





'''simple docstring'''







from __future__ import annotations

lowerCAmelCase_					:    Tuple      	=			list[list[int]]

# assigning initial values to the grid
lowerCAmelCase_					:    Optional[Any]      	=			[
    [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_					:    Optional[int]      	=			[
    [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				( lowercase						:      List[Any]    ,							lowercase						:      Union[str, Any]    ,							lowercase						:      str    ,							lowercase						:      Optional[int]						)	->	Tuple:
				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				( lowercase						:      Any						)	->	Dict:
				for i in range(9						):
								for j in range(9						):
												if grid[i][j] == 0:
																return i, j
				return None






def  _lowerCamelCase				( lowercase						:      List[str]						)	->	Dict:
				if location := find_empty_location(a_						):
								_a	,					_a								= location
				else:
								# If the location is ``None``, then the grid is solved.
								return grid

				for digit in range(1    ,							10						):
								if is_safe(a_    ,							a_    ,							a_    ,							a_						):
												_a								= digit

												if sudoku(a_						) is not None:
																return grid

												_a								= 0

				return None






def  _lowerCamelCase				( lowercase						:      Optional[int]						)	->	Tuple:
				for row in grid:
								for cell in row:
												print(a_    ,							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_tf_available, is_torch_available


lowercase  							=					{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TapasForMaskedLM",
	    "TapasForQuestionAnswering",
	    "TapasForSequenceClassification",
	    "TapasModel",
	    "TapasPreTrainedModel",
	    "load_tf_weights_in_tapas",
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFTapasForMaskedLM",
	    "TFTapasForQuestionAnswering",
	    "TFTapasForSequenceClassification",
	    "TFTapasModel",
	    "TFTapasPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





