



from __future__ import annotations

SCREAMING_SNAKE_CASE     				=						list[list[int]]

# assigning initial values to the grid
SCREAMING_SNAKE_CASE     				=						[
    [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
SCREAMING_SNAKE_CASE     				=						[
    [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__  (			__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 snake_case__  (			__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 snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Matrix | None:
		if location := find_empty_location(__SCREAMING_SNAKE_CASE ):
				UpperCAmelCase_					,						UpperCAmelCase_						=				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 ):
						UpperCAmelCase_						=				digit

						if sudoku(__SCREAMING_SNAKE_CASE ) is not None:
								return grid

						UpperCAmelCase_						=				0

		return None







def snake_case__  (			__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:")
						SCREAMING_SNAKE_CASE     				=						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,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

