

import datasets


__A      		=     '''\\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'''

__A      		=     '''\\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'''

__A      		=     '''\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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



		"""simple docstring"""






		return (preds == labels).mean()



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



						def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					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 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Any      , A__				:    Dict		)   ->					Union[str, Any]:
								return {"accuracy": simple_accuracy(lowercase_      , lowercase_		)}




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    int		)   ->					None:
        warnings.warn(
            '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DeformableDetrImageProcessor instead.'''      , A__      , )
        super().__init__(*A__      , **A__		)

