




import datasets


__lowerCamelCase   :						str				=			'''\
@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",
}
'''

__lowerCamelCase   :						Optional[Any]				=			'''\
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).
'''

__lowerCamelCase   :						Optional[Any]				=			'''
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 		lowercase__   (		__A:	Optional[Any]							,__A:	int				):





					'''simple docstring'''






					return (preds == labels).mean()


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






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Dict:
								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			:   int       ,						lowerCamelCase_			:   Any       ,						lowerCamelCase_			:   Tuple  )						->   Optional[Any]:
								return {"accuracy": simple_accuracy(lowerCamelCase_       ,						lowerCamelCase_  )}









import requests







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{'''Content-Type''': '''application/json'''}
					__magic_name__							:	Union[str, Any]    							=							requests.post(__A							,json={'''text''': message_body}							,headers=__A				)
					if response.status_code != 2_0_0:
										__magic_name__							:	int    							=							(
										    '''Request to slack returned an error '''
										    F'''{response.status_code}, the response is:\n{response.text}'''
										)
										raise ValueError(__A				)


if __name__ == "__main__":
	# Set the slack url to the one provided by Slack when you create the webhook at
	# https://my.slack.com/services/new/incoming-webhook/
	send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')



