


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__    					=      {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TrOCRForCausalLM',
      'TrOCRPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
  from .processing_trocr import TrOCRProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
  import sys

  UpperCamelCase__    					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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

import datasets


_snake_case  :    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'


_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'


_snake_case  :    List[str]						    =  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 :		str     )							->						int:
     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 :		str					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Optional[int]     )							->						List[Any]:
     __lowerCAmelCase								=	0.0
     for i, j in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_					,  lowerCAmelCase_     ) else 0.0
     __lowerCAmelCase								=	n_correct / len(lowerCAmelCase_     )
     return {
         "accuracy": accuracy,
     }

