


"""simple docstring"""







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

import datasets


lowerCAmelCase__				    =       '''\
@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}
}
'''


lowerCAmelCase__				    =       '''\
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.
'''


lowerCAmelCase__				    =       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 				SCREAMING_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		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							0.0
         for i, j in zip(__a		,					__a						):
           n_correct += 1.0 if math_equivalence.is_equiv(__a		,					__a						) else 0.0
         lowerCAmelCase					:					Optional[Any]								=							n_correct / len(__a						)
         return {
             "accuracy": accuracy,
         }









"""simple docstring"""







import re

from filelock import FileLock


try:
     import nltk

     lowerCAmelCase__				    =       True
except (ImportError, ModuleNotFoundError):
     lowerCAmelCase__				    =       False

if NLTK_AVAILABLE:
     with FileLock('''.lock''') as lock:
          nltk.download('''punkt''', quiet=True)
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  re.sub("<n>"    ,				""    ,				SCREAMING_SNAKE_CASE    )  # remove pegasus newline char
  assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
  return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE    )    )





