






'''simple docstring'''

from pathlib import Path

import fire
from tqdm import tqdm



def 				snake_case       (  a_				:			Any="ro"     , a_				:			Any="en"     , a_				:			Optional[Any]="wmt16"     , a_				:			Dict=None						)							->					None:





      """simple docstring"""

      try:
            import datasets
      except (ModuleNotFoundError, ImportError):
            raise ImportError("""run pip install datasets"""						)
      UpperCamelCase_					: int            =				f"{src_lang}-{tgt_lang}"
      print(f"Converting {dataset}-{pair}"						)
      UpperCamelCase_					: Dict            =				datasets.load_dataset(a_     , a_						)
      if save_dir is None:
            UpperCamelCase_					: List[str]            =				f"{dataset}-{pair}"
      UpperCamelCase_					: Dict            =				Path(a_						)
      save_dir.mkdir(exist_ok=a_						)

      for split in ds.keys():
            print(f"Splitting {split} with {ds[split].num_rows} records"						)

            # to save to val.source, val.target like summary datasets
            UpperCamelCase_					: List[str]            =				"""val""" if split == """validation""" else split
            UpperCamelCase_					: List[Any]            =				save_dir.joinpath(f"{fn}.source"						)
            UpperCamelCase_					: int            =				save_dir.joinpath(f"{fn}.target"						)
            UpperCamelCase_					: Union[str, Any]            =				src_path.open("""w+"""						)
            UpperCamelCase_					: Any            =				tgt_path.open("""w+"""						)

            # reader is the bottleneck so writing one record at a time doesn't slow things down
            for x in tqdm(ds[split]						):
                  UpperCamelCase_					: List[Any]            =				x["""translation"""]
                  src_fp.write(ex[src_lang] + """\n"""						)
                  tgt_fp.write(ex[tgt_lang] + """\n"""						)

      print(f"Saved {dataset} dataset to {save_dir}"						)


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














'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






