
from __future__ import annotations

_snake_case      :							List[str]     	=    list[list[int]]

# assigning initial values to the grid
_snake_case      :							Matrix     	=    [
    [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
_snake_case      :							Matrix     	=    [
    [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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				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  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(9						):
								for j in range(9						):
												if grid[i][j] == 0:
																return i, j
				return None

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if location := find_empty_location(__magic_name__						):
								lowercase     ,	lowercase         =      location
				else:
								# If the location is ``None``, then the grid is solved.
								return grid

				for digit in range(1		,    10						):
								if is_safe(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):
												lowercase         =      digit

												if sudoku(__magic_name__						) is not None:
																return grid

												lowercase         =      0

				return None

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for row in grid:
								for cell in row:
												print(__magic_name__		,    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:")
												_snake_case      :							Optional[int]     	=    sudoku(example_grid)
												if solution is not None:
																		print_solution(solution)
												else:
																		print("Cannot find a solution.")


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "WavLMForAudioFrameClassification",
						    "WavLMForCTC",
						    "WavLMForSequenceClassification",
						    "WavLMForXVector",
						    "WavLMModel",
						    "WavLMPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
