




'''simple docstring'''



import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
UpperCAmelCase_							:	Union[str, Any]	     =		logging.getLogger(__name__)

if __name__ == "__main__":
     UpperCAmelCase_							:	Optional[int]	     =		argparse.ArgumentParser(
         description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
     )
     parser.add_argument(
         '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
     )
     parser.add_argument(
         '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
     )
     parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
     UpperCAmelCase_							:	Union[str, Any]	     =		parser.parse_args()

     logger.info(F"""Loading data from {args.data_file}""")
     with open(args.data_file, '''rb''') as fp:
          UpperCAmelCase_							:	Optional[int]	     =		pickle.load(fp)

     logger.info('''Counting occurrences for MLM.''')
     UpperCAmelCase_							:	Union[str, Any]	     =		Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     UpperCAmelCase_							:	Dict	     =		[0] * args.vocab_size
     for k, v in counter.items():
          UpperCAmelCase_							:	str	     =		v

     logger.info(F"""Dump to {args.token_counts_dump}""")
     with open(args.token_counts_dump, '''wb''') as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)









'''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__   )}





