'''simple docstring'''


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

import datasets


snake_case__					:      str				      =  '''\
@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}
}
'''


snake_case__					:      List[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.
'''


snake_case__					:      Union[str, 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 _UpperCamelCase					(			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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				0.0
       for i, j in zip(snake_case_				,		snake_case_						):
            n_correct += 1.0 if math_equivalence.is_equiv(snake_case_				,		snake_case_						) else 0.0
       UpperCAmelCase_							:       List[Any]       =				n_correct / len(snake_case_						)
       return {
           "accuracy": accuracy,
       }







'''simple docstring'''


from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  @register_to_config
  def __init__(			self				,		snake_case_ = 7_6_8				,		):




       '''simple docstring'''

       super().__init__()

       UpperCAmelCase_							:       int       =				nn.Parameter(torch.zeros(1				,		snake_case_						)						)
       UpperCAmelCase_							:       str       =				nn.Parameter(torch.ones(1				,		snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_ = None				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				nn.Parameter(self.mean.to(snake_case_						).to(snake_case_						)						)
       UpperCAmelCase_							:       Tuple       =				nn.Parameter(self.std.to(snake_case_						).to(snake_case_						)						)
       return self


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				(embeds - self.mean) * 1.0 / self.std
       return embeds






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				(embeds * self.std) + self.mean
       return embeds






