





'''simple docstring'''






import datasets


lowerCAmelCase       		=      """\
@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       		=      """\
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       		=      """
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						(							a_						:  Tuple      ,a_						:  List[str]	):
		return (preds == labels).mean()

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



	def   _lowerCamelCase    (   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   _lowerCamelCase    (   self		,      a_		,      a_	):
			return {"accuracy": simple_accuracy(a_		,      a_	)}










'''simple docstring'''






from __future__ import annotations






def      __A						(							a_						:  float      ,a_						:  float      ,a_						:  float      ,):
		if (stress, tangential_force, area).count(0	) != 1:
				raise ValueError("You cannot supply more or less than 2 values"	)
		elif stress < 0:
				raise ValueError("Stress cannot be negative"	)
		elif tangential_force < 0:
				raise ValueError("Tangential Force cannot be negative"	)
		elif area < 0:
				raise ValueError("Area cannot be negative"	)
		elif stress == 0:
				return (
				    "stress",
				    tangential_force / area,
				)
		elif tangential_force == 0:
				return (
				    "tangential_force",
				    stress * area,
				)
		else:
				return (
				    "area",
				    tangential_force / stress,
				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



