


"""simple docstring"""
import datasets


__SCREAMING_SNAKE_CASE							:      int						=      '\\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							:      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							:      int						=      '\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			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							return (preds == labels).mean()





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





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[Any]:



														"""simple docstring"""



														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 						lowerCAmelCase    (			self							: Any						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: str    )   ->Tuple:



														"""simple docstring"""



														return {"accuracy": simple_accuracy(UpperCAmelCase_						,    UpperCAmelCase_    )}







"""simple docstring"""
from collections import defaultdict
from math import gcd







def   _a			( _SCREAMING_SNAKE_CASE = 1_500_000			)      ->			int:
							snake_case_          =						defaultdict(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						2
							while 2 * euclid_m * (euclid_m + 1) <= limit:
														for euclid_n in range((euclid_m % 2) + 1			,	_SCREAMING_SNAKE_CASE			,	2			):
																					if gcd(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) > 1:
																												continue
																					snake_case_          =						2 * euclid_m * (euclid_m + euclid_n)
																					for perimeter in range(_SCREAMING_SNAKE_CASE			,	limit + 1			,	_SCREAMING_SNAKE_CASE			):
																												frequencies[perimeter] += 1
														euclid_m += 1
							return sum(1 for frequency in frequencies.values() if frequency == 1			)


if __name__ == "__main__":
				print(f"""{solution() = }""")



