




'''simple docstring'''



import datasets


lowerCamelCase_			=						'\\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_			=						'\\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_			=						'\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_			(							__A    :			Union[str, Any]	,					__A    :			List[str]  )			->			Union[str, Any]:
   return (preds == labels).mean()







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


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     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 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      return {"accuracy": simple_accuracy(__lowerCamelCase  ,				__lowerCamelCase   )}







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			list  )			->			list:
   if len(__A  ) <= 1:
      return lst

   _SCREAMING_SNAKE_CASE	=				1

   while i < len(__A  ):
      if lst[i - 1] <= lst[i]:
         i += 1
      else:
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				lst[i], lst[i - 1]
         i -= 1
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				1

   return lst


if __name__ == "__main__":
 lowerCamelCase_			=						input('Enter numbers separated by a comma:\n').strip()
 lowerCamelCase_			=						[int(item) for item in user_input.split(',')]
 print(gnome_sort(unsorted))

