

'''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			(   __magic_name__						:     Dict   , __magic_name__						:     Dict   , __magic_name__						:     Optional[Any]=1024   , __magic_name__						:     Dict=1024   , __magic_name__						:     Dict=False   , **__magic_name__						:     List[Any]						)				->  str:
						lowercase :							int    		=AutoTokenizer.from_pretrained(__magic_name__						)
						lowercase :							int    		=SeqaSeqDataset(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , type_path='''train'''   , **__magic_name__						)
						lowercase :							List[Any]    		=tok.pad_token_id

						def get_lens(__magic_name__						:     List[Any]						):
												lowercase :							str    		=tqdm(
												    DataLoader(__magic_name__   , batch_size=512   , num_workers=8   , shuffle=__magic_name__   , collate_fn=ds.collate_fn						)   , desc=str(ds.len_file						)   , )
												lowercase :							Optional[int]    		=[]
												for batch in dl:
																		lowercase :							Optional[Any]    		=batch['''input_ids'''].ne(__magic_name__						).sum(1						).tolist()
																		lowercase :							Optional[int]    		=batch['''labels'''].ne(__magic_name__						).sum(1						).tolist()
																		if consider_target:
																								for src, tgt in zip(__magic_name__   , __magic_name__						):
																														max_lens.append(max(__magic_name__   , __magic_name__						)						)
																		else:
																								max_lens.extend(__magic_name__						)
												return max_lens

						lowercase :							int    		=get_lens(__magic_name__						)
						lowercase :							List[str]    		=SeqaSeqDataset(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , type_path='''val'''   , **__magic_name__						)
						lowercase :							Dict    		=get_lens(__magic_name__						)
						pickle_save(__magic_name__   , train_ds.len_file						)
						pickle_save(__magic_name__   , val_ds.len_file						)


if __name__ == "__main__":
							fire.Fire(save_len_file)








'''simple docstring'''





import datasets


UpperCamelCase_    						=       """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

UpperCamelCase_    						=       """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

UpperCamelCase_    						=       """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""






def 				_lowerCAmelCase			(   __magic_name__						:     Dict   , __magic_name__						:     Union[str, Any]						)				->  Union[str, Any]:
						return (preds == labels).mean()







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,							_KWARGS_DESCRIPTION      )
class 		__SCREAMING_SNAKE_CASE							(			datasets.Metric      ):







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return datasets.MetricInfo(
										    description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''   ),
										            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''   ),
										        }   )		,					codebase_urls=[]		,					reference_urls=[]		,					format='''numpy'''		,					)







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										return {"accuracy": simple_accuracy(UpperCAmelCase__		,					UpperCAmelCase__   )}





