



"""simple docstring"""

from __future__ import annotations

lowercase__			   =  list[list[int]]

# assigning initial values to the grid
lowercase__			   =  [
    [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
lowercase__			   =  [
    [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       __magic_name__    (   _lowerCamelCase				: Matrix  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							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       __magic_name__    (   _lowerCamelCase				: Matrix					):
							for i in range(9					):
														for j in range(9					):
																					if grid[i][j] == 0:
																												return i, j
							return None





def       __magic_name__    (   _lowerCamelCase				: Matrix					):
							if location := find_empty_location(_lowerCamelCase					):
														__a							,     __a		:		List[Any]									=       location
							else:
														# If the location is ``None``, then the grid is solved.
														return grid

							for digit in range(1  ,       1_0					):
														if is_safe(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					):
																					__a		:		int									=       digit

																					if sudoku(_lowerCamelCase					) is not None:
																												return grid

																					__a		:		Optional[int]									=       0

							return None





def       __magic_name__    (   _lowerCamelCase				: Matrix					):
							for row in grid:
														for cell in row:
																					print(_lowerCamelCase  ,       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:")
		lowercase__			   =  sudoku(example_grid)
		if solution is not None:
			print_solution(solution)
		else:
			print("Cannot find a solution.")






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

