





import datasets


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

snake_case_				=			'\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'

snake_case_				=			'\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    \'accuracy\': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric("xnli")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[Any]       )      ->				Dict:
   return (preds == labels).mean()







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


      def    a (self       :      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    a (self       :      Any				,			a__       :      List[str]				,			a__       :      Dict      ):





         """simple docstring"""



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








def lowerCamelCase__    (							)      ->				int:
   return [
       a * b * (1000 - a - b)
       for a in range(1       ,      999       )
       for b in range(snake_case_       ,      999       )
       if (a * a + b * b == (1000 - a - b) ** 2)
   ][0]


if __name__ == "__main__":
    print(F'{solution() = }')

