

"""simple docstring"""


def        a__       (       __SCREAMING_SNAKE_CASE					)      ->      str:
		__lowerCAmelCase:			str        =     current_set.copy()
		for row_index, row in enumerate(_SCREAMING_SNAKE_CASE					):
				__lowerCAmelCase:			Optional[Any]        =     row[0]
				for column_index, column in enumerate(_SCREAMING_SNAKE_CASE					):
						if magnitude == 0:
								__lowerCAmelCase:			List[Any]        =     column
								continue
						__lowerCAmelCase:			Optional[Any]        =     column / magnitude
    # Subtract to cancel term
		__lowerCAmelCase:			Optional[int]        =     current_set[0]
		__lowerCAmelCase:			int        =     [first_row]
		__lowerCAmelCase:			Optional[Any]        =     current_set[1::]
		for row in current_set:
				__lowerCAmelCase:			List[str]        =     []
				# If first term is 0, it is already in form we want, so we preserve it
				if row[0] == 0:
						final_set.append(_SCREAMING_SNAKE_CASE					)
						continue
				for column_index in range(len(_SCREAMING_SNAKE_CASE					)					):
						temp_row.append(first_row[column_index] - row[column_index]					)
				final_set.append(_SCREAMING_SNAKE_CASE					)
		# Create next recursion iteration set
		if len(final_set[0]					) != 3:
				__lowerCAmelCase:			Optional[Any]        =     final_set[0]
				__lowerCAmelCase:			Tuple        =     []
				__lowerCAmelCase:			Tuple        =     []
				for row in final_set[1::]:
						current_first_column.append(row[0]					)
						next_iteration.append(row[1::]					)
				__lowerCAmelCase:			Optional[Any]        =     simplify(_SCREAMING_SNAKE_CASE					)
				for i in range(len(_SCREAMING_SNAKE_CASE					)					):
						resultant[i].insert(0   ,					current_first_column[i]					)
				resultant.insert(0   ,					_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase:			Tuple        =     resultant
		return final_set






def        a__       (       __SCREAMING_SNAKE_CASE					)      ->      List[str]:
		if len(_SCREAMING_SNAKE_CASE					) == 0:
				raise IndexError("solve_simultaneous() requires n lists of length n+1"					)
		__lowerCAmelCase:			Dict        =     len(_SCREAMING_SNAKE_CASE					) + 1
		if any(len(_SCREAMING_SNAKE_CASE					) != _length for item in equations					):
				raise IndexError("solve_simultaneous() requires n lists of length n+1"					)
		for row in equations:
				if any(not isinstance(_SCREAMING_SNAKE_CASE   ,					(int, float)					) for column in row					):
						raise ValueError("solve_simultaneous() requires lists of integers"					)
		if len(_SCREAMING_SNAKE_CASE					) == 1:
				return [equations[0][-1] / equations[0][0]]
		__lowerCAmelCase:			Union[str, Any]        =     equations.copy()
		if any(0 in row for row in data_set					):
				__lowerCAmelCase:			Union[str, Any]        =     data_set.copy()
				__lowerCAmelCase:			List[str]        =     []
				for row_index, row in enumerate(_SCREAMING_SNAKE_CASE					):
						if 0 not in row:
								__lowerCAmelCase:			Dict        =     data_set.pop(_SCREAMING_SNAKE_CASE					)
								break
				if not full_row:
						raise ValueError("solve_simultaneous() requires at least 1 full equation"					)
				data_set.insert(0   ,					_SCREAMING_SNAKE_CASE					)
		__lowerCAmelCase:			Tuple        =     data_set.copy()
		__lowerCAmelCase:			List[str]        =     simplify(_SCREAMING_SNAKE_CASE					)
		__lowerCAmelCase:			Union[str, Any]        =     simplified[::-1]
		__lowerCAmelCase:			list        =     []
		for row in simplified:
				__lowerCAmelCase:			Union[str, Any]        =     row[-1]
				if not solutions:
						if row[-2] == 0:
								solutions.append(0					)
								continue
						solutions.append(current_solution / row[-2]					)
						continue
				__lowerCAmelCase:			Optional[int]        =     row.copy()[: len(_SCREAMING_SNAKE_CASE					) - 1 :]
				while temp_row[0] == 0:
						temp_row.pop(0					)
				if len(_SCREAMING_SNAKE_CASE					) == 0:
						solutions.append(0					)
						continue
				__lowerCAmelCase:			int        =     temp_row[1::]
				__lowerCAmelCase:			Dict        =     temp_row[::-1]
				for column_index, column in enumerate(_SCREAMING_SNAKE_CASE					):
						current_solution -= column * solutions[column_index]
				solutions.append(_SCREAMING_SNAKE_CASE					)
		__lowerCAmelCase:			Optional[Any]        =     []
		for item in solutions:
				final.append(float(round(_SCREAMING_SNAKE_CASE   ,					5					)					)					)
		return final[::-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			__A		  =     [
			    [2, 1, 1, 1, 1, 4],
			    [1, 2, 1, 1, 1, 5],
			    [1, 1, 2, 1, 1, 6],
			    [1, 1, 1, 2, 1, 7],
			    [1, 1, 1, 1, 2, 8],
			]
			print(solve_simultaneous(eq))
			print(solve_simultaneous([[4, 2]]))



import math
from typing import Optional

import numpy as np

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     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        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		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__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





						@property
						def        A_							(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        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






