



"""simple docstring"""






from __future__ import annotations




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->				None:
  _snake_case   	=      len(lowerCAmelCase_	)

  # 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(lowerCAmelCase_	):
    # 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]  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)




def 			snake_case		(		lowerCAmelCase_	)						->				None:
  _snake_case   	=      []
  depth_first_search([]  ,						[]  ,						[]  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

  # Print all the boards
  for board in boards:
    for column in board:
      print(lowerCAmelCase_	)
    print(''''''	)

  print(len(lowerCAmelCase_	)  ,						'''solutions were found.'''	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     n_queens_solution(4)










"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





