




import datasets


UpperCamelCase__					    =    """\
@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\",
}
"""

UpperCamelCase__					    =    """\
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).
"""

UpperCamelCase__					    =    """
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 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       str  ):
					return (preds == labels).mean()


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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""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        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



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










from math import ceil, sqrt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = 0

					for outer_width in range(3   ,   (limit // 4) + 2  ):
										if outer_width**2 > limit:
															__lowerCAmelCase            = max(ceil(sqrt(outer_width**2 - limit  )  )   ,   1  )
										else:
															__lowerCAmelCase            = 1
										if (outer_width - hole_width_lower_bound) % 2:
															hole_width_lower_bound += 1

										answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

					return answer


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




