



'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	):


     '''simple docstring'''







     a_       		=len(lowercase__       )

     # 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(lowercase__       ):
          # 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]       ,	lowercase__       ,	lowercase__       ,	)






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=[]
     depth_first_search([]       ,	[]       ,	[]       ,	lowercase__       ,	lowercase__       )

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

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  n_queens_solution(4)







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


