






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

import datasets


UpperCAmelCase__				   =		"\\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"


UpperCAmelCase__				   =		"\\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"


UpperCAmelCase__				   =		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 		lowercase_					(	datasets.Metric   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Optional[int]:




       """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       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						List[Any]  )    ->Tuple:




       """simple docstring"""


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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_groupvit": [
        "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "GroupViTConfig",
        "GroupViTOnnxConfig",
        "GroupViTTextConfig",
        "GroupViTVisionConfig",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GroupViTModel",
         "GroupViTPreTrainedModel",
         "GroupViTTextModel",
         "GroupViTVisionModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFGroupViTModel",
         "TFGroupViTPreTrainedModel",
         "TFGroupViTTextModel",
         "TFGroupViTVisionModel",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


