






import math
from typing import Optional

import numpy as np

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


__a:     Any   					=					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 		SCREAMING_SNAKE_CASE__	(  UpperCamelCase__     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''encodec'''


  def __init__(      self		:			str       ,				lowerCamelCase		:			Any=[1.5, 3.0, 6.0, 12.0, 24.0]       ,				lowerCamelCase		:			Dict=2_4000       ,				lowerCamelCase		:			Optional[int]=1       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Optional[int]=None       ,				lowerCamelCase		:			int=None       ,				lowerCamelCase		:			Any=128       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=1       ,				lowerCamelCase		:			Union[str, Any]=[8, 5, 4, 2]       ,				lowerCamelCase		:			Optional[int]="weight_norm"       ,				lowerCamelCase		:			Dict=7       ,				lowerCamelCase		:			str=7       ,				lowerCamelCase		:			int=3       ,				lowerCamelCase		:			Union[str, Any]=2       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			Any="reflect"       ,				lowerCamelCase		:			List[Any]=2       ,				lowerCamelCase		:			Any=2       ,				lowerCamelCase		:			Tuple=1.0       ,				lowerCamelCase		:			int=1024       ,				lowerCamelCase		:			Optional[int]=None       ,				lowerCamelCase		:			str=True       ,				**lowerCamelCase		:			Optional[int]       ,				)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    target_bandwidths
         _UpperCAmelCase							=    sampling_rate
         _UpperCAmelCase							=    audio_channels
         _UpperCAmelCase							=    normalize
         _UpperCAmelCase							=    chunk_length_s
         _UpperCAmelCase							=    overlap
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_filters
         _UpperCAmelCase							=    num_residual_layers
         _UpperCAmelCase							=    upsampling_ratios
         _UpperCAmelCase							=    norm_type
         _UpperCAmelCase							=    kernel_size
         _UpperCAmelCase							=    last_kernel_size
         _UpperCAmelCase							=    residual_kernel_size
         _UpperCAmelCase							=    dilation_growth_rate
         _UpperCAmelCase							=    use_causal_conv
         _UpperCAmelCase							=    pad_mode
         _UpperCAmelCase							=    compress
         _UpperCAmelCase							=    num_lstm_layers
         _UpperCAmelCase							=    trim_right_ratio
         _UpperCAmelCase							=    codebook_size
         _UpperCAmelCase							=    codebook_dim if codebook_dim is not None else hidden_size
         _UpperCAmelCase							=    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__(**__snake_case							)


  @property
  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


         """simple docstring"""

         if self.chunk_length_s is None:
                return None
         else:
                return int(self.chunk_length_s * self.sampling_rate							)


  @property
  def  lowerCamelCase       (      self		:			List[str]							)						->	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  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    np.prod(self.upsampling_ratios							)
         return math.ceil(self.sampling_rate / hop_length							)





  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	Union[str, Any]:


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





UpperCAmelCase_		:					Tuple          =  0  # The first color of the flag.
UpperCAmelCase_		:					Any          =  1  # The second color of the flag.
UpperCAmelCase_		:					str          =  2  # The third color of the flag.
UpperCAmelCase_		:					Tuple          =  (red, white, blue)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if not sequence:
								return []
				if len(lowerCamelCase ) == 1:
								return list(lowerCamelCase )
				__magic_name__						:						int										=0
				__magic_name__						:						str										=len(lowerCamelCase ) - 1
				__magic_name__						:						Optional[Any]										=0
				while mid <= high:
								if sequence[mid] == colors[0]:
												__magic_name__					,			__magic_name__						:						Tuple										=sequence[mid], sequence[low]
												low += 1
												mid += 1
								elif sequence[mid] == colors[1]:
												mid += 1
								elif sequence[mid] == colors[2]:
												__magic_name__					,			__magic_name__						:						Optional[Any]										=sequence[high], sequence[mid]
												high -= 1
								else:
												__magic_name__						:						Optional[int]										=F"The elements inside the sequence must contains only {colors} values"
												raise ValueError(lowerCamelCase )
				return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					List[Any]          =  input("Enter numbers separated by commas:\n").strip()
							UpperCAmelCase_		:					Optional[int]          =  [int(item.strip()) for item in user_input.split(",")]
							print(F"""{dutch_national_flag_sort(unsorted)}""")




