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

import datasets


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


SCREAMING_SNAKE_CASE__		:					str						=					"\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n"

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




		def 	__lowercase(      self     :	List[str]				)->       Union[str, Any]:




							"""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     :	Optional[int]     , a_     :	Optional[int]     , a_     :	Union[str, Any]				)->       Tuple:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0.0
							for i, j in zip(lowerCamelCase_     , lowerCamelCase_				):
												n_correct += 1.0 if math_equivalence.is_equiv(lowerCamelCase_     , lowerCamelCase_				) else 0.0
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		n_correct / len(lowerCamelCase_				)
							return {
							    "accuracy": accuracy,
							}




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



