
import math
from typing import Optional

import numpy as np

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


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "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 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''encodec'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=[1.5, 3.0, 6.0, 12.0, 24.0]						,						__SCREAMING_SNAKE_CASE=2_4_0_0_0						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=1_2_8						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=[8, 5, 4, 2]						,						__SCREAMING_SNAKE_CASE="weight_norm"						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE="reflect"						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=1.0						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=True						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				target_bandwidths
           UpperCamelCase__    :   List[Any]					=				sampling_rate
           UpperCamelCase__    :   List[Any]					=				audio_channels
           UpperCamelCase__    :   int					=				normalize
           UpperCamelCase__    :   Optional[Any]					=				chunk_length_s
           UpperCamelCase__    :   Union[str, Any]					=				overlap
           UpperCamelCase__    :   List[Any]					=				hidden_size
           UpperCamelCase__    :   Dict					=				num_filters
           UpperCamelCase__    :   Dict					=				num_residual_layers
           UpperCamelCase__    :   Tuple					=				upsampling_ratios
           UpperCamelCase__    :   List[Any]					=				norm_type
           UpperCamelCase__    :   Optional[Any]					=				kernel_size
           UpperCamelCase__    :   int					=				last_kernel_size
           UpperCamelCase__    :   int					=				residual_kernel_size
           UpperCamelCase__    :   int					=				dilation_growth_rate
           UpperCamelCase__    :   List[Any]					=				use_causal_conv
           UpperCamelCase__    :   Any					=				pad_mode
           UpperCamelCase__    :   List[str]					=				compress
           UpperCamelCase__    :   Union[str, Any]					=				num_lstm_layers
           UpperCamelCase__    :   str					=				trim_right_ratio
           UpperCamelCase__    :   Any					=				codebook_size
           UpperCamelCase__    :   Tuple					=				codebook_dim if codebook_dim is not None else hidden_size
           UpperCamelCase__    :   Optional[int]					=				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__(**__SCREAMING_SNAKE_CASE  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							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    __SCREAMING_SNAKE_CASE			(							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    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				np.prod(self.upsampling_ratios  )
           return math.ceil(self.sampling_rate / hop_length  )






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)  )




import doctest
from collections import deque

import numpy as np




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self  )  ->   None:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				[2, 1, 2, -1]
           UpperCamelCase__    :   Union[str, Any]					=				[1, 2, 3, 4]






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   list[float]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				len(self.first_signal  )
           UpperCamelCase__    :   Any					=				len(self.second_signal  )

           UpperCamelCase__    :   Dict					=				max(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           # create a zero matrix of max_length x max_length
           UpperCamelCase__    :   List[Any]					=				[[0] * max_length for i in range(__SCREAMING_SNAKE_CASE  )]

           # fills the smaller signal with zeros to make both signals of same length
           if length_first_signal < length_second_signal:
                 self.first_signal += [0] * (max_length - length_first_signal)
           elif length_first_signal > length_second_signal:
                 self.second_signal += [0] * (max_length - length_second_signal)
           for i in range(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Optional[int]					=				deque(self.second_signal  )
                 rotated_signal.rotate(__SCREAMING_SNAKE_CASE  )
                 for j, item in enumerate(__SCREAMING_SNAKE_CASE  ):
                       matrix[i][j] += item

        # multiply the matrix with the first signal
           UpperCamelCase__    :   Optional[Any]					=				np.matmul(np.transpose(__SCREAMING_SNAKE_CASE  )						,						np.transpose(self.first_signal  )  )

           # rounding-off to two decimal places
           return [round(__SCREAMING_SNAKE_CASE						,						2  ) for i in final_signal]


if __name__ == "__main__":
    doctest.testmod()


