


'''simple docstring'''

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

import datasets


a_  :				Dict						= """\
@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_  :				Union[str, 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_  :				Dict						= 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   snake_case		(    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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







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







'''simple docstring'''

import os





def __snake_case					(      UpperCAmelCase_						:						str = "matrix.txt"		):
 with open(os.path.join(os.path.dirname(UpperCAmelCase_		)					,				UpperCAmelCase_		)		) as in_file:
  lowerCamelCase_      =  in_file.read()

 lowerCamelCase_      =  [[int(UpperCAmelCase_		) for cell in row.split(","		)] for row in data.strip().splitlines()]
 lowerCamelCase_      =  [[0 for cell in row] for row in grid]
 lowerCamelCase_      =  len(grid[0]		)

 lowerCamelCase_      =  [[0 for i in range(UpperCAmelCase_		)] for j in range(UpperCAmelCase_		)]
 lowerCamelCase_      =  grid[0][0]
 for i in range(1					,				UpperCAmelCase_		):
  lowerCamelCase_      =  grid[0][i] + dp[0][i - 1]
 for i in range(1					,				UpperCAmelCase_		):
  lowerCamelCase_      =  grid[i][0] + dp[i - 1][0]

 for i in range(1					,				UpperCAmelCase_		):
  for j in range(1					,				UpperCAmelCase_		):
   lowerCamelCase_      =  grid[i][j] + min(dp[i - 1][j]					,				dp[i][j - 1]		)

 return dp[-1][-1]


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



