





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate




@dataclass(frozen=a     )
class 					_lowerCamelCase			(     a     ):



      """simple docstring"""






      UpperCAmelCase_    :   str      =field(default="audio-classification"			, metadata={"include_in_asdict_even_if_is_default": True}     )
      UpperCAmelCase_    :   ClassVar[Features]      =Features({"audio": Audio()}     )
      UpperCAmelCase_    :   ClassVar[Features]      =Features({"labels": ClassLabel}     )
      UpperCAmelCase_    :   str      ="audio"
      UpperCAmelCase_    :   str      ="labels"







      def     UpperCAmelCase		(			self					,    UpperCAmelCase )							-> Any:






        '''simple docstring'''






        if self.label_column not in features:
          raise ValueError(F"""Column {self.label_column} is not present in features.""" )
        if not isinstance(features[self.label_column]					,    lowerCAmelCase__ ):
          raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""" )
        __snake_case :					List[Any]          =							copy.deepcopy(self )
        __snake_case :					Optional[Any]          =							self.label_schema.copy()
        __snake_case :					List[Any]          =							features[self.label_column]
        __snake_case :					int          =							label_schema
        return task_template







      @property
      def     UpperCAmelCase		(			self )							-> Dict[str, str]:






        '''simple docstring'''






        return {
            self.audio_column: "audio",
            self.label_column: "labels",
        }








'''simple docstring'''

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

import datasets


lowercase__		:     str			=							'\\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'


lowercase__		:     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'


lowercase__		:     Dict			=							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 							__lowerCAmelCase	(       datasets.Metric					):






       """simple docstring"""





       def snake_case__		(				self  :						Dict	)       ->	Any:

            '''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 snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




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



