



"""simple docstring"""

import datasets


lowercase__    :  Any							    =	'''\
@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",
}
'''

lowercase__    :  str							    =	'''\
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).
'''

lowercase__    :  Tuple							    =	'''
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 __lowercase			(							_a		,			_a    ):
    return (preds == labels).mean()






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

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










"""simple docstring"""

import numpy as np




def __lowercase			(							_a    ):
    return (2 / (1 + np.exp(-2 * vector    ))) - 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





