'''simple docstring'''





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

import datasets


lowerCAmelCase_	:		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}
}
"""


lowerCAmelCase_	:		int			=     """\
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_	:		Optional[Any]			=     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  snake_case__				(						self					:						Optional[int]					)							->List[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  snake_case__				(						self					:						Tuple       ,		lowercase__					:						Dict       ,		lowercase__					:						int					)							->str:


										'''simple docstring'''




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



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

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

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

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


