





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,
     }








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case  :    Union[str, Any]						    =  {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Union[str, Any]						    =  ['LayoutLMv3TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    List[str]						    =  [
         'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'LayoutLMv3ForQuestionAnswering',
         'LayoutLMv3ForSequenceClassification',
         'LayoutLMv3ForTokenClassification',
         'LayoutLMv3Model',
         'LayoutLMv3PreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[Any]						    =  [
         'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFLayoutLMv3ForQuestionAnswering',
         'TFLayoutLMv3ForSequenceClassification',
         'TFLayoutLMv3ForTokenClassification',
         'TFLayoutLMv3Model',
         'TFLayoutLMv3PreTrainedModel',
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Tuple						    =  ['LayoutLMv3FeatureExtractor']
     _snake_case  :    str						    =  ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
     from .configuration_layoutlmva import (
         LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LayoutLMvaConfig,
         LayoutLMvaOnnxConfig,
     )
     from .processing_layoutlmva import LayoutLMvaProcessor
     from .tokenization_layoutlmva import LayoutLMvaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlmva import (
              TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
              TFLayoutLMvaModel,
              TFLayoutLMvaPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
          from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
     import sys

     _snake_case  :    Any						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

