





"""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 	a_    (							_lowercase	,						_lowercase       ):
						return (preds == labels).mean()






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






				def     snake_case     (			self		:   List[str]							)     ->				Any:




										'''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     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   int							)     ->				int:




										'''simple docstring'''



										return {"accuracy": simple_accuracy(lowerCAmelCase__,			lowerCAmelCase__							)}












"""simple docstring"""




from functools import lru_cache





@lru_cache
def 	a_    (							_lowercase       ):
						if num < 0:
												raise ValueError('''Number should not be negative.'''       )

						return 1 if num in (0, 1) else num * factorial(num - 1       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



