




import datasets


lowerCamelCase    :		Dict			   =       "\\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"

lowerCamelCase    :		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"

lowerCamelCase    :		str			   =       "\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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



      return (preds == labels).mean()







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



 '''simple docstring'''


 def     a__			(    self :  Optional[int]  )       ->     Optional[int]:

       """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 :  Optional[Any]						,							A_ :  Union[str, Any]						,							A_ :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






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









import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	float					):


      '''simple docstring'''



      return math.pow(lowercase     ,							2					) - a

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      return 2 * x

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float					):


      '''simple docstring'''



      lowerCamelCase_							=   2.0

      while start <= a:
            lowerCamelCase_							=   math.pow(lowercase     ,							2					)

      return start

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	float     ,							lowercase     :	int = 99_99     ,							lowercase     :	float = 0.00_0000_0000_0001					):


      '''simple docstring'''



      if a < 0:
            raise ValueError('math domain error'					)

      lowerCamelCase_							=   get_initial_point(lowercase					)

      for _ in range(lowercase					):
            lowerCamelCase_							=   value
            lowerCamelCase_							=   value - fx(lowercase     ,							lowercase					) / fx_derivative(lowercase					)
            if abs(prev_value - value					) < tolerance:
                  return value

      return value


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

 testmod()



