


'''simple docstring'''






import math
from typing import Optional

import numpy as np

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


a_					:    str          =    logging.get_logger(__name__)

a_					:    List[Any]          =    {
    "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  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""encodec"""
 def __init__( self      ,				__magic_name__=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]      ,				__magic_name__=2_40_00      ,				__magic_name__=1      ,				__magic_name__=False      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=1_28      ,				__magic_name__=32      ,				__magic_name__=1      ,				__magic_name__=[8, 5, 4, 2]      ,				__magic_name__="weight_norm"      ,				__magic_name__=7      ,				__magic_name__=7      ,				__magic_name__=3      ,				__magic_name__=2      ,				__magic_name__=True      ,				__magic_name__="reflect"      ,				__magic_name__=2      ,				__magic_name__=2      ,				__magic_name__=1.0      ,				__magic_name__=10_24      ,				__magic_name__=None      ,				__magic_name__=True      ,				**__magic_name__      ,				)					->							Optional[Any]:
      _a			      =							target_bandwidths
      _a			      =							sampling_rate
      _a			      =							audio_channels
      _a			      =							normalize
      _a			      =							chunk_length_s
      _a			      =							overlap
      _a			      =							hidden_size
      _a			      =							num_filters
      _a			      =							num_residual_layers
      _a			      =							upsampling_ratios
      _a			      =							norm_type
      _a			      =							kernel_size
      _a			      =							last_kernel_size
      _a			      =							residual_kernel_size
      _a			      =							dilation_growth_rate
      _a			      =							use_causal_conv
      _a			      =							pad_mode
      _a			      =							compress
      _a			      =							num_lstm_layers
      _a			      =							trim_right_ratio
      _a			      =							codebook_size
      _a			      =							codebook_dim if codebook_dim is not None else hidden_size
      _a			      =							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__(**__magic_name__						)
 @property
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      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]:
      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:
      _a			      =							np.prod(self.upsampling_ratios						)
      return math.ceil(self.sampling_rate / hop_length						)




 @property
 def 							__UpperCAmelCase		( self						)					->							int:
      return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

