


def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
  raise ValueError('Input series is not valid, valid series - [2, 4, 6]'						)
 if len(SCREAMING_SNAKE_CASE__						) == 0:
  raise ValueError('Input list must be a non empty list'						)
 if len(SCREAMING_SNAKE_CASE__						) == 1:
  return True
 __lowerCamelCase		:							Tuple		 =						series[1] - series[0]
 for index in range(len(SCREAMING_SNAKE_CASE__						) - 1						):
  if series[index + 1] - series[index] != common_diff:
   return False
 return True
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
  raise ValueError('Input series is not valid, valid series - [2, 4, 6]'						)
 if len(SCREAMING_SNAKE_CASE__						) == 0:
  raise ValueError('Input list must be a non empty list'						)
 __lowerCamelCase		:							Union[str, Any]		 =						0
 for val in series:
  answer += val
 return answer / len(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





import datasets


lowercase_	   =     '\\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'

lowercase_	   =     '\\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'

lowercase_	   =     '\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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return (preds == labels).mean()



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




      '''simple docstring'''


      def 					_snake_case     (    self:	Union[str, 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 					_snake_case     (    self:	int						,				a:	Optional[Any]						,				a:	Optional[Any]    ):
       return {"accuracy": simple_accuracy(a						,				a    )}

