


import datasets


_lowerCAmelCase:						List[str]   				=       '\\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'

_lowerCAmelCase:						List[Any]   				=       '\\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'

_lowerCAmelCase:						Union[str, Any]   				=       '\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(					__a				:						Optional[int]    ,     __a				:						List[Any]     ):
							return (preds == labels).mean()







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





				def 						__UpperCamelCase    (				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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> List[str]:
											return {"accuracy": simple_accuracy(lowercase_       ,  lowercase_)}






'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Optional[int]      ,*_UpperCAmelCase   :			List[Any]      ,**_UpperCAmelCase   :			Union[str, Any]						):
									warnings.warn(
									    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use GLPNImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




