


import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE       :      Union[str, Any]												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      int												= {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	int     =			"""encodec"""
       def __init__(					self,      lowerCamelCase=[1.5, 3.0, 6.0, 1_2.0, 2_4.0],      lowerCamelCase=2_40_00,      lowerCamelCase=1,      lowerCamelCase=False,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=1_28,      lowerCamelCase=32,      lowerCamelCase=1,      lowerCamelCase=[8, 5, 4, 2],      lowerCamelCase="weight_norm",      lowerCamelCase=7,      lowerCamelCase=7,      lowerCamelCase=3,      lowerCamelCase=2,      lowerCamelCase=True,      lowerCamelCase="reflect",      lowerCamelCase=2,      lowerCamelCase=2,      lowerCamelCase=1.0,      lowerCamelCase=10_24,      lowerCamelCase=None,      lowerCamelCase=True,      **lowerCamelCase,      )       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		target_bandwidths
           _lowercase       :							Dict      		=		sampling_rate
           _lowercase       :							List[Any]      		=		audio_channels
           _lowercase       :							Any      		=		normalize
           _lowercase       :							str      		=		chunk_length_s
           _lowercase       :							Tuple      		=		overlap
           _lowercase       :							str      		=		hidden_size
           _lowercase       :							List[Any]      		=		num_filters
           _lowercase       :							Tuple      		=		num_residual_layers
           _lowercase       :							Union[str, Any]      		=		upsampling_ratios
           _lowercase       :							Union[str, Any]      		=		norm_type
           _lowercase       :							Optional[int]      		=		kernel_size
           _lowercase       :							List[str]      		=		last_kernel_size
           _lowercase       :							Optional[Any]      		=		residual_kernel_size
           _lowercase       :							Union[str, Any]      		=		dilation_growth_rate
           _lowercase       :							Tuple      		=		use_causal_conv
           _lowercase       :							Tuple      		=		pad_mode
           _lowercase       :							str      		=		compress
           _lowercase       :							List[str]      		=		num_lstm_layers
           _lowercase       :							Optional[Any]      		=		trim_right_ratio
           _lowercase       :							List[Any]      		=		codebook_size
           _lowercase       :							Dict      		=		codebook_dim if codebook_dim is not None else hidden_size
           _lowercase       :							int      		=		use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   F'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}''')

           super().__init__(**lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           if self.chunk_length_s is None or self.overlap is None:
               return None
           else:
               return max(1,      int((1.0 - self.overlap) * self.chunk_length))
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		np.prod(self.upsampling_ratios)
           return math.ceil(self.sampling_rate / hop_length)




       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10))



'''simple docstring'''





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

import datasets


SCREAMING_SNAKE_CASE_:     Optional[Any]  ='\\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'


SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='\\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'


SCREAMING_SNAKE_CASE_:     List[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        __A      ( datasets.Metric	):



    def 					_lowercase						(self    :    Optional[Any] ):
         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    :    Tuple   ,			__a    :    Optional[int]   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   0.0
         for i, j in zip(__a   ,			__a ):
              n_correct += 1.0 if math_equivalence.is_equiv(__a   ,			__a ) else 0.0
         UpperCAmelCase_           =   n_correct / len(__a )
         return {
             "accuracy": accuracy,
         }




