





'''simple docstring'''



import math
from typing import Optional

import numpy as np

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


__magic_name__   :						Optional[Any]    =			logging.get_logger(__name__)

__magic_name__   :						Optional[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       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   '''encodec'''

     def __init__(					self						,   lowerCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]						,   lowerCamelCase=24_000						,   lowerCamelCase=1						,   lowerCamelCase=False						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=128						,   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=1_024						,   lowerCamelCase=None						,   lowerCamelCase=True						,   **lowerCamelCase						,   ):
       _snake_case     		=				target_bandwidths
       _snake_case     		=				sampling_rate
       _snake_case     		=				audio_channels
       _snake_case     		=				normalize
       _snake_case     		=				chunk_length_s
       _snake_case     		=				overlap
       _snake_case     		=				hidden_size
       _snake_case     		=				num_filters
       _snake_case     		=				num_residual_layers
       _snake_case     		=				upsampling_ratios
       _snake_case     		=				norm_type
       _snake_case     		=				kernel_size
       _snake_case     		=				last_kernel_size
       _snake_case     		=				residual_kernel_size
       _snake_case     		=				dilation_growth_rate
       _snake_case     		=				use_causal_conv
       _snake_case     		=				pad_mode
       _snake_case     		=				compress
       _snake_case     		=				num_lstm_layers
       _snake_case     		=				trim_right_ratio
       _snake_case     		=				codebook_size
       _snake_case     		=				codebook_dim if codebook_dim is not None else hidden_size
       _snake_case     		=				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	):
       if self.chunk_length_s is None:
         return None
       else:
         return int(self.chunk_length_s * self.sampling_rate	)

     @property
     def 	UpperCamelCase(					self	):
       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	):
       _snake_case     		=				np.prod(self.upsampling_ratios	)
       return math.ceil(self.sampling_rate / hop_length	)






     @property
     def 	UpperCamelCase(					self	):
       return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)	)









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


