






'''simple docstring'''



import math
from typing import Optional

import numpy as np

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


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

__UpperCamelCase      :       Tuple      			=      {
    """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       (							_lowerCAmelCase     ):
      __a  					="encodec"




      def __init__(  self ,   lowerCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0] ,   lowerCamelCase=2_4000 ,   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=1024 ,   lowerCamelCase=None ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->int:



         '''simple docstring'''
         __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__(**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'''
         __a				     =    np.prod(self.upsampling_ratios      )
         return math.ceil(self.sampling_rate / hop_length      )






      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)      )







'''simple docstring'''



from maths.prime_factors import prime_factors



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    f"""Input value of [number={number}] must be an integer"""
      raise TypeError(SCREAMING_SNAKE_CASE__    )
   if number < 1:
      raise ValueError('Input must be a positive integer'    )
   return -1 if len(prime_factors(SCREAMING_SNAKE_CASE__    )    ) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()