


'''simple docstring'''






import math
from typing import Optional

import numpy as np

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


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''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__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				"encodec"







							def __init__(		self ,   lowerCamelCase_=[1.5, 3.0, 6.0, 12.0, 24.0] ,   lowerCamelCase_=2_40_00 ,   lowerCamelCase_=1 ,   lowerCamelCase_=False ,   lowerCamelCase_=None ,   lowerCamelCase_=None ,   lowerCamelCase_=1_28 ,   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_=10_24 ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   **lowerCamelCase_ ,   )	->							Optional[Any]:
														lowerCAmelCase__	       =     target_bandwidths
														lowerCAmelCase__	       =     sampling_rate
														lowerCAmelCase__	       =     audio_channels
														lowerCAmelCase__	       =     normalize
														lowerCAmelCase__	       =     chunk_length_s
														lowerCAmelCase__	       =     overlap
														lowerCAmelCase__	       =     hidden_size
														lowerCAmelCase__	       =     num_filters
														lowerCAmelCase__	       =     num_residual_layers
														lowerCAmelCase__	       =     upsampling_ratios
														lowerCAmelCase__	       =     norm_type
														lowerCAmelCase__	       =     kernel_size
														lowerCAmelCase__	       =     last_kernel_size
														lowerCAmelCase__	       =     residual_kernel_size
														lowerCAmelCase__	       =     dilation_growth_rate
														lowerCAmelCase__	       =     use_causal_conv
														lowerCAmelCase__	       =     pad_mode
														lowerCAmelCase__	       =     compress
														lowerCAmelCase__	       =     num_lstm_layers
														lowerCAmelCase__	       =     trim_right_ratio
														lowerCAmelCase__	       =     codebook_size
														lowerCAmelCase__	       =     codebook_dim if codebook_dim is not None else hidden_size
														lowerCAmelCase__	       =     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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														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]:
														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:
														lowerCAmelCase__	       =     np.prod(self.upsampling_ratios       )
														return math.ceil(self.sampling_rate / hop_length       )







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )



'''simple docstring'''






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def  _snake_case       (					A			,   A			,   A			,   A			,   )						->   list[float]:
							lowerCAmelCase__       , lowerCAmelCase__	       =     coefficient_matrix.shape
							lowerCAmelCase__       , lowerCAmelCase__	       =     constant_matrix.shape

							if rowsa != colsa:
														lowerCAmelCase__	       =     F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if colsa != 1:
														lowerCAmelCase__	       =     F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(A	)

							if rowsa != rowsa:
														lowerCAmelCase__	       =     (
														    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
														    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(A	)

							if len(A	) != rowsa:
														lowerCAmelCase__	       =     (
														    '''Number of initial values must be equal to number of rows in coefficient '''
														    F"""matrix but received {len(A	)} and {rowsa}"""
														)
														raise ValueError(A	)

							if iterations <= 0:
														raise ValueError('''Iterations must be at least 1'''	)

							lowerCAmelCase__	       =     np.concatenate(
							    (coefficient_matrix, constant_matrix)			,   axis=1	)

							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							strictly_diagonally_dominant(A	)

							# Iterates the whole matrix for given number of times
							for _ in range(A	):
														lowerCAmelCase__	       =     []
														for row in range(A	):
																					lowerCAmelCase__	       =     0
																					for col in range(A	):
																												if col == row:
																																			lowerCAmelCase__	       =     table[row][col]
																												elif col == cols - 1:
																																			lowerCAmelCase__	       =     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					lowerCAmelCase__	       =     (temp + val) / denom
																					new_val.append(A	)
														lowerCAmelCase__	       =     new_val

							return [float(A	) for i in new_val]


def  _snake_case       (					A	)						->   bool:
							lowerCAmelCase__       , lowerCAmelCase__	       =     table.shape

							lowerCAmelCase__	       =     True

							for i in range(0			,   A	):
														lowerCAmelCase__	       =     0
														for j in range(0			,   cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()