



"""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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      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      __UpperCAmelCase      (    __lowerCamelCase	)							->      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      __UpperCAmelCase      (    __lowerCamelCase	)							->      Matrix | None:
		if location := find_empty_location(__lowerCamelCase	):
				lowercase__		,			lowercase__   :    Dict				=			location
		else:
				# If the location is ``None``, then the grid is solved.
				return grid

		for digit in range(1						,     10	):
				if is_safe(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	):
						lowercase__   :    Any				=			digit

						if sudoku(__lowerCamelCase	) is not None:
								return grid

						lowercase__   :    Tuple				=			0

		return None



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      None:
		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:')
														lowerCAmelCase_   						=   sudoku(example_grid)
														if solution is not None:
																					print_solution(solution)
														else:
																					print('Cannot find a solution.')





"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__   :    Tuple				=			val

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

		print(model.load_state_dict(__lowerCamelCase	)	)
		model.eval()
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
