


'''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_    :			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   (   snake_case_				):
 _lowerCAmelCase           =		"""encodec"""
 def __init__(       self					, __magic_name__=[1.5, 3.0, 6.0, 12.0, 24.0]					, __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__					, )    ->							Tuple:
      _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)  )



__A				={str(digit): digit**5 for digit in range(1_0)}





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase__    )    )





def 			lowerCamelCase_ (				):
				return sum(
				    number
				    for number in range(1_0_0_0	, 1_0_0_0_0_0_0    )
				    if number == digits_fifth_powers_sum(lowerCamelCase__    )    )


if __name__ == "__main__":
			print(solution())


