


'''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  _snake_case       (					A			,   A	)						->   Any:
							return (preds == labels).mean()






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





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														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   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														return {"accuracy": simple_accuracy(lowerCamelCase_ ,   lowerCamelCase_       )}



'''simple docstring'''






def  _snake_case       (					A = 10	)						->   str:
							if not isinstance(A			,   A	) or n < 0:
														raise ValueError('''Invalid input'''	)
							lowerCAmelCase__	       =     10**n
							lowerCAmelCase__	       =     28433 * (pow(2			,   7830457			,   A	)) + 1
							return str(number % modulus	)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print(f"""{solution(10) = }""")