"""simple docstring"""







import datasets


__SCREAMING_SNAKE_CASE								="\\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								="\\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								="\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     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     return (preds == labels).mean()







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION						)
class  UpperCamelCase	(    datasets.Metric						):
   def     _UpperCAmelCase     (    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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[int]:


        '''simple docstring'''


        return {"accuracy": simple_accuracy(__UpperCamelCase  ,__UpperCamelCase    )}


"""simple docstring"""







from argparse import ArgumentParser

from .env import EnvironmentCommand



def     lowercase__( ):
     lowercase_			:       List[Any]									=			ArgumentParser('Diffusers CLI tool'  ,    usage='diffusers-cli <command> [<args>]'   )
     lowercase_			:       int									=			parser.add_subparsers(help='diffusers-cli command helpers'   )

     # Register commands
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE   )

     # Let's go
     lowercase_			:       str									=			parser.parse_args()

     if not hasattr(__SCREAMING_SNAKE_CASE  ,    'func'   ):
          parser.print_help()
          exit(1   )

     # Run
     lowercase_			:       Optional[Any]									=			args.func(__SCREAMING_SNAKE_CASE   )
     service.run()


if __name__ == "__main__":
       main()

