
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    a_ :
			a	:			str					   =							None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )
									_lowercase					       =					json.loads(feat_extract.to_json_string()  )
									for key, value in self.feat_extract_dict.items():
															self.assertEqual(obj[key]				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )

									with tempfile.TemporaryDirectory() as tmpdirname:
															_lowercase					       =					os.path.join(__UpperCamelCase				,			"""feat_extract.json"""  )
															feat_extract_first.to_json_file(__UpperCamelCase  )
															_lowercase					       =					self.feature_extraction_class.from_json_file(__UpperCamelCase  )

									self.assertEqual(feat_extract_second.to_dict()				,			feat_extract_first.to_dict()  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class(**self.feat_extract_dict  )

									with tempfile.TemporaryDirectory() as tmpdirname:
															_lowercase					       =					feat_extract_first.save_pretrained(__UpperCamelCase  )[0]
															check_json_file_has_correct_format(__UpperCamelCase  )
															_lowercase					       =					self.feature_extraction_class.from_pretrained(__UpperCamelCase  )

									self.assertEqual(feat_extract_second.to_dict()				,			feat_extract_first.to_dict()  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.feature_extraction_class()
									self.assertIsNotNone(__UpperCamelCase  )

import datasets


A		: Optional[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",
}
'''

A		: Optional[int]								=     '''\
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).
'''

A		: str								=     '''
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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     str:
						return (preds == labels).mean()



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






			def    UpperCamelCase_				(      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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return {"accuracy": simple_accuracy(__UpperCamelCase				,			__UpperCamelCase  )}