






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

import datasets


A__	:			Tuple			   = '\\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'


A__	:			Dict			   = '\\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'


A__	:			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       _UpperCAmelCase					(       datasets.Metric	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Dict  ):

								'''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						:    Dict,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple  ):

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












from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




