import datasets


__SCREAMING_SNAKE_CASE						:   Dict         =			'\\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'

__SCREAMING_SNAKE_CASE						:   Union[str, 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'

__SCREAMING_SNAKE_CASE						:   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  snake_case						(__lowercase ,      __lowercase						)	->	Tuple:

						'''simple docstring'''



						return (preds == labels).mean()



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION		)
class        lowercase_   (					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				,    lowercase_				,    lowercase_    ):
													return {"accuracy": simple_accuracy(lowercase_				,    lowercase_    )}
from __future__ import annotations
def  snake_case						(__lowercase ,      __lowercase						)	->	float:

						'''simple docstring'''



						_snake_case						:					Any    =   sorted(numsa + numsa						)
						_snake_case    ,_snake_case						:					Any    =   divmod(len(__lowercase						) ,      2						)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[float(x) for x in input('Enter the elements of first array: ').split()]
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			[float(x) for x in input('Enter the elements of second array: ').split()]
				print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')