
import math
from typing import Optional

import numpy as np

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


_lowerCamelCase	:    Any          =      logging.get_logger(__name__)

_lowerCamelCase	:    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	:		str          = "encodec"



	def __init__(  self     :   Optional[int]      ,  lowercase     :   Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]      ,  lowercase     :   Tuple=24_000      ,  lowercase     :   str=1      ,  lowercase     :   Optional[Any]=False      ,  lowercase     :   Optional[int]=None      ,  lowercase     :   Union[str, Any]=None      ,  lowercase     :   str=128      ,  lowercase     :   Tuple=32      ,  lowercase     :   Dict=1      ,  lowercase     :   Optional[Any]=[8, 5, 4, 2]      ,  lowercase     :   Optional[int]="weight_norm"      ,  lowercase     :   Tuple=7      ,  lowercase     :   Union[str, Any]=7      ,  lowercase     :   Dict=3      ,  lowercase     :   Union[str, Any]=2      ,  lowercase     :   List[Any]=True      ,  lowercase     :   List[Any]="reflect"      ,  lowercase     :   str=2      ,  lowercase     :   Any=2      ,  lowercase     :   Tuple=1.0      ,  lowercase     :   Dict=1_024      ,  lowercase     :   List[Any]=None      ,  lowercase     :   Dict=True      ,  **lowercase     :   str      ,  ):


				'''simple docstring'''


				_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__(**lowercase_     )



	@property
	def 			A							(  self     :   str     ):


				'''simple docstring'''


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



	@property
	def 			A							(  self     :   str     ):


				'''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 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      np.prod(self.upsampling_ratios     )
				return math.ceil(self.sampling_rate / hop_length     )





	@property
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)     )

import random




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	@staticmethod
	def 			A							(  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      [ord(lowercase     ) for i in text]
				_snake_case          =      []
				_snake_case          =      []
				for i in plain:
							_snake_case          =      random.randint(1      ,  300     )
							_snake_case          =      (i + k) * k
							cipher.append(lowercase     )
							key.append(lowercase     )
				return cipher, key





	@staticmethod
	def 			A							(  lowercase     :   list[int]      ,  lowercase     :   list[int]     ):


				'''simple docstring'''


				_snake_case          =      []
				for i in range(len(lowercase     )     ):
							_snake_case          =      int((cipher[i] - (key[i]) ** 2) / key[i]     )
							plain.append(chr(lowercase     )     )
				return "".join(lowercase     )


if __name__ == "__main__":
			_lowerCamelCase   ,					_lowerCamelCase	:    Optional[int]          =      Onepad().encrypt('''Hello''')
			print(c, k)
			print(Onepad().decrypt(c, k))