snake_case ,return_tensors='''np''' ,return_attention_mask=_snake_case ,)
lowercase__ : List[str] = inputs.input_features
lowercase__ : List[Any] = inputs.attention_mask
lowercase__ : Dict = np.sum(attention_mask == 1 ,axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape ,(3, 6, 24) )
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
import torch
lowercase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : int = np.random.rand(100 ,32 ).astype(np.floataa )
lowercase__ : Tuple = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowercase__ : Tuple = feature_extractor.pad([{'''input_features''': inputs}] ,return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowercase__ : Union[str, Any] = feature_extractor.pad([{'''input_features''': inputs}] ,return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase ( self : Any ,_snake_case : str ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
lowercase__ : List[str] = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,'''clean''' ,split='''validation''' )
# automatic decoding with librispeech
lowercase__ : int = ds.sort('''id''' ).select(range(_snake_case ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ : str = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
lowercase__ : List[Any] = self._load_datasamples(1 )
lowercase__ : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : Optional[Any] = feature_extractor(_snake_case ,return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape ,(1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] ,_snake_case ,atol=1e-4 ) )
| 16 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
SCREAMING_SNAKE_CASE :Optional[int] = NewType('DataClass', Any)
SCREAMING_SNAKE_CASE :int = NewType('DataClassType', Any)
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
if isinstance(a_ , a_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def UpperCAmelCase ( a_ ) -> Callable[[str], Any]:
"""simple docstring"""
__A = {str(a_ ): choice for choice in choices}
return lambda a_ : str_to_choice.get(a_ , a_ )
def UpperCAmelCase ( *,
a_ = None , a_ = None , a_ = dataclasses.MISSING , a_ = dataclasses.MISSING , a_ = None , **a_ , ) -> dataclasses.Field:
"""simple docstring"""
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__A = {}
if aliases is not None:
__A = aliases
if help is not None:
__A = help
return dataclasses.field(metadata=a_ , default=a_ , default_factory=a_ , **a_ )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
def __init__( self : Union[str, Any] ,A : Union[DataClassType, Iterable[DataClassType]] ,**A : List[Any] ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__A = ArgumentDefaultsHelpFormatter
super().__init__(**A )
if dataclasses.is_dataclass(A ):
__A = [dataclass_types]
__A = list(A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(A )
@staticmethod
def UpperCamelCase_ ( A : ArgumentParser ,A : dataclasses.Field ):
__A = f'''--{field.name}'''
__A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type ,A ):
raise RuntimeError(
"Unresolved type detected, which should have been done with the help of "
"`typing.get_type_hints` method by default" )
__A = kwargs.pop("aliases" ,[] )
if isinstance(A ,A ):
__A = [aliases]
__A = getattr(field.type ,"__origin__" ,field.type )
if origin_type is Union or (hasattr(A ,"UnionType" ) and isinstance(A ,types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(A ) not in field.type.__args__
):
raise ValueError(
"Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
" the argument parser only supports one type per argument."
f''' Problem encountered in field \'{field.name}\'.''' )
if type(A ) not in field.type.__args__:
# filter `str` in Union
__A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__A = getattr(field.type ,"__origin__" ,field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__A = (
field.type.__args__[0] if isinstance(A ,field.type.__args__[1] ) else field.type.__args__[1]
)
__A = getattr(field.type ,"__origin__" ,field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__A = {}
if origin_type is Literal or (isinstance(field.type ,A ) and issubclass(field.type ,A )):
if origin_type is Literal:
__A = field.type.__args__
else:
__A = [x.value for x in field.type]
__A = make_choice_type_function(kwargs["choices"] )
if field.default is not dataclasses.MISSING:
__A = field.default
else:
__A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__A = copy(A )
# Hack because type=bool in argparse does not behave as we want.
__A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__A = default
# This tells argparse we accept 0 or 1 value after --field_name
__A = "?"
# This is the value that will get picked if we do --field_name (without value)
__A = True
elif isclass(A ) and issubclass(A ,A ):
__A = field.type.__args__[0]
__A = "+"
if field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
elif field.default is dataclasses.MISSING:
__A = True
else:
__A = field.type
if field.default is not dataclasses.MISSING:
__A = field.default
elif field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
else:
__A = True
parser.add_argument(A ,*A ,**A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__A = False
parser.add_argument(f'''--no_{field.name}''' ,action="store_false" ,dest=field.name ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : DataClassType ):
if hasattr(A ,"_argument_group_name" ):
__A = self.add_argument_group(dtype._argument_group_name )
else:
__A = self
try:
__A = get_type_hints(A )
except NameError:
raise RuntimeError(
f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
"removing line of `from __future__ import annotations` which opts in Postponed "
"Evaluation of Annotations (PEP 563)" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A ):
__A = ".".join(map(A ,sys.version_info[:3] ) )
raise RuntimeError(
f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
"line of `from __future__ import annotations` which opts in union types as "
"`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
"support Python versions that lower than 3.10, you need to use "
"`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
"`X | None`." ) from ex
raise
for field in dataclasses.fields(A ):
if not field.init:
continue
__A = type_hints[field.name]
self._parse_dataclass_field(A ,A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[Any]=None ,A : List[Any]=False ,A : Optional[Any]=True ,A : Union[str, Any]=None ,A : Union[str, Any]=None ,):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
__A = []
if args_filename:
args_files.append(Path(A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__A = ArgumentParser()
args_file_parser.add_argument(A ,type=A ,action="append" )
# Use only remaining args for further parsing (remove the args_file_flag)
__A , __A = args_file_parser.parse_known_args(args=A )
__A = vars(A ).get(args_file_flag.lstrip("-" ) ,A )
if cmd_args_file_paths:
args_files.extend([Path(A ) for p in cmd_args_file_paths] )
__A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__A = file_args + args if args is not None else file_args + sys.argv[1:]
__A , __A = self.parse_known_args(args=A )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in vars(A ).items() if k in keys}
for k in keys:
delattr(A ,A )
__A = dtype(**A )
outputs.append(A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def UpperCamelCase_ ( self : Dict ,A : Dict[str, Any] ,A : bool = False ):
__A = set(args.keys() )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
__A = dtype(**A )
outputs.append(A )
if not allow_extra_keys and unused_keys:
raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(A )}''' )
return tuple(A )
def UpperCamelCase_ ( self : List[str] ,A : str ,A : bool = False ):
with open(Path(A ) ,encoding="utf-8" ) as open_json_file:
__A = json.loads(open_json_file.read() )
__A = self.parse_dict(A ,allow_extra_keys=A )
return tuple(A )
def UpperCamelCase_ ( self : int ,A : str ,A : bool = False ):
__A = self.parse_dict(yaml.safe_load(Path(A ).read_text() ) ,allow_extra_keys=A )
return tuple(A )
| 15 | 0 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : List[Any], UpperCamelCase_ : Any, UpperCamelCase_ : List[str]) -> Optional[int]:
'''simple docstring'''
if isinstance(UpperCamelCase_, UpperCamelCase_):
__lowercase = np.full((len(UpperCamelCase_), sequence_length, 2), UpperCamelCase_)
else:
__lowercase = np.full((len(UpperCamelCase_), sequence_length), UpperCamelCase_)
for i, tensor in enumerate(UpperCamelCase_):
if padding_side == "right":
if isinstance(UpperCamelCase_, UpperCamelCase_):
__lowercase = tensor[:sequence_length]
else:
__lowercase = tensor[:sequence_length]
else:
if isinstance(UpperCamelCase_, UpperCamelCase_):
__lowercase = tensor[:sequence_length]
else:
__lowercase = tensor[:sequence_length]
return out_tensor.tolist()
def _A ( UpperCamelCase_ : Dict) -> str:
'''simple docstring'''
__lowercase = ord(UpperCamelCase_)
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__lowercase = unicodedata.category(UpperCamelCase_)
if cat.startswith("P"):
return True
return False
@dataclass
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : PreTrainedTokenizerBase
__UpperCAmelCase : Union[bool, str, PaddingStrategy] = True
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : int = -1_0_0
__UpperCAmelCase : str = "pt"
def _lowercase ( self : str, UpperCAmelCase__ : List[str] ):
import torch
__lowercase = "label" if "label" in features[0].keys() else "labels"
__lowercase = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__lowercase = self.tokenizer.pad(
UpperCAmelCase__, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt" if labels is None else None, )
if labels is None:
return batch
__lowercase = torch.tensor(batch["entity_ids"] ).shape[1]
__lowercase = self.tokenizer.padding_side
if padding_side == "right":
__lowercase = [
list(UpperCAmelCase__ ) + [self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase__ )) for label in labels
]
else:
__lowercase = [
[self.label_pad_token_id] * (sequence_length - len(UpperCAmelCase__ )) + list(UpperCAmelCase__ ) for label in labels
]
__lowercase = [feature["ner_tags"] for feature in features]
__lowercase = padding_tensor(UpperCAmelCase__, -1, UpperCAmelCase__, UpperCAmelCase__ )
__lowercase = [feature["original_entity_spans"] for feature in features]
__lowercase = padding_tensor(UpperCAmelCase__, (-1, -1), UpperCAmelCase__, UpperCAmelCase__ )
__lowercase = {k: torch.tensor(UpperCAmelCase__, dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 17 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
return False
__A = 0
__A = 0
__A = 1
# Calculating the hash of pattern and substring of text
for i in range(a_ ):
__A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
__A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
__A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
__A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = "abc1abc12"
__A = "alskfjaldsabc1abc1abc12k23adsfabcabc"
__A = "alskfjaldsk23adsfabcabc"
assert rabin_karp(a_ , a_ ) and not rabin_karp(a_ , a_ )
# Test 2)
__A = "ABABX"
__A = "ABABZABABYABABX"
assert rabin_karp(a_ , a_ )
# Test 3)
__A = "AAAB"
__A = "ABAAAAAB"
assert rabin_karp(a_ , a_ )
# Test 4)
__A = "abcdabcy"
__A = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(a_ , a_ )
# Test 5)
__A = "Lü"
__A = "Lüsai"
assert rabin_karp(a_ , a_ )
__A = "Lue"
assert not rabin_karp(a_ , a_ )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 15 | 0 |
import argparse
import os
import re
__lowerCamelCase : int = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__lowerCamelCase : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCamelCase : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCamelCase : Optional[Any] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCamelCase : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCamelCase : List[str] = re.compile(R'''\[([^\]]+)\]''')
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = _re_indent.search(lowerCAmelCase )
return "" if search is None else search.groups()[0]
def _snake_case ( lowerCAmelCase : List[str] , lowerCAmelCase : List[Any]="" , lowerCAmelCase : Dict=None , lowerCAmelCase : Any=None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = 0
SCREAMING_SNAKE_CASE_ : Optional[Any] = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(lowerCAmelCase ):
index += 1
SCREAMING_SNAKE_CASE_ : List[Any] = ["\n".join(lines[:index] )]
else:
SCREAMING_SNAKE_CASE_ : List[str] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
SCREAMING_SNAKE_CASE_ : List[Any] = [lines[index]]
index += 1
while index < len(lowerCAmelCase ) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(lowerCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(lowerCAmelCase ) )
if index < len(lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE_ : Any = [lines[index + 1]]
index += 1
else:
SCREAMING_SNAKE_CASE_ : Tuple = []
else:
blocks.append("\n".join(lowerCAmelCase ) )
SCREAMING_SNAKE_CASE_ : Dict = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(lowerCAmelCase ) > 0:
blocks.append("\n".join(lowerCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lowerCAmelCase ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def _snake_case ( lowerCAmelCase : Tuple ):
"""simple docstring"""
def _inner(lowerCAmelCase : List[str] ):
return key(lowerCAmelCase ).lower().replace("_" , "" )
return _inner
def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : Dict=None ):
"""simple docstring"""
def noop(lowerCAmelCase : Optional[Any] ):
return x
if key is None:
SCREAMING_SNAKE_CASE_ : Dict = noop
# Constants are all uppercase, they go first.
SCREAMING_SNAKE_CASE_ : List[Any] = [obj for obj in objects if key(lowerCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
SCREAMING_SNAKE_CASE_ : Optional[Any] = [obj for obj in objects if key(lowerCAmelCase )[0].isupper() and not key(lowerCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
SCREAMING_SNAKE_CASE_ : str = [obj for obj in objects if not key(lowerCAmelCase )[0].isupper()]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ignore_underscore(lowerCAmelCase )
return sorted(lowerCAmelCase , key=lowerCAmelCase ) + sorted(lowerCAmelCase , key=lowerCAmelCase ) + sorted(lowerCAmelCase , key=lowerCAmelCase )
def _snake_case ( lowerCAmelCase : Tuple ):
"""simple docstring"""
def _replace(lowerCAmelCase : Any ):
SCREAMING_SNAKE_CASE_ : Optional[int] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
SCREAMING_SNAKE_CASE_ : List[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:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(lowerCAmelCase )] ) + "]"
SCREAMING_SNAKE_CASE_ : str = import_statement.split("\n" )
if len(lowerCAmelCase ) > 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.
SCREAMING_SNAKE_CASE_ : str = 2 if lines[1].strip() == "[" else 1
SCREAMING_SNAKE_CASE_ : Tuple = [(i, _re_strip_line.search(lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
SCREAMING_SNAKE_CASE_ : Tuple = sort_objects(lowerCAmelCase , key=lambda lowerCAmelCase : x[1] )
SCREAMING_SNAKE_CASE_ : Optional[int] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(lowerCAmelCase ) == 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:
SCREAMING_SNAKE_CASE_ : Any = _re_bracket_content.sub(_replace , lines[1] )
else:
SCREAMING_SNAKE_CASE_ : int = [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:
SCREAMING_SNAKE_CASE_ : Tuple = keys[:-1]
SCREAMING_SNAKE_CASE_ : Optional[int] = get_indent(lines[1] ) + ", ".join([f'"{k}"' for k in sort_objects(lowerCAmelCase )] )
return "\n".join(lowerCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
SCREAMING_SNAKE_CASE_ : Dict = _re_bracket_content.sub(_replace , lowerCAmelCase )
return import_statement
def _snake_case ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : List[Any]=True ):
"""simple docstring"""
with open(lowerCAmelCase , "r" ) as f:
SCREAMING_SNAKE_CASE_ : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
SCREAMING_SNAKE_CASE_ : Tuple = split_code_in_indented_blocks(
lowerCAmelCase , 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(lowerCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
SCREAMING_SNAKE_CASE_ : int = main_blocks[block_idx]
SCREAMING_SNAKE_CASE_ : Any = block.split("\n" )
# Get to the start of the imports.
SCREAMING_SNAKE_CASE_ : str = 0
while line_idx < len(lowerCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
SCREAMING_SNAKE_CASE_ : Optional[int] = len(lowerCAmelCase )
else:
line_idx += 1
if line_idx >= len(lowerCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
SCREAMING_SNAKE_CASE_ : Any = "\n".join(block_lines[line_idx:-1] )
SCREAMING_SNAKE_CASE_ : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
SCREAMING_SNAKE_CASE_ : int = split_code_in_indented_blocks(lowerCAmelCase , indent_level=lowerCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
SCREAMING_SNAKE_CASE_ : 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.
SCREAMING_SNAKE_CASE_ : List[Any] = [(pattern.search(lowerCAmelCase ).groups()[0] if pattern.search(lowerCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
SCREAMING_SNAKE_CASE_ : Any = [(i, key) for i, key in enumerate(lowerCAmelCase ) if key is not None]
SCREAMING_SNAKE_CASE_ : int = [x[0] for x in sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
SCREAMING_SNAKE_CASE_ : List[str] = 0
SCREAMING_SNAKE_CASE_ : Any = []
for i in range(len(lowerCAmelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
SCREAMING_SNAKE_CASE_ : Dict = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(lowerCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
SCREAMING_SNAKE_CASE_ : Dict = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(lowerCAmelCase ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(lowerCAmelCase , "w" ) as f:
f.write("\n".join(lowerCAmelCase ) )
def _snake_case ( lowerCAmelCase : Union[str, Any]=True ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = []
for root, _, files in os.walk(lowerCAmelCase ):
if "__init__.py" in files:
SCREAMING_SNAKE_CASE_ : int = sort_imports(os.path.join(lowerCAmelCase , "__init__.py" ) , check_only=lowerCAmelCase )
if result:
SCREAMING_SNAKE_CASE_ : Optional[int] = [os.path.join(lowerCAmelCase , "__init__.py" )]
if len(lowerCAmelCase ) > 0:
raise ValueError(f'Would overwrite {len(lowerCAmelCase )} files, run `make style`.' )
if __name__ == "__main__":
__lowerCamelCase : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__lowerCamelCase : Any = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 18 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE :Dict = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
SCREAMING_SNAKE_CASE :Dict = reader.read()
SCREAMING_SNAKE_CASE :List[str] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
else:
SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
SCREAMING_SNAKE_CASE :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
SCREAMING_SNAKE_CASE :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
SCREAMING_SNAKE_CASE :Optional[Any] = config[key]
del config[key]
SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
SCREAMING_SNAKE_CASE :Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
SCREAMING_SNAKE_CASE :List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
SCREAMING_SNAKE_CASE :List[Any] = param_value
SCREAMING_SNAKE_CASE :str = True
if not has_changed:
SCREAMING_SNAKE_CASE :List[str] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 15 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.get_logger(__name__)
__A =[
['''attention''', '''attn'''],
['''encoder_attention''', '''encoder_attn'''],
['''q_lin''', '''q_proj'''],
['''k_lin''', '''k_proj'''],
['''v_lin''', '''v_proj'''],
['''out_lin''', '''out_proj'''],
['''norm_embeddings''', '''layernorm_embedding'''],
['''position_embeddings''', '''embed_positions'''],
['''embeddings''', '''embed_tokens'''],
['''ffn.lin''', '''fc'''],
]
def lowerCamelCase_ ( lowerCamelCase__ ):
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
lowerCamelCase_ = k.replace(lowerCamelCase__ , lowerCamelCase__ )
if k.startswith("encoder" ):
lowerCamelCase_ = k.replace(".attn" , ".self_attn" )
lowerCamelCase_ = k.replace("norm1" , "self_attn_layer_norm" )
lowerCamelCase_ = k.replace("norm2" , "final_layer_norm" )
elif k.startswith("decoder" ):
lowerCamelCase_ = k.replace("norm1" , "self_attn_layer_norm" )
lowerCamelCase_ = k.replace("norm2" , "encoder_attn_layer_norm" )
lowerCamelCase_ = k.replace("norm3" , "final_layer_norm" )
return k
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = [
"model.encoder.layernorm_embedding.weight",
"model.encoder.layernorm_embedding.bias",
"model.decoder.layernorm_embedding.weight",
"model.decoder.layernorm_embedding.bias",
]
for k in keys:
lowerCamelCase_ = sd.pop(lowerCamelCase__ )
lowerCamelCase_ = k.replace("layernorm_embedding" , "layer_norm" )
assert new_k not in sd
lowerCamelCase_ = v
__A =['''START''']
@torch.no_grad()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = torch.load(lowerCamelCase__ , map_location="cpu" )
lowerCamelCase_ = model["model"]
lowerCamelCase_ = BlenderbotConfig.from_json_file(lowerCamelCase__ )
lowerCamelCase_ = BlenderbotForConditionalGeneration(lowerCamelCase__ )
lowerCamelCase_ = m.model.state_dict().keys()
lowerCamelCase_ = []
lowerCamelCase_ = {}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
lowerCamelCase_ = rename_state_dict_key(lowerCamelCase__ )
if new_k not in valid_keys:
failures.append([k, new_k] )
else:
lowerCamelCase_ = v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(lowerCamelCase__ )
m.model.load_state_dict(lowerCamelCase__ , strict=lowerCamelCase__ )
m.half()
m.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
parser.add_argument(
'''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
)
__A =parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
| 19 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
__A = date_parser.parse(a_ )
__A = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__A = start
__A = end
__A = duration_in_min
return job_info
def UpperCAmelCase ( a_ , a_=None ) -> str:
"""simple docstring"""
__A = None
if token is not None:
__A = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
__A = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__A = requests.get(a_ , headers=a_ ).json()
__A = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
__A = math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )
for i in range(a_ ):
__A = requests.get(url + F'''&page={i + 2}''' , headers=a_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
SCREAMING_SNAKE_CASE :Union[str, Any] = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE :Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f'''{k}: {v["duration"]}''')
| 15 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case ( lowerCAmelCase , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
_a : List[str]= "ssube/stable-diffusion-x4-upscaler-onnx"
def _SCREAMING_SNAKE_CASE ( self ,snake_case=0 ):
'''simple docstring'''
lowercase : List[Any] = floats_tensor((1, 3, 128, 128) ,rng=random.Random(snake_case ) )
lowercase : str = torch.manual_seed(snake_case )
lowercase : Dict = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=snake_case )
lowercase : Tuple = self.get_dummy_inputs()
lowercase : List[str] = pipe(**snake_case ).images
lowercase : List[str] = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
lowercase : List[Any] = np.array(
[0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
lowercase : Optional[int] = PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=snake_case )
pipe.set_progress_bar_config(disable=snake_case )
lowercase : Union[str, Any] = self.get_dummy_inputs()
lowercase : Tuple = pipe(**snake_case ).images
lowercase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase : List[str] = np.array(
[0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : int = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
lowercase : Dict = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case )
lowercase : int = self.get_dummy_inputs()
lowercase : str = pipe(**snake_case ).images
lowercase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase : Any = np.array(
[0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
lowercase : str = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case )
lowercase : List[Any] = self.get_dummy_inputs()
lowercase : Dict = pipe(**snake_case ).images
lowercase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase : Optional[int] = np.array(
[0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : str = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider="""CPUExecutionProvider""" )
lowercase : List[Any] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case )
lowercase : Tuple = self.get_dummy_inputs()
lowercase : Optional[int] = pipe(**snake_case ).images
lowercase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowercase : str = np.array(
[0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
@property
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Dict = ort.SessionOptions()
lowercase : Tuple = False
return options
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowercase : Dict = init_image.resize((128, 128) )
# using the PNDM scheduler by default
lowercase : List[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=snake_case )
lowercase : List[str] = """A fantasy landscape, trending on artstation"""
lowercase : str = torch.manual_seed(0 )
lowercase : Any = pipe(
prompt=snake_case ,image=snake_case ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=snake_case ,output_type="""np""" ,)
lowercase : Optional[Any] = output.images
lowercase : str = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowercase : Optional[int] = np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowercase : Dict = init_image.resize((128, 128) )
lowercase : Union[str, Any] = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" ,subfolder="""scheduler""" )
lowercase : List[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" ,scheduler=snake_case ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=snake_case )
lowercase : int = """A fantasy landscape, trending on artstation"""
lowercase : Tuple = torch.manual_seed(0 )
lowercase : Any = pipe(
prompt=snake_case ,image=snake_case ,guidance_scale=7.5 ,num_inference_steps=20 ,generator=snake_case ,output_type="""np""" ,)
lowercase : str = output.images
lowercase : int = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowercase : Tuple = np.array(
[0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 20 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.model_name_or_path.rstrip("/" )
__A = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
__A = torch.load(os.path.join(a_ , "pytorch_model.bin" ) )
__A = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
__A = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__A = MagnitudeBinarizer.apply(inputs=a_ , threshold=a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = TopKBinarizer.apply(a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = ThresholdBinarizer.apply(a_ , a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A , __A = -0.1, 1.1
__A = torch.sigmoid(a_ )
__A = s * (r - l) + l
__A = s_bar.clamp(min=0.0 , max=1.0 )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
__A = os.path.join(
os.path.dirname(a_ ) , F'''bertarized_{os.path.basename(a_ )}''' )
if not os.path.isdir(a_ ):
shutil.copytree(a_ , a_ )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(a_ , os.path.join(a_ , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
SCREAMING_SNAKE_CASE :str = parser.parse_args()
main(args)
| 15 | 0 |
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE : Any = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
SCREAMING_SNAKE_CASE : List[Any] = "\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {'f1': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results['f1'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric(\"f1\")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results['f1'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n >>> print(round(results['f1'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n >>> print(round(results['f1'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {'f1': array([0.8, 0. , 0. ])}\n"
SCREAMING_SNAKE_CASE : List[Any] = "\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowerCamelCase( datasets.Metric ):
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('int32')),
'references': datasets.Sequence(datasets.Value('int32')),
}
if self.config_name == 'multilabel'
else {
'predictions': datasets.Value('int32'),
'references': datasets.Value('int32'),
}), reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html'], )
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=1, lowerCamelCase="binary", lowerCamelCase=None) -> Union[str, Any]:
"""simple docstring"""
_lowercase : int = fa_score(
lowerCamelCase, lowerCamelCase, labels=lowerCamelCase, pos_label=lowerCamelCase, average=lowerCamelCase, sample_weight=lowerCamelCase)
return {"f1": float(lowerCamelCase) if score.size == 1 else score}
| 21 |
import os
import re
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 logging
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :int = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
SCREAMING_SNAKE_CASE :int = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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_ = []
def __init__( self : Any ,A : List[str] ,A : str="<unk>" ,A : int="<s>" ,A : Union[str, Any]="</s>" ,A : List[str]="<pad>" ,A : int="[SEP]" ,A : Optional[Any]="[MASK]" ,A : Tuple="[CLS]" ,A : Optional[Dict[str, Any]] = None ,**A : Any ,):
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else bos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else eos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else unk_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else pad_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else cls_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sep_token=A ,mask_token=A ,cls_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def UpperCamelCase_ ( self : List[str] ):
return self.sp_model.get_piece_size()
def UpperCamelCase_ ( self : Optional[Any] ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : str ,A : Optional[Any] ):
__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 UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Tuple ):
return self.sp_model.piece_to_id(A )
def UpperCamelCase_ ( self : List[Any] ,A : Tuple ):
__A = self.sp_model.IdToPiece(A )
return token
def UpperCamelCase_ ( self : List[Any] ,A : int ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def UpperCamelCase_ ( self : Tuple ,A : List[int] ,A : bool = False ,A : bool = None ,A : bool = True ,**A : Union[str, Any] ,):
__A = kwargs.pop("use_source_tokenizer" ,A )
__A = self.convert_ids_to_tokens(A ,skip_special_tokens=A )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
__A = []
__A = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
__A = []
sub_texts.append(A )
else:
current_sub_text.append(A )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
__A = re.sub(R" (\[(MASK|SEP)\])" ,R"\1" ," ".join(A ) )
else:
__A = "".join(A )
__A = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__A = self.clean_up_tokenization(A )
return clean_text
else:
return text
def UpperCamelCase_ ( self : str ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Optional[int] ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Any ,A : List[int] ,A : Optional[List[int]] = None ):
__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]
| 15 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> str:
'''simple docstring'''
if isinstance(__lowercase , __lowercase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(__lowercase , __lowercase ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if num == 0:
return "0b0"
_UpperCAmelCase = False
if num < 0:
_UpperCAmelCase = True
_UpperCAmelCase = -num
_UpperCAmelCase = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(__lowercase ) for e in binary )
return "0b" + "".join(str(__lowercase ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
SCREAMING_SNAKE_CASE :int = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = (images / 2 + 0.5).clamp(0 , 1 )
__A = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A = numpy_to_pil(a_ )
return images
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if images.ndim == 3:
__A = images[None, ...]
__A = (images * 2_5_5).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
__A = [Image.fromarray(a_ ) for image in images]
return pil_images
| 15 | 0 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Optional[Any] ) -> None:
UpperCAmelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCAmelCase : str = False
def A ( self : Tuple , __snake_case : list[str] ) -> None:
for word in words:
self.insert(__snake_case )
def A ( self : Union[str, Any] , __snake_case : str ) -> None:
UpperCAmelCase : Any = self
for char in word:
if char not in curr.nodes:
UpperCAmelCase : str = TrieNode()
UpperCAmelCase : str = curr.nodes[char]
UpperCAmelCase : Optional[Any] = True
def A ( self : Optional[Any] , __snake_case : str ) -> bool:
UpperCAmelCase : str = self
for char in word:
if char not in curr.nodes:
return False
UpperCAmelCase : List[str] = curr.nodes[char]
return curr.is_leaf
def A ( self : List[str] , __snake_case : str ) -> None:
def _delete(__snake_case : TrieNode , __snake_case : str , __snake_case : int ) -> bool:
if index == len(__snake_case ):
# If word does not exist
if not curr.is_leaf:
return False
UpperCAmelCase : Union[str, Any] = False
return len(curr.nodes ) == 0
UpperCAmelCase : Optional[Any] = word[index]
UpperCAmelCase : str = curr.nodes.get(__snake_case )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
UpperCAmelCase : Union[str, Any] = _delete(__snake_case , __snake_case , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , __snake_case , 0 )
def snake_case_ ( _lowerCAmelCase : TrieNode , _lowerCAmelCase : str ) -> None:
if node.is_leaf:
print(_lowerCAmelCase , end=''' ''' )
for key, value in node.nodes.items():
print_words(_lowerCAmelCase , word + key )
def snake_case_ ( ) -> bool:
UpperCAmelCase : List[str] = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase : int = TrieNode()
root.insert_many(_lowerCAmelCase )
# print_words(root, "")
assert all(root.find(_lowerCAmelCase ) for word in words )
assert root.find('''banana''' )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
assert root.find('''apple''' )
assert root.find('''all''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : bool ) -> None:
print(str(_lowerCAmelCase ) , '''works!''' if passes else '''doesn\'t work :(''' )
def snake_case_ ( ) -> None:
assert test_trie()
def snake_case_ ( ) -> None:
print_results('''Testing trie functionality''' , test_trie() )
if __name__ == "__main__":
main()
| 23 |
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
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "yolos"
def __init__( self : Any ,A : Optional[Any]=7_68 ,A : Dict=12 ,A : Any=12 ,A : str=30_72 ,A : Any="gelu" ,A : str=0.0 ,A : List[str]=0.0 ,A : Dict=0.02 ,A : int=1E-12 ,A : Tuple=[5_12, 8_64] ,A : List[Any]=16 ,A : str=3 ,A : str=True ,A : Any=1_00 ,A : Dict=True ,A : Dict=False ,A : Tuple=1 ,A : Union[str, Any]=5 ,A : Optional[Any]=2 ,A : Union[str, Any]=5 ,A : int=2 ,A : int=0.1 ,**A : List[str] ,):
super().__init__(**A )
__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 = initializer_range
__A = layer_norm_eps
__A = image_size
__A = patch_size
__A = num_channels
__A = qkv_bias
__A = num_detection_tokens
__A = use_mid_position_embeddings
__A = auxiliary_loss
# Hungarian matcher
__A = class_cost
__A = bbox_cost
__A = giou_cost
# Loss coefficients
__A = bbox_loss_coefficient
__A = giou_loss_coefficient
__A = eos_coefficient
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = version.parse("1.11" )
@property
def UpperCamelCase_ ( self : str ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase_ ( self : List[Any] ):
return 1E-4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 12
| 15 | 0 |
import re
def lowerCamelCase__ ( snake_case_ : str ) -> list:
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def lowerCamelCase__ ( snake_case_ : str ) -> str:
__snake_case = split_input(str_ )
return "".join(
[''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : bool , snake_case_ : str ) -> str:
try:
__snake_case = split_input(snake_case_ )
if upper:
__snake_case = ''''''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
__snake_case = ''''''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def lowerCamelCase__ ( snake_case_ : str ) -> str:
return to_simple_case(snake_case_ )
def lowerCamelCase__ ( snake_case_ : str ) -> str:
try:
__snake_case = to_simple_case(snake_case_ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : bool ) -> str:
return to_complex_case(snake_case_ , snake_case_ , '''_''' )
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : bool ) -> str:
return to_complex_case(snake_case_ , snake_case_ , '''-''' )
if __name__ == "__main__":
__import__('doctest').testmod()
| 24 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : List[Any] = '''vit_mae'''
def __init__(self , SCREAMING_SNAKE_CASE__=7_68 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=12 , SCREAMING_SNAKE_CASE__=30_72 , SCREAMING_SNAKE_CASE__="gelu" , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=0.02 , SCREAMING_SNAKE_CASE__=1E-12 , SCREAMING_SNAKE_CASE__=2_24 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=5_12 , SCREAMING_SNAKE_CASE__=8 , SCREAMING_SNAKE_CASE__=20_48 , SCREAMING_SNAKE_CASE__=0.75 , SCREAMING_SNAKE_CASE__=False , **SCREAMING_SNAKE_CASE__ , ) -> Tuple:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = qkv_bias
SCREAMING_SNAKE_CASE__ : Any = decoder_num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] = decoder_hidden_size
SCREAMING_SNAKE_CASE__ : List[str] = decoder_num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_intermediate_size
SCREAMING_SNAKE_CASE__ : str = mask_ratio
SCREAMING_SNAKE_CASE__ : List[Any] = norm_pix_loss
| 25 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__A = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE :List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 15 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_ ( snake_case_ ):
return np.dot(snake_case_,snake_case_ )
class lowercase :
def __init__( self , *,
_a = np.inf , _a = "linear" , _a = 0.0 , ) -> None:
_A : List[Any] = regularization
_A : Optional[int] = gamma
if kernel == "linear":
_A : List[str] = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("""rbf kernel requires gamma""" )
if not isinstance(self.gamma , (float, int) ):
raise ValueError("""gamma must be float or int""" )
if not self.gamma > 0:
raise ValueError("""gamma must be > 0""" )
_A : Optional[int] = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
_A : Dict = F'''Unknown kernel: {kernel}'''
raise ValueError(_a )
def a__ ( self , _a , _a ) -> float:
return np.dot(_a , _a )
def a__ ( self , _a , _a ) -> float:
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def a__ ( self , _a , _a ) -> None:
_A : List[Any] = observations
_A : Optional[int] = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((_A) , ) : Union[str, Any] = np.shape(_a )
def to_minimize(_a ) -> float:
_A : str = 0
((_A) , ) : Optional[int] = np.shape(_a )
for i in range(_a ):
for j in range(_a ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(_a )
_A : Optional[int] = LinearConstraint(_a , 0 , 0 )
_A : List[str] = Bounds(0 , self.regularization )
_A : Dict = minimize(
_a , np.ones(_a ) , bounds=_a , constraints=[ly_contraint] ).x
_A : Union[str, Any] = l_star
# calculating mean offset of separation plane to points
_A : Optional[int] = 0
for i in range(_a ):
for j in range(_a ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
_A : str = s / n
def a__ ( self , _a ) -> int:
_A : Union[str, Any] = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , _a )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 26 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
__A = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__A = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
__A = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
__A = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
__A = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
__A = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
__A = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
__A = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
__A = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
__A = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
__A = key.replace("image_encoder.module" , "flava.image_model" )
__A = key.replace("text_encoder.module" , "flava.text_model" )
__A = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
__A = key.replace("mm_encoder.module" , "flava.multimodal_model" )
__A = key.replace("text_projection" , "flava.text_projection" )
__A = key.replace("image_projection" , "flava.image_projection" )
__A = value.float()
for key, value in codebook_state_dict.items():
__A = value
return upgrade
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__A = FlavaConfig.from_pretrained(a_ )
else:
__A = FlavaConfig()
__A = FlavaForPreTraining(a_ ).eval()
__A = convert_dalle_checkpoint(a_ , a_ , save_checkpoint=a_ )
if os.path.exists(a_ ):
__A = torch.load(a_ , map_location="cpu" )
else:
__A = torch.hub.load_state_dict_from_url(a_ , map_location="cpu" )
__A = upgrade_state_dict(a_ , a_ )
hf_model.load_state_dict(a_ )
__A = hf_model.state_dict()
__A = count_parameters(a_ )
__A = count_parameters(a_ ) + count_parameters(a_ )
assert torch.allclose(a_ , a_ , atol=1E-3 )
hf_model.save_pretrained(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 15 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any]=1_024 , _SCREAMING_SNAKE_CASE : Optional[int]=1_024 , _SCREAMING_SNAKE_CASE : Tuple=False , **_SCREAMING_SNAKE_CASE : Union[str, Any] ):
__a : Dict = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
__a : Tuple = SeqaSeqDataset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , type_path='train' , **_SCREAMING_SNAKE_CASE )
__a : int = tok.pad_token_id
def get_lens(_SCREAMING_SNAKE_CASE : str ):
__a : List[Any] = tqdm(
DataLoader(_SCREAMING_SNAKE_CASE , batch_size=512 , num_workers=8 , shuffle=_SCREAMING_SNAKE_CASE , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__a : Dict = []
for batch in dl:
__a : Optional[int] = batch['input_ids'].ne(_SCREAMING_SNAKE_CASE ).sum(1 ).tolist()
__a : Any = batch['labels'].ne(_SCREAMING_SNAKE_CASE ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
max_lens.append(max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
else:
max_lens.extend(_SCREAMING_SNAKE_CASE )
return max_lens
__a : Optional[int] = get_lens(_SCREAMING_SNAKE_CASE )
__a : Optional[int] = SeqaSeqDataset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , type_path='val' , **_SCREAMING_SNAKE_CASE )
__a : int = get_lens(_SCREAMING_SNAKE_CASE )
pickle_save(_SCREAMING_SNAKE_CASE , train_ds.len_file )
pickle_save(_SCREAMING_SNAKE_CASE , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 27 |
import os
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 logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE :Tuple = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
SCREAMING_SNAKE_CASE :List[Any] = {
'camembert-base': 512,
}
SCREAMING_SNAKE_CASE :List[str] = '▁'
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[Any] ,A : List[str] ,A : List[Any]="<s>" ,A : Tuple="</s>" ,A : Any="</s>" ,A : Optional[Any]="<s>" ,A : Tuple="<unk>" ,A : str="<pad>" ,A : int="<mask>" ,A : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] ,A : Optional[Dict[str, Any]] = None ,**A : Optional[Any] ,):
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,sep_token=A ,cls_token=A ,pad_token=A ,mask_token=A ,additional_special_tokens=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
__A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__A = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
__A = len(self.fairseq_tokens_to_ids )
__A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
__A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self : int ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__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 + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase_ ( self : Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self : int ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Dict ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase_ ( self : Optional[Any] ,A : Dict ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self : Dict ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Any ):
__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 UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 15 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowerCamelCase : int = "examples/"
_lowerCamelCase : Dict = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
"doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
_lowerCamelCase : List[str] = {
"init": "src/transformers/__init__.py",
"setup": "setup.py",
}
_lowerCamelCase : Union[str, Any] = "README.md"
def __lowerCamelCase ( A__ , A__ , A__ ) -> Optional[Any]:
"""simple docstring"""
with open(A__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
UpperCamelCase = f.read()
UpperCamelCase , UpperCamelCase = REPLACE_PATTERNS[pattern]
UpperCamelCase = replace.replace('VERSION' , A__ )
UpperCamelCase = re_pattern.sub(A__ , A__ )
with open(A__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(A__ )
def __lowerCamelCase ( A__ ) -> Optional[int]:
"""simple docstring"""
for folder, directories, fnames in os.walk(A__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(A__ , A__ ) , A__ , pattern='examples' )
def __lowerCamelCase ( A__ , A__=False ) -> List[str]:
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(A__ , A__ , A__ )
if not patch:
update_version_in_examples(A__ )
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = '🤗 Transformers currently provides the following architectures'
UpperCamelCase = '1. Want to contribute a new model?'
with open(A__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
UpperCamelCase = f.readlines()
# Find the start of the list.
UpperCamelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
UpperCamelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
UpperCamelCase = lines[index].replace(
'https://huggingface.co/docs/transformers/main/model_doc' , 'https://huggingface.co/docs/transformers/model_doc' , )
index += 1
with open(A__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(A__ )
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
with open(REPLACE_FILES['init'] , 'r' ) as f:
UpperCamelCase = f.read()
UpperCamelCase = REPLACE_PATTERNS['init'][0].search(A__ ).groups()[0]
return packaging.version.parse(A__ )
def __lowerCamelCase ( A__=False ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
UpperCamelCase = default_version.base_version
elif patch:
UpperCamelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
UpperCamelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
UpperCamelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(A__ ) == 0:
UpperCamelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(A__ , patch=A__ )
if not patch:
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
def __lowerCamelCase ( ) -> int:
"""simple docstring"""
UpperCamelCase = get_version()
UpperCamelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
UpperCamelCase = current_version.base_version
# Check with the user we got that right.
UpperCamelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(A__ ) == 0:
UpperCamelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(A__ )
print('Cleaning main README, don\'t forget to run `make fix-copies`.' )
clean_main_ref_in_model_list()
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
_lowerCamelCase : Dict = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 28 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , a_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__A , __A = arr[k - 1], arr[i]
else: # k is odd
__A , __A = arr[k - 1], arr[0]
generate(k - 1 , a_ )
generate(len(a_ ) , a_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :Dict = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 15 | 0 |
__UpperCAmelCase = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def lowercase__ ( __snake_case : float ):
'''simple docstring'''
assert type(__snake_case ) in (int, float) and decimal == int(__snake_case )
UpperCAmelCase_ : Tuple = int(__snake_case )
UpperCAmelCase_ : int = ''
UpperCAmelCase_ : Dict = False
if decimal < 0:
UpperCAmelCase_ : Any = True
decimal *= -1
while decimal > 0:
UpperCAmelCase_ , UpperCAmelCase_ : Dict = divmod(__snake_case , 16 )
UpperCAmelCase_ : Optional[int] = values[remainder] + hexadecimal
UpperCAmelCase_ : str = '0x' + hexadecimal
if negative:
UpperCAmelCase_ : Optional[int] = '-' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 29 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
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__":
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :List[Any] = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 15 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'AutoformerForPrediction',
'AutoformerModel',
'AutoformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 30 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 2
@register_to_config
def __init__( self : str ,A : float = 0.02 ,A : float = 1_00 ,A : float = 1.0_07 ,A : float = 80 ,A : float = 0.05 ,A : float = 50 ,):
# standard deviation of the initial noise distribution
__A = sigma_max
# setable values
__A = None
__A = None
__A = None # sigma(t_i)
def UpperCamelCase_ ( self : str ,A : torch.FloatTensor ,A : Optional[int] = None ):
return sample
def UpperCamelCase_ ( self : Dict ,A : int ,A : Union[str, torch.device] = None ):
__A = num_inference_steps
__A = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__A = torch.from_numpy(A ).to(A )
__A = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__A = torch.tensor(A ,dtype=torch.floataa ,device=A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : float ,A : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
__A = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__A = 0
# sample eps ~ N(0, S_noise^2 * I)
__A = self.config.s_noise * randn_tensor(sample.shape ,generator=A ).to(sample.device )
__A = sigma + gamma * sigma
__A = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase_ ( self : Dict ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_hat + sigma_hat * model_output
__A = (sample_hat - pred_original_sample) / sigma_hat
__A = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : Optional[int] ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_prev + sigma_prev * model_output
__A = (sample_prev - pred_original_sample) / sigma_prev
__A = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : str ):
raise NotImplementedError()
| 15 | 0 |
'''simple docstring'''
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return input_array.reshape((input_array.size, 1) )
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : int ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Dict = np.nan
for i in range(_UpperCAmelCase ):
_UpperCAmelCase : Union[str, Any] = features[:, labels == i]
_UpperCAmelCase : Dict = data.mean(1 )
# Centralize the data of class i
_UpperCAmelCase : Any = data - column_reshape(_UpperCAmelCase )
if i > 0:
# If covariance_sum is not None
covariance_sum += np.dot(_UpperCAmelCase , centered_data.T )
else:
# If covariance_sum is np.nan (i.e. first loop)
_UpperCAmelCase : Tuple = np.dot(_UpperCAmelCase , centered_data.T )
return covariance_sum / features.shape[1]
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : int ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : Any = features.mean(1 )
_UpperCAmelCase : Tuple = np.nan
for i in range(_UpperCAmelCase ):
_UpperCAmelCase : int = features[:, labels == i]
_UpperCAmelCase : Dict = data.shape[1]
_UpperCAmelCase : Dict = data.mean(1 )
if i > 0:
# If covariance_sum is not None
covariance_sum += device_data * np.dot(
column_reshape(_UpperCAmelCase ) - column_reshape(_UpperCAmelCase ) , (column_reshape(_UpperCAmelCase ) - column_reshape(_UpperCAmelCase )).T , )
else:
# If covariance_sum is np.nan (i.e. first loop)
_UpperCAmelCase : int = device_data * np.dot(
column_reshape(_UpperCAmelCase ) - column_reshape(_UpperCAmelCase ) , (column_reshape(_UpperCAmelCase ) - column_reshape(_UpperCAmelCase )).T , )
return covariance_sum / features.shape[1]
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : int ) -> np.ndarray:
"""simple docstring"""
if features.any():
_UpperCAmelCase : Any = features.mean(1 )
# Center the dataset
_UpperCAmelCase : Tuple = features - np.reshape(_UpperCAmelCase , (data_mean.size, 1) )
_UpperCAmelCase : List[str] = np.dot(_UpperCAmelCase , centered_data.T ) / features.shape[1]
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = np.linalg.eigh(_UpperCAmelCase )
# Take all the columns in the reverse order (-1), and then takes only the first
_UpperCAmelCase : Tuple = eigenvectors[:, ::-1][:, 0:dimensions]
# Project the database on the new space
_UpperCAmelCase : List[Any] = np.dot(filtered_eigenvectors.T , _UpperCAmelCase )
logging.info("Principal Component Analysis computed" )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format="%(message)s" , force=_UpperCAmelCase )
logging.error("Dataset empty" )
raise AssertionError
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> np.ndarray:
"""simple docstring"""
assert classes > dimensions
# Check if features have been already loaded
if features.any:
_UpperCAmelCase , _UpperCAmelCase : str = eigh(
covariance_between_classes(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) , covariance_within_classes(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) , )
_UpperCAmelCase : Any = eigenvectors[:, ::-1][:, :dimensions]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[Any] = np.linalg.svd(_UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = svd_matrix[:, 0:dimensions]
_UpperCAmelCase : List[str] = np.dot(filtered_svd_matrix.T , _UpperCAmelCase )
logging.info("Linear Discriminant Analysis computed" )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format="%(message)s" , force=_UpperCAmelCase )
logging.error("Dataset empty" )
raise AssertionError
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : Dict = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
_UpperCAmelCase : Dict = np.array([0, 0, 0, 1, 1] )
_UpperCAmelCase : Tuple = 2
_UpperCAmelCase : int = 2
# Assert that the function raises an AssertionError if dimensions > classes
with pytest.raises(_UpperCAmelCase ) as error_info:
_UpperCAmelCase : Any = linear_discriminant_analysis(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if isinstance(_UpperCAmelCase , np.ndarray ):
raise AssertionError(
"Did not raise AssertionError for dimensions > classes" )
assert error_info.type is AssertionError
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
_UpperCAmelCase : Optional[int] = 2
_UpperCAmelCase : List[Any] = np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]] )
with pytest.raises(_UpperCAmelCase ) as error_info:
_UpperCAmelCase : Optional[Any] = principal_component_analysis(_UpperCAmelCase , _UpperCAmelCase )
if not np.allclose(_UpperCAmelCase , _UpperCAmelCase ):
raise AssertionError
assert error_info.type is AssertionError
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE :Union[str, Any] = get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = "dummy_data"
snake_case_ = "datasets"
snake_case_ = False
def __init__( self : Optional[int] ,A : str ,A : str ,A : Union[Version, str] ,A : Optional[str] = None ,A : bool = False ,A : bool = True ,A : Optional[List[Callable]] = None ,):
__A = 0
__A = dataset_name
__A = cache_dir
__A = use_local_dummy_data
__A = config
# download_callbacks take a single url as input
__A = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__A = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__A = str(A )
# to be downloaded
__A = None
__A = None
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
if self._dummy_file is None:
__A = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase_ ( self : Optional[Any] ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def UpperCamelCase_ ( self : List[Any] ):
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def UpperCamelCase_ ( self : Tuple ):
__A = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__A = cached_path(
A ,cache_dir=self.cache_dir ,extract_compressed_file=A ,force_extract=A )
return os.path.join(A ,self.dummy_file_name )
@property
def UpperCamelCase_ ( self : str ):
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def UpperCamelCase_ ( self : Any ):
if self._bucket_url is None:
__A = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def UpperCamelCase_ ( self : Tuple ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def UpperCamelCase_ ( self : List[str] ,A : List[Any] ,*A : Dict ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__A = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__A = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A ,A ):
return self.create_dummy_data_dict(A ,A )
elif isinstance(A ,(list, tuple) ):
return self.create_dummy_data_list(A ,A )
else:
return self.create_dummy_data_single(A ,A )
def UpperCamelCase_ ( self : str ,A : List[Any] ,*A : List[Any] ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : List[str] ,A : List[str] ,A : Tuple ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : Any ,A : Any ,*A : Optional[Any] ,**A : List[str] ):
return path
def UpperCamelCase_ ( self : str ):
return {}
def UpperCamelCase_ ( self : int ,A : int ,A : Tuple ):
__A = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A ,A ):
for single_url in single_urls:
download_callback(A )
else:
__A = single_urls
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A ,A ):
__A = [os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) ) for x in single_urls]
else:
__A = single_urls
__A = os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) )
__A = value
# make sure that values are unique
if all(isinstance(A ,A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__A = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,A : str ):
__A = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__A = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,A ) ) for url in data_url )
__A = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__A = [data_url[0]] * len(A )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(A )
return dummy_data_list
def UpperCamelCase_ ( self : str ,A : List[Any] ,A : Optional[Any] ):
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(A ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : Dict ):
pass
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ):
def _iter_archive_members(A : Optional[Any] ):
# this preserves the order of the members inside the ZIP archive
__A = Path(self.dummy_file ).parent
__A = path.relative_to(A )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__A = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A )
__A = Path(A )
__A = _iter_archive_members(A ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(A ).as_posix(), file_path.open("rb" )
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
if not isinstance(A ,A ):
__A = [paths]
for path in paths:
if os.path.isfile(A ):
if os.path.basename(A ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A ):
if os.path.basename(A ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(A ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(A ,A )
| 15 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
UpperCAmelCase_ : Optional[int] = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def SCREAMING_SNAKE_CASE_ ( __A : Tuple=True ) -> Union[str, Any]:
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=lowercase__ ) )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : List[Any] = None
snake_case__ : List[str] = None
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple ) -> List[str]:
with TemporaryDirectory() as tmp_dir:
a_ : Optional[Any] = dataset_module_factory(SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = import_main_class(dataset_module.module_path , dataset=SCREAMING_SNAKE_CASE__ )
a_ : DatasetBuilder = builder_cls(
cache_dir=SCREAMING_SNAKE_CASE__ , config_name=SCREAMING_SNAKE_CASE__ , hash=dataset_module.hash , )
a_ : Any = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=SCREAMING_SNAKE_CASE__ ).replace(os.sep , '/' ),
config.DATASET_INFO_FILENAME,
] )
a_ : Dict = cached_path(SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ )
self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE__ ) )
@pytest.mark.integration
def SCREAMING_SNAKE_CASE_ ( __A : List[str] ) -> int:
"""simple docstring"""
a_ : List[str] = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
a_ : Union[str, Any] = dataset_module_factory('wikipedia' , cache_dir=__A )
a_ : Dict = import_main_class(dataset_module.module_path )
a_ : DatasetBuilder = builder_cls(
cache_dir=__A , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
a_ : Optional[Any] = None
builder_instance.download_and_prepare()
a_ : List[Any] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def SCREAMING_SNAKE_CASE_ ( __A : Tuple ) -> List[Any]:
"""simple docstring"""
a_ : Optional[int] = dataset_module_factory('wikipedia' , cache_dir=__A )
a_ : Any = import_main_class(dataset_module.module_path , dataset=__A )
a_ : DatasetBuilder = builder_cls(
cache_dir=__A , config_name='20220301.frr' , hash=dataset_module.hash , )
a_ : Tuple = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(__A , __A )
assert "train" in ds
assert isinstance(ds['train'] , __A )
assert next(iter(ds['train'] ) )
| 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[str] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 | 0 |
"""simple docstring"""
from typing import Dict, 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,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__A : Union[str, Any] = logging.get_logger(__name__)
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : str = ["pixel_values"]
def __init__( self : str , A : bool = True , A : Optional[Dict[str, int]] = None , A : PILImageResampling = PILImageResampling.BICUBIC , A : bool = True , A : bool = True , A : Union[int, float] = 1 / 2_55 , A : Dict[str, int] = None , A : bool = True , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , **A : Dict , ) -> None:
super().__init__(**A )
lowercase_ : Tuple = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
lowercase_ : Dict = get_size_dict(A )
lowercase_ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
lowercase_ : List[Any] = get_size_dict(A , default_to_square=A , param_name='''crop_size''' )
lowercase_ : Optional[Any] = do_resize
lowercase_ : List[str] = do_rescale
lowercase_ : Optional[int] = do_normalize
lowercase_ : List[str] = do_center_crop
lowercase_ : Union[str, Any] = crop_size
lowercase_ : Optional[Any] = size
lowercase_ : Optional[Any] = resample
lowercase_ : Optional[Any] = rescale_factor
lowercase_ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase_ : Tuple = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def A ( self : str , A : np.ndarray , A : Dict[str, int] , A : PILImageResampling = PILImageResampling.BILINEAR , A : Optional[Union[str, ChannelDimension]] = None , **A : List[str] , ) -> np.ndarray:
lowercase_ : str = get_size_dict(A )
if "shortest_edge" in size:
lowercase_ : Dict = get_resize_output_image_size(A , size=size['''shortest_edge'''] , default_to_square=A )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
lowercase_ : List[Any] = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(A , size=A , resample=A , data_format=A , **A )
def A ( self : str , A : np.ndarray , A : Dict[str, int] , A : Optional[Union[str, ChannelDimension]] = None , **A : Dict , ) -> np.ndarray:
lowercase_ : List[Any] = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(A , size=(size['''height'''], size['''width''']) , data_format=A , **A )
def A ( self : Tuple , A : np.ndarray , A : float , A : Optional[Union[str, ChannelDimension]] = None , **A : Any ) -> np.ndarray:
return rescale(A , scale=A , data_format=A , **A )
def A ( self : Optional[int] , A : np.ndarray , A : Union[float, List[float]] , A : Union[float, List[float]] , A : Optional[Union[str, ChannelDimension]] = None , **A : Tuple , ) -> np.ndarray:
return normalize(A , mean=A , std=A , data_format=A , **A )
def A ( self : Dict , A : ImageInput , A : Optional[bool] = None , A : Dict[str, int] = None , A : PILImageResampling = None , A : bool = None , A : int = None , A : Optional[bool] = None , A : Optional[float] = None , A : Optional[bool] = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[str, TensorType]] = None , A : Union[str, ChannelDimension] = ChannelDimension.FIRST , **A : List[str] , ) -> BatchFeature:
lowercase_ : str = do_resize if do_resize is not None else self.do_resize
lowercase_ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase_ : str = do_normalize if do_normalize is not None else self.do_normalize
lowercase_ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase_ : str = crop_size if crop_size is not None else self.crop_size
lowercase_ : int = get_size_dict(A , param_name='''crop_size''' , default_to_square=A )
lowercase_ : Union[str, Any] = resample if resample is not None else self.resample
lowercase_ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase_ : Tuple = image_mean if image_mean is not None else self.image_mean
lowercase_ : Tuple = image_std if image_std is not None else self.image_std
lowercase_ : Union[str, Any] = size if size is not None else self.size
lowercase_ : str = get_size_dict(A )
if not is_batched(A ):
lowercase_ : List[Any] = [images]
if not valid_images(A ):
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.''' )
# All transformations expect numpy arrays.
lowercase_ : Tuple = [to_numpy_array(A ) for image in images]
if do_resize:
lowercase_ : List[str] = [self.resize(image=A , size=A , resample=A ) for image in images]
if do_center_crop:
lowercase_ : List[Any] = [self.center_crop(image=A , size=A ) for image in images]
if do_rescale:
lowercase_ : List[Any] = [self.rescale(image=A , scale=A ) for image in images]
if do_normalize:
lowercase_ : Dict = [self.normalize(image=A , mean=A , std=A ) for image in images]
lowercase_ : List[str] = [to_channel_dimension_format(A , A ) for image in images]
lowercase_ : Optional[Any] = {'''pixel_values''': images}
return BatchFeature(data=A , tensor_type=A )
| 33 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
SCREAMING_SNAKE_CASE :List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
SCREAMING_SNAKE_CASE :str = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Tuple:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
__A = new_id
# turn into Numpy arrays
__A = np.array(a_ )
__A = np.array(a_ )
if reduce_labels:
__A = 2_5_5
__A = label - 1
__A = 2_5_5
__A = label != ignore_index
__A = np.not_equal(a_ , a_ )
__A = pred_label[mask]
__A = np.array(a_ )[mask]
__A = pred_label[pred_label == label]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Union[str, Any]:
"""simple docstring"""
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a_ , a_ ):
__A , __A , __A , __A = intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = None , a_ = False , ) -> str:
"""simple docstring"""
__A , __A , __A , __A = total_intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
# compute metrics
__A = {}
__A = total_area_intersect.sum() / total_area_label.sum()
__A = total_area_intersect / total_area_union
__A = total_area_intersect / total_area_label
__A = np.nanmean(a_ )
__A = np.nanmean(a_ )
__A = all_acc
__A = iou
__A = acc
if nan_to_num is not None:
__A = {metric: np.nan_to_num(a_ , nan=a_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) ,reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] ,)
def UpperCamelCase_ ( self : int ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : bool ,A : Optional[int] = None ,A : Optional[Dict[int, int]] = None ,A : bool = False ,):
__A = mean_iou(
results=A ,gt_seg_maps=A ,num_labels=A ,ignore_index=A ,nan_to_num=A ,label_map=A ,reduce_labels=A ,)
return iou_result
| 15 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
),
}
class _a ( __a ):
__a : Union[str, Any] = """xlm-roberta"""
def __init__( self : List[str] , lowercase : Any=30_522 , lowercase : Optional[Any]=768 , lowercase : Optional[int]=12 , lowercase : List[str]=12 , lowercase : Optional[Any]=3_072 , lowercase : Any="gelu" , lowercase : Union[str, Any]=0.1 , lowercase : List[str]=0.1 , lowercase : Union[str, Any]=512 , lowercase : List[str]=2 , lowercase : Dict=0.02 , lowercase : Union[str, Any]=1E-12 , lowercase : Tuple=1 , lowercase : int=0 , lowercase : Dict=2 , lowercase : List[Any]="absolute" , lowercase : List[str]=True , lowercase : Dict=None , **lowercase : str , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = use_cache
UpperCAmelCase = classifier_dropout
class _a ( __a ):
@property
def A ( self : Optional[Any] ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 34 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[str] = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Dict = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE :Optional[Any] = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[int] ,A : Optional[Any] ,A : Optional[int]=False ,A : int=False ,A : Union[str, Any]=False ,A : int=None ,A : Optional[Any]=None ,A : Union[str, Any]=None ,A : Optional[Any]=None ,A : Optional[Dict[str, Any]] = None ,**A : Tuple ,):
__A = {} if sp_model_kwargs is None else sp_model_kwargs
__A = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
__A = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__A = "<|endoftext|>" if eos_token is None else eos_token
__A = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__A = unk_token if pad_token is None else pad_token
__A = eos_token if bos_token is None else bos_token
else:
__A = "<pad>" if pad_token is None else pad_token
__A = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=A ,remove_space=A ,keep_accents=A ,bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = do_lower_case
__A = remove_space
__A = keep_accents
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
__A = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__A = re.compile(
f'''[{''.join(map(A ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(1_27 ,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Optional[Any] ,A : Union[str, Any] ):
__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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase_ ( self : List[str] ):
return len(self.sp_model )
def UpperCamelCase_ ( self : int ,A : str ):
__A = self.non_printing_characters_re.sub("" ,A )
# Normalize whitespaces
__A = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
__A = unicodedata.normalize("NFC" ,A )
return text
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,**A : Optional[int] ):
__A = self.preprocess_text(A )
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : int ):
return self.sp_model.IdToPiece(A )
@staticmethod
def UpperCamelCase_ ( A : str ):
return out_string
def UpperCamelCase_ ( self : str ,A : List[str] ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string
def UpperCamelCase_ ( self : str ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : List[str] ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, List[str]] ,A : Union[str, bool] = False ):
if isinstance(A ,A ):
__A = self.preprocess_text(A )
__A = self.sp_model.encode(A )
else:
__A = [self.preprocess_text(A ) for t in text]
__A = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
__A = torch.tensor(A )
return token_ids
def UpperCamelCase_ ( self : List[Any] ,A : Union[int, List[int]] ):
return self.sp_model.decode(A )
def UpperCamelCase_ ( self : List[str] ,A : "Conversation" ):
__A = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__A = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 15 | 0 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__a = "src/transformers"
# This is to make sure the transformers module imported is the one in the repo.
__a = direct_transformers_import(PATH_TO_TRANSFORMERS)
__a = transformers.models.auto.configuration_auto.CONFIG_MAPPING
__a = {
# used to compute the property `self.chunk_length`
"EncodecConfig": ["overlap"],
# used as `self.bert_model = BertModel(config, ...)`
"DPRConfig": True,
# not used in modeling files, but it's an important information
"FSMTConfig": ["langs"],
# used internally in the configuration class file
"GPTNeoConfig": ["attention_types"],
# used internally in the configuration class file
"EsmConfig": ["is_folding_model"],
# used during training (despite we don't have training script for these models yet)
"Mask2FormerConfig": ["ignore_value"],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"OneFormerConfig": ["ignore_value", "norm"],
# used during preprocessing and collation, see `collating_graphormer.py`
"GraphormerConfig": ["spatial_pos_max"],
# used internally in the configuration class file
"T5Config": ["feed_forward_proj"],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"MT5Config": ["feed_forward_proj", "tokenizer_class"],
"UMT5Config": ["feed_forward_proj", "tokenizer_class"],
# used internally in the configuration class file
"LongT5Config": ["feed_forward_proj"],
# used internally in the configuration class file
"SwitchTransformersConfig": ["feed_forward_proj"],
# having default values other than `1e-5` - we can't fix them without breaking
"BioGptConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"GLPNConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"SegformerConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"CvtConfig": ["layer_norm_eps"],
# having default values other than `1e-5` - we can't fix them without breaking
"PerceiverConfig": ["layer_norm_eps"],
# used internally to calculate the feature size
"InformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate the feature size
"AutoformerConfig": ["num_static_real_features", "num_time_features"],
# used internally to calculate `mlp_dim`
"SamVisionConfig": ["mlp_ratio"],
# For (head) training, but so far not implemented
"ClapAudioConfig": ["num_classes"],
# Not used, but providing useful information to users
"SpeechT5HifiGanConfig": ["sampling_rate"],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"CLIPSegConfig": True,
"DeformableDetrConfig": True,
"DetaConfig": True,
"DinatConfig": True,
"DonutSwinConfig": True,
"EfficientFormerConfig": True,
"FSMTConfig": True,
"JukeboxConfig": True,
"LayoutLMv2Config": True,
"MaskFormerSwinConfig": True,
"MT5Config": True,
"NatConfig": True,
"OneFormerConfig": True,
"PerceiverConfig": True,
"RagConfig": True,
"SpeechT5Config": True,
"SwinConfig": True,
"Swin2SRConfig": True,
"Swinv2Config": True,
"SwitchTransformersConfig": True,
"TableTransformerConfig": True,
"TapasConfig": True,
"TransfoXLConfig": True,
"UniSpeechConfig": True,
"UniSpeechSatConfig": True,
"WavLMConfig": True,
"WhisperConfig": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"JukeboxPriorConfig": True,
# TODO: @Younes (for `is_decoder`)
"Pix2StructTextConfig": True,
}
)
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
snake_case__ : List[str] = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
f"config.{attribute}" in modeling_source
or f"getattr(config, \"{attribute}\"" in modeling_source
or f"getattr(self.config, \"{attribute}\"" in modeling_source
):
snake_case__ : str = True
# Deal with multi-line cases
elif (
re.search(
rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"" , _lowerCAmelCase , )
is not None
):
snake_case__ : Optional[Any] = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
snake_case__ : Union[str, Any] = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
snake_case__ : List[Any] = [
"""bos_index""",
"""eos_index""",
"""pad_index""",
"""unk_index""",
"""mask_index""",
"""image_size""",
"""use_cache""",
"""out_features""",
"""out_indices""",
]
snake_case__ : Union[str, Any] = ["""encoder_no_repeat_ngram_size"""]
# Special cases to be allowed
snake_case__ : Union[str, Any] = True
if not attribute_used:
snake_case__ : Any = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
snake_case__ : Dict = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
snake_case__ : Any = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
snake_case__ : Union[str, Any] = True
elif attribute.endswith("""_token_id""" ):
snake_case__ : Union[str, Any] = True
# configuration class specific cases
if not case_allowed:
snake_case__ : str = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
snake_case__ : Tuple = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def __snake_case( _lowerCAmelCase ) -> str:
snake_case__ : List[str] = dict(inspect.signature(config_class.__init__ ).parameters )
snake_case__ : Tuple = [x for x in list(signature.keys() ) if x not in ["""self""", """kwargs"""]]
snake_case__ : List[str] = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
snake_case__ : str = {}
if len(config_class.attribute_map ) > 0:
snake_case__ : Dict = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
snake_case__ : int = inspect.getsourcefile(_lowerCAmelCase )
snake_case__ : int = os.path.dirname(_lowerCAmelCase )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
snake_case__ : List[str] = [os.path.join(_lowerCAmelCase , _lowerCAmelCase ) for fn in os.listdir(_lowerCAmelCase ) if fn.startswith("""modeling_""" )]
# Get the source code strings
snake_case__ : Dict = []
for path in modeling_paths:
if os.path.isfile(_lowerCAmelCase ):
with open(_lowerCAmelCase ) as fp:
modeling_sources.append(fp.read() )
snake_case__ : List[str] = []
for config_param, default_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
# `attributes` here is all the variant names for `config_param`
snake_case__ : Tuple = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
unused_attributes.append(attributes[0] )
return sorted(_lowerCAmelCase )
def __snake_case( ) -> List[str]:
snake_case__ : str = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
snake_case__ : List[Any] = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda _lowerCAmelCase : inspect.isclass(_lowerCAmelCase )
and issubclass(_lowerCAmelCase , _lowerCAmelCase )
and inspect.getmodule(_lowerCAmelCase ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
snake_case__ : Union[str, Any] = check_config_attributes_being_used(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
snake_case__ : Union[str, Any] = unused_attributes
if len(_lowerCAmelCase ) > 0:
snake_case__ : str = """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
for name, attributes in configs_with_unused_attributes.items():
error += f"{name}: {attributes}\n"
raise ValueError(_lowerCAmelCase )
if __name__ == "__main__":
check_config_attributes()
| 35 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 0 |
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = set()
# edges = list of graph's edges
_lowerCAmelCase : Dict = get_edges(_lowerCamelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
_lowerCAmelCase , _lowerCAmelCase : List[Any] = edges.pop()
chosen_vertices.add(_lowerCamelCase )
chosen_vertices.add(_lowerCamelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_lowerCamelCase )
return chosen_vertices
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 36 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE :str = 'RegNetConfig'
# Base docstring
SCREAMING_SNAKE_CASE :List[str] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 1088, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE :Optional[int] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Any = 'tabby, tabby cat'
SCREAMING_SNAKE_CASE :Optional[int] = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : int ,A : int = 3 ,A : int = 1 ,A : int = 1 ,A : Optional[str] = "relu" ,**A : Dict ,):
super().__init__(**A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__A = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=A ,strides=A ,padding="VALID" ,groups=A ,use_bias=A ,name="convolution" ,)
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
__A = ACTaFN[activation] if activation is not None else tf.identity
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
__A = self.convolution(self.padding(A ) )
__A = self.normalization(A )
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : RegNetConfig ,**A : str ):
super().__init__(**A )
__A = config.num_channels
__A = TFRegNetConvLayer(
out_channels=config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act ,name="embedder" ,)
def UpperCamelCase_ ( self : Tuple ,A : Optional[Any] ):
__A = shape_list(A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__A = tf.transpose(A ,perm=(0, 2, 3, 1) )
__A = self.embedder(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : int ,A : int = 2 ,**A : Tuple ):
super().__init__(**A )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=1 ,strides=A ,use_bias=A ,name="convolution" )
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
def UpperCamelCase_ ( self : Union[str, Any] ,A : tf.Tensor ,A : bool = False ):
return self.normalization(self.convolution(A ) ,training=A )
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Dict ,A : int ,A : int ,**A : str ):
super().__init__(**A )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
__A = [
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="relu" ,name="attention.0" ),
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="sigmoid" ,name="attention.2" ),
]
def UpperCamelCase_ ( self : Dict ,A : List[Any] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__A = self.pooler(A )
for layer_module in self.attention:
__A = layer_module(A )
__A = hidden_state * pooled
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : Optional[int] ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.2" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : int ,A : Optional[int] ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : str ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetSELayer(A ,reduced_channels=int(round(in_channels / 4 ) ) ,name="layer.2" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.3" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : Dict ,A : Any ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 2 ,A : int = 2 ,**A : Optional[int] ):
super().__init__(**A )
__A = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
__A = [
# downsampling is done in the first layer with stride of 2
layer(A ,A ,A ,stride=A ,name="layers.0" ),
*[layer(A ,A ,A ,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def UpperCamelCase_ ( self : Any ,A : List[str] ):
for layer_module in self.layers:
__A = layer_module(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any ,A : RegNetConfig ,**A : List[str] ):
super().__init__(**A )
__A = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
A ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,name="stages.0" ,) )
__A = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(A ,config.depths[1:] ) ):
self.stages.append(TFRegNetStage(A ,A ,A ,depth=A ,name=f'''stages.{i+1}''' ) )
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor ,A : bool = False ,A : bool = True ):
__A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__A = hidden_states + (hidden_state,)
__A = stage_module(A )
if output_hidden_states:
__A = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=A ,hidden_states=A )
@keras_serializable
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
snake_case_ = RegNetConfig
def __init__( self : int ,A : Optional[int] ,**A : Dict ):
super().__init__(**A )
__A = config
__A = TFRegNetEmbeddings(A ,name="embedder" )
__A = TFRegNetEncoder(A ,name="encoder" )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
@unpack_inputs
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : bool = False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.embedder(A ,training=A )
__A = self.encoder(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = encoder_outputs[0]
__A = self.pooler(A )
# Change to NCHW output format have uniformity in the modules
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__A = tuple([tf.transpose(A ,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A ,pooler_output=A ,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = RegNetConfig
snake_case_ = "regnet"
snake_case_ = "pixel_values"
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) ,dtype=tf.floataa )}
SCREAMING_SNAKE_CASE :Dict = R'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE :Dict = R'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,*A : List[Any] ,**A : str ):
super().__init__(A ,*A ,**A )
__A = TFRegNetMainLayer(A ,name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=A ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : int=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
pixel_values=A ,output_hidden_states=A ,return_dict=A ,training=A ,)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state ,pooler_output=outputs.pooler_output ,hidden_states=outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : RegNetConfig ,*A : str ,**A : Tuple ):
super().__init__(A ,*A ,**A )
__A = config.num_labels
__A = TFRegNetMainLayer(A ,name="regnet" )
# classification head
__A = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels ,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=A ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor = None ,A : tf.Tensor = None ,A : bool = None ,A : bool = None ,A : Union[str, Any]=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = outputs.pooler_output if return_dict else outputs[1]
__A = self.classifier[0](A )
__A = self.classifier[1](A )
__A = None if labels is None else self.hf_compute_loss(labels=A ,logits=A )
if not return_dict:
__A = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=A ,logits=A ,hidden_states=outputs.hidden_states )
| 15 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> None:
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" ,__UpperCAmelCase ,)
super().__init__(*__UpperCAmelCase ,**__UpperCAmelCase )
| 37 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ ) -> None: # Max Heap
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) == 0:
return array
__A = 2 * math.ceil(math.loga(len(a_ ) ) )
__A = 1_6
return intro_sort(a_ , 0 , len(a_ ) , a_ , a_ )
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> list:
"""simple docstring"""
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()
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma : ').strip()
SCREAMING_SNAKE_CASE :str = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 15 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : Optional[int] , **__lowerCamelCase : Optional[int] ):
requires_backends(self , ["""bs4"""] )
super().__init__(**__lowerCamelCase )
def _A ( self : List[str] , __lowerCamelCase : Any ):
UpperCamelCase :Optional[int] = []
UpperCamelCase :List[str] = []
UpperCamelCase :Union[str, Any] = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
UpperCamelCase :Optional[Any] = parent.find_all(child.name , recursive=__lowerCamelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(__lowerCamelCase ) else next(i for i, s in enumerate(__lowerCamelCase , 1 ) if s is child ) )
UpperCamelCase :Any = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def _A ( self : Any , __lowerCamelCase : Tuple ):
UpperCamelCase :Any = BeautifulSoup(__lowerCamelCase , """html.parser""" )
UpperCamelCase :Union[str, Any] = []
UpperCamelCase :Tuple = []
UpperCamelCase :Tuple = []
for element in html_code.descendants:
if type(__lowerCamelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCamelCase :Any = html.unescape(__lowerCamelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(__lowerCamelCase )
UpperCamelCase , UpperCamelCase :Optional[Any] = self.xpath_soup(__lowerCamelCase )
stringaxtag_seq.append(__lowerCamelCase )
stringaxsubs_seq.append(__lowerCamelCase )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def _A ( self : int , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] ):
UpperCamelCase :Tuple = """"""
for tagname, subs in zip(__lowerCamelCase , __lowerCamelCase ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self : Any , __lowerCamelCase : Dict ):
UpperCamelCase :Any = False
# Check that strings has a valid type
if isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCamelCase :List[Any] = True
elif isinstance(__lowerCamelCase , (list, tuple) ):
if len(__lowerCamelCase ) == 0 or isinstance(html_strings[0] , __lowerCamelCase ):
UpperCamelCase :Any = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F"""but is of type {type(__lowerCamelCase )}.""" )
UpperCamelCase :str = bool(isinstance(__lowerCamelCase , (list, tuple) ) and (isinstance(html_strings[0] , __lowerCamelCase )) )
if not is_batched:
UpperCamelCase :Any = [html_strings]
# Get nodes + xpaths
UpperCamelCase :Union[str, Any] = []
UpperCamelCase :str = []
for html_string in html_strings:
UpperCamelCase , UpperCamelCase , UpperCamelCase :int = self.get_three_from_single(__lowerCamelCase )
nodes.append(__lowerCamelCase )
UpperCamelCase :int = []
for node, tag_list, sub_list in zip(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
UpperCamelCase :str = self.construct_xpath(__lowerCamelCase , __lowerCamelCase )
xpath_strings.append(__lowerCamelCase )
xpaths.append(__lowerCamelCase )
# return as Dict
UpperCamelCase :Optional[int] = {"""nodes""": nodes, """xpaths""": xpaths}
UpperCamelCase :Any = BatchFeature(data=__lowerCamelCase , tensor_type=__lowerCamelCase )
return encoded_inputs
| 38 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
SCREAMING_SNAKE_CASE :Optional[int] = NewType('DataClass', Any)
SCREAMING_SNAKE_CASE :int = NewType('DataClassType', Any)
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
if isinstance(a_ , a_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def UpperCAmelCase ( a_ ) -> Callable[[str], Any]:
"""simple docstring"""
__A = {str(a_ ): choice for choice in choices}
return lambda a_ : str_to_choice.get(a_ , a_ )
def UpperCAmelCase ( *,
a_ = None , a_ = None , a_ = dataclasses.MISSING , a_ = dataclasses.MISSING , a_ = None , **a_ , ) -> dataclasses.Field:
"""simple docstring"""
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__A = {}
if aliases is not None:
__A = aliases
if help is not None:
__A = help
return dataclasses.field(metadata=a_ , default=a_ , default_factory=a_ , **a_ )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
def __init__( self : Union[str, Any] ,A : Union[DataClassType, Iterable[DataClassType]] ,**A : List[Any] ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__A = ArgumentDefaultsHelpFormatter
super().__init__(**A )
if dataclasses.is_dataclass(A ):
__A = [dataclass_types]
__A = list(A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(A )
@staticmethod
def UpperCamelCase_ ( A : ArgumentParser ,A : dataclasses.Field ):
__A = f'''--{field.name}'''
__A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type ,A ):
raise RuntimeError(
"Unresolved type detected, which should have been done with the help of "
"`typing.get_type_hints` method by default" )
__A = kwargs.pop("aliases" ,[] )
if isinstance(A ,A ):
__A = [aliases]
__A = getattr(field.type ,"__origin__" ,field.type )
if origin_type is Union or (hasattr(A ,"UnionType" ) and isinstance(A ,types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(A ) not in field.type.__args__
):
raise ValueError(
"Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
" the argument parser only supports one type per argument."
f''' Problem encountered in field \'{field.name}\'.''' )
if type(A ) not in field.type.__args__:
# filter `str` in Union
__A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__A = getattr(field.type ,"__origin__" ,field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__A = (
field.type.__args__[0] if isinstance(A ,field.type.__args__[1] ) else field.type.__args__[1]
)
__A = getattr(field.type ,"__origin__" ,field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__A = {}
if origin_type is Literal or (isinstance(field.type ,A ) and issubclass(field.type ,A )):
if origin_type is Literal:
__A = field.type.__args__
else:
__A = [x.value for x in field.type]
__A = make_choice_type_function(kwargs["choices"] )
if field.default is not dataclasses.MISSING:
__A = field.default
else:
__A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__A = copy(A )
# Hack because type=bool in argparse does not behave as we want.
__A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__A = default
# This tells argparse we accept 0 or 1 value after --field_name
__A = "?"
# This is the value that will get picked if we do --field_name (without value)
__A = True
elif isclass(A ) and issubclass(A ,A ):
__A = field.type.__args__[0]
__A = "+"
if field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
elif field.default is dataclasses.MISSING:
__A = True
else:
__A = field.type
if field.default is not dataclasses.MISSING:
__A = field.default
elif field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
else:
__A = True
parser.add_argument(A ,*A ,**A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__A = False
parser.add_argument(f'''--no_{field.name}''' ,action="store_false" ,dest=field.name ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : DataClassType ):
if hasattr(A ,"_argument_group_name" ):
__A = self.add_argument_group(dtype._argument_group_name )
else:
__A = self
try:
__A = get_type_hints(A )
except NameError:
raise RuntimeError(
f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
"removing line of `from __future__ import annotations` which opts in Postponed "
"Evaluation of Annotations (PEP 563)" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A ):
__A = ".".join(map(A ,sys.version_info[:3] ) )
raise RuntimeError(
f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
"line of `from __future__ import annotations` which opts in union types as "
"`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
"support Python versions that lower than 3.10, you need to use "
"`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
"`X | None`." ) from ex
raise
for field in dataclasses.fields(A ):
if not field.init:
continue
__A = type_hints[field.name]
self._parse_dataclass_field(A ,A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[Any]=None ,A : List[Any]=False ,A : Optional[Any]=True ,A : Union[str, Any]=None ,A : Union[str, Any]=None ,):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
__A = []
if args_filename:
args_files.append(Path(A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__A = ArgumentParser()
args_file_parser.add_argument(A ,type=A ,action="append" )
# Use only remaining args for further parsing (remove the args_file_flag)
__A , __A = args_file_parser.parse_known_args(args=A )
__A = vars(A ).get(args_file_flag.lstrip("-" ) ,A )
if cmd_args_file_paths:
args_files.extend([Path(A ) for p in cmd_args_file_paths] )
__A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__A = file_args + args if args is not None else file_args + sys.argv[1:]
__A , __A = self.parse_known_args(args=A )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in vars(A ).items() if k in keys}
for k in keys:
delattr(A ,A )
__A = dtype(**A )
outputs.append(A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def UpperCamelCase_ ( self : Dict ,A : Dict[str, Any] ,A : bool = False ):
__A = set(args.keys() )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
__A = dtype(**A )
outputs.append(A )
if not allow_extra_keys and unused_keys:
raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(A )}''' )
return tuple(A )
def UpperCamelCase_ ( self : List[str] ,A : str ,A : bool = False ):
with open(Path(A ) ,encoding="utf-8" ) as open_json_file:
__A = json.loads(open_json_file.read() )
__A = self.parse_dict(A ,allow_extra_keys=A )
return tuple(A )
def UpperCamelCase_ ( self : int ,A : str ,A : bool = False ):
__A = self.parse_dict(yaml.safe_load(Path(A ).read_text() ) ,allow_extra_keys=A )
return tuple(A )
| 15 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
_a = logging.getLogger(__name__)
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None ):
"""simple docstring"""
_UpperCAmelCase = self.layer[current_layer](UpperCAmelCase , UpperCAmelCase , head_mask[current_layer] )
_UpperCAmelCase = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , snake_case__ , )
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __init__( self , UpperCAmelCase ):
"""simple docstring"""
super().__init__(UpperCAmelCase )
_UpperCAmelCase = BertEncoderWithPabee(UpperCAmelCase )
self.init_weights()
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 0
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = threshold
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = patience
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = 0
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self.inference_layers_num / self.inference_instances_num
_UpperCAmelCase = (
F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(UpperCAmelCase )
@add_start_docstrings_to_model_forward(UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
_UpperCAmelCase = input_ids.size()
elif inputs_embeds is not None:
_UpperCAmelCase = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
_UpperCAmelCase = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
_UpperCAmelCase = torch.ones(UpperCAmelCase , device=UpperCAmelCase )
if token_type_ids is None:
_UpperCAmelCase = torch.zeros(UpperCAmelCase , dtype=torch.long , device=UpperCAmelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
_UpperCAmelCase = self.get_extended_attention_mask(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = encoder_hidden_states.size()
_UpperCAmelCase = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
_UpperCAmelCase = torch.ones(UpperCAmelCase , device=UpperCAmelCase )
_UpperCAmelCase = self.invert_attention_mask(UpperCAmelCase )
else:
_UpperCAmelCase = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
_UpperCAmelCase = self.get_head_mask(UpperCAmelCase , self.config.num_hidden_layers )
_UpperCAmelCase = self.embeddings(
input_ids=UpperCAmelCase , position_ids=UpperCAmelCase , token_type_ids=UpperCAmelCase , inputs_embeds=UpperCAmelCase )
_UpperCAmelCase = embedding_output
if self.training:
_UpperCAmelCase = []
for i in range(self.config.num_hidden_layers ):
_UpperCAmelCase = self.encoder.adaptive_forward(
UpperCAmelCase , current_layer=UpperCAmelCase , attention_mask=UpperCAmelCase , head_mask=UpperCAmelCase )
_UpperCAmelCase = self.pooler(UpperCAmelCase )
_UpperCAmelCase = output_layers[i](output_dropout(UpperCAmelCase ) )
res.append(UpperCAmelCase )
elif self.patience == 0: # Use all layers for inference
_UpperCAmelCase = self.encoder(
UpperCAmelCase , attention_mask=UpperCAmelCase , head_mask=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=UpperCAmelCase , )
_UpperCAmelCase = self.pooler(encoder_outputs[0] )
_UpperCAmelCase = [output_layers[self.config.num_hidden_layers - 1](UpperCAmelCase )]
else:
_UpperCAmelCase = 0
_UpperCAmelCase = None
_UpperCAmelCase = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
_UpperCAmelCase = self.encoder.adaptive_forward(
UpperCAmelCase , current_layer=UpperCAmelCase , attention_mask=UpperCAmelCase , head_mask=UpperCAmelCase )
_UpperCAmelCase = self.pooler(UpperCAmelCase )
_UpperCAmelCase = output_layers[i](UpperCAmelCase )
if regression:
_UpperCAmelCase = logits.detach()
if patient_result is not None:
_UpperCAmelCase = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
_UpperCAmelCase = 0
else:
_UpperCAmelCase = logits.detach().argmax(dim=1 )
if patient_result is not None:
_UpperCAmelCase = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(UpperCAmelCase ) ):
patient_counter += 1
else:
_UpperCAmelCase = 0
_UpperCAmelCase = logits
if patient_counter == self.patience:
break
_UpperCAmelCase = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , snake_case__ , )
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __init__( self , UpperCAmelCase ):
"""simple docstring"""
super().__init__(UpperCAmelCase )
_UpperCAmelCase = config.num_labels
_UpperCAmelCase = BertModelWithPabee(UpperCAmelCase )
_UpperCAmelCase = nn.Dropout(config.hidden_dropout_prob )
_UpperCAmelCase = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , ):
"""simple docstring"""
_UpperCAmelCase = self.bert(
input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , position_ids=UpperCAmelCase , head_mask=UpperCAmelCase , inputs_embeds=UpperCAmelCase , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
_UpperCAmelCase = (logits[-1],)
if labels is not None:
_UpperCAmelCase = None
_UpperCAmelCase = 0
for ix, logits_item in enumerate(UpperCAmelCase ):
if self.num_labels == 1:
# We are doing regression
_UpperCAmelCase = MSELoss()
_UpperCAmelCase = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
_UpperCAmelCase = CrossEntropyLoss()
_UpperCAmelCase = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
_UpperCAmelCase = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
_UpperCAmelCase = (total_loss / total_weights,) + outputs
return outputs
| 39 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
return False
__A = 0
__A = 0
__A = 1
# Calculating the hash of pattern and substring of text
for i in range(a_ ):
__A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
__A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
__A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
__A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = "abc1abc12"
__A = "alskfjaldsabc1abc1abc12k23adsfabcabc"
__A = "alskfjaldsk23adsfabcabc"
assert rabin_karp(a_ , a_ ) and not rabin_karp(a_ , a_ )
# Test 2)
__A = "ABABX"
__A = "ABABZABABYABABX"
assert rabin_karp(a_ , a_ )
# Test 3)
__A = "AAAB"
__A = "ABAAAAAB"
assert rabin_karp(a_ , a_ )
# Test 4)
__A = "abcdabcy"
__A = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(a_ , a_ )
# Test 5)
__A = "Lü"
__A = "Lüsai"
assert rabin_karp(a_ , a_ )
__A = "Lue"
assert not rabin_karp(a_ , a_ )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 15 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowercase = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ["""CLIPFeatureExtractor"""]
__lowercase = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 40 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE :Dict = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
SCREAMING_SNAKE_CASE :Dict = reader.read()
SCREAMING_SNAKE_CASE :List[str] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
else:
SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
SCREAMING_SNAKE_CASE :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
SCREAMING_SNAKE_CASE :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
SCREAMING_SNAKE_CASE :Optional[Any] = config[key]
del config[key]
SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
SCREAMING_SNAKE_CASE :Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
SCREAMING_SNAKE_CASE :List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
SCREAMING_SNAKE_CASE :List[Any] = param_value
SCREAMING_SNAKE_CASE :str = True
if not has_changed:
SCREAMING_SNAKE_CASE :List[str] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 15 | 0 |
'''simple docstring'''
_A : Any ={'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
_A : Optional[Any] =['''a''', '''b''', '''c''', '''d''', '''e''']
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Union[str, Any]:
lowerCamelCase__ : str = start
# add current to visited
visited.append(UpperCamelCase )
lowerCamelCase__ : Optional[Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
lowerCamelCase__ : str = topological_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# if all neighbors visited add current to sort
sort.append(UpperCamelCase )
# if all vertices haven't been visited select a new one to visit
if len(UpperCamelCase ) != len(UpperCamelCase ):
for vertice in vertices:
if vertice not in visited:
lowerCamelCase__ : Union[str, Any] = topological_sort(UpperCamelCase , UpperCamelCase , UpperCamelCase )
# return sort
return sort
if __name__ == "__main__":
_A : Optional[Any] =topological_sort('''a''', [], [])
print(sort)
| 41 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
__A = date_parser.parse(a_ )
__A = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__A = start
__A = end
__A = duration_in_min
return job_info
def UpperCAmelCase ( a_ , a_=None ) -> str:
"""simple docstring"""
__A = None
if token is not None:
__A = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
__A = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__A = requests.get(a_ , headers=a_ ).json()
__A = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
__A = math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )
for i in range(a_ ):
__A = requests.get(url + F'''&page={i + 2}''' , headers=a_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
SCREAMING_SNAKE_CASE :Union[str, Any] = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE :Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f'''{k}: {v["duration"]}''')
| 15 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_=0.01 , lowerCAmelCase_=10_00 ):
"""simple docstring"""
_snake_case = p_stop
_snake_case = max_length
def __iter__( self ):
"""simple docstring"""
_snake_case = 0
_snake_case = False
while not stop and count < self.max_length:
yield count
count += 1
_snake_case = random.random() < self.p_stop
class __UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False , lowerCAmelCase_=True ):
"""simple docstring"""
_snake_case = [
BatchSamplerShard(lowerCAmelCase_ , 2 , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
for i in range(2 )
]
_snake_case = [list(lowerCAmelCase_ ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(lowerCAmelCase_ ) for shard in batch_sampler_shards] , [len(lowerCAmelCase_ ) for e in expected] )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
_snake_case = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
_snake_case = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
_snake_case = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
# Check the shards when the dataset is very small.
_snake_case = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
_snake_case = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [[], []]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size.
_snake_case = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
_snake_case = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
# Check the shards when the dataset is very small.
_snake_case = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [[], []]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
_snake_case = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
_snake_case = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
_snake_case = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is very small.
_snake_case = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [[[0, 1]], []]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowerCAmelCase_ )
_snake_case = [[], []]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
# Expected shouldn't change
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size.
_snake_case = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
_snake_case = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
# Check the shards when the dataset is very small.
_snake_case = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [[[0, 1]], []]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
_snake_case = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = [[], []]
self.check_batch_sampler_shards(lowerCAmelCase_ , lowerCAmelCase_ , split_batches=lowerCAmelCase_ , even_batches=lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
_snake_case = [BatchSamplerShard(lowerCAmelCase_ , 2 , lowerCAmelCase_ , even_batches=lowerCAmelCase_ ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False , lowerCAmelCase_=2 , lowerCAmelCase_=False ):
"""simple docstring"""
random.seed(lowerCAmelCase_ )
_snake_case = list(lowerCAmelCase_ )
_snake_case = [
IterableDatasetShard(
lowerCAmelCase_ , batch_size=lowerCAmelCase_ , drop_last=lowerCAmelCase_ , num_processes=lowerCAmelCase_ , process_index=lowerCAmelCase_ , split_batches=lowerCAmelCase_ , )
for i in range(lowerCAmelCase_ )
]
_snake_case = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(lowerCAmelCase_ )
iterable_dataset_lists.append(list(lowerCAmelCase_ ) )
_snake_case = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
_snake_case = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(lowerCAmelCase_ ) , len(lowerCAmelCase_ ) )
self.assertTrue(len(lowerCAmelCase_ ) % shard_batch_size == 0 )
_snake_case = []
for idx in range(0 , len(lowerCAmelCase_ ) , lowerCAmelCase_ ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(lowerCAmelCase_ ) < len(lowerCAmelCase_ ):
reference += reference
self.assertListEqual(lowerCAmelCase_ , reference[: len(lowerCAmelCase_ )] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 42
_snake_case = RandomIterableDataset()
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
# Edge case with a very small dataset
_snake_case = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
self.check_iterable_dataset_shards(lowerCAmelCase_ , lowerCAmelCase_ , batch_size=4 , drop_last=lowerCAmelCase_ , split_batches=lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BatchSampler(range(16 ) , batch_size=4 , drop_last=lowerCAmelCase_ )
_snake_case = SkipBatchSampler(lowerCAmelCase_ , 2 )
self.assertListEqual(list(lowerCAmelCase_ ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = DataLoader(list(range(16 ) ) , batch_size=4 )
_snake_case = skip_first_batches(lowerCAmelCase_ , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(lowerCAmelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowerCAmelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowerCamelCase ( self ):
"""simple docstring"""
Accelerator()
_snake_case = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(lowerCAmelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowerCAmelCase_ ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 42 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.model_name_or_path.rstrip("/" )
__A = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
__A = torch.load(os.path.join(a_ , "pytorch_model.bin" ) )
__A = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
__A = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__A = MagnitudeBinarizer.apply(inputs=a_ , threshold=a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = TopKBinarizer.apply(a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = ThresholdBinarizer.apply(a_ , a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A , __A = -0.1, 1.1
__A = torch.sigmoid(a_ )
__A = s * (r - l) + l
__A = s_bar.clamp(min=0.0 , max=1.0 )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
__A = os.path.join(
os.path.dirname(a_ ) , F'''bertarized_{os.path.basename(a_ )}''' )
if not os.path.isdir(a_ ):
shutil.copytree(a_ , a_ )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(a_ , os.path.join(a_ , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
SCREAMING_SNAKE_CASE :str = parser.parse_args()
main(args)
| 15 | 0 |
from timeit import timeit
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__UpperCamelCase :str = 0
while number:
number &= number - 1
result += 1
return result
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__UpperCamelCase :Optional[Any] = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def lowerCamelCase ( ):
'''simple docstring'''
def do_benchmark(SCREAMING_SNAKE_CASE ) -> None:
__UpperCamelCase :List[str] = '''import __main__ as z'''
print(f"""Benchmark when {number = }:""" )
print(f"""{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE ) = }""" )
__UpperCamelCase :Optional[int] = timeit('''z.get_set_bits_count_using_modulo_operator(25)''' , setup=SCREAMING_SNAKE_CASE )
print(f"""timeit() runs in {timing} seconds""" )
print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE ) = }""" )
__UpperCamelCase :Union[str, Any] = timeit(
'''z.get_set_bits_count_using_brian_kernighans_algorithm(25)''' , setup=SCREAMING_SNAKE_CASE , )
print(f"""timeit() runs in {timing} seconds""" )
for number in (25, 37, 58, 0):
do_benchmark(SCREAMING_SNAKE_CASE )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 43 |
import os
import re
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 logging
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :int = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
SCREAMING_SNAKE_CASE :int = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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_ = []
def __init__( self : Any ,A : List[str] ,A : str="<unk>" ,A : int="<s>" ,A : Union[str, Any]="</s>" ,A : List[str]="<pad>" ,A : int="[SEP]" ,A : Optional[Any]="[MASK]" ,A : Tuple="[CLS]" ,A : Optional[Dict[str, Any]] = None ,**A : Any ,):
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else bos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else eos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else unk_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else pad_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else cls_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sep_token=A ,mask_token=A ,cls_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def UpperCamelCase_ ( self : List[str] ):
return self.sp_model.get_piece_size()
def UpperCamelCase_ ( self : Optional[Any] ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : str ,A : Optional[Any] ):
__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 UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Tuple ):
return self.sp_model.piece_to_id(A )
def UpperCamelCase_ ( self : List[Any] ,A : Tuple ):
__A = self.sp_model.IdToPiece(A )
return token
def UpperCamelCase_ ( self : List[Any] ,A : int ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def UpperCamelCase_ ( self : Tuple ,A : List[int] ,A : bool = False ,A : bool = None ,A : bool = True ,**A : Union[str, Any] ,):
__A = kwargs.pop("use_source_tokenizer" ,A )
__A = self.convert_ids_to_tokens(A ,skip_special_tokens=A )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
__A = []
__A = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
__A = []
sub_texts.append(A )
else:
current_sub_text.append(A )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
__A = re.sub(R" (\[(MASK|SEP)\])" ,R"\1" ," ".join(A ) )
else:
__A = "".join(A )
__A = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__A = self.clean_up_tokenization(A )
return clean_text
else:
return text
def UpperCamelCase_ ( self : str ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Optional[int] ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Any ,A : List[int] ,A : Optional[List[int]] = None ):
__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]
| 15 | 0 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Optional[int]=0 ) -> Dict:
# Format the message.
if name is None:
_lowerCAmelCase : int = None
else:
_lowerCAmelCase : int = """.""" * max(0 ,spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
_lowerCAmelCase : Optional[int] = fmt.format(_lowerCamelCase )
# Print and recurse (if needed).
if isinstance(_lowerCamelCase ,_lowerCamelCase ):
if msg is not None:
print(_lowerCamelCase )
for k in val.keys():
recursive_print(_lowerCamelCase ,val[k] ,spaces + 2 )
elif isinstance(_lowerCamelCase ,torch.Tensor ):
print(_lowerCamelCase ,""":""" ,val.size() )
else:
print(_lowerCamelCase ,""":""" ,_lowerCamelCase )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Optional[int] ,_lowerCamelCase : int ,_lowerCamelCase : Dict ,_lowerCamelCase : Dict ) -> Tuple:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
_lowerCAmelCase : int = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
_lowerCAmelCase : Union[str, Any] = (num_heads, hidden_size, num_splits) + input_shape[1:]
_lowerCAmelCase : List[str] = param.view(*_lowerCamelCase )
_lowerCAmelCase : Tuple = param.transpose(0 ,2 )
_lowerCAmelCase : List[Any] = param.transpose(1 ,2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
_lowerCAmelCase : List[Any] = (num_heads, num_splits, hidden_size) + input_shape[1:]
_lowerCAmelCase : str = param.view(*_lowerCamelCase )
_lowerCAmelCase : int = param.transpose(0 ,1 ).contiguous()
_lowerCAmelCase : List[Any] = param.view(*_lowerCamelCase )
return param
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : List[Any] ,_lowerCamelCase : Dict ) -> Optional[int]:
# The converted output model.
_lowerCAmelCase : Tuple = {}
# old versions did not store training args
_lowerCAmelCase : List[Any] = input_state_dict.get("""args""" ,_lowerCamelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
_lowerCAmelCase : Any = ds_args.padded_vocab_size
_lowerCAmelCase : Tuple = ds_args.max_position_embeddings
_lowerCAmelCase : Dict = ds_args.hidden_size
_lowerCAmelCase : Dict = ds_args.num_layers
_lowerCAmelCase : str = ds_args.num_attention_heads
_lowerCAmelCase : List[Any] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
_lowerCAmelCase : Tuple = config.n_head
# The hidden_size per head.
_lowerCAmelCase : int = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
_lowerCAmelCase : List[Any] = input_state_dict["""checkpoint_version"""]
else:
_lowerCAmelCase : Union[str, Any] = 0.0
# The model.
_lowerCAmelCase : Tuple = input_state_dict["""model"""]
# The language model.
_lowerCAmelCase : List[Any] = model["""language_model"""]
# The embeddings.
_lowerCAmelCase : List[str] = lm["""embedding"""]
# The word embeddings.
_lowerCAmelCase : Union[str, Any] = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
_lowerCAmelCase : Tuple = word_embeddings[: config.vocab_size, :]
_lowerCAmelCase : Tuple = word_embeddings
# The position embeddings.
_lowerCAmelCase : List[str] = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
_lowerCAmelCase : int = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match" )
# Store the position embeddings.
_lowerCAmelCase : Tuple = pos_embeddings
# The transformer.
_lowerCAmelCase : List[Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
_lowerCAmelCase : Optional[Any] = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
_lowerCAmelCase : Any = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
_lowerCAmelCase : Optional[Any] = layer_re.match(_lowerCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
_lowerCAmelCase : Any = int(m.group(1 ) )
# The name of the operation.
_lowerCAmelCase : Any = m.group(2 )
# Is it a weight or a bias?
_lowerCAmelCase : Optional[Any] = m.group(3 )
# The name of the layer.
_lowerCAmelCase : Union[str, Any] = f"transformer.h.{layer_idx}"
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
_lowerCAmelCase : Optional[int] = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
_lowerCAmelCase : Union[str, Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
_lowerCAmelCase : Any = torch.tril(torch.ones((n_positions, n_positions) ,dtype=torch.floataa ) ).view(
1 ,1 ,_lowerCamelCase ,_lowerCamelCase )
_lowerCAmelCase : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
_lowerCAmelCase : str = torch.tensor(-1e4 ,dtype=torch.floataa )
_lowerCAmelCase : Union[str, Any] = masked_bias
_lowerCAmelCase : Tuple = fix_query_key_value_ordering(_lowerCamelCase ,_lowerCamelCase ,3 ,_lowerCamelCase ,_lowerCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
_lowerCAmelCase : Dict = out_val.transpose(0 ,1 ).contiguous()
# Store.
_lowerCAmelCase : List[Any] = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
_lowerCAmelCase : Dict = fix_query_key_value_ordering(_lowerCamelCase ,_lowerCamelCase ,3 ,_lowerCamelCase ,_lowerCamelCase )
# Store. No change of shape.
_lowerCAmelCase : List[Any] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
_lowerCAmelCase : Dict = megatron_to_transformers[op_name]
_lowerCAmelCase : Dict = val.transpose(0 ,1 )
# Copy the bias.
elif weight_or_bias == "bias":
_lowerCAmelCase : Union[str, Any] = megatron_to_transformers[op_name]
_lowerCAmelCase : List[str] = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
_lowerCAmelCase : Union[str, Any] = transformer["""final_layernorm.weight"""]
_lowerCAmelCase : Union[str, Any] = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
_lowerCAmelCase : Dict = word_embeddings
# It should be done!
return output_state_dict
def SCREAMING_SNAKE_CASE ( ) -> int:
# Create the argument parser.
_lowerCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" ,action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" ,type=_lowerCamelCase ,help="""Path to the checkpoint file (.zip archive or direct .pt file)""" ,)
parser.add_argument(
"""--config_file""" ,default="""""" ,type=_lowerCamelCase ,help="""An optional config json file describing the pre-trained model.""" ,)
_lowerCAmelCase : Dict = parser.parse_args()
# Extract the basename.
_lowerCAmelCase : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}" )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint ,"""r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
_lowerCAmelCase : int = torch.load(_lowerCamelCase ,map_location="""cpu""" )
else:
_lowerCAmelCase : List[Any] = torch.load(args.path_to_checkpoint ,map_location="""cpu""" )
_lowerCAmelCase : Union[str, Any] = input_state_dict.get("""args""" ,_lowerCamelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
_lowerCAmelCase : Optional[Any] = """gelu_fast"""
elif ds_args.openai_gelu:
_lowerCAmelCase : Any = """gelu_new"""
else:
_lowerCAmelCase : int = """gelu"""
else:
# in the very early days this used to be "gelu_new"
_lowerCAmelCase : Tuple = """gelu_new"""
# Spell out all parameters in case the defaults change.
_lowerCAmelCase : Dict = GPTaConfig(
vocab_size=50257 ,n_positions=1024 ,n_embd=1024 ,n_layer=24 ,n_head=16 ,n_inner=4096 ,activation_function=_lowerCamelCase ,resid_pdrop=0.1 ,embd_pdrop=0.1 ,attn_pdrop=0.1 ,layer_norm_epsilon=1e-5 ,initializer_range=0.02 ,summary_type="""cls_index""" ,summary_use_proj=_lowerCamelCase ,summary_activation=_lowerCamelCase ,summary_proj_to_labels=_lowerCamelCase ,summary_first_dropout=0.1 ,scale_attn_weights=_lowerCamelCase ,use_cache=_lowerCamelCase ,bos_token_id=50256 ,eos_token_id=50256 ,)
else:
_lowerCAmelCase : int = GPTaConfig.from_json_file(args.config_file )
_lowerCAmelCase : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
_lowerCAmelCase : int = convert_megatron_checkpoint(_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(_lowerCamelCase ,_lowerCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
_lowerCAmelCase : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
_lowerCAmelCase : List[Any] = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
_lowerCAmelCase : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}" )
else:
_lowerCAmelCase : Optional[Any] = """gpt2"""
_lowerCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained(_lowerCamelCase )
_lowerCAmelCase : int = type(_lowerCamelCase ).__name__
_lowerCAmelCase : str = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(_lowerCamelCase )
# Save tokenizer based on args
print(f"Adding {tokenizer_class} tokenizer files" )
tokenizer.save_pretrained(_lowerCamelCase )
# Store the state_dict to file.
_lowerCAmelCase : int = os.path.join(_lowerCamelCase ,"""pytorch_model.bin""" )
print(f"Saving checkpoint to \"{output_checkpoint_file}\"" )
torch.save(_lowerCamelCase ,_lowerCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 44 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
SCREAMING_SNAKE_CASE :int = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = (images / 2 + 0.5).clamp(0 , 1 )
__A = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A = numpy_to_pil(a_ )
return images
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if images.ndim == 3:
__A = images[None, ...]
__A = (images * 2_5_5).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
__A = [Image.fromarray(a_ ) for image in images]
return pil_images
| 15 | 0 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class __lowerCAmelCase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def __UpperCAmelCase ( _a ):
__a = PartialState()
return not main_process_only or (main_process_only and state.is_main_process)
def __UpperCAmelCase ( self , _a , _a , *_a , **_a ):
if PartialState._shared_state == {}:
raise RuntimeError(
'''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.''' )
__a = kwargs.pop('''main_process_only''' , _a )
__a = kwargs.pop('''in_order''' , _a )
if self.isEnabledFor(_a ):
if self._should_log(_a ):
__a , __a = self.process(_a , _a )
self.logger.log(_a , _a , *_a , **_a )
elif in_order:
__a = PartialState()
for i in range(state.num_processes ):
if i == state.process_index:
__a , __a = self.process(_a , _a )
self.logger.log(_a , _a , *_a , **_a )
state.wait_for_everyone()
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : str = None ) -> Any:
if log_level is None:
__a = os.environ.get('''ACCELERATE_LOG_LEVEL''' , lowerCAmelCase__ )
__a = logging.getLogger(lowerCAmelCase__ )
if log_level is not None:
logger.setLevel(log_level.upper() )
logger.root.setLevel(log_level.upper() )
return MultiProcessAdapter(lowerCAmelCase__ , {} )
| 45 |
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
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "yolos"
def __init__( self : Any ,A : Optional[Any]=7_68 ,A : Dict=12 ,A : Any=12 ,A : str=30_72 ,A : Any="gelu" ,A : str=0.0 ,A : List[str]=0.0 ,A : Dict=0.02 ,A : int=1E-12 ,A : Tuple=[5_12, 8_64] ,A : List[Any]=16 ,A : str=3 ,A : str=True ,A : Any=1_00 ,A : Dict=True ,A : Dict=False ,A : Tuple=1 ,A : Union[str, Any]=5 ,A : Optional[Any]=2 ,A : Union[str, Any]=5 ,A : int=2 ,A : int=0.1 ,**A : List[str] ,):
super().__init__(**A )
__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 = initializer_range
__A = layer_norm_eps
__A = image_size
__A = patch_size
__A = num_channels
__A = qkv_bias
__A = num_detection_tokens
__A = use_mid_position_embeddings
__A = auxiliary_loss
# Hungarian matcher
__A = class_cost
__A = bbox_cost
__A = giou_cost
# Loss coefficients
__A = bbox_loss_coefficient
__A = giou_loss_coefficient
__A = eos_coefficient
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = version.parse("1.11" )
@property
def UpperCamelCase_ ( self : str ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase_ ( self : List[Any] ):
return 1E-4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 12
| 15 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = StableDiffusionSAGPipeline
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_BATCH_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = False
def _snake_case ( self ) -> Union[str, Any]:
torch.manual_seed(0 )
lowerCAmelCase = 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 , )
lowerCAmelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=lowercase , set_alpha_to_one=lowercase , )
torch.manual_seed(0 )
lowerCAmelCase = 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 , )
torch.manual_seed(0 )
lowerCAmelCase = 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=1_000 , )
lowerCAmelCase = CLIPTextModel(lowercase )
lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _snake_case ( self , lowercase , lowercase=0 ) -> Union[str, Any]:
if str(lowercase ).startswith("""mps""" ):
lowerCAmelCase = torch.manual_seed(lowercase )
else:
lowerCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
lowerCAmelCase = {
"""prompt""": """.""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 1.0,
"""sag_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def _snake_case ( self ) -> str:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowercase ( unittest.TestCase ):
def _snake_case ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
lowerCAmelCase = sag_pipe.to(lowercase )
sag_pipe.set_progress_bar_config(disable=lowercase )
lowerCAmelCase = """."""
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sag_pipe(
[prompt] , generator=lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase = np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def _snake_case ( self ) -> str:
lowerCAmelCase = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
lowerCAmelCase = sag_pipe.to(lowercase )
sag_pipe.set_progress_bar_config(disable=lowercase )
lowerCAmelCase = """."""
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sag_pipe(
[prompt] , generator=lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase = np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2
def _snake_case ( self ) -> Tuple:
lowerCAmelCase = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
lowerCAmelCase = sag_pipe.to(lowercase )
sag_pipe.set_progress_bar_config(disable=lowercase )
lowerCAmelCase = """."""
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sag_pipe(
[prompt] , width=768 , height=512 , generator=lowercase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" , )
lowerCAmelCase = output.images
assert image.shape == (1, 512, 768, 3)
| 46 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 0 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def _lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : Any ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
_SCREAMING_SNAKE_CASE =Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw ).convert('RGB' )
_SCREAMING_SNAKE_CASE =transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73) , (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11) ),
] )
_SCREAMING_SNAKE_CASE =transform(_UpperCamelCase ).unsqueeze(0 ).to(_UpperCamelCase )
return image
def _lowerCAmelCase ( _UpperCamelCase : Dict ) -> str:
"""simple docstring"""
if "visual_encoder" in key:
_SCREAMING_SNAKE_CASE =re.sub('visual_encoder*' , 'vision_model.encoder' , _UpperCamelCase )
if "blocks" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'blocks' , 'layers' , _UpperCamelCase )
if "attn" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'attn' , 'self_attn' , _UpperCamelCase )
if "norm1" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'norm1' , 'layer_norm1' , _UpperCamelCase )
if "norm2" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'norm2' , 'layer_norm2' , _UpperCamelCase )
if "encoder.norm" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'encoder.norm' , 'post_layernorm' , _UpperCamelCase )
if "encoder.patch_embed.proj" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'encoder.patch_embed.proj' , 'embeddings.patch_embedding' , _UpperCamelCase )
if "encoder.pos_embed" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'encoder.pos_embed' , 'embeddings.position_embedding' , _UpperCamelCase )
if "encoder.cls_token" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'encoder.cls_token' , 'embeddings.class_embedding' , _UpperCamelCase )
if "self_attn" in key:
_SCREAMING_SNAKE_CASE =re.sub(r'self_attn.proj' , 'self_attn.projection' , _UpperCamelCase )
return key
@torch.no_grad()
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : int=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
_SCREAMING_SNAKE_CASE =BlipConfig.from_pretrained(_UpperCamelCase )
else:
_SCREAMING_SNAKE_CASE =BlipConfig(projection_dim=5_12 , text_config={} , vision_config={} )
_SCREAMING_SNAKE_CASE =BlipForConditionalGeneration(_UpperCamelCase ).eval()
_SCREAMING_SNAKE_CASE ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
_SCREAMING_SNAKE_CASE =blip_decoder(pretrained=_UpperCamelCase , image_size=3_84 , vit='base' )
_SCREAMING_SNAKE_CASE =pt_model.eval()
_SCREAMING_SNAKE_CASE =pt_model.state_dict()
for key in modified_state_dict.copy():
_SCREAMING_SNAKE_CASE =modified_state_dict.pop(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =rename_key(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =value
hf_model.load_state_dict(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =3_84
_SCREAMING_SNAKE_CASE =load_demo_image(image_size=_UpperCamelCase , device='cpu' )
_SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('bert-base-uncased' )
_SCREAMING_SNAKE_CASE =tokenizer(['a picture of'] ).input_ids
_SCREAMING_SNAKE_CASE =hf_model.generate(_UpperCamelCase , _UpperCamelCase )
assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
_SCREAMING_SNAKE_CASE =hf_model.generate(_UpperCamelCase )
assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(_UpperCamelCase )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
_SCREAMING_SNAKE_CASE =(
'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
)
_SCREAMING_SNAKE_CASE =blip_vqa(pretrained=_UpperCamelCase , image_size=_UpperCamelCase , vit='base' )
vqa_model.eval()
_SCREAMING_SNAKE_CASE =vqa_model.state_dict()
for key in modified_state_dict.copy():
_SCREAMING_SNAKE_CASE =modified_state_dict.pop(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =rename_key(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =value
_SCREAMING_SNAKE_CASE =BlipForQuestionAnswering(_UpperCamelCase )
hf_vqa_model.load_state_dict(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =['How many dogs are in this image?']
_SCREAMING_SNAKE_CASE =tokenizer(_UpperCamelCase , return_tensors='pt' ).input_ids
_SCREAMING_SNAKE_CASE =hf_vqa_model.generate(_UpperCamelCase , _UpperCamelCase )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa' )
_SCREAMING_SNAKE_CASE ='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'
_SCREAMING_SNAKE_CASE =blip_itm(pretrained=_UpperCamelCase , image_size=_UpperCamelCase , vit='base' )
itm_model.eval()
_SCREAMING_SNAKE_CASE =itm_model.state_dict()
for key in modified_state_dict.copy():
_SCREAMING_SNAKE_CASE =modified_state_dict.pop(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =rename_key(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =value
_SCREAMING_SNAKE_CASE =BlipForImageTextRetrieval(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =['A picture of a woman with a dog sitting in a beach']
_SCREAMING_SNAKE_CASE =tokenizer(
_UpperCamelCase , return_tensors='pt' , padding='max_length' , truncation=_UpperCamelCase , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(_UpperCamelCase )
hf_itm_model.eval()
_SCREAMING_SNAKE_CASE =hf_itm_model(_UpperCamelCase , _UpperCamelCase , use_itm_head=_UpperCamelCase )
_SCREAMING_SNAKE_CASE =hf_itm_model(_UpperCamelCase , _UpperCamelCase , use_itm_head=_UpperCamelCase )
assert out[0].item() == 0.21_10_68_74_94_27_79_54
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm' )
if __name__ == "__main__":
lowerCamelCase : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
lowerCamelCase : int = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 47 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__A = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE :List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 15 | 0 |
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def A ( _SCREAMING_SNAKE_CASE ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
lowerCamelCase : int = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Optional[int] = "".join(bin(_SCREAMING_SNAKE_CASE )[2:].zfill(8 ) for byte in data )
lowerCamelCase : List[str] = len(_SCREAMING_SNAKE_CASE ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowerCamelCase : Optional[int] = B"=" * ((6 - len(_SCREAMING_SNAKE_CASE ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_SCREAMING_SNAKE_CASE ) % 6)
else:
lowerCamelCase : List[Any] = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] ,2 )]
for index in range(0 ,len(_SCREAMING_SNAKE_CASE ) ,6 ) ).encode()
+ padding
)
def A ( _SCREAMING_SNAKE_CASE ) -> bytes:
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) and not isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
lowerCamelCase : List[Any] = (
"argument should be a bytes-like object or ASCII string, "
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(_SCREAMING_SNAKE_CASE )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
try:
lowerCamelCase : Optional[Any] = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
lowerCamelCase : Optional[Any] = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_SCREAMING_SNAKE_CASE ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowerCamelCase : Optional[Any] = encoded_data[:-padding]
lowerCamelCase : Tuple = "".join(
bin(B64_CHARSET.index(_SCREAMING_SNAKE_CASE ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowerCamelCase : Dict = "".join(
bin(B64_CHARSET.index(_SCREAMING_SNAKE_CASE ) )[2:].zfill(6 ) for char in encoded_data )
lowerCamelCase : int = [
int(binary_stream[index : index + 8] ,2 )
for index in range(0 ,len(_SCREAMING_SNAKE_CASE ) ,8 )
]
return bytes(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
__A = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__A = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
__A = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
__A = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
__A = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
__A = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
__A = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
__A = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
__A = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
__A = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
__A = key.replace("image_encoder.module" , "flava.image_model" )
__A = key.replace("text_encoder.module" , "flava.text_model" )
__A = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
__A = key.replace("mm_encoder.module" , "flava.multimodal_model" )
__A = key.replace("text_projection" , "flava.text_projection" )
__A = key.replace("image_projection" , "flava.image_projection" )
__A = value.float()
for key, value in codebook_state_dict.items():
__A = value
return upgrade
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__A = FlavaConfig.from_pretrained(a_ )
else:
__A = FlavaConfig()
__A = FlavaForPreTraining(a_ ).eval()
__A = convert_dalle_checkpoint(a_ , a_ , save_checkpoint=a_ )
if os.path.exists(a_ ):
__A = torch.load(a_ , map_location="cpu" )
else:
__A = torch.hub.load_state_dict_from_url(a_ , map_location="cpu" )
__A = upgrade_state_dict(a_ , a_ )
hf_model.load_state_dict(a_ )
__A = hf_model.state_dict()
__A = count_parameters(a_ )
__A = count_parameters(a_ ) + count_parameters(a_ )
assert torch.allclose(a_ , a_ , atol=1E-3 )
hf_model.save_pretrained(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 15 | 0 |
from timeit import timeit
def __snake_case ( _UpperCAmelCase ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__a = 0
while number:
number &= number - 1
result += 1
return result
def __snake_case ( _UpperCAmelCase ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__a = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def __snake_case ( ):
def do_benchmark(_UpperCAmelCase ) -> None:
__a = '''import __main__ as z'''
print(f'Benchmark when {number = }:' )
print(f'{get_set_bits_count_using_modulo_operator(_UpperCAmelCase ) = }' )
__a = timeit('''z.get_set_bits_count_using_modulo_operator(25)''' , setup=_UpperCAmelCase )
print(f'timeit() runs in {timing} seconds' )
print(f'{get_set_bits_count_using_brian_kernighans_algorithm(_UpperCAmelCase ) = }' )
__a = timeit(
'''z.get_set_bits_count_using_brian_kernighans_algorithm(25)''' , setup=_UpperCAmelCase , )
print(f'timeit() runs in {timing} seconds' )
for number in (25, 37, 58, 0):
do_benchmark(_UpperCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 49 |
import os
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 logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE :Tuple = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
SCREAMING_SNAKE_CASE :List[Any] = {
'camembert-base': 512,
}
SCREAMING_SNAKE_CASE :List[str] = '▁'
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[Any] ,A : List[str] ,A : List[Any]="<s>" ,A : Tuple="</s>" ,A : Any="</s>" ,A : Optional[Any]="<s>" ,A : Tuple="<unk>" ,A : str="<pad>" ,A : int="<mask>" ,A : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] ,A : Optional[Dict[str, Any]] = None ,**A : Optional[Any] ,):
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,sep_token=A ,cls_token=A ,pad_token=A ,mask_token=A ,additional_special_tokens=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
__A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__A = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
__A = len(self.fairseq_tokens_to_ids )
__A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
__A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self : int ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__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 + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase_ ( self : Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self : int ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Dict ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase_ ( self : Optional[Any] ,A : Dict ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self : Dict ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Any ):
__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 UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 15 | 0 |
_UpperCAmelCase : Dict = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_UpperCAmelCase : Optional[int] = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_UpperCAmelCase : List[Any] = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 50 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , a_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__A , __A = arr[k - 1], arr[i]
else: # k is odd
__A , __A = arr[k - 1], arr[0]
generate(k - 1 , a_ )
generate(len(a_ ) , a_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :Dict = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 15 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Tuple = ["MBartTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : str = ["MBartTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[Any] = [
"MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"MBartForCausalLM",
"MBartForConditionalGeneration",
"MBartForQuestionAnswering",
"MBartForSequenceClassification",
"MBartModel",
"MBartPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Any = [
"TFMBartForConditionalGeneration",
"TFMBartModel",
"TFMBartPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[str] = [
"FlaxMBartForConditionalGeneration",
"FlaxMBartForQuestionAnswering",
"FlaxMBartForSequenceClassification",
"FlaxMBartModel",
"FlaxMBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
snake_case_ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 51 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
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__":
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :List[Any] = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 15 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : List[Any] = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
__lowerCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 52 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 2
@register_to_config
def __init__( self : str ,A : float = 0.02 ,A : float = 1_00 ,A : float = 1.0_07 ,A : float = 80 ,A : float = 0.05 ,A : float = 50 ,):
# standard deviation of the initial noise distribution
__A = sigma_max
# setable values
__A = None
__A = None
__A = None # sigma(t_i)
def UpperCamelCase_ ( self : str ,A : torch.FloatTensor ,A : Optional[int] = None ):
return sample
def UpperCamelCase_ ( self : Dict ,A : int ,A : Union[str, torch.device] = None ):
__A = num_inference_steps
__A = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__A = torch.from_numpy(A ).to(A )
__A = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__A = torch.tensor(A ,dtype=torch.floataa ,device=A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : float ,A : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
__A = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__A = 0
# sample eps ~ N(0, S_noise^2 * I)
__A = self.config.s_noise * randn_tensor(sample.shape ,generator=A ).to(sample.device )
__A = sigma + gamma * sigma
__A = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase_ ( self : Dict ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_hat + sigma_hat * model_output
__A = (sample_hat - pred_original_sample) / sigma_hat
__A = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : Optional[int] ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_prev + sigma_prev * model_output
__A = (sample_prev - pred_original_sample) / sigma_prev
__A = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : str ):
raise NotImplementedError()
| 15 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowercase__ ( __lowercase : Any ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'_float_tensor',
'decoder.output_projection.weight',
]
for k in ignore_keys:
state_dict.pop(__lowercase , __lowercase )
def lowercase__ ( __lowercase : Tuple ) -> int:
"""simple docstring"""
__UpperCamelCase , __UpperCamelCase = emb.weight.shape
__UpperCamelCase = nn.Linear(__lowercase , __lowercase , bias=__lowercase )
__UpperCamelCase = emb.weight.data
return lin_layer
def lowercase__ ( __lowercase : int , __lowercase : List[str]="facebook/mbart-large-en-ro" , __lowercase : str=False , __lowercase : List[Any]=False ) -> int:
"""simple docstring"""
__UpperCamelCase = torch.load(__lowercase , map_location='cpu' )['model']
remove_ignore_keys_(__lowercase )
__UpperCamelCase = state_dict['encoder.embed_tokens.weight'].shape[0]
__UpperCamelCase = MBartConfig.from_pretrained(__lowercase , vocab_size=__lowercase )
if mbart_aa and finetuned:
__UpperCamelCase = 'relu'
__UpperCamelCase = state_dict['decoder.embed_tokens.weight']
__UpperCamelCase = MBartForConditionalGeneration(__lowercase )
model.model.load_state_dict(__lowercase )
if finetuned:
__UpperCamelCase = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
a__ : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
)
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--hf_config''',
default='''facebook/mbart-large-cc25''',
type=str,
help='''Which huggingface architecture to use: mbart-large''',
)
parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
a__ : Union[str, Any] =parser.parse_args()
a__ : str =convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path)
| 53 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE :Union[str, Any] = get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = "dummy_data"
snake_case_ = "datasets"
snake_case_ = False
def __init__( self : Optional[int] ,A : str ,A : str ,A : Union[Version, str] ,A : Optional[str] = None ,A : bool = False ,A : bool = True ,A : Optional[List[Callable]] = None ,):
__A = 0
__A = dataset_name
__A = cache_dir
__A = use_local_dummy_data
__A = config
# download_callbacks take a single url as input
__A = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__A = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__A = str(A )
# to be downloaded
__A = None
__A = None
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
if self._dummy_file is None:
__A = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase_ ( self : Optional[Any] ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def UpperCamelCase_ ( self : List[Any] ):
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def UpperCamelCase_ ( self : Tuple ):
__A = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__A = cached_path(
A ,cache_dir=self.cache_dir ,extract_compressed_file=A ,force_extract=A )
return os.path.join(A ,self.dummy_file_name )
@property
def UpperCamelCase_ ( self : str ):
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def UpperCamelCase_ ( self : Any ):
if self._bucket_url is None:
__A = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def UpperCamelCase_ ( self : Tuple ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def UpperCamelCase_ ( self : List[str] ,A : List[Any] ,*A : Dict ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__A = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__A = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A ,A ):
return self.create_dummy_data_dict(A ,A )
elif isinstance(A ,(list, tuple) ):
return self.create_dummy_data_list(A ,A )
else:
return self.create_dummy_data_single(A ,A )
def UpperCamelCase_ ( self : str ,A : List[Any] ,*A : List[Any] ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : List[str] ,A : List[str] ,A : Tuple ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : Any ,A : Any ,*A : Optional[Any] ,**A : List[str] ):
return path
def UpperCamelCase_ ( self : str ):
return {}
def UpperCamelCase_ ( self : int ,A : int ,A : Tuple ):
__A = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A ,A ):
for single_url in single_urls:
download_callback(A )
else:
__A = single_urls
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A ,A ):
__A = [os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) ) for x in single_urls]
else:
__A = single_urls
__A = os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) )
__A = value
# make sure that values are unique
if all(isinstance(A ,A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__A = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,A : str ):
__A = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__A = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,A ) ) for url in data_url )
__A = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__A = [data_url[0]] * len(A )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(A )
return dummy_data_list
def UpperCamelCase_ ( self : str ,A : List[Any] ,A : Optional[Any] ):
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(A ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : Dict ):
pass
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ):
def _iter_archive_members(A : Optional[Any] ):
# this preserves the order of the members inside the ZIP archive
__A = Path(self.dummy_file ).parent
__A = path.relative_to(A )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__A = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A )
__A = Path(A )
__A = _iter_archive_members(A ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(A ).as_posix(), file_path.open("rb" )
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
if not isinstance(A ,A ):
__A = [paths]
for path in paths:
if os.path.isfile(A ):
if os.path.basename(A ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A ):
if os.path.basename(A ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(A ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(A ,A )
| 15 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ = 10 ):
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or n < 0:
raise ValueError("Invalid input" )
__SCREAMING_SNAKE_CASE = 10**n
__SCREAMING_SNAKE_CASE = 2_8433 * (pow(2 , 783_0457 , lowerCAmelCase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"{solution(1_0) = }")
| 54 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[str] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a_ : Dict = logging.get_logger(__name__)
a_ : Tuple = {
"""post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def __snake_case ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any] ):
for attribute in key.split("." ):
lowerCamelCase_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
lowerCamelCase_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
lowerCamelCase_ = 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":
lowerCamelCase_ = value
elif weight_type == "weight_g":
lowerCamelCase_ = value
elif weight_type == "weight_v":
lowerCamelCase_ = value
elif weight_type == "bias":
lowerCamelCase_ = value
else:
lowerCamelCase_ = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def __snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : str ):
lowerCamelCase_ = []
lowerCamelCase_ = fairseq_model.state_dict()
lowerCamelCase_ = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
lowerCamelCase_ = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hf_model.config.feat_extract_norm == "group" , )
lowerCamelCase_ = True
else:
for key, mapped_key in MAPPING.items():
lowerCamelCase_ = "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
lowerCamelCase_ = True
if "*" in mapped_key:
lowerCamelCase_ = name.split(UpperCAmelCase_ )[0].split("." )[-2]
lowerCamelCase_ = mapped_key.replace("*" , UpperCAmelCase_ )
if "weight_g" in name:
lowerCamelCase_ = "weight_g"
elif "weight_v" in name:
lowerCamelCase_ = "weight_v"
elif "weight" in name:
lowerCamelCase_ = "weight"
elif "bias" in name:
lowerCamelCase_ = "bias"
else:
lowerCamelCase_ = None
set_recursively(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def __snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Any] ):
lowerCamelCase_ = full_name.split("conv_layers." )[-1]
lowerCamelCase_ = name.split("." )
lowerCamelCase_ = int(items[0] )
lowerCamelCase_ = 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.'''
)
lowerCamelCase_ = 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.'''
)
lowerCamelCase_ = 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."
)
lowerCamelCase_ = 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.'''
)
lowerCamelCase_ = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(UpperCAmelCase_ )
def __snake_case ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Any ):
lowerCamelCase_ = SEWConfig()
if is_finetuned:
lowerCamelCase_ = model.wav_encoder.wav_model.cfg
else:
lowerCamelCase_ = model.cfg
lowerCamelCase_ = fs_config.conv_bias
lowerCamelCase_ = eval(fs_config.conv_feature_layers )
lowerCamelCase_ = [x[0] for x in conv_layers]
lowerCamelCase_ = [x[1] for x in conv_layers]
lowerCamelCase_ = [x[2] for x in conv_layers]
lowerCamelCase_ = "gelu"
lowerCamelCase_ = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
lowerCamelCase_ = 0.0
lowerCamelCase_ = fs_config.activation_fn.name
lowerCamelCase_ = fs_config.encoder_embed_dim
lowerCamelCase_ = 0.02
lowerCamelCase_ = fs_config.encoder_ffn_embed_dim
lowerCamelCase_ = 1E-5
lowerCamelCase_ = fs_config.encoder_layerdrop
lowerCamelCase_ = fs_config.encoder_attention_heads
lowerCamelCase_ = fs_config.conv_pos_groups
lowerCamelCase_ = fs_config.conv_pos
lowerCamelCase_ = len(UpperCAmelCase_ )
lowerCamelCase_ = fs_config.encoder_layers
lowerCamelCase_ = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
lowerCamelCase_ = model.cfg
lowerCamelCase_ = fs_config.final_dropout
lowerCamelCase_ = fs_config.layerdrop
lowerCamelCase_ = fs_config.activation_dropout
lowerCamelCase_ = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
lowerCamelCase_ = fs_config.attention_dropout
lowerCamelCase_ = fs_config.dropout_input
lowerCamelCase_ = fs_config.dropout
lowerCamelCase_ = fs_config.mask_channel_length
lowerCamelCase_ = fs_config.mask_channel_prob
lowerCamelCase_ = fs_config.mask_length
lowerCamelCase_ = fs_config.mask_prob
lowerCamelCase_ = "Wav2Vec2FeatureExtractor"
lowerCamelCase_ = "Wav2Vec2CTCTokenizer"
return config
@torch.no_grad()
def __snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : List[Any]=None , UpperCAmelCase_ : int=True ):
if is_finetuned:
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
lowerCamelCase_ = SEWConfig.from_pretrained(UpperCAmelCase_ )
else:
lowerCamelCase_ = convert_config(model[0] , UpperCAmelCase_ )
lowerCamelCase_ = model[0].eval()
lowerCamelCase_ = True if config.feat_extract_norm == "layer" else False
lowerCamelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , )
if is_finetuned:
if dict_path:
lowerCamelCase_ = Dictionary.load(UpperCAmelCase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCamelCase_ = target_dict.pad_index
lowerCamelCase_ = target_dict.bos_index
lowerCamelCase_ = target_dict.pad_index
lowerCamelCase_ = target_dict.bos_index
lowerCamelCase_ = target_dict.eos_index
lowerCamelCase_ = len(target_dict.symbols )
lowerCamelCase_ = os.path.join(UpperCAmelCase_ , "vocab.json" )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_ ) )
return
os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
with open(UpperCAmelCase_ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(target_dict.indices , UpperCAmelCase_ )
lowerCamelCase_ = WavaVecaCTCTokenizer(
UpperCAmelCase_ , 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=UpperCAmelCase_ , )
lowerCamelCase_ = WavaVecaProcessor(feature_extractor=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ )
processor.save_pretrained(UpperCAmelCase_ )
lowerCamelCase_ = SEWForCTC(UpperCAmelCase_ )
else:
lowerCamelCase_ = SEWModel(UpperCAmelCase_ )
feature_extractor.save_pretrained(UpperCAmelCase_ )
recursively_load_weights(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
hf_model.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
a_ : 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(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
a_ : int = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 55 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
SCREAMING_SNAKE_CASE :List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
SCREAMING_SNAKE_CASE :str = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Tuple:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
__A = new_id
# turn into Numpy arrays
__A = np.array(a_ )
__A = np.array(a_ )
if reduce_labels:
__A = 2_5_5
__A = label - 1
__A = 2_5_5
__A = label != ignore_index
__A = np.not_equal(a_ , a_ )
__A = pred_label[mask]
__A = np.array(a_ )[mask]
__A = pred_label[pred_label == label]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Union[str, Any]:
"""simple docstring"""
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a_ , a_ ):
__A , __A , __A , __A = intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = None , a_ = False , ) -> str:
"""simple docstring"""
__A , __A , __A , __A = total_intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
# compute metrics
__A = {}
__A = total_area_intersect.sum() / total_area_label.sum()
__A = total_area_intersect / total_area_union
__A = total_area_intersect / total_area_label
__A = np.nanmean(a_ )
__A = np.nanmean(a_ )
__A = all_acc
__A = iou
__A = acc
if nan_to_num is not None:
__A = {metric: np.nan_to_num(a_ , nan=a_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) ,reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] ,)
def UpperCamelCase_ ( self : int ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : bool ,A : Optional[int] = None ,A : Optional[Dict[int, int]] = None ,A : bool = False ,):
__A = mean_iou(
results=A ,gt_seg_maps=A ,num_labels=A ,ignore_index=A ,nan_to_num=A ,label_map=A ,reduce_labels=A ,)
return iou_result
| 15 | 0 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class a ( _lowerCamelCase , unittest.TestCase ):
snake_case_ = BarthezTokenizer
snake_case_ = BarthezTokenizerFast
snake_case_ = True
snake_case_ = True
def A_ ( self : List[str] ):
super().setUp()
snake_case_ = BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=lowercase_ )
snake_case_ = tokenizer
def A_ ( self : Optional[Any] ):
snake_case_ = '''<pad>'''
snake_case_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowercase_ ) , 10_1122 )
def A_ ( self : int ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_1122 )
@require_torch
def A_ ( self : str ):
snake_case_ = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
snake_case_ = [0, 57, 3018, 7_0307, 91, 2]
snake_case_ = self.tokenizer(
lowercase_ , max_length=len(lowercase_ ) , padding=lowercase_ , truncation=lowercase_ , return_tensors='''pt''' )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
snake_case_ = batch.input_ids.tolist()[0]
self.assertListEqual(lowercase_ , lowercase_ )
def A_ ( self : Tuple ):
if not self.test_rust_tokenizer:
return
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_rust_tokenizer()
snake_case_ = '''I was born in 92000, and this is falsé.'''
snake_case_ = tokenizer.tokenize(lowercase_ )
snake_case_ = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
snake_case_ = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
snake_case_ = self.get_rust_tokenizer()
snake_case_ = tokenizer.encode(lowercase_ )
snake_case_ = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def A_ ( self : List[Any] ):
# fmt: off
snake_case_ = {'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
snake_case_ = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='''moussaKam/mbarthez''' , revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' , sequences=lowercase_ , )
| 56 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[str] = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Dict = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE :Optional[Any] = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[int] ,A : Optional[Any] ,A : Optional[int]=False ,A : int=False ,A : Union[str, Any]=False ,A : int=None ,A : Optional[Any]=None ,A : Union[str, Any]=None ,A : Optional[Any]=None ,A : Optional[Dict[str, Any]] = None ,**A : Tuple ,):
__A = {} if sp_model_kwargs is None else sp_model_kwargs
__A = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
__A = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__A = "<|endoftext|>" if eos_token is None else eos_token
__A = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__A = unk_token if pad_token is None else pad_token
__A = eos_token if bos_token is None else bos_token
else:
__A = "<pad>" if pad_token is None else pad_token
__A = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=A ,remove_space=A ,keep_accents=A ,bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = do_lower_case
__A = remove_space
__A = keep_accents
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
__A = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__A = re.compile(
f'''[{''.join(map(A ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(1_27 ,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Optional[Any] ,A : Union[str, Any] ):
__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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase_ ( self : List[str] ):
return len(self.sp_model )
def UpperCamelCase_ ( self : int ,A : str ):
__A = self.non_printing_characters_re.sub("" ,A )
# Normalize whitespaces
__A = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
__A = unicodedata.normalize("NFC" ,A )
return text
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,**A : Optional[int] ):
__A = self.preprocess_text(A )
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : int ):
return self.sp_model.IdToPiece(A )
@staticmethod
def UpperCamelCase_ ( A : str ):
return out_string
def UpperCamelCase_ ( self : str ,A : List[str] ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string
def UpperCamelCase_ ( self : str ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : List[str] ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, List[str]] ,A : Union[str, bool] = False ):
if isinstance(A ,A ):
__A = self.preprocess_text(A )
__A = self.sp_model.encode(A )
else:
__A = [self.preprocess_text(A ) for t in text]
__A = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
__A = torch.tensor(A )
return token_ids
def UpperCamelCase_ ( self : List[Any] ,A : Union[int, List[int]] ):
return self.sp_model.decode(A )
def UpperCamelCase_ ( self : List[str] ,A : "Conversation" ):
__A = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__A = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 15 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case ( self ):
__lowerCAmelCase = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
__lowerCAmelCase = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
model.to(__a )
from datasets import load_dataset
__lowerCAmelCase = load_dataset("nielsr/rvlcdip-demo" )
__lowerCAmelCase = dataset["train"][0]["image"].convert("RGB" )
__lowerCAmelCase = image_processor(__a , return_tensors="pt" ).to(__a )
# forward pass
with torch.no_grad():
__lowerCAmelCase = model(**__a )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = torch.Size((1, 16) )
self.assertEqual(logits.shape , __a )
__lowerCAmelCase = torch.tensor(
[-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7] , device=__a , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , __a , atol=1e-4 ) )
| 57 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def lowerCamelCase ( __lowerCamelCase : Sequence[float] , __lowerCamelCase : bool = False ) ->float:
if not arr:
return 0
_SCREAMING_SNAKE_CASE = 0 if allow_empty_subarrays else float("""-inf""" )
_SCREAMING_SNAKE_CASE = 0.0
for num in arr:
_SCREAMING_SNAKE_CASE = max(0 if allow_empty_subarrays else num , curr_sum + num )
_SCREAMING_SNAKE_CASE = max(__lowerCamelCase , __lowerCamelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
lowercase_ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(f"""{max_subarray_sum(nums) = }""")
| 58 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE :str = 'RegNetConfig'
# Base docstring
SCREAMING_SNAKE_CASE :List[str] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 1088, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE :Optional[int] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Any = 'tabby, tabby cat'
SCREAMING_SNAKE_CASE :Optional[int] = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : int ,A : int = 3 ,A : int = 1 ,A : int = 1 ,A : Optional[str] = "relu" ,**A : Dict ,):
super().__init__(**A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__A = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=A ,strides=A ,padding="VALID" ,groups=A ,use_bias=A ,name="convolution" ,)
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
__A = ACTaFN[activation] if activation is not None else tf.identity
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
__A = self.convolution(self.padding(A ) )
__A = self.normalization(A )
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : RegNetConfig ,**A : str ):
super().__init__(**A )
__A = config.num_channels
__A = TFRegNetConvLayer(
out_channels=config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act ,name="embedder" ,)
def UpperCamelCase_ ( self : Tuple ,A : Optional[Any] ):
__A = shape_list(A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__A = tf.transpose(A ,perm=(0, 2, 3, 1) )
__A = self.embedder(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : int ,A : int = 2 ,**A : Tuple ):
super().__init__(**A )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=1 ,strides=A ,use_bias=A ,name="convolution" )
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
def UpperCamelCase_ ( self : Union[str, Any] ,A : tf.Tensor ,A : bool = False ):
return self.normalization(self.convolution(A ) ,training=A )
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Dict ,A : int ,A : int ,**A : str ):
super().__init__(**A )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
__A = [
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="relu" ,name="attention.0" ),
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="sigmoid" ,name="attention.2" ),
]
def UpperCamelCase_ ( self : Dict ,A : List[Any] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__A = self.pooler(A )
for layer_module in self.attention:
__A = layer_module(A )
__A = hidden_state * pooled
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : Optional[int] ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.2" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : int ,A : Optional[int] ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : str ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetSELayer(A ,reduced_channels=int(round(in_channels / 4 ) ) ,name="layer.2" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.3" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : Dict ,A : Any ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 2 ,A : int = 2 ,**A : Optional[int] ):
super().__init__(**A )
__A = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
__A = [
# downsampling is done in the first layer with stride of 2
layer(A ,A ,A ,stride=A ,name="layers.0" ),
*[layer(A ,A ,A ,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def UpperCamelCase_ ( self : Any ,A : List[str] ):
for layer_module in self.layers:
__A = layer_module(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any ,A : RegNetConfig ,**A : List[str] ):
super().__init__(**A )
__A = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
A ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,name="stages.0" ,) )
__A = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(A ,config.depths[1:] ) ):
self.stages.append(TFRegNetStage(A ,A ,A ,depth=A ,name=f'''stages.{i+1}''' ) )
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor ,A : bool = False ,A : bool = True ):
__A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__A = hidden_states + (hidden_state,)
__A = stage_module(A )
if output_hidden_states:
__A = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=A ,hidden_states=A )
@keras_serializable
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
snake_case_ = RegNetConfig
def __init__( self : int ,A : Optional[int] ,**A : Dict ):
super().__init__(**A )
__A = config
__A = TFRegNetEmbeddings(A ,name="embedder" )
__A = TFRegNetEncoder(A ,name="encoder" )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
@unpack_inputs
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : bool = False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.embedder(A ,training=A )
__A = self.encoder(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = encoder_outputs[0]
__A = self.pooler(A )
# Change to NCHW output format have uniformity in the modules
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__A = tuple([tf.transpose(A ,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A ,pooler_output=A ,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = RegNetConfig
snake_case_ = "regnet"
snake_case_ = "pixel_values"
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) ,dtype=tf.floataa )}
SCREAMING_SNAKE_CASE :Dict = R'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE :Dict = R'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,*A : List[Any] ,**A : str ):
super().__init__(A ,*A ,**A )
__A = TFRegNetMainLayer(A ,name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=A ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : int=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
pixel_values=A ,output_hidden_states=A ,return_dict=A ,training=A ,)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state ,pooler_output=outputs.pooler_output ,hidden_states=outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : RegNetConfig ,*A : str ,**A : Tuple ):
super().__init__(A ,*A ,**A )
__A = config.num_labels
__A = TFRegNetMainLayer(A ,name="regnet" )
# classification head
__A = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels ,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=A ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor = None ,A : tf.Tensor = None ,A : bool = None ,A : bool = None ,A : Union[str, Any]=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = outputs.pooler_output if return_dict else outputs[1]
__A = self.classifier[0](A )
__A = self.classifier[1](A )
__A = None if labels is None else self.hf_compute_loss(labels=A ,logits=A )
if not return_dict:
__A = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=A ,logits=A ,hidden_states=outputs.hidden_states )
| 15 | 0 |
import operator as op
__lowerCamelCase = """scaler.pt"""
__lowerCamelCase = """pytorch_model"""
__lowerCamelCase = """random_states"""
__lowerCamelCase = """optimizer"""
__lowerCamelCase = """scheduler"""
__lowerCamelCase = """pytorch_model.bin"""
__lowerCamelCase = """pytorch_model.bin.index.json"""
__lowerCamelCase = """model.safetensors"""
__lowerCamelCase = """model.safetensors.index.json"""
__lowerCamelCase = """1.10.2"""
__lowerCamelCase = """py38"""
__lowerCamelCase = """4.17.0"""
__lowerCamelCase = ["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
__lowerCamelCase = ["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
__lowerCamelCase = ["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
__lowerCamelCase = ["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
__lowerCamelCase = ["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
__lowerCamelCase = """2.0.1"""
__lowerCamelCase = ["""pdsh""", """standard""", """openmpi""", """mvapich"""]
__lowerCamelCase = ["""default""", """reduce-overhead""", """max-autotune"""]
__lowerCamelCase = {""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
__lowerCamelCase = [
"""nnodes""",
"""nproc_per_node""",
"""rdzv_backend""",
"""rdzv_endpoint""",
"""rdzv_id""",
"""rdzv_conf""",
"""standalone""",
"""max_restarts""",
"""monitor_interval""",
"""start_method""",
"""role""",
"""module""",
"""m""",
"""no_python""",
"""run_path""",
"""log_dir""",
"""r""",
"""redirects""",
"""t""",
"""tee""",
"""node_rank""",
"""master_addr""",
"""master_port""",
]
__lowerCamelCase = ["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
__lowerCamelCase = ["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]
| 59 |
import math
def UpperCAmelCase ( a_ , a_ = 0 , a_ = 0 ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ ) -> None: # Max Heap
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
__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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
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 UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
__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 UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) == 0:
return array
__A = 2 * math.ceil(math.loga(len(a_ ) ) )
__A = 1_6
return intro_sort(a_ , 0 , len(a_ ) , a_ , a_ )
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ ) -> list:
"""simple docstring"""
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()
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma : ').strip()
SCREAMING_SNAKE_CASE :str = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 15 | 0 |
"""simple docstring"""
import os
def _snake_case ( _snake_case : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as in_file:
lowerCAmelCase : Tuple = in_file.read()
lowerCAmelCase : Dict = [[int(_snake_case ) for cell in row.split(''',''' )] for row in data.strip().splitlines()]
lowerCAmelCase : Dict = [[0 for cell in row] for row in grid]
lowerCAmelCase : List[str] = len(grid[0] )
lowerCAmelCase : Tuple = [[0 for i in range(_snake_case )] for j in range(_snake_case )]
lowerCAmelCase : Union[str, Any] = grid[0][0]
for i in range(1 , _snake_case ):
lowerCAmelCase : List[str] = grid[0][i] + dp[0][i - 1]
for i in range(1 , _snake_case ):
lowerCAmelCase : Tuple = grid[i][0] + dp[i - 1][0]
for i in range(1 , _snake_case ):
for j in range(1 , _snake_case ):
lowerCAmelCase : Optional[int] = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 60 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
SCREAMING_SNAKE_CASE :Optional[int] = NewType('DataClass', Any)
SCREAMING_SNAKE_CASE :int = NewType('DataClassType', Any)
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
if isinstance(a_ , a_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''' )
def UpperCAmelCase ( a_ ) -> Callable[[str], Any]:
"""simple docstring"""
__A = {str(a_ ): choice for choice in choices}
return lambda a_ : str_to_choice.get(a_ , a_ )
def UpperCAmelCase ( *,
a_ = None , a_ = None , a_ = dataclasses.MISSING , a_ = dataclasses.MISSING , a_ = None , **a_ , ) -> dataclasses.Field:
"""simple docstring"""
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
__A = {}
if aliases is not None:
__A = aliases
if help is not None:
__A = help
return dataclasses.field(metadata=a_ , default=a_ , default_factory=a_ , **a_ )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
def __init__( self : Union[str, Any] ,A : Union[DataClassType, Iterable[DataClassType]] ,**A : List[Any] ):
# To make the default appear when using --help
if "formatter_class" not in kwargs:
__A = ArgumentDefaultsHelpFormatter
super().__init__(**A )
if dataclasses.is_dataclass(A ):
__A = [dataclass_types]
__A = list(A )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(A )
@staticmethod
def UpperCamelCase_ ( A : ArgumentParser ,A : dataclasses.Field ):
__A = f'''--{field.name}'''
__A = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type ,A ):
raise RuntimeError(
"Unresolved type detected, which should have been done with the help of "
"`typing.get_type_hints` method by default" )
__A = kwargs.pop("aliases" ,[] )
if isinstance(A ,A ):
__A = [aliases]
__A = getattr(field.type ,"__origin__" ,field.type )
if origin_type is Union or (hasattr(A ,"UnionType" ) and isinstance(A ,types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(A ) not in field.type.__args__
):
raise ValueError(
"Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
" the argument parser only supports one type per argument."
f''' Problem encountered in field \'{field.name}\'.''' )
if type(A ) not in field.type.__args__:
# filter `str` in Union
__A = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
__A = getattr(field.type ,"__origin__" ,field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
__A = (
field.type.__args__[0] if isinstance(A ,field.type.__args__[1] ) else field.type.__args__[1]
)
__A = getattr(field.type ,"__origin__" ,field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
__A = {}
if origin_type is Literal or (isinstance(field.type ,A ) and issubclass(field.type ,A )):
if origin_type is Literal:
__A = field.type.__args__
else:
__A = [x.value for x in field.type]
__A = make_choice_type_function(kwargs["choices"] )
if field.default is not dataclasses.MISSING:
__A = field.default
else:
__A = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
__A = copy(A )
# Hack because type=bool in argparse does not behave as we want.
__A = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
__A = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
__A = default
# This tells argparse we accept 0 or 1 value after --field_name
__A = "?"
# This is the value that will get picked if we do --field_name (without value)
__A = True
elif isclass(A ) and issubclass(A ,A ):
__A = field.type.__args__[0]
__A = "+"
if field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
elif field.default is dataclasses.MISSING:
__A = True
else:
__A = field.type
if field.default is not dataclasses.MISSING:
__A = field.default
elif field.default_factory is not dataclasses.MISSING:
__A = field.default_factory()
else:
__A = True
parser.add_argument(A ,*A ,**A )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
__A = False
parser.add_argument(f'''--no_{field.name}''' ,action="store_false" ,dest=field.name ,**A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : DataClassType ):
if hasattr(A ,"_argument_group_name" ):
__A = self.add_argument_group(dtype._argument_group_name )
else:
__A = self
try:
__A = get_type_hints(A )
except NameError:
raise RuntimeError(
f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
"removing line of `from __future__ import annotations` which opts in Postponed "
"Evaluation of Annotations (PEP 563)" )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A ):
__A = ".".join(map(A ,sys.version_info[:3] ) )
raise RuntimeError(
f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
"line of `from __future__ import annotations` which opts in union types as "
"`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
"support Python versions that lower than 3.10, you need to use "
"`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
"`X | None`." ) from ex
raise
for field in dataclasses.fields(A ):
if not field.init:
continue
__A = type_hints[field.name]
self._parse_dataclass_field(A ,A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[Any]=None ,A : List[Any]=False ,A : Optional[Any]=True ,A : Union[str, Any]=None ,A : Union[str, Any]=None ,):
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
__A = []
if args_filename:
args_files.append(Path(A ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
__A = ArgumentParser()
args_file_parser.add_argument(A ,type=A ,action="append" )
# Use only remaining args for further parsing (remove the args_file_flag)
__A , __A = args_file_parser.parse_known_args(args=A )
__A = vars(A ).get(args_file_flag.lstrip("-" ) ,A )
if cmd_args_file_paths:
args_files.extend([Path(A ) for p in cmd_args_file_paths] )
__A = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
__A = file_args + args if args is not None else file_args + sys.argv[1:]
__A , __A = self.parse_known_args(args=A )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in vars(A ).items() if k in keys}
for k in keys:
delattr(A ,A )
__A = dtype(**A )
outputs.append(A )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(A )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' )
return (*outputs,)
def UpperCamelCase_ ( self : Dict ,A : Dict[str, Any] ,A : bool = False ):
__A = set(args.keys() )
__A = []
for dtype in self.dataclass_types:
__A = {f.name for f in dataclasses.fields(A ) if f.init}
__A = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
__A = dtype(**A )
outputs.append(A )
if not allow_extra_keys and unused_keys:
raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(A )}''' )
return tuple(A )
def UpperCamelCase_ ( self : List[str] ,A : str ,A : bool = False ):
with open(Path(A ) ,encoding="utf-8" ) as open_json_file:
__A = json.loads(open_json_file.read() )
__A = self.parse_dict(A ,allow_extra_keys=A )
return tuple(A )
def UpperCamelCase_ ( self : int ,A : str ,A : bool = False ):
__A = self.parse_dict(yaml.safe_load(Path(A ).read_text() ) ,allow_extra_keys=A )
return tuple(A )
| 15 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Any = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(__lowerCamelCase ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
UpperCAmelCase_ : Optional[Any] = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError("This matrix has no inverse." )
# Creates a copy of the matrix with swapped positions of the elements
UpperCAmelCase_ : Union[str, Any] = [[0.0, 0.0], [0.0, 0.0]]
UpperCAmelCase_ , UpperCAmelCase_ : Any = matrix[1][1], matrix[0][0]
UpperCAmelCase_ , UpperCAmelCase_ : Dict = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(__lowerCamelCase ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(__lowerCamelCase ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
UpperCAmelCase_ : int = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError("This matrix has no inverse." )
# Creating cofactor matrix
UpperCAmelCase_ : Optional[Any] = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
UpperCAmelCase_ : int = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
UpperCAmelCase_ : int = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
UpperCAmelCase_ : int = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
UpperCAmelCase_ : Dict = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
UpperCAmelCase_ : List[str] = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
UpperCAmelCase_ : int = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
UpperCAmelCase_ : List[Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
UpperCAmelCase_ : int = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
UpperCAmelCase_ : str = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
UpperCAmelCase_ : Dict = array(__lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
UpperCAmelCase_ : Optional[int] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
UpperCAmelCase_ : Union[str, Any] = array(__lowerCamelCase )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(__lowerCamelCase )
# Calculate the inverse of the matrix
return [[float(d(__lowerCamelCase ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("Please provide a matrix of size 2x2 or 3x3." )
| 61 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
return False
__A = 0
__A = 0
__A = 1
# Calculating the hash of pattern and substring of text
for i in range(a_ ):
__A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
__A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
__A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
__A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = "abc1abc12"
__A = "alskfjaldsabc1abc1abc12k23adsfabcabc"
__A = "alskfjaldsk23adsfabcabc"
assert rabin_karp(a_ , a_ ) and not rabin_karp(a_ , a_ )
# Test 2)
__A = "ABABX"
__A = "ABABZABABYABABX"
assert rabin_karp(a_ , a_ )
# Test 3)
__A = "AAAB"
__A = "ABAAAAAB"
assert rabin_karp(a_ , a_ )
# Test 4)
__A = "abcdabcy"
__A = "abcxabcdabxabcdabcdabcy"
assert rabin_karp(a_ , a_ )
# Test 5)
__A = "Lü"
__A = "Lüsai"
assert rabin_karp(a_ , a_ )
__A = "Lue"
assert not rabin_karp(a_ , a_ )
print("Success." )
if __name__ == "__main__":
test_rabin_karp()
| 15 | 0 |
import requests
_A = 'YOUR API KEY'
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str = giphy_api_key ):
__UpperCamelCase ='+'.join(query.split() )
__UpperCamelCase =F'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
__UpperCamelCase =requests.get(SCREAMING_SNAKE_CASE__ ).json()['data']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 62 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE :Dict = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
SCREAMING_SNAKE_CASE :Dict = reader.read()
SCREAMING_SNAKE_CASE :List[str] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
else:
SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
SCREAMING_SNAKE_CASE :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
SCREAMING_SNAKE_CASE :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
SCREAMING_SNAKE_CASE :Optional[Any] = config[key]
del config[key]
SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
SCREAMING_SNAKE_CASE :Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
SCREAMING_SNAKE_CASE :List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
SCREAMING_SNAKE_CASE :List[Any] = param_value
SCREAMING_SNAKE_CASE :str = True
if not has_changed:
SCREAMING_SNAKE_CASE :List[str] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 15 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a =['image_processor', 'tokenizer']
__a ='LayoutLMv3ImageProcessor'
__a =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Tuple , __a : int=None , __a : Union[str, Any]=None , **__a : Optional[Any] ):
_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." , __a , )
_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__(__a , __a )
def __call__( self : Any , __a : List[str] , __a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __a : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , __a : Union[List[List[int]], List[List[List[int]]]] = None , __a : Optional[Union[List[int], List[List[int]]]] = None , __a : bool = True , __a : Union[bool, str, PaddingStrategy] = False , __a : Union[bool, str, TruncationStrategy] = None , __a : Optional[int] = None , __a : int = 0 , __a : Optional[int] = None , __a : Optional[bool] = None , __a : Optional[bool] = None , __a : bool = False , __a : bool = False , __a : bool = False , __a : bool = False , __a : bool = True , __a : Optional[Union[str, TensorType]] = None , **__a : Dict , ):
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
# first, apply the image processor
_a = self.image_processor(images=__a , return_tensors=__a )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__a , __a ):
_a = [text] # add batch dimension (as the image processor always adds a batch dimension)
_a = features["words"]
_a = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=__a , add_special_tokens=__a , padding=__a , truncation=__a , max_length=__a , stride=__a , pad_to_multiple_of=__a , return_token_type_ids=__a , return_attention_mask=__a , return_overflowing_tokens=__a , return_special_tokens_mask=__a , return_offsets_mapping=__a , return_length=__a , verbose=__a , return_tensors=__a , **__a , )
# add pixel values
_a = features.pop("pixel_values" )
if return_overflowing_tokens is True:
_a = self.get_overflowing_images(__a , encoded_inputs["overflow_to_sample_mapping"] )
_a = images
return encoded_inputs
def UpperCamelCase__ ( self : Optional[int] , __a : str , __a : List[Any] ):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
_a = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__a ) != len(__a ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
f' {len(__a )} and {len(__a )}' )
return images_with_overflow
def UpperCamelCase__ ( self : int , *__a : str , **__a : Tuple ):
return self.tokenizer.batch_decode(*__a , **__a )
def UpperCamelCase__ ( self : str , *__a : List[Any] , **__a : List[str] ):
return self.tokenizer.decode(*__a , **__a )
@property
def UpperCamelCase__ ( self : Tuple ):
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCamelCase__ ( self : int ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __a , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self : List[str] ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __a , )
return self.image_processor
| 63 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
__A = date_parser.parse(a_ )
__A = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__A = start
__A = end
__A = duration_in_min
return job_info
def UpperCAmelCase ( a_ , a_=None ) -> str:
"""simple docstring"""
__A = None
if token is not None:
__A = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
__A = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__A = requests.get(a_ , headers=a_ ).json()
__A = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
__A = math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )
for i in range(a_ ):
__A = requests.get(url + F'''&page={i + 2}''' , headers=a_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(a_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
SCREAMING_SNAKE_CASE :Union[str, Any] = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE :Optional[int] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f'''{k}: {v["duration"]}''')
| 15 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase( __a , unittest.TestCase ):
'''simple docstring'''
lowercase__ = DiTPipeline
lowercase__ = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
lowercase__ = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
lowercase__ = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
lowercase__ = False
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case : Tuple = TransformeraDModel(
sample_size=16, num_layers=2, patch_size=4, attention_head_dim=8, num_attention_heads=2, in_channels=4, out_channels=8, attention_bias=a_, activation_fn="""gelu-approximate""", num_embeds_ada_norm=1_000, norm_type="""ada_norm_zero""", norm_elementwise_affine=a_, )
_snake_case : List[Any] = AutoencoderKL()
_snake_case : Any = DDIMScheduler()
_snake_case : Optional[Any] = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def UpperCamelCase_ ( self: Any, a_: Dict, a_: List[Any]=0 ):
'''simple docstring'''
if str(a_ ).startswith("""mps""" ):
_snake_case : str = torch.manual_seed(a_ )
else:
_snake_case : Dict = torch.Generator(device=a_ ).manual_seed(a_ )
_snake_case : Union[str, Any] = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
_snake_case : Tuple = """cpu"""
_snake_case : str = self.get_dummy_components()
_snake_case : Union[str, Any] = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
_snake_case : int = self.get_dummy_inputs(a_ )
_snake_case : Any = pipe(**a_ ).images
_snake_case : Tuple = image[0, -3:, -3:, -1]
self.assertEqual(image.shape, (1, 16, 16, 3) )
_snake_case : List[str] = np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457] )
_snake_case : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a_, 1E-3 )
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=a_, expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available(), reason="""XFormers attention is only available with CUDA and `xformers` installed""", )
def UpperCamelCase_ ( self: Any ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self: Optional[int] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase_ ( self: str ):
'''simple docstring'''
_snake_case : List[Any] = torch.manual_seed(0 )
_snake_case : Dict = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_snake_case : Optional[Any] = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_snake_case : List[str] = pipe.get_label_ids(a_ )
_snake_case : List[str] = pipe(a_, generator=a_, num_inference_steps=40, output_type="""np""" ).images
for word, image in zip(a_, a_ ):
_snake_case : Dict = load_numpy(
f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" )
assert np.abs((expected_image - image).max() ) < 1E-2
def UpperCamelCase_ ( self: Dict ):
'''simple docstring'''
_snake_case : Dict = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_snake_case : List[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_snake_case : Dict = ["""vase""", """umbrella"""]
_snake_case : List[str] = pipe.get_label_ids(a_ )
_snake_case : Tuple = torch.manual_seed(0 )
_snake_case : Dict = pipe(a_, generator=a_, num_inference_steps=25, output_type="""np""" ).images
for word, image in zip(a_, a_ ):
_snake_case : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"/dit/{word}_512.npy" )
assert np.abs((expected_image - image).max() ) < 1E-1
| 64 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = args.pruning_method
__A = args.threshold
__A = args.model_name_or_path.rstrip("/" )
__A = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
__A = torch.load(os.path.join(a_ , "pytorch_model.bin" ) )
__A = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
__A = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
__A = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
__A = MagnitudeBinarizer.apply(inputs=a_ , threshold=a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = TopKBinarizer.apply(a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A = ThresholdBinarizer.apply(a_ , a_ , a_ )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
__A = name[:-6]
__A = model[F'''{prefix_}mask_scores''']
__A , __A = -0.1, 1.1
__A = torch.sigmoid(a_ )
__A = s * (r - l) + l
__A = s_bar.clamp(min=0.0 , max=1.0 )
__A = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
__A = os.path.join(
os.path.dirname(a_ ) , F'''bertarized_{os.path.basename(a_ )}''' )
if not os.path.isdir(a_ ):
shutil.copytree(a_ , a_ )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(a_ , os.path.join(a_ , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--pruning_method',
choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
type=str,
required=True,
help=(
'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
' sigmoied_threshold = Soft movement pruning)'
),
)
parser.add_argument(
'--threshold',
type=float,
required=False,
help=(
'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
'Not needed for `l0`'
),
)
parser.add_argument(
'--model_name_or_path',
type=str,
required=True,
help='Folder containing the model that was previously fine-pruned',
)
parser.add_argument(
'--target_model_path',
default=None,
type=str,
required=False,
help='Folder containing the model that was previously fine-pruned',
)
SCREAMING_SNAKE_CASE :str = parser.parse_args()
main(args)
| 15 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : List[str] = 'align_text_model'
def __init__(self : Dict , __UpperCAmelCase : List[str]=3_0_5_2_2 , __UpperCAmelCase : str=7_6_8 , __UpperCAmelCase : int=1_2 , __UpperCAmelCase : List[str]=1_2 , __UpperCAmelCase : Any=3_0_7_2 , __UpperCAmelCase : Any="gelu" , __UpperCAmelCase : Optional[int]=0.1 , __UpperCAmelCase : int=0.1 , __UpperCAmelCase : Dict=5_1_2 , __UpperCAmelCase : Dict=2 , __UpperCAmelCase : Tuple=0.02 , __UpperCAmelCase : int=1E-12 , __UpperCAmelCase : Dict=0 , __UpperCAmelCase : Optional[int]="absolute" , __UpperCAmelCase : str=True , **__UpperCAmelCase : List[Any] , ) -> Dict:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = type_vocab_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = position_embedding_type
UpperCAmelCase__ = use_cache
UpperCAmelCase__ = pad_token_id
@classmethod
def lowercase_ (cls : Tuple , __UpperCAmelCase : Union[str, os.PathLike] , **__UpperCAmelCase : int ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = cls.get_config_dict(__UpperCAmelCase , **__UpperCAmelCase )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
UpperCAmelCase__ = 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(__UpperCAmelCase , **__UpperCAmelCase )
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : List[Any] = 'align_vision_model'
def __init__(self : Any , __UpperCAmelCase : int = 3 , __UpperCAmelCase : int = 6_0_0 , __UpperCAmelCase : float = 2.0 , __UpperCAmelCase : float = 3.1 , __UpperCAmelCase : int = 8 , __UpperCAmelCase : List[int] = [3, 3, 5, 3, 5, 5, 3] , __UpperCAmelCase : List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , __UpperCAmelCase : List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , __UpperCAmelCase : List[int] = [] , __UpperCAmelCase : List[int] = [1, 2, 2, 2, 1, 2, 1] , __UpperCAmelCase : List[int] = [1, 2, 2, 3, 3, 4, 1] , __UpperCAmelCase : List[int] = [1, 6, 6, 6, 6, 6, 6] , __UpperCAmelCase : float = 0.25 , __UpperCAmelCase : str = "swish" , __UpperCAmelCase : int = 2_5_6_0 , __UpperCAmelCase : str = "mean" , __UpperCAmelCase : float = 0.02 , __UpperCAmelCase : float = 0.001 , __UpperCAmelCase : float = 0.99 , __UpperCAmelCase : float = 0.2 , **__UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
UpperCAmelCase__ = num_channels
UpperCAmelCase__ = image_size
UpperCAmelCase__ = width_coefficient
UpperCAmelCase__ = depth_coefficient
UpperCAmelCase__ = depth_divisor
UpperCAmelCase__ = kernel_sizes
UpperCAmelCase__ = in_channels
UpperCAmelCase__ = out_channels
UpperCAmelCase__ = depthwise_padding
UpperCAmelCase__ = strides
UpperCAmelCase__ = num_block_repeats
UpperCAmelCase__ = expand_ratios
UpperCAmelCase__ = squeeze_expansion_ratio
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = hidden_dim
UpperCAmelCase__ = pooling_type
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = batch_norm_eps
UpperCAmelCase__ = batch_norm_momentum
UpperCAmelCase__ = drop_connect_rate
UpperCAmelCase__ = sum(__UpperCAmelCase ) * 4
@classmethod
def lowercase_ (cls : List[str] , __UpperCAmelCase : Union[str, os.PathLike] , **__UpperCAmelCase : Any ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = cls.get_config_dict(__UpperCAmelCase , **__UpperCAmelCase )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("model_type" ) == "align":
UpperCAmelCase__ = config_dict["vision_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(__UpperCAmelCase , **__UpperCAmelCase )
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Any = 'align'
__UpperCAmelCase : str = True
def __init__(self : str , __UpperCAmelCase : Any=None , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : Tuple=6_4_0 , __UpperCAmelCase : int=1.0 , __UpperCAmelCase : List[str]=0.02 , **__UpperCAmelCase : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
if text_config is None:
UpperCAmelCase__ = {}
logger.info("text_config is None. Initializing the AlignTextConfig with default values." )
if vision_config is None:
UpperCAmelCase__ = {}
logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )
UpperCAmelCase__ = AlignTextConfig(**__UpperCAmelCase )
UpperCAmelCase__ = AlignVisionConfig(**__UpperCAmelCase )
UpperCAmelCase__ = projection_dim
UpperCAmelCase__ = temperature_init_value
UpperCAmelCase__ = initializer_range
@classmethod
def lowercase_ (cls : int , __UpperCAmelCase : AlignTextConfig , __UpperCAmelCase : AlignVisionConfig , **__UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__UpperCAmelCase )
def lowercase_ (self : str ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ = self.text_config.to_dict()
UpperCAmelCase__ = self.vision_config.to_dict()
UpperCAmelCase__ = self.__class__.model_type
return output
| 65 |
import os
import re
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 logging
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :int = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
SCREAMING_SNAKE_CASE :int = {
'google/bigbird-roberta-base': 4096,
'google/bigbird-roberta-large': 4096,
'google/bigbird-base-trivia-itc': 4096,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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_ = []
def __init__( self : Any ,A : List[str] ,A : str="<unk>" ,A : int="<s>" ,A : Union[str, Any]="</s>" ,A : List[str]="<pad>" ,A : int="[SEP]" ,A : Optional[Any]="[MASK]" ,A : Tuple="[CLS]" ,A : Optional[Dict[str, Any]] = None ,**A : Any ,):
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else bos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else eos_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else unk_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else pad_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else cls_token
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sep_token=A ,mask_token=A ,cls_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def UpperCamelCase_ ( self : List[str] ):
return self.sp_model.get_piece_size()
def UpperCamelCase_ ( self : Optional[Any] ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : str ,A : Optional[Any] ):
__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 UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Tuple ):
return self.sp_model.piece_to_id(A )
def UpperCamelCase_ ( self : List[Any] ,A : Tuple ):
__A = self.sp_model.IdToPiece(A )
return token
def UpperCamelCase_ ( self : List[Any] ,A : int ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def UpperCamelCase_ ( self : Tuple ,A : List[int] ,A : bool = False ,A : bool = None ,A : bool = True ,**A : Union[str, Any] ,):
__A = kwargs.pop("use_source_tokenizer" ,A )
__A = self.convert_ids_to_tokens(A ,skip_special_tokens=A )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
__A = []
__A = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
__A = []
sub_texts.append(A )
else:
current_sub_text.append(A )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(A ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
__A = re.sub(R" (\[(MASK|SEP)\])" ,R"\1" ," ".join(A ) )
else:
__A = "".join(A )
__A = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__A = self.clean_up_tokenization(A )
return clean_text
else:
return text
def UpperCamelCase_ ( self : str ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Optional[int] ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Any ,A : List[int] ,A : Optional[List[int]] = None ):
__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]
| 15 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class lowerCamelCase ( _lowerCAmelCase ):
'''simple docstring'''
def __init__( self: List[Any] , snake_case: str = "▁" , snake_case: bool = True , snake_case: Union[str, AddedToken] = "<unk>" , snake_case: Union[str, AddedToken] = "</s>" , snake_case: Union[str, AddedToken] = "<pad>" , ) -> Any:
snake_case_ :Any = {
"""pad""": {"""id""": 0, """token""": pad_token},
"""eos""": {"""id""": 1, """token""": eos_token},
"""unk""": {"""id""": 2, """token""": unk_token},
}
snake_case_ :Dict = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
snake_case_ :Tuple = token_dict["""token"""]
snake_case_ :Union[str, Any] = Tokenizer(Unigram() )
snake_case_ :Tuple = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(""" {2,}""" ) , """ """ ),
normalizers.Lowercase(),
] )
snake_case_ :str = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=snake_case , add_prefix_space=snake_case ),
pre_tokenizers.Digits(individual_digits=snake_case ),
pre_tokenizers.Punctuation(),
] )
snake_case_ :Dict = decoders.Metaspace(replacement=snake_case , add_prefix_space=snake_case )
snake_case_ :str = TemplateProcessing(
single=f"""$A {self.special_tokens['eos']['token']}""" , special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])] , )
snake_case_ :Tuple = {
"""model""": """SentencePieceUnigram""",
"""replacement""": replacement,
"""add_prefix_space""": add_prefix_space,
}
super().__init__(snake_case , snake_case )
def lowerCAmelCase_ ( self: Dict , snake_case: Union[str, List[str]] , snake_case: int = 8_000 , snake_case: bool = True , ) -> int:
snake_case_ :List[Any] = trainers.UnigramTrainer(
vocab_size=snake_case , special_tokens=self.special_tokens_list , show_progress=snake_case , )
if isinstance(snake_case , snake_case ):
snake_case_ :int = [files]
self._tokenizer.train(snake_case , trainer=snake_case )
self.add_unk_id()
def lowerCAmelCase_ ( self: Dict , snake_case: Union[Iterator[str], Iterator[Iterator[str]]] , snake_case: int = 8_000 , snake_case: bool = True , ) -> List[str]:
snake_case_ :Optional[Any] = trainers.UnigramTrainer(
vocab_size=snake_case , special_tokens=self.special_tokens_list , show_progress=snake_case , )
self._tokenizer.train_from_iterator(snake_case , trainer=snake_case )
self.add_unk_id()
def lowerCAmelCase_ ( self: List[Any] ) -> Tuple:
snake_case_ :Dict = json.loads(self._tokenizer.to_str() )
snake_case_ :Optional[int] = self.special_tokens["""unk"""]["""id"""]
snake_case_ :Optional[int] = Tokenizer.from_str(json.dumps(snake_case ) )
| 66 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
SCREAMING_SNAKE_CASE :int = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = (images / 2 + 0.5).clamp(0 , 1 )
__A = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A = numpy_to_pil(a_ )
return images
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if images.ndim == 3:
__A = images[None, ...]
__A = (images * 2_5_5).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
__A = [Image.fromarray(a_ ) for image in images]
return pil_images
| 15 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}
class a__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCamelCase : Optional[int] ="convnextv2"
def __init__( self : int , a : Dict=3 , a : List[str]=4 , a : str=4 , a : Dict=None , a : List[str]=None , a : Tuple="gelu" , a : Tuple=0.02 , a : Dict=1e-1_2 , a : Any=0.0 , a : List[str]=2_24 , a : Optional[Any]=None , a : int=None , **a : Any , ):
"""simple docstring"""
super().__init__(**a )
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_stages
__lowerCamelCase = [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
__lowerCamelCase = [3, 3, 9, 3] if depths is None else depths
__lowerCamelCase = hidden_act
__lowerCamelCase = initializer_range
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = drop_path_rate
__lowerCamelCase = image_size
__lowerCamelCase = ['''stem'''] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
__lowerCamelCase , __lowerCamelCase = get_aligned_output_features_output_indices(
out_features=a , out_indices=a , stage_names=self.stage_names )
| 67 |
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
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = "yolos"
def __init__( self : Any ,A : Optional[Any]=7_68 ,A : Dict=12 ,A : Any=12 ,A : str=30_72 ,A : Any="gelu" ,A : str=0.0 ,A : List[str]=0.0 ,A : Dict=0.02 ,A : int=1E-12 ,A : Tuple=[5_12, 8_64] ,A : List[Any]=16 ,A : str=3 ,A : str=True ,A : Any=1_00 ,A : Dict=True ,A : Dict=False ,A : Tuple=1 ,A : Union[str, Any]=5 ,A : Optional[Any]=2 ,A : Union[str, Any]=5 ,A : int=2 ,A : int=0.1 ,**A : List[str] ,):
super().__init__(**A )
__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 = initializer_range
__A = layer_norm_eps
__A = image_size
__A = patch_size
__A = num_channels
__A = qkv_bias
__A = num_detection_tokens
__A = use_mid_position_embeddings
__A = auxiliary_loss
# Hungarian matcher
__A = class_cost
__A = bbox_cost
__A = giou_cost
# Loss coefficients
__A = bbox_loss_coefficient
__A = giou_loss_coefficient
__A = eos_coefficient
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = version.parse("1.11" )
@property
def UpperCamelCase_ ( self : str ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCamelCase_ ( self : List[Any] ):
return 1E-4
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return 12
| 15 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
class a__ ( enum.Enum ):
"""simple docstring"""
__lowerCamelCase = 'all_checks'
__lowerCamelCase = 'basic_checks'
__lowerCamelCase = 'no_checks'
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[dict] , SCREAMING_SNAKE_CASE_: dict , SCREAMING_SNAKE_CASE_: Tuple=None ) -> Union[str, Any]:
'''simple docstring'''
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
A__ = " for " + verification_name if verification_name is not None else ""
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise NonMatchingChecksumError(
F'Checksums didn\'t match{for_verification_name}:\n'
F'{bad_urls}\n'
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
class a__ ( snake_case ):
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[dict] , SCREAMING_SNAKE_CASE_: dict ) -> List[str]:
'''simple docstring'''
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
if len(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) > 0:
raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) ) )
A__ = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE_ ) )
logger.info("All the splits matched successfully." )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: bool = True ) -> dict:
'''simple docstring'''
if record_checksum:
A__ = shaaaa()
with open(SCREAMING_SNAKE_CASE_ , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 2_0 ) , b"" ):
m.update(SCREAMING_SNAKE_CASE_ )
A__ = m.hexdigest()
else:
A__ = None
return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE_ ), "checksum": checksum}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] ) -> str:
'''simple docstring'''
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 68 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
SCREAMING_SNAKE_CASE :List[str] = 'pytorch_model.bin'
SCREAMING_SNAKE_CASE :str = 'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE :Optional[int] = 'adapter_config.json'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.bin'
SCREAMING_SNAKE_CASE :Dict = 'adapter_model.safetensors'
SCREAMING_SNAKE_CASE :str = 'tf_model.h5'
SCREAMING_SNAKE_CASE :List[Any] = 'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE :str = 'model.ckpt'
SCREAMING_SNAKE_CASE :List[Any] = 'flax_model.msgpack'
SCREAMING_SNAKE_CASE :Optional[int] = 'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE :Tuple = 'model.safetensors'
SCREAMING_SNAKE_CASE :List[Any] = 'model.safetensors.index.json'
SCREAMING_SNAKE_CASE :str = 'config.json'
SCREAMING_SNAKE_CASE :int = 'preprocessor_config.json'
SCREAMING_SNAKE_CASE :Optional[Any] = FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE :Optional[int] = 'generation_config.json'
SCREAMING_SNAKE_CASE :List[str] = 'modelcard.json'
SCREAMING_SNAKE_CASE :Optional[int] = '▁'
SCREAMING_SNAKE_CASE :Optional[Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
SCREAMING_SNAKE_CASE :str = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE :Optional[Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE :List[Any] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
__A = (
"This example requires a source install from HuggingFace Transformers (see "
"`https://huggingface.co/docs/transformers/installation#install-from-source`),"
)
else:
__A = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
"versions of HuggingFace Transformers." )
| 15 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCamelCase ( lowerCAmelCase__ ):
def __init__( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__) -> List[Any]:
snake_case_ = dataset
snake_case_ = process
snake_case_ = params
def __len__( self) -> Optional[Any]:
return len(self.dataset)
def __getitem__( self, lowerCAmelCase__) -> Optional[int]:
snake_case_ = self.dataset[i]
snake_case_ = self.process(lowerCAmelCase__, **self.params)
return processed
class UpperCamelCase ( lowerCAmelCase__ ):
def __init__( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__=None) -> Optional[Any]:
snake_case_ = loader
snake_case_ = infer
snake_case_ = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
snake_case_ = None
snake_case_ = loader_batch_size
# Internal bookkeeping
snake_case_ = None
snake_case_ = None
def __len__( self) -> str:
return len(self.loader)
def __iter__( self) -> List[Any]:
snake_case_ = iter(self.loader)
return self
def a_ ( self) -> Dict:
if isinstance(self._loader_batch_data, torch.Tensor):
# Batch data is simple tensor, just fetch the slice
snake_case_ = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
snake_case_ = {}
for k, element in self._loader_batch_data.items():
if isinstance(lowerCAmelCase__, lowerCAmelCase__):
# Convert ModelOutput to tuple first
snake_case_ = element.to_tuple()
if isinstance(element[0], torch.Tensor):
snake_case_ = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
snake_case_ = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCAmelCase__, lowerCAmelCase__):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0], torch.Tensor):
snake_case_ = tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
elif isinstance(element[0], np.ndarray):
snake_case_ = tuple(np.expand_dims(el[self._loader_batch_index], 0) for el in element)
continue
if element is None:
# This can happen for optional data that get passed around
snake_case_ = None
elif isinstance(element[self._loader_batch_index], torch.Tensor):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
snake_case_ = element[self._loader_batch_index].unsqueeze(0)
elif isinstance(element[self._loader_batch_index], np.ndarray):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
snake_case_ = np.expand_dims(element[self._loader_batch_index], 0)
else:
# This is typically a list, so no need to `unsqueeze`.
snake_case_ = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
snake_case_ = self._loader_batch_data.__class__(lowerCAmelCase__)
self._loader_batch_index += 1
return result
def a_ ( self) -> Optional[int]:
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
snake_case_ = next(self.iterator)
snake_case_ = self.infer(lowerCAmelCase__, **self.params)
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(lowerCAmelCase__, torch.Tensor):
snake_case_ = processed
else:
snake_case_ = list(processed.keys())[0]
snake_case_ = processed[key]
if isinstance(lowerCAmelCase__, lowerCAmelCase__):
snake_case_ = len(lowerCAmelCase__)
else:
snake_case_ = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
snake_case_ = observed_batch_size
# Setting internal index to unwrap the batch
snake_case_ = processed
snake_case_ = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class UpperCamelCase ( lowerCAmelCase__ ):
def __init__( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__=None) -> Any:
super().__init__(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__)
def __iter__( self) -> str:
snake_case_ = iter(self.loader)
snake_case_ = None
return self
def a_ ( self) -> Optional[int]:
if self.subiterator is None:
snake_case_ = self.infer(next(self.iterator), **self.params)
try:
# Try to return next item
snake_case_ = next(self.subiterator)
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
snake_case_ = self.infer(next(self.iterator), **self.params)
snake_case_ = next(self.subiterator)
return processed
class UpperCamelCase ( lowerCAmelCase__ ):
def __iter__( self) -> Any:
snake_case_ = iter(self.loader)
return self
def a_ ( self) -> Tuple:
# Extremely similar to PipelineIterator in its unpacking mechanism
# BUT, we have an extra required item which is the presence of `is_last`
# That is because everything is flattened by `PipelineChunkIterator` we
# need to keep track of how to regroup here in the original `process`
# boundaries so that `process` and `postprocess` see the same data.
# This iterator accumulates items (possibly while unbatching) until it
# its a `is_last` and then just passes it on to the caller.
snake_case_ = False
snake_case_ = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
snake_case_ = self.loader_batch_item()
snake_case_ = item.pop('is_last')
accumulator.append(lowerCAmelCase__)
if is_last:
return accumulator
while not is_last:
snake_case_ = self.infer(next(self.iterator), **self.params)
if self.loader_batch_size is not None:
if isinstance(lowerCAmelCase__, torch.Tensor):
snake_case_ = processed
else:
snake_case_ = list(processed.keys())[0]
snake_case_ = processed[key]
if isinstance(lowerCAmelCase__, lowerCAmelCase__):
snake_case_ = len(lowerCAmelCase__)
else:
snake_case_ = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
snake_case_ = observed_batch_size
snake_case_ = processed
snake_case_ = 0
while self._loader_batch_index < self.loader_batch_size:
snake_case_ = self.loader_batch_item()
snake_case_ = item.pop('is_last')
accumulator.append(lowerCAmelCase__)
if is_last:
return accumulator
else:
snake_case_ = processed
snake_case_ = item.pop('is_last')
accumulator.append(lowerCAmelCase__)
return accumulator
class UpperCamelCase ( lowerCAmelCase__ ):
def __init__( self, lowerCAmelCase__, lowerCAmelCase__) -> Tuple:
snake_case_ = dataset
snake_case_ = key
def __len__( self) -> str:
return len(self.dataset)
def __getitem__( self, lowerCAmelCase__) -> List[Any]:
return self.dataset[i][self.key]
class UpperCamelCase ( lowerCAmelCase__ ):
def __init__( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__) -> List[str]:
snake_case_ = dataset
snake_case_ = keya
snake_case_ = keya
def __len__( self) -> str:
return len(self.dataset)
def __getitem__( self, lowerCAmelCase__) -> Any:
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 69 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
if indegree[i] == 0:
queue.append(a_ )
while queue:
__A = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__A = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(a_ )
print(max(a_ ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE :List[Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 15 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
A__ : Dict =logging.getLogger(__name__)
@dataclass(frozen=snake_case_ )
class UpperCAmelCase :
_lowercase: str
_lowercase: str
_lowercase: Optional[str] = None
_lowercase: Optional[str] = None
_lowercase: Optional[str] = None
@dataclass(frozen=snake_case_ )
class UpperCAmelCase :
_lowercase: List[int]
_lowercase: Optional[List[int]] = None
_lowercase: Optional[List[int]] = None
_lowercase: Optional[Union[int, float]] = None
_lowercase: Optional[int] = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class UpperCAmelCase ( snake_case_ ):
_lowercase: List[InputFeatures]
def __init__( self : str , __snake_case : str , __snake_case : PreTrainedTokenizer , __snake_case : str , __snake_case : Optional[int] = None , __snake_case : int=False , __snake_case : bool = False , ) -> Optional[int]:
_lowerCAmelCase = hans_processors[task]()
_lowerCAmelCase = os.path.join(
__snake_case , """cached_{}_{}_{}_{}""".format(
"""dev""" if evaluate else """train""" , tokenizer.__class__.__name__ , str(__snake_case ) , __snake_case , ) , )
_lowerCAmelCase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_lowerCAmelCase , _lowerCAmelCase = label_list[2], label_list[1]
_lowerCAmelCase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__snake_case ):
if os.path.exists(__snake_case ) and not overwrite_cache:
logger.info(f"Loading features from cached file {cached_features_file}" )
_lowerCAmelCase = torch.load(__snake_case )
else:
logger.info(f"Creating features from dataset file at {data_dir}" )
_lowerCAmelCase = (
processor.get_dev_examples(__snake_case ) if evaluate else processor.get_train_examples(__snake_case )
)
logger.info("""Training examples: %s""" , len(__snake_case ) )
_lowerCAmelCase = hans_convert_examples_to_features(__snake_case , __snake_case , __snake_case , __snake_case )
logger.info("""Saving features into cached file %s""" , __snake_case )
torch.save(self.features , __snake_case )
def __len__( self : List[str] ) -> List[Any]:
return len(self.features )
def __getitem__( self : Union[str, Any] , __snake_case : Union[str, Any] ) -> InputFeatures:
return self.features[i]
def lowercase__ ( self : List[Any] ) -> int:
return self.label_list
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase :
_lowercase: List[InputFeatures]
def __init__( self : List[Any] , __snake_case : str , __snake_case : PreTrainedTokenizer , __snake_case : str , __snake_case : Optional[int] = 1_28 , __snake_case : Dict=False , __snake_case : bool = False , ) -> Union[str, Any]:
_lowerCAmelCase = hans_processors[task]()
_lowerCAmelCase = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
_lowerCAmelCase , _lowerCAmelCase = label_list[2], label_list[1]
_lowerCAmelCase = label_list
_lowerCAmelCase = processor.get_dev_examples(__snake_case ) if evaluate else processor.get_train_examples(__snake_case )
_lowerCAmelCase = hans_convert_examples_to_features(__snake_case , __snake_case , __snake_case , __snake_case )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="""convert examples to features""" ):
if ex_index % 1_00_00 == 0:
logger.info("""Writing example %d of %d""" % (ex_index, len(__snake_case )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
_lowerCAmelCase = tf.data.Dataset.from_generator(
__snake_case , (
{
"""example_id""": tf.intaa,
"""input_ids""": tf.intaa,
"""attention_mask""": tf.intaa,
"""token_type_ids""": tf.intaa,
},
tf.intaa,
) , (
{
"""example_id""": tf.TensorShape([] ),
"""input_ids""": tf.TensorShape([None, None] ),
"""attention_mask""": tf.TensorShape([None, None] ),
"""token_type_ids""": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def lowercase__ ( self : Optional[Any] ) -> Optional[int]:
return self.dataset
def __len__( self : Any ) -> List[Any]:
return len(self.features )
def __getitem__( self : Any , __snake_case : Optional[Any] ) -> InputFeatures:
return self.features[i]
def lowercase__ ( self : Dict ) -> str:
return self.label_list
class UpperCAmelCase ( snake_case_ ):
def lowercase__ ( self : Optional[Any] , __snake_case : str ) -> int:
return self._create_examples(self._read_tsv(os.path.join(__snake_case , """heuristics_train_set.txt""" ) ) , """train""" )
def lowercase__ ( self : str , __snake_case : List[Any] ) -> Optional[int]:
return self._create_examples(self._read_tsv(os.path.join(__snake_case , """heuristics_evaluation_set.txt""" ) ) , """dev""" )
def lowercase__ ( self : int ) -> Optional[int]:
return ["contradiction", "entailment", "neutral"]
def lowercase__ ( self : List[Any] , __snake_case : Any , __snake_case : Optional[Any] ) -> int:
_lowerCAmelCase = []
for i, line in enumerate(__snake_case ):
if i == 0:
continue
_lowerCAmelCase = """%s-%s""" % (set_type, line[0])
_lowerCAmelCase = line[5]
_lowerCAmelCase = line[6]
_lowerCAmelCase = line[7][2:] if line[7].startswith("""ex""" ) else line[7]
_lowerCAmelCase = line[0]
examples.append(InputExample(guid=__snake_case , text_a=__snake_case , text_b=__snake_case , label=__snake_case , pairID=__snake_case ) )
return examples
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ):
"""simple docstring"""
_lowerCAmelCase = {label: i for i, label in enumerate(lowerCAmelCase )}
_lowerCAmelCase = []
for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase ) , desc="""convert examples to features""" ):
if ex_index % 1_00_00 == 0:
logger.info("""Writing example %d""" % (ex_index) )
_lowerCAmelCase = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowerCAmelCase , max_length=lowerCAmelCase , padding="""max_length""" , truncation=lowerCAmelCase , return_overflowing_tokens=lowerCAmelCase , )
_lowerCAmelCase = label_map[example.label] if example.label in label_map else 0
_lowerCAmelCase = int(example.pairID )
features.append(InputFeatures(**lowerCAmelCase , label=lowerCAmelCase , pairID=lowerCAmelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info("""*** Example ***""" )
logger.info(f"guid: {example}" )
logger.info(f"features: {features[i]}" )
return features
A__ : List[Any] ={
'''hans''': 3,
}
A__ : Any ={
'''hans''': HansProcessor,
}
| 70 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""simple docstring"""
__A = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__A = key.replace("heads.cmd.mim_head.cls.predictions" , "mmm_image_head" )
__A = key.replace("heads.cmd.mlm_head.cls.predictions" , "mmm_text_head" )
__A = key.replace("heads.cmd.itm_head.cls" , "itm_head" )
__A = key.replace("heads.cmd.itm_head.pooler" , "itm_head.pooler" )
__A = key.replace("heads.cmd.clip_head.logit_scale" , "flava.logit_scale" )
__A = key.replace("heads.fairseq_mlm.cls.predictions" , "mlm_head" )
__A = key.replace("heads.imagenet.mim_head.cls.predictions" , "mim_head" )
__A = key.replace("mm_text_projection" , "flava.text_to_mm_projection" )
__A = key.replace("mm_image_projection" , "flava.image_to_mm_projection" )
__A = key.replace("image_encoder.module" , "flava.image_model" )
__A = key.replace("text_encoder.module" , "flava.text_model" )
__A = key.replace("mm_encoder.module.encoder.cls_token" , "flava.multimodal_model.cls_token" )
__A = key.replace("mm_encoder.module" , "flava.multimodal_model" )
__A = key.replace("text_projection" , "flava.text_projection" )
__A = key.replace("image_projection" , "flava.image_projection" )
__A = value.float()
for key, value in codebook_state_dict.items():
__A = value
return upgrade
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_=None ) -> Tuple:
"""simple docstring"""
if config_path is not None:
__A = FlavaConfig.from_pretrained(a_ )
else:
__A = FlavaConfig()
__A = FlavaForPreTraining(a_ ).eval()
__A = convert_dalle_checkpoint(a_ , a_ , save_checkpoint=a_ )
if os.path.exists(a_ ):
__A = torch.load(a_ , map_location="cpu" )
else:
__A = torch.hub.load_state_dict_from_url(a_ , map_location="cpu" )
__A = upgrade_state_dict(a_ , a_ )
hf_model.load_state_dict(a_ )
__A = hf_model.state_dict()
__A = count_parameters(a_ )
__A = count_parameters(a_ ) + count_parameters(a_ )
assert torch.allclose(a_ , a_ , atol=1E-3 )
hf_model.save_pretrained(a_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
SCREAMING_SNAKE_CASE :Optional[int] = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 15 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
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.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class __A :
"""simple docstring"""
UpperCamelCase__ : int =XGLMConfig
UpperCamelCase__ : Optional[Any] ={}
UpperCamelCase__ : List[str] ="""gelu"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=14 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=99 , lowerCamelCase__=32 , lowerCamelCase__=2 , lowerCamelCase__=4 , lowerCamelCase__=37 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=512 , lowerCamelCase__=0.02 , ):
"""simple docstring"""
__UpperCamelCase : Tuple =parent
__UpperCamelCase : List[str] =batch_size
__UpperCamelCase : str =seq_length
__UpperCamelCase : Dict =is_training
__UpperCamelCase : Tuple =use_input_mask
__UpperCamelCase : List[Any] =use_labels
__UpperCamelCase : Any =vocab_size
__UpperCamelCase : List[Any] =d_model
__UpperCamelCase : Optional[int] =num_hidden_layers
__UpperCamelCase : List[str] =num_attention_heads
__UpperCamelCase : Optional[int] =ffn_dim
__UpperCamelCase : str =activation_function
__UpperCamelCase : Any =activation_dropout
__UpperCamelCase : Optional[int] =attention_dropout
__UpperCamelCase : Optional[int] =max_position_embeddings
__UpperCamelCase : Any =initializer_range
__UpperCamelCase : Dict =None
__UpperCamelCase : Optional[int] =0
__UpperCamelCase : Optional[Any] =2
__UpperCamelCase : str =1
def __lowercase ( self ):
"""simple docstring"""
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[Any] =tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
__UpperCamelCase : Union[str, Any] =None
if self.use_input_mask:
__UpperCamelCase : Dict =random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : Any =self.get_config()
__UpperCamelCase : Optional[Any] =floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def __lowercase ( self ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=lowerCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=lowerCamelCase__ , )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : int =config_and_inputs
__UpperCamelCase : Optional[Any] ={
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class __A ( a , a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] =(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
UpperCamelCase__ : str =(TFXGLMForCausalLM,) if is_tf_available() else ()
UpperCamelCase__ : Optional[Any] =(
{"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
)
UpperCamelCase__ : Tuple =False
UpperCamelCase__ : Tuple =False
UpperCamelCase__ : Optional[Any] =False
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =TFXGLMModelTester(self )
__UpperCamelCase : Dict =ConfigTester(self , config_class=lowerCamelCase__ , n_embd=37 )
def __lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def __lowercase ( self ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase : Optional[Any] =TFXGLMModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def __lowercase ( self ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class __A ( unittest.TestCase ):
"""simple docstring"""
@slow
def __lowercase ( self , lowerCamelCase__=True ):
"""simple docstring"""
__UpperCamelCase : int =TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__UpperCamelCase : List[str] =tf.convert_to_tensor([[2, 268, 9865]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
__UpperCamelCase : str =[2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
# fmt: on
__UpperCamelCase : Optional[Any] =model.generate(lowerCamelCase__ , do_sample=lowerCamelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , lowerCamelCase__ )
@slow
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__UpperCamelCase : Union[str, Any] =TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
__UpperCamelCase : str =tokenizer('Today is a nice day and' , return_tensors='tf' )
__UpperCamelCase : Union[str, Any] =tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
__UpperCamelCase : Any =model.generate(lowerCamelCase__ , do_sample=lowerCamelCase__ , seed=[7, 0] )
__UpperCamelCase : Tuple =tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCamelCase__ )
__UpperCamelCase : List[Any] =(
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
__UpperCamelCase : Optional[Any] =XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
__UpperCamelCase : Optional[Any] ='left'
# use different length sentences to test batching
__UpperCamelCase : Optional[int] =[
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
__UpperCamelCase : List[Any] =tokenizer(lowerCamelCase__ , return_tensors='tf' , padding=lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =inputs['input_ids']
__UpperCamelCase : Dict =model.generate(input_ids=lowerCamelCase__ , attention_mask=inputs['attention_mask'] , max_new_tokens=12 )
__UpperCamelCase : List[Any] =tokenizer(sentences[0] , return_tensors='tf' ).input_ids
__UpperCamelCase : Dict =model.generate(input_ids=lowerCamelCase__ , max_new_tokens=12 )
__UpperCamelCase : Any =tokenizer(sentences[1] , return_tensors='tf' ).input_ids
__UpperCamelCase : Optional[Any] =model.generate(input_ids=lowerCamelCase__ , max_new_tokens=12 )
__UpperCamelCase : Optional[int] =tokenizer.batch_decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCamelCase__ )
__UpperCamelCase : int =tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCamelCase__ )
__UpperCamelCase : Any =[
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , [non_padded_sentence, padded_sentence] )
| 71 |
import os
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 logging
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE :Tuple = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
SCREAMING_SNAKE_CASE :List[Any] = {
'camembert-base': 512,
}
SCREAMING_SNAKE_CASE :List[str] = '▁'
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[Any] ,A : List[str] ,A : List[Any]="<s>" ,A : Tuple="</s>" ,A : Any="</s>" ,A : Optional[Any]="<s>" ,A : Tuple="<unk>" ,A : str="<pad>" ,A : int="<mask>" ,A : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] ,A : Optional[Dict[str, Any]] = None ,**A : Optional[Any] ,):
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A ,eos_token=A ,unk_token=A ,sep_token=A ,cls_token=A ,pad_token=A ,mask_token=A ,additional_special_tokens=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
__A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__A = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
__A = len(self.fairseq_tokens_to_ids )
__A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
__A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self : int ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A ,token_ids_a=A ,already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self : Union[str, Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__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 + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase_ ( self : Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self : int ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Dict ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase_ ( self : Optional[Any] ,A : Dict ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self : Dict ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Any ):
__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 UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 15 | 0 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class __snake_case ( _lowercase):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead." , _lowercase , )
| 72 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , a_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__A , __A = arr[k - 1], arr[i]
else: # k is odd
__A , __A = arr[k - 1], arr[0]
generate(k - 1 , a_ )
generate(len(a_ ) , a_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :Dict = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 15 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__=None , lowerCamelCase__=None ) -> Any:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class A_ :
_UpperCAmelCase : str = field(
metadata={'''help''': '''The csv file to plot.'''} , )
_UpperCAmelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''} , )
_UpperCAmelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''} , )
_UpperCAmelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Disable logarithmic scale when plotting'''} , )
_UpperCAmelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
} , )
_UpperCAmelCase : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''} , )
_UpperCAmelCase : Optional[List[str]] = list_field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Any:
try:
int(lowerCamelCase__ )
return True
except ValueError:
return False
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[Any]:
try:
float(lowerCamelCase__ )
return True
except ValueError:
return False
class A_ :
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Dict):
__lowerCamelCase : str = args
__lowerCamelCase : Any = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})
with open(self.args.csv_file ,newline='') as csv_file:
__lowerCamelCase : Any = csv.DictReader(SCREAMING_SNAKE_CASE__)
for row in reader:
__lowerCamelCase : Any = row['model']
self.result_dict[model_name]["bsz"].append(int(row['batch_size']))
self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']))
if can_convert_to_int(row['result']):
# value is not None
__lowerCamelCase : Tuple = int(row['result'])
elif can_convert_to_float(row['result']):
# value is not None
__lowerCamelCase : List[Any] = float(row['result'])
def lowerCAmelCase ( self : List[str]):
__lowerCamelCase , __lowerCamelCase : List[Any] = plt.subplots()
__lowerCamelCase : Union[str, Any] = 'Time usage' if self.args.is_time else 'Memory usage'
__lowerCamelCase : Optional[Any] = title_str + ' for training' if self.args.is_train else title_str + ' for inference'
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('log')
ax.set_yscale('log')
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter())
for model_name_idx, model_name in enumerate(self.result_dict.keys()):
__lowerCamelCase : int = sorted(set(self.result_dict[model_name]['bsz']))
__lowerCamelCase : Tuple = sorted(set(self.result_dict[model_name]['seq_len']))
__lowerCamelCase : int = self.result_dict[model_name]['result']
((__lowerCamelCase) , (__lowerCamelCase)) : List[str] = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
__lowerCamelCase : List[str] = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
__lowerCamelCase : int = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,dtype=SCREAMING_SNAKE_CASE__ ,)
else:
__lowerCamelCase : Union[str, Any] = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,dtype=np.floataa ,)
((__lowerCamelCase) , (__lowerCamelCase)) : Optional[Any] = (
('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
)
__lowerCamelCase : Tuple = np.asarray(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)[: len(SCREAMING_SNAKE_CASE__)]
plt.scatter(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}")
plt.plot(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,'--')
title_str += F" {label_model_name} vs."
__lowerCamelCase : List[Any] = title_str[:-4]
__lowerCamelCase : int = 'Time in s' if self.args.is_time else 'Memory in MB'
# plot
plt.title(SCREAMING_SNAKE_CASE__)
plt.xlabel(SCREAMING_SNAKE_CASE__)
plt.ylabel(SCREAMING_SNAKE_CASE__)
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file)
else:
plt.show()
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
__lowerCamelCase : Optional[Any] = HfArgumentParser(lowerCamelCase__ )
__lowerCamelCase : List[str] = parser.parse_args_into_dataclasses()[0]
__lowerCamelCase : str = Plot(args=lowerCamelCase__ )
plot.plot()
if __name__ == "__main__":
main()
| 73 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
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__":
SCREAMING_SNAKE_CASE :List[Any] = input('Enter numbers separated by a comma:\n').strip()
SCREAMING_SNAKE_CASE :List[Any] = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 15 | 0 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
_lowercase = '''naver-clova-ix/donut-base'''
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
A = DonutProcessor.from_pretrained(A_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[int]:
A = {
'name': 'John Doe',
'age': '99',
'city': 'Atlanta',
'state': 'GA',
'zip': '30301',
'phone': '123-4567',
'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
}
A = (
'<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
'<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
'<s_nicknames><s_nickname>Johnny</s_nickname>'
'<sep/><s_nickname>JD</s_nickname></s_nicknames>'
)
A = self.processor.tokenajson(A_ )
self.assertDictEqual(A_ ,A_ ) | 74 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = 2
@register_to_config
def __init__( self : str ,A : float = 0.02 ,A : float = 1_00 ,A : float = 1.0_07 ,A : float = 80 ,A : float = 0.05 ,A : float = 50 ,):
# standard deviation of the initial noise distribution
__A = sigma_max
# setable values
__A = None
__A = None
__A = None # sigma(t_i)
def UpperCamelCase_ ( self : str ,A : torch.FloatTensor ,A : Optional[int] = None ):
return sample
def UpperCamelCase_ ( self : Dict ,A : int ,A : Union[str, torch.device] = None ):
__A = num_inference_steps
__A = np.arange(0 ,self.num_inference_steps )[::-1].copy()
__A = torch.from_numpy(A ).to(A )
__A = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in self.timesteps
]
__A = torch.tensor(A ,dtype=torch.floataa ,device=A )
def UpperCamelCase_ ( self : Union[str, Any] ,A : torch.FloatTensor ,A : float ,A : Optional[torch.Generator] = None ):
if self.config.s_min <= sigma <= self.config.s_max:
__A = min(self.config.s_churn / self.num_inference_steps ,2**0.5 - 1 )
else:
__A = 0
# sample eps ~ N(0, S_noise^2 * I)
__A = self.config.s_noise * randn_tensor(sample.shape ,generator=A ).to(sample.device )
__A = sigma + gamma * sigma
__A = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def UpperCamelCase_ ( self : Dict ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_hat + sigma_hat * model_output
__A = (sample_hat - pred_original_sample) / sigma_hat
__A = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : Optional[int] ,A : torch.FloatTensor ,A : float ,A : float ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : torch.FloatTensor ,A : bool = True ,):
__A = sample_prev + sigma_prev * model_output
__A = (sample_prev - pred_original_sample) / sigma_prev
__A = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative)
return KarrasVeOutput(
prev_sample=A ,derivative=A ,pred_original_sample=A )
def UpperCamelCase_ ( self : List[Any] ,A : Dict ,A : List[str] ,A : str ):
raise NotImplementedError()
| 15 | 0 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
a_ : Dict = logging.getLogger(__name__)
def a_ ( __snake_case : str ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =git.Repo(search_parent_directories=__snake_case )
lowerCamelCase_ ={
'''repo_id''': str(__snake_case ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
}
with open(os.path.join(__snake_case , '''git_log.json''' ) , '''w''' ) as f:
json.dump(__snake_case , __snake_case , indent=4 )
def a_ ( __snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if params.n_gpu <= 0:
lowerCamelCase_ =0
lowerCamelCase_ =-1
lowerCamelCase_ =True
lowerCamelCase_ =False
return
assert torch.cuda.is_available()
logger.info('''Initializing GPUs''' )
if params.n_gpu > 1:
assert params.local_rank != -1
lowerCamelCase_ =int(os.environ['''WORLD_SIZE'''] )
lowerCamelCase_ =int(os.environ['''N_GPU_NODE'''] )
lowerCamelCase_ =int(os.environ['''RANK'''] )
# number of nodes / node ID
lowerCamelCase_ =params.world_size // params.n_gpu_per_node
lowerCamelCase_ =params.global_rank // params.n_gpu_per_node
lowerCamelCase_ =True
assert params.n_nodes == int(os.environ['''N_NODES'''] )
assert params.node_id == int(os.environ['''NODE_RANK'''] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowerCamelCase_ =1
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =0
lowerCamelCase_ =1
lowerCamelCase_ =1
lowerCamelCase_ =False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowerCamelCase_ =params.node_id == 0 and params.local_rank == 0
lowerCamelCase_ =params.n_nodes > 1
# summary
lowerCamelCase_ =F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes )
logger.info(PREFIX + '''Node ID : %i''' % params.node_id )
logger.info(PREFIX + '''Local rank : %i''' % params.local_rank )
logger.info(PREFIX + '''World size : %i''' % params.world_size )
logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node )
logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) )
logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) )
logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) )
logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('''Initializing PyTorch distributed''' )
torch.distributed.init_process_group(
init_method='''env://''' , backend='''nccl''' , )
def a_ ( __snake_case : Optional[int] ) -> Tuple:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 75 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
SCREAMING_SNAKE_CASE :Union[str, Any] = get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = "dummy_data"
snake_case_ = "datasets"
snake_case_ = False
def __init__( self : Optional[int] ,A : str ,A : str ,A : Union[Version, str] ,A : Optional[str] = None ,A : bool = False ,A : bool = True ,A : Optional[List[Callable]] = None ,):
__A = 0
__A = dataset_name
__A = cache_dir
__A = use_local_dummy_data
__A = config
# download_callbacks take a single url as input
__A = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
__A = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__A = str(A )
# to be downloaded
__A = None
__A = None
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
if self._dummy_file is None:
__A = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase_ ( self : Optional[Any] ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def UpperCamelCase_ ( self : List[Any] ):
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def UpperCamelCase_ ( self : Tuple ):
__A = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__A = cached_path(
A ,cache_dir=self.cache_dir ,extract_compressed_file=A ,force_extract=A )
return os.path.join(A ,self.dummy_file_name )
@property
def UpperCamelCase_ ( self : str ):
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def UpperCamelCase_ ( self : Any ):
if self._bucket_url is None:
__A = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def UpperCamelCase_ ( self : Tuple ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def UpperCamelCase_ ( self : List[str] ,A : List[Any] ,*A : Dict ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__A = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__A = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A ,A ):
return self.create_dummy_data_dict(A ,A )
elif isinstance(A ,(list, tuple) ):
return self.create_dummy_data_list(A ,A )
else:
return self.create_dummy_data_single(A ,A )
def UpperCamelCase_ ( self : str ,A : List[Any] ,*A : List[Any] ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : List[str] ,A : List[str] ,A : Tuple ):
return self.download_and_extract(A )
def UpperCamelCase_ ( self : Any ,A : Any ,*A : Optional[Any] ,**A : List[str] ):
return path
def UpperCamelCase_ ( self : str ):
return {}
def UpperCamelCase_ ( self : int ,A : int ,A : Tuple ):
__A = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A ,A ):
for single_url in single_urls:
download_callback(A )
else:
__A = single_urls
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A ,A ):
__A = [os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) ) for x in single_urls]
else:
__A = single_urls
__A = os.path.join(A ,urllib.parse.quote_plus(Path(A ).name ) )
__A = value
# make sure that values are unique
if all(isinstance(A ,A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
__A = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,A : str ):
__A = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__A = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,A ) ) for url in data_url )
__A = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
__A = [data_url[0]] * len(A )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(A )
return dummy_data_list
def UpperCamelCase_ ( self : str ,A : List[Any] ,A : Optional[Any] ):
for download_callback in self.download_callbacks:
download_callback(A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
__A = os.path.join(A ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(A ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : Dict ):
pass
def UpperCamelCase_ ( self : Optional[Any] ,A : List[Any] ):
def _iter_archive_members(A : Optional[Any] ):
# this preserves the order of the members inside the ZIP archive
__A = Path(self.dummy_file ).parent
__A = path.relative_to(A )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__A = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A )
__A = Path(A )
__A = _iter_archive_members(A ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(A ).as_posix(), file_path.open("rb" )
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
if not isinstance(A ,A ):
__A = [paths]
for path in paths:
if os.path.isfile(A ):
if os.path.basename(A ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A ):
if os.path.basename(A ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(A ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(A ,A )
| 15 | 0 |
import requests
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Dict = {"Content-Type": "application/json"}
SCREAMING_SNAKE_CASE : Optional[int] = requests.post(_a , json={"text": message_body} , headers=_a)
if response.status_code != 200:
SCREAMING_SNAKE_CASE : Dict = (
"Request to slack returned an error "
f"{response.status_code}, the response is:\n{response.text}"
)
raise ValueError(_a)
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>') | 76 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE :List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE :List[str] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 15 | 0 |
"""simple docstring"""
import math
class UpperCAmelCase_ :
def _UpperCAmelCase ( self , a , a ) -> int:
lowercase__ : Tuple = 0.0
lowercase__ : str = 0.0
for i in range(len(a ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def _UpperCAmelCase ( self , a , a , a , a ) -> list[list[int | float]]:
for i in range(len(a ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def a_ ( ):
'''simple docstring'''
lowercase__ : str = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
lowercase__ : int = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
lowercase__ : Optional[int] = SelfOrganizingMap()
lowercase__ : Dict = 3
lowercase__ : List[Any] = 0.5
for _ in range(_lowerCAmelCase ):
for j in range(len(_lowerCAmelCase ) ):
# training sample
lowercase__ : Tuple = training_samples[j]
# Compute the winning vector
lowercase__ : str = self_organizing_map.get_winner(_lowerCAmelCase , _lowerCAmelCase )
# Update the winning vector
lowercase__ : List[Any] = self_organizing_map.update(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# classify test sample
lowercase__ : int = [0, 0, 0, 1]
lowercase__ : Union[str, Any] = self_organizing_map.get_winner(_lowerCAmelCase , _lowerCAmelCase )
# results
print(f"""Clusters that the test sample belongs to : {winner}""" )
print(f"""Weights that have been trained : {weights}""" )
# running the main() function
if __name__ == "__main__":
main()
| 77 |
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE :List[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
SCREAMING_SNAKE_CASE :List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
SCREAMING_SNAKE_CASE :str = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Tuple:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
__A = new_id
# turn into Numpy arrays
__A = np.array(a_ )
__A = np.array(a_ )
if reduce_labels:
__A = 2_5_5
__A = label - 1
__A = 2_5_5
__A = label != ignore_index
__A = np.not_equal(a_ , a_ )
__A = pred_label[mask]
__A = np.array(a_ )[mask]
__A = pred_label[pred_label == label]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = np.histogram(a_ , bins=a_ , range=(0, num_labels - 1) )[0]
__A = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = False , ) -> Union[str, Any]:
"""simple docstring"""
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
__A = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(a_ , a_ ):
__A , __A , __A , __A = intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ = None , a_ = None , a_ = False , ) -> str:
"""simple docstring"""
__A , __A , __A , __A = total_intersect_and_union(
a_ , a_ , a_ , a_ , a_ , a_ )
# compute metrics
__A = {}
__A = total_area_intersect.sum() / total_area_label.sum()
__A = total_area_intersect / total_area_union
__A = total_area_intersect / total_area_label
__A = np.nanmean(a_ )
__A = np.nanmean(a_ )
__A = all_acc
__A = iou
__A = acc
if nan_to_num is not None:
__A = {metric: np.nan_to_num(a_ , nan=a_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self : List[Any] ):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) ,reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] ,)
def UpperCamelCase_ ( self : int ,A : Optional[Any] ,A : Optional[Any] ,A : int ,A : bool ,A : Optional[int] = None ,A : Optional[Dict[int, int]] = None ,A : bool = False ,):
__A = mean_iou(
results=A ,gt_seg_maps=A ,num_labels=A ,ignore_index=A ,nan_to_num=A ,label_map=A ,reduce_labels=A ,)
return iou_result
| 15 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
snake_case_ = 25_0004
snake_case_ = 25_0020
@require_sentencepiece
@require_tokenizers
class A_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = MBartaaTokenizer
__UpperCamelCase = MBartaaTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
def UpperCAmelCase__ ( self :Dict ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase = MBartaaTokenizer(lowercase_ , src_lang='en_XX' , tgt_lang='ro_RO' , keep_accents=lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self :Optional[int] ) -> Optional[Any]:
UpperCAmelCase = '<s>'
UpperCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :Any ) -> Optional[Any]:
UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(lowercase_ ) , 10_54 )
def UpperCAmelCase__ ( self :Optional[Any] ) -> Optional[int]:
self.assertEqual(self.get_tokenizer().vocab_size , 10_54 )
def UpperCAmelCase__ ( self :Any ) -> Dict:
UpperCAmelCase = MBartaaTokenizer(lowercase_ , src_lang='en_XX' , tgt_lang='ro_RO' , keep_accents=lowercase_ )
UpperCAmelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowercase_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
UpperCAmelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowercase_ , [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', '.'] , )
UpperCAmelCase = tokenizer.convert_tokens_to_ids(lowercase_ )
self.assertListEqual(
lowercase_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
UpperCAmelCase = tokenizer.convert_ids_to_tokens(lowercase_ )
self.assertListEqual(
lowercase_ , [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>', '.'] , )
@slow
def UpperCAmelCase__ ( self :Optional[Any] ) -> Any:
# fmt: off
UpperCAmelCase = {'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '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, 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, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 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=lowercase_ , model_name='facebook/mbart-large-50' , revision='d3913889c59cd5c9e456b269c376325eabad57e2' , )
def UpperCAmelCase__ ( self :List[str] ) -> Dict:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
UpperCAmelCase = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = tokenizer_r.save_pretrained(lowercase_ )
UpperCAmelCase = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
UpperCAmelCase = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
UpperCAmelCase = tokenizer_r.from_pretrained(lowercase_ )
UpperCAmelCase = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=True
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
UpperCAmelCase = tokenizer_p.save_pretrained(lowercase_ )
# Checks it save with the same files
self.assertSequenceEqual(lowercase_ , lowercase_ )
# Checks everything loads correctly in the same way
UpperCAmelCase = tokenizer_r.from_pretrained(lowercase_ )
UpperCAmelCase = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
# Save tokenizer rust, legacy_format=False
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = tokenizer_r.save_pretrained(lowercase_ , legacy_format=lowercase_ )
UpperCAmelCase = tokenizer_p.save_pretrained(lowercase_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
UpperCAmelCase = tokenizer_r.from_pretrained(lowercase_ )
UpperCAmelCase = tokenizer_p.from_pretrained(lowercase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowercase_ , lowercase_ ) )
shutil.rmtree(lowercase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class A_ ( unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = """facebook/mbart-large-50-one-to-many-mmt"""
__UpperCamelCase = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
__UpperCamelCase = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
__UpperCamelCase = [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
@classmethod
def UpperCAmelCase__ ( cls :int ) -> Optional[int]:
UpperCAmelCase = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
UpperCAmelCase = 1
return cls
def UpperCAmelCase__ ( self :Optional[Any] ) -> Optional[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 25_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 25_00_04 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 25_00_20 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN'] , 25_00_38 )
def UpperCAmelCase__ ( self :Dict ) -> Tuple:
UpperCAmelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowercase_ )
def UpperCAmelCase__ ( self :int ) -> List[Any]:
self.assertIn(lowercase_ , self.tokenizer.all_special_ids )
UpperCAmelCase = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
UpperCAmelCase = self.tokenizer.decode(lowercase_ , skip_special_tokens=lowercase_ )
UpperCAmelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.assertNotIn(self.tokenizer.eos_token , lowercase_ )
def UpperCAmelCase__ ( self :Tuple ) -> str:
UpperCAmelCase = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , lowercase_ )
UpperCAmelCase = 10
UpperCAmelCase = self.tokenizer(lowercase_ , max_length=lowercase_ , truncation=lowercase_ ).input_ids[0]
self.assertEqual(ids[0] , lowercase_ )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(lowercase_ ) , lowercase_ )
def UpperCAmelCase__ ( self :List[Any] ) -> List[Any]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_00_53, 25_00_01] )
def UpperCAmelCase__ ( self :List[str] ) -> Optional[Any]:
UpperCAmelCase = tempfile.mkdtemp()
UpperCAmelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowercase_ )
UpperCAmelCase = MBartaaTokenizer.from_pretrained(lowercase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowercase_ )
@require_torch
def UpperCAmelCase__ ( self :Tuple ) -> Any:
UpperCAmelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowercase_ , return_tensors='pt' )
UpperCAmelCase = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def UpperCAmelCase__ ( self :Tuple ) -> Dict:
UpperCAmelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowercase_ , truncation=lowercase_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
UpperCAmelCase = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowercase_ )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def UpperCAmelCase__ ( self :Tuple ) -> Any:
UpperCAmelCase = self.tokenizer(self.src_text , padding=lowercase_ , truncation=lowercase_ , max_length=3 , return_tensors='pt' )
UpperCAmelCase = self.tokenizer(
text_target=self.tgt_text , padding=lowercase_ , truncation=lowercase_ , max_length=10 , return_tensors='pt' )
UpperCAmelCase = targets['input_ids']
UpperCAmelCase = shift_tokens_right(lowercase_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCAmelCase__ ( self :Tuple ) -> Any:
UpperCAmelCase = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(lowercase_ ) , {
# en_XX, A, test, EOS
'input_ids': [[25_00_04, 62, 30_34, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_00_01,
} , )
| 78 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[str] = {'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE :Dict = {
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE :Optional[Any] = {
'AI-Sweden/gpt-sw3-126m': 2048,
'AI-Sweden/gpt-sw3-350m': 2048,
'AI-Sweden/gpt-sw3-1.6b': 2048,
'AI-Sweden/gpt-sw3-6.7b': 2048,
'AI-Sweden/gpt-sw3-20b': 2048,
}
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''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"]
def __init__( self : Optional[int] ,A : Optional[Any] ,A : Optional[int]=False ,A : int=False ,A : Union[str, Any]=False ,A : int=None ,A : Optional[Any]=None ,A : Union[str, Any]=None ,A : Optional[Any]=None ,A : Optional[Dict[str, Any]] = None ,**A : Tuple ,):
__A = {} if sp_model_kwargs is None else sp_model_kwargs
__A = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
__A = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
__A = "<|endoftext|>" if eos_token is None else eos_token
__A = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
__A = unk_token if pad_token is None else pad_token
__A = eos_token if bos_token is None else bos_token
else:
__A = "<pad>" if pad_token is None else pad_token
__A = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=A ,remove_space=A ,keep_accents=A ,bos_token=A ,eos_token=A ,unk_token=A ,pad_token=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = do_lower_case
__A = remove_space
__A = keep_accents
__A = vocab_file
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
# Used for whitespace normalization in input texts
# fmt : off
__A = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
__A = re.compile(
f'''[{''.join(map(A ,list(range(0 ,9 ) ) + list(range(11 ,32 ) ) + list(range(1_27 ,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )
def __getstate__( self : Optional[int] ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Optional[Any] ,A : Union[str, Any] ):
__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 )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCamelCase_ ( self : List[str] ):
return len(self.sp_model )
def UpperCamelCase_ ( self : int ,A : str ):
__A = self.non_printing_characters_re.sub("" ,A )
# Normalize whitespaces
__A = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
__A = unicodedata.normalize("NFC" ,A )
return text
def UpperCamelCase_ ( self : Union[str, Any] ,A : str ,**A : Optional[int] ):
__A = self.preprocess_text(A )
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : int ):
return self.sp_model.IdToPiece(A )
@staticmethod
def UpperCamelCase_ ( A : str ):
return out_string
def UpperCamelCase_ ( self : str ,A : List[str] ):
__A = []
__A = ""
__A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string
def UpperCamelCase_ ( self : str ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : List[str] ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : Union[str, List[str]] ,A : Union[str, bool] = False ):
if isinstance(A ,A ):
__A = self.preprocess_text(A )
__A = self.sp_model.encode(A )
else:
__A = [self.preprocess_text(A ) for t in text]
__A = self.sp_model.encode(A )
if return_tensors is True or return_tensors == "pt":
__A = torch.tensor(A )
return token_ids
def UpperCamelCase_ ( self : List[Any] ,A : Union[int, List[int]] ):
return self.sp_model.decode(A )
def UpperCamelCase_ ( self : List[str] ,A : "Conversation" ):
__A = [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
__A = (
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=A )
| 15 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
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
lowerCamelCase_ = 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''')
lowerCamelCase_ = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase_ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
snake_case = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
snake_case = field(
default=snake_case_ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
snake_case = field(
default=snake_case_ , metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''} , )
snake_case = field(default=snake_case_ , metadata={'''help''': '''A folder containing the training data.'''} )
snake_case = field(default=snake_case_ , metadata={'''help''': '''A folder containing the validation data.'''} )
snake_case = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
snake_case = field(default=32 , metadata={'''help''': '''The size of the square patches to use for masking.'''} )
snake_case = field(
default=0.6 , metadata={'''help''': '''Percentage of patches to mask.'''} , )
snake_case = field(
default=snake_case_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
snake_case = field(
default=snake_case_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
_A = {}
if self.train_dir is not None:
_A = self.train_dir
if self.validation_dir is not None:
_A = self.validation_dir
_A = data_files if data_files else None
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
snake_case = field(
default=snake_case_ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
'''checkpoint identifier on the hub. '''
'''Don\'t set if you want to train a model from scratch.'''
)
} , )
snake_case = field(
default=snake_case_ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_ )} , )
snake_case = field(
default=snake_case_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
snake_case = field(
default=snake_case_ , 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'''
)
} , )
snake_case = field(
default=snake_case_ , metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''} , )
snake_case = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
snake_case = field(default=snake_case_ , metadata={'''help''': '''Name or path of preprocessor config.'''} )
snake_case = field(
default=snake_case_ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
snake_case = field(
default=snake_case_ , metadata={
'''help''': (
'''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
)
} , )
snake_case = field(
default=snake_case_ , metadata={
'''help''': (
'''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
)
} , )
snake_case = field(
default=snake_case_ , metadata={'''help''': '''Stride to use for the encoder.'''} , )
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , __UpperCAmelCase : Optional[int]=192 , __UpperCAmelCase : Dict=32 , __UpperCAmelCase : int=4 , __UpperCAmelCase : int=0.6 ):
'''simple docstring'''
_A = input_size
_A = mask_patch_size
_A = model_patch_size
_A = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError("Input size must be divisible by mask patch size" )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError("Mask patch size must be divisible by model patch size" )
_A = self.input_size // self.mask_patch_size
_A = self.mask_patch_size // self.model_patch_size
_A = self.rand_size**2
_A = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self : Any ):
'''simple docstring'''
_A = np.random.permutation(self.token_count )[: self.mask_count]
_A = np.zeros(self.token_count , dtype=__UpperCAmelCase )
_A = 1
_A = mask.reshape((self.rand_size, self.rand_size) )
_A = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def __lowercase ( __lowercase ) -> str:
'''simple docstring'''
_A = torch.stack([example["pixel_values"] for example in examples] )
_A = torch.stack([example["mask"] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def __lowercase ( ) -> Dict:
'''simple docstring'''
_A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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.
_A , _A , _A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A = 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_mim" , __lowercase , __lowercase )
# 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()
_A = training_args.get_process_log_level()
logger.setLevel(__lowercase )
transformers.utils.logging.set_verbosity(__lowercase )
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.
_A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A = 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.
_A = 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.
_A = None if "validation" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __lowercase ) and data_args.train_val_split > 0.0:
_A = ds["train"].train_test_split(data_args.train_val_split )
_A = split["train"]
_A = split["test"]
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A = {
"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_or_path:
_A = AutoConfig.from_pretrained(model_args.config_name_or_path , **__lowercase )
elif model_args.model_name_or_path:
_A = AutoConfig.from_pretrained(model_args.model_name_or_path , **__lowercase )
else:
_A = CONFIG_MAPPING[model_args.model_type]()
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}''' )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(__lowercase , "decoder_type" ):
_A = "simmim"
# adapt config
_A = model_args.image_size if model_args.image_size is not None else config.image_size
_A = model_args.patch_size if model_args.patch_size is not None else config.patch_size
_A = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
"image_size": model_args.image_size,
"patch_size": model_args.patch_size,
"encoder_stride": model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
_A = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **__lowercase )
elif model_args.model_name_or_path:
_A = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **__lowercase )
else:
_A = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
_A = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
_A = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__lowercase , 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" )
_A = AutoModelForMaskedImageModeling.from_config(__lowercase )
if training_args.do_train:
_A = ds["train"].column_names
else:
_A = ds["validation"].column_names
if data_args.image_column_name is not None:
_A = data_args.image_column_name
elif "image" in column_names:
_A = "image"
elif "img" in column_names:
_A = "img"
else:
_A = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
_A = Compose(
[
Lambda(lambda __lowercase : img.convert("RGB" ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
_A = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(__lowercase ):
_A = [transforms(__lowercase ) for image in examples[image_column_name]]
_A = [mask_generator() for i in range(len(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:
_A = ds["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__lowercase )
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:
_A = (
ds["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__lowercase )
# Initialize our trainer
_A = Trainer(
model=__lowercase , args=__lowercase , train_dataset=ds["train"] if training_args.do_train else None , eval_dataset=ds["validation"] if training_args.do_eval else None , tokenizer=__lowercase , data_collator=__lowercase , )
# Training
if training_args.do_train:
_A = None
if training_args.resume_from_checkpoint is not None:
_A = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_A = last_checkpoint
_A = trainer.train(resume_from_checkpoint=__lowercase )
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:
_A = trainer.evaluate()
trainer.log_metrics("eval" , __lowercase )
trainer.save_metrics("eval" , __lowercase )
# Write model card and (optionally) push to hub
_A = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "masked-image-modeling",
"dataset": data_args.dataset_name,
"tags": ["masked-image-modeling"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__lowercase )
else:
trainer.create_model_card(**__lowercase )
if __name__ == "__main__":
main()
| 79 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a_ ) == np.iscomplexobj(a_ )
__A = np.iscomplexobj(a_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__A = False
__A = 0
__A = 0
__A = 1E12
while not convergence:
# Multiple matrix by the vector.
__A = np.dot(a_ , a_ )
# Normalize the resulting output vector.
__A = w / np.linalg.norm(a_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__A = vector.conj().T if is_complex else vector.T
__A = np.dot(a_ , np.dot(a_ , a_ ) )
# Check convergence.
__A = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__A = True
__A = lambda_
if is_complex:
__A = np.real(lambda_ )
return lambda_, vector
def UpperCAmelCase ( ) -> None:
"""simple docstring"""
__A = np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]] )
__A = np.array([4_1, 4, 2_0] )
__A = real_input_matrix.astype(np.complexaaa )
__A = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__A = np.array([4_1, 4, 2_0] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__A = real_input_matrix
__A = real_vector
elif problem_type == "complex":
__A = complex_input_matrix
__A = complex_vector
# Our implementation.
__A , __A = power_iteration(a_ , a_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__A , __A = np.linalg.eigh(a_ )
# Last eigenvalue is the maximum one.
__A = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__A = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a_ ) - np.abs(a_ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__ : Any = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Any = ['BloomTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[Any] = [
'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
'BloomForCausalLM',
'BloomModel',
'BloomPreTrainedModel',
'BloomForSequenceClassification',
'BloomForTokenClassification',
'BloomForQuestionAnswering',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
a__ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 80 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE :str = 'RegNetConfig'
# Base docstring
SCREAMING_SNAKE_CASE :List[str] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 1088, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE :Optional[int] = 'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE :Any = 'tabby, tabby cat'
SCREAMING_SNAKE_CASE :Optional[int] = [
'facebook/regnet-y-040',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : int ,A : int = 3 ,A : int = 1 ,A : int = 1 ,A : Optional[str] = "relu" ,**A : Dict ,):
super().__init__(**A )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
__A = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=A ,strides=A ,padding="VALID" ,groups=A ,use_bias=A ,name="convolution" ,)
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
__A = ACTaFN[activation] if activation is not None else tf.identity
def UpperCamelCase_ ( self : List[Any] ,A : Any ):
__A = self.convolution(self.padding(A ) )
__A = self.normalization(A )
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Tuple ,A : RegNetConfig ,**A : str ):
super().__init__(**A )
__A = config.num_channels
__A = TFRegNetConvLayer(
out_channels=config.embedding_size ,kernel_size=3 ,stride=2 ,activation=config.hidden_act ,name="embedder" ,)
def UpperCamelCase_ ( self : Tuple ,A : Optional[Any] ):
__A = shape_list(A )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
__A = tf.transpose(A ,perm=(0, 2, 3, 1) )
__A = self.embedder(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : int ,A : int = 2 ,**A : Tuple ):
super().__init__(**A )
__A = tf.keras.layers.ConvaD(
filters=A ,kernel_size=1 ,strides=A ,use_bias=A ,name="convolution" )
__A = tf.keras.layers.BatchNormalization(epsilon=1E-5 ,momentum=0.9 ,name="normalization" )
def UpperCamelCase_ ( self : Union[str, Any] ,A : tf.Tensor ,A : bool = False ):
return self.normalization(self.convolution(A ) ,training=A )
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Dict ,A : int ,A : int ,**A : str ):
super().__init__(**A )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
__A = [
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="relu" ,name="attention.0" ),
tf.keras.layers.ConvaD(filters=A ,kernel_size=1 ,activation="sigmoid" ,name="attention.2" ),
]
def UpperCamelCase_ ( self : Dict ,A : List[Any] ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
__A = self.pooler(A )
for layer_module in self.attention:
__A = layer_module(A )
__A = hidden_state * pooled
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : Optional[int] ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.2" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : int ,A : Optional[int] ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,A : int ,A : int ,A : int = 1 ,**A : str ):
super().__init__(**A )
__A = in_channels != out_channels or stride != 1
__A = max(1 ,out_channels // config.groups_width )
__A = (
TFRegNetShortCut(A ,stride=A ,name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" ,name="shortcut" )
)
__A = [
TFRegNetConvLayer(A ,kernel_size=1 ,activation=config.hidden_act ,name="layer.0" ),
TFRegNetConvLayer(
A ,stride=A ,groups=A ,activation=config.hidden_act ,name="layer.1" ),
TFRegNetSELayer(A ,reduced_channels=int(round(in_channels / 4 ) ) ,name="layer.2" ),
TFRegNetConvLayer(A ,kernel_size=1 ,activation=A ,name="layer.3" ),
]
__A = ACTaFN[config.hidden_act]
def UpperCamelCase_ ( self : Dict ,A : Any ):
__A = hidden_state
for layer_module in self.layers:
__A = layer_module(A )
__A = self.shortcut(A )
hidden_state += residual
__A = self.activation(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : List[str] ,A : RegNetConfig ,A : int ,A : int ,A : int = 2 ,A : int = 2 ,**A : Optional[int] ):
super().__init__(**A )
__A = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
__A = [
# downsampling is done in the first layer with stride of 2
layer(A ,A ,A ,stride=A ,name="layers.0" ),
*[layer(A ,A ,A ,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def UpperCamelCase_ ( self : Any ,A : List[str] ):
for layer_module in self.layers:
__A = layer_module(A )
return hidden_state
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Any ,A : RegNetConfig ,**A : List[str] ):
super().__init__(**A )
__A = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
A ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,name="stages.0" ,) )
__A = zip(config.hidden_sizes ,config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(A ,config.depths[1:] ) ):
self.stages.append(TFRegNetStage(A ,A ,A ,depth=A ,name=f'''stages.{i+1}''' ) )
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor ,A : bool = False ,A : bool = True ):
__A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__A = hidden_states + (hidden_state,)
__A = stage_module(A )
if output_hidden_states:
__A = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=A ,hidden_states=A )
@keras_serializable
class UpperCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
snake_case_ = RegNetConfig
def __init__( self : int ,A : Optional[int] ,**A : Dict ):
super().__init__(**A )
__A = config
__A = TFRegNetEmbeddings(A ,name="embedder" )
__A = TFRegNetEncoder(A ,name="encoder" )
__A = tf.keras.layers.GlobalAveragePoolingaD(keepdims=A ,name="pooler" )
@unpack_inputs
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : bool = False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.embedder(A ,training=A )
__A = self.encoder(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = encoder_outputs[0]
__A = self.pooler(A )
# Change to NCHW output format have uniformity in the modules
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
__A = tf.transpose(A ,perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
__A = tuple([tf.transpose(A ,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=A ,pooler_output=A ,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,)
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = RegNetConfig
snake_case_ = "regnet"
snake_case_ = "pixel_values"
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) ,dtype=tf.floataa )}
SCREAMING_SNAKE_CASE :Dict = R'\n Parameters:\n This model is a Tensorflow\n [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n behavior.\n config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'
SCREAMING_SNAKE_CASE :Dict = R'\n Args:\n pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConveNextImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] ,A : RegNetConfig ,*A : List[Any] ,**A : str ):
super().__init__(A ,*A ,**A )
__A = TFRegNetMainLayer(A ,name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=A ,config_class=_CONFIG_FOR_DOC ,modality="vision" ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def UpperCamelCase_ ( self : Tuple ,A : tf.Tensor ,A : Optional[bool] = None ,A : Optional[bool] = None ,A : int=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
pixel_values=A ,output_hidden_states=A ,return_dict=A ,training=A ,)
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state ,pooler_output=outputs.pooler_output ,hidden_states=outputs.hidden_states ,)
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , __SCREAMING_SNAKE_CASE , )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[int] ,A : RegNetConfig ,*A : str ,**A : Tuple ):
super().__init__(A ,*A ,**A )
__A = config.num_labels
__A = TFRegNetMainLayer(A ,name="regnet" )
# classification head
__A = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels ,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=A ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def UpperCamelCase_ ( self : List[str] ,A : tf.Tensor = None ,A : tf.Tensor = None ,A : bool = None ,A : bool = None ,A : Union[str, Any]=False ,):
__A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__A = return_dict if return_dict is not None else self.config.use_return_dict
__A = self.regnet(
A ,output_hidden_states=A ,return_dict=A ,training=A )
__A = outputs.pooler_output if return_dict else outputs[1]
__A = self.classifier[0](A )
__A = self.classifier[1](A )
__A = None if labels is None else self.hf_compute_loss(labels=A ,logits=A )
if not return_dict:
__A = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=A ,logits=A ,hidden_states=outputs.hidden_states )
| 15 | 0 |
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