


'''simple docstring'''






import datasets


a_   :      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"

a_   :      Dict				    =      "\\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_   :      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 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Tuple						)				->					Union[str, Any]:




     '''simple docstring'''



     return (preds == labels).mean()






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,			_KWARGS_DESCRIPTION    )
class 	a       (       datasets.Metric    ):
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, 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 ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      return {"accuracy": simple_accuracy(__magic_name__ ,			__magic_name__  )}





'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline






@pytest.mark.integration
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
          with pytest.raises(lowerCAmelCase__						):
               requests.request('GET'			,  'https://huggingface.co'						)
          with pytest.raises(requests.exceptions.ConnectTimeout						):
               requests.request('GET'			,  'https://huggingface.co'			,  timeout=1.0						)






@pytest.mark.integration
def 		_A			()				->					Any:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_FAILS						):
          with pytest.raises(requests.exceptions.ConnectionError						):
               requests.request('GET'			,  'https://huggingface.co'						)






def 		_A			()				->					Dict:




     '''simple docstring'''



     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
          with pytest.raises(lowerCAmelCase__						):
               http_head('https://huggingface.co'						)

