





import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


_A					:				List[Any]				=			"""\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


_A					:				List[Any]				=			"""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


_A					:				Optional[int]				=			r"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""



@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION			,     _KWARGS_DESCRIPTION    )
class 					__snake_case	(  datasets.Metric    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        return datasets.MetricInfo(
            description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''     ),
                    '''references''': datasets.Value('''string'''     ),
                }     )   ,				homepage='''https://github.com/hendrycks/math'''   ,				codebase_urls=['''https://github.com/hendrycks/math''']   ,				)




    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			0.0
        for i, j in zip(A_   ,				A_     ):
            n_correct += 1.0 if math_equivalence.is_equiv(A_   ,				A_     ) else 0.0
        SCREAMING_SNAKE_CASE__											=			n_correct / len(A_     )
        return {
            "accuracy": accuracy,
        }





'''simple docstring'''





from __future__ import annotations





def   a_   (		_UpperCAmelCase    :      list[int]				)    ->  bool:
      return len(set(_UpperCAmelCase				)				) == len(_UpperCAmelCase				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



