



from __future__ import annotations

lowerCAmelCase__		:					Dict							=list[list[int]]

# assigning initial values to the grid
lowerCAmelCase__		:					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
lowerCAmelCase__		:					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      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			Union[str, Any]:
							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      __lowercase       (			a__       )    ->			Tuple:
							for i in range(9       ):
														for j in range(9       ):
																					if grid[i][j] == 0:
																												return i, j
							return None






def      __lowercase       (			a__       )    ->			Any:
							if location := find_empty_location(UpperCamelCase__       ):
														__SCREAMING_SNAKE_CASE									= location
							else:
														# If the location is ``None``, then the grid is solved.
														return grid

							for digit in range(1							,       10       ):
														if is_safe(UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__							,       UpperCamelCase__       ):
																					__SCREAMING_SNAKE_CASE									= digit

																					if sudoku(UpperCamelCase__       ) is not None:
																												return grid

																					__SCREAMING_SNAKE_CASE									= 0

							return None






def      __lowercase       (			a__       )    ->			List[Any]:
							for row in grid:
														for cell in row:
																					print(UpperCamelCase__							,       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__		:					str							=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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






