"""simple docstring"""





from __future__ import annotations

lowerCAmelCase_     =	list[list[int]]

# assigning initial values to the grid
lowerCAmelCase_     =	[
    [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_     =	[
    [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_(__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      lowerCamelCase_(__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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Matrix | None:
							if location := find_empty_location(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    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  ):
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    digit

																					if sudoku(__SCREAMING_SNAKE_CASE  ) is not None:
																												return grid

																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0

							return None



def      lowerCamelCase_(__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:''')
												lowerCAmelCase_     =	sudoku(example_grid)
												if solution is not None:
																		print_solution(solution)
												else:
																		print('''Cannot find a solution.''')



"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"M-CLIP"





				def __init__(      self							:							Optional[Any]      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							Union[str, Any]=7_6_8      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    transformerDimSize
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    imageDimSize
											super().__init__(**_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						MCLIPConfig





				def __init__(      self							:							Dict      ,    _A							:							Optional[Any]      ,    *_A							:							Any      ,    **_A							:							Dict):



											"""simple docstring"""







											super().__init__(_A      ,    *_A      ,    **_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    XLMRobertaModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.nn.Linear(
											    in_features=config.transformerDimensions      ,    out_features=config.numDims)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.transformer(input_ids=_A      ,    attention_mask=_A)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
											return self.LinearTransformation(_A), embs


