


import datasets


__UpperCamelCase    :						int     =							'\\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'

__UpperCamelCase    :						Union[str, Any]     =							'\\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'

__UpperCamelCase    :						List[Any]     =							'\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   _UpperCAmelCase       (     UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      return (preds == labels).mean()




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

   '''simple docstring'''







   def   _snake_case    (  self  : Optional[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  : str       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Optional[Any]							):






         '''simple docstring'''



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







def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						len(UpperCAmelCase      )
      for _ in range(UpperCAmelCase      ):
            for i in range(_ % 2						,	arr_size - 1						,	2      ):
                  if arr[i + 1] < arr[i]:
                        __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						arr[i + 1], arr[i]

      return arr


if __name__ == "__main__":
    __UpperCamelCase    :						str     =							list(range(10, 0, -1))
    print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')



