






from __future__ import annotations

UpperCAmelCase_ :						List[Any]      =      list[list[int]]

# assigning initial values to the grid
UpperCAmelCase_ :						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
UpperCAmelCase_ :						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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Matrix	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  int					)		->			bool:






	"""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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Matrix					)		->			tuple[int, int] | None:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Matrix					)		->			Matrix | None:






	"""simple docstring"""



	if location := find_empty_location(__magic_name__					):
		UpperCamelCase     ,      UpperCamelCase     :Tuple          =	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__					):
			UpperCamelCase     :Dict          =	digit

			if sudoku(__magic_name__					) is not None:
				return grid

			UpperCamelCase     :List[str]          =	0

	return None

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Matrix					)		->			None:






	"""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:''')
		UpperCAmelCase_ :						Union[str, Any]      =      sudoku(example_grid)
		if solution is not None:
			print_solution(solution)
		else:
			print('''Cannot find a solution.''')





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =		{
    "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		["BloomTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a          =		[
           "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BloomForCausalLM",
           "BloomModel",
           "BloomPreTrainedModel",
           "BloomForSequenceClassification",
           "BloomForTokenClassification",
           "BloomForQuestionAnswering",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

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