


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


snake_case							:       List[str]										=	{}


try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Tuple										=	['''MLukeTokenizer''']

if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mluke import MLukeTokenizer


else:
     import sys

     snake_case							:       Optional[Any]										=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









import datasets


snake_case							:       int										=	'''\
@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",
}
'''

snake_case							:       Tuple										=	'''\
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).
'''

snake_case							:       Union[str, Any]										=	'''
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 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						List[str]		):




      """simple docstring"""





      return (preds == labels).mean()







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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return {"accuracy": simple_accuracy(_lowerCamelCase   ,    _lowerCamelCase       )}





