

'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str    ):
  try:
    UpperCAmelCase    				=						float(SCREAMING_SNAKE_CASE_    )
  except ValueError:
    raise ValueError('Please enter a valid number'    )
  UpperCAmelCase    				=						decimal - int(SCREAMING_SNAKE_CASE_    )
  if fractional_part == 0:
    return int(SCREAMING_SNAKE_CASE_    ), 1
  else:
    UpperCAmelCase    				=						len(str(SCREAMING_SNAKE_CASE_    ).split('.'    )[1]    )
    UpperCAmelCase    				=						int(decimal * (10**number_of_frac_digits)    )
    UpperCAmelCase    				=						10**number_of_frac_digits
    UpperCAmelCase				,   UpperCAmelCase    				=						denominator, numerator
    while True:
      UpperCAmelCase    				=						dividend % divisor
      if remainder == 0:
        break
      UpperCAmelCase				,   UpperCAmelCase    				=						divisor, remainder
    UpperCAmelCase				,   UpperCAmelCase    				=						numerator / divisor, denominator / divisor
    return int(SCREAMING_SNAKE_CASE_    ), int(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
  print(F'''{decimal_to_fraction(2) = }''')
  print(F'''{decimal_to_fraction(89.0) = }''')
  print(F'''{decimal_to_fraction('67') = }''')
  print(F'''{decimal_to_fraction('45.0') = }''')
  print(F'''{decimal_to_fraction(1.5) = }''')
  print(F'''{decimal_to_fraction('6.25') = }''')
  print(F'''{decimal_to_fraction('78td') = }''')




import datasets


A		: Optional[int]								=     '''\
@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",
}
'''

A		: Optional[int]								=     '''\
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).
'''

A		: str								=     '''
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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     str:
						return (preds == labels).mean()



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






			def    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return {"accuracy": simple_accuracy(__UpperCamelCase				,			__UpperCamelCase  )}