






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

import datasets


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		"""\
@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}
}
"""


SCREAMING_SNAKE_CASE__   :      str				=		"""\
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.
"""


SCREAMING_SNAKE_CASE__   :      List[str]				=		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     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   0.0
								for i, j in zip(__lowerCAmelCase							,       __lowerCAmelCase  ):
												n_correct += 1.0 if math_equivalence.is_equiv(__lowerCAmelCase							,       __lowerCAmelCase  ) else 0.0
								__magic_name__		:Dict   =   n_correct / len(__lowerCAmelCase  )
								return {
								    "accuracy": accuracy,
								}










def 						_lowerCAmelCase							(     _lowerCAmelCase = 100			)    ->						int:


						'''simple docstring'''






						__snake_case				=		n * (n + 1) * (2 * n + 1) / 6
						__snake_case				=		(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares			)


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





