






'''simple docstring'''







import math
from typing import Optional

import numpy as np

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


_A	:			List[Any]         =logging.get_logger(__name__)

_A	:			Union[str, 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 _lowercase	(				_lowercase		):
							a				      =       """encodec"""




							def __init__(							self:				Dict			,							UpperCamelCase__:				Dict=[1.5, 3.0, 6.0, 12.0, 24.0]			,							UpperCamelCase__:				Optional[int]=24_000			,							UpperCamelCase__:				Optional[int]=1			,							UpperCamelCase__:				Dict=False			,							UpperCamelCase__:				List[Any]=None			,							UpperCamelCase__:				str=None			,							UpperCamelCase__:				List[str]=128			,							UpperCamelCase__:				str=32			,							UpperCamelCase__:				Dict=1			,							UpperCamelCase__:				List[str]=[8, 5, 4, 2]			,							UpperCamelCase__:				List[Any]="weight_norm"			,							UpperCamelCase__:				str=7			,							UpperCamelCase__:				Any=7			,							UpperCamelCase__:				Optional[Any]=3			,							UpperCamelCase__:				str=2			,							UpperCamelCase__:				List[Any]=True			,							UpperCamelCase__:				Dict="reflect"			,							UpperCamelCase__:				Union[str, Any]=2			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				List[str]=1.0			,							UpperCamelCase__:				Any=1_024			,							UpperCamelCase__:				Dict=None			,							UpperCamelCase__:				Optional[Any]=True			,							**UpperCamelCase__:				int			,							):
														lowerCamelCase__					:	Any         =       target_bandwidths
														lowerCamelCase__					:	List[Any]         =       sampling_rate
														lowerCamelCase__					:	Union[str, Any]         =       audio_channels
														lowerCamelCase__					:	Optional[int]         =       normalize
														lowerCamelCase__					:	Tuple         =       chunk_length_s
														lowerCamelCase__					:	List[Any]         =       overlap
														lowerCamelCase__					:	Optional[Any]         =       hidden_size
														lowerCamelCase__					:	Union[str, Any]         =       num_filters
														lowerCamelCase__					:	Optional[Any]         =       num_residual_layers
														lowerCamelCase__					:	Dict         =       upsampling_ratios
														lowerCamelCase__					:	List[Any]         =       norm_type
														lowerCamelCase__					:	Union[str, Any]         =       kernel_size
														lowerCamelCase__					:	List[Any]         =       last_kernel_size
														lowerCamelCase__					:	str         =       residual_kernel_size
														lowerCamelCase__					:	List[Any]         =       dilation_growth_rate
														lowerCamelCase__					:	Dict         =       use_causal_conv
														lowerCamelCase__					:	List[str]         =       pad_mode
														lowerCamelCase__					:	int         =       compress
														lowerCamelCase__					:	List[str]         =       num_lstm_layers
														lowerCamelCase__					:	List[str]         =       trim_right_ratio
														lowerCamelCase__					:	List[Any]         =       codebook_size
														lowerCamelCase__					:	List[Any]         =       codebook_dim if codebook_dim is not None else hidden_size
														lowerCamelCase__					:	Any         =       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 		lowerCamelCase_				(							self:				int     ):
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate     )




							@property
							def 		lowerCamelCase_				(							self:				Any     ):
														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:				Tuple     ):
														lowerCamelCase__					:	Union[str, Any]         =       np.prod(self.upsampling_ratios     )
														return math.ceil(self.sampling_rate / hop_length     )




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



from __future__ import annotations

lowerCamelCase__          =		list[list[int]]

# assigning initial values to the grid
lowerCamelCase__          =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__          =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		bool:
			for i in range(9     ):
						if grid[row][i] == n or grid[i][column] == n:
									return False

			for i in range(3     ):
						for j in range(3     ):
									if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
												return False

			return True
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		tuple[int, int] | None:
			for i in range(9     ):
						for j in range(9     ):
									if grid[i][j] == 0:
												return i, j
			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Matrix | None:
			if location := find_empty_location(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		location
			else:
						# If the location is ``None``, then the grid is solved.
						return grid

			for digit in range(1       ,							10     ):
						if is_safe(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
									lowerCAmelCase__      :      Optional[Any]         =		digit

									if sudoku(SCREAMING_SNAKE_CASE_     ) is not None:
												return grid

									lowerCAmelCase__      :      List[Any]         =		0

			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		None:
			for row in grid:
						for cell in row:
									print(SCREAMING_SNAKE_CASE_       ,							end=' '     )
						print()


if __name__ == "__main__":
					# make a copy of grid so that you can compare with the unmodified grid
					for example_grid in (initial_grid, no_solution):
										print("""\nExample grid:\n""" + """=""" * 20)
										print_solution(example_grid)
										print("""\nExample grid solution:""")
										lowerCamelCase__          =		sudoku(example_grid)
										if solution is not None:
															print_solution(solution)
										else:
															print("""Cannot find a solution.""")