




import datasets


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

SCREAMING_SNAKE_CASE__       :      int  =   '\\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'

SCREAMING_SNAKE_CASE__       :      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      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       return (preds == labels).mean()




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

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           return {"accuracy": simple_accuracy(UpperCamelCase__     ,  UpperCamelCase__     )}












import random

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> tuple:
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					[], [], []
       for element in data:
              if element < pivot:
                     less.append(_SCREAMING_SNAKE_CASE    )
              elif element > pivot:
                     greater.append(_SCREAMING_SNAKE_CASE    )
              else:
                     equal.append(_SCREAMING_SNAKE_CASE    )
       return less, equal, greater


def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(_SCREAMING_SNAKE_CASE    ) or index < 0:
              return None

       lowerCamelCase   :      List[Any]			 =					items[random.randint(0 ,len(_SCREAMING_SNAKE_CASE    ) - 1    )]
       lowerCamelCase   :      Dict			 =					0
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					_partition(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					len(_SCREAMING_SNAKE_CASE    )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # must be in larger
       else:
              return quick_select(_SCREAMING_SNAKE_CASE ,index - (m + count)    )






