



import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def     a(      A       :    Dict ,     A       :    Union[str, Any] ,     A       :    List[str]=1024 ,     A       :    str=1024 ,     A       :    Union[str, Any]=False ,     **A       :    Dict       )		->       List[str]:
					"""simple docstring"""





					a					     =   AutoTokenizer.from_pretrained(A       )
					a					     =   SeqaSeqDataset(A ,     A ,     A ,     A ,     type_path="train" ,     **A       )
					a					     =   tok.pad_token_id

					def get_lens(A       :    str       ):
										a					     =   tqdm(
										    DataLoader(A ,     batch_size=512 ,     num_workers=8 ,     shuffle=A ,     collate_fn=ds.collate_fn       ) ,     desc=str(ds.len_file       ) ,     )
										a					     =   []
										for batch in dl:
															a					     =   batch["input_ids"].ne(A       ).sum(1       ).tolist()
															a					     =   batch["labels"].ne(A       ).sum(1       ).tolist()
															if consider_target:
																				for src, tgt in zip(A ,     A       ):
																									max_lens.append(max(A ,     A       )       )
															else:
																				max_lens.extend(A       )
										return max_lens

					a					     =   get_lens(A       )
					a					     =   SeqaSeqDataset(A ,     A ,     A ,     A ,     type_path="val" ,     **A       )
					a					     =   get_lens(A       )
					pickle_save(A ,     train_ds.len_file       )
					pickle_save(A ,     val_ds.len_file       )


if __name__ == "__main__":
				fire.Fire(save_len_file)





from __future__ import annotations

_lowercase: Tuple      			=		list[list[int]]

# assigning initial values to the grid
_lowercase: 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
_lowercase: 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     a(      A       :    Matrix ,     A       :    int ,     A       :    int ,     A       :    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     a(      A       :    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     a(      A       :    Matrix       )		->       Matrix | None:
					"""simple docstring"""





					if location := find_empty_location(A       ):
										a     ,				a					     =   location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1 ,     10       ):
										if is_safe(A ,     A ,     A ,     A       ):
															a					     =   digit

															if sudoku(A       ) is not None:
																				return grid

															a					     =   0

					return None






def     a(      A       :    Matrix       )		->       None:
					"""simple docstring"""





					for row in grid:
										for cell in row:
															print(A ,     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: List[str]      			=		sudoku(example_grid)
								if solution is not None:
												print_solution(solution)
								else:
												print("Cannot find a solution.")
