'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class lowercase_       (		SCREAMING_SNAKE_CASE__			):







   """simple docstring"""

   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   42
   def __init__(      self      :			Tuple, UpperCamelCase__      :			Any, UpperCamelCase__      :			str     )  ->			Dict:
        super().__init__()
        self.register_modules(unet=__lowerCAmelCase, scheduler=__lowerCAmelCase     )

   @torch.no_grad()
   def __call__(      self      :			Tuple, UpperCamelCase__      :			Dict = 1, UpperCamelCase__      :			int = 20_00, UpperCamelCase__      :			Tuple = None, UpperCamelCase__      :			Optional[int] = "pil", UpperCamelCase__      :			Tuple = True, **UpperCamelCase__      :			Tuple, )  ->			int:
        _A    		=  self.unet.config.sample_size
        _A    		=  (batch_size, 3, img_size, img_size)

        _A    		=  self.unet

        _A    		=  randn_tensor(__lowerCAmelCase, generator=__lowerCAmelCase     ) * self.scheduler.init_noise_sigma
        _A    		=  sample.to(self.device     )

        self.scheduler.set_timesteps(__lowerCAmelCase     )
        self.scheduler.set_sigmas(__lowerCAmelCase     )

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
             _A    		=  self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device     )

             # correction step
             for _ in range(self.scheduler.config.correct_steps     ):
                  _A    		=  self.unet(__lowerCAmelCase, __lowerCAmelCase     ).sample
                  _A    		=  self.scheduler.step_correct(__lowerCAmelCase, __lowerCAmelCase, generator=__lowerCAmelCase     ).prev_sample

             # prediction step
             _A    		=  model(__lowerCAmelCase, __lowerCAmelCase     ).sample
             _A    		=  self.scheduler.step_pred(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase, generator=__lowerCAmelCase     )

             _A    		=  output.prev_sample, output.prev_sample_mean

        _A    		=  sample_mean.clamp(0, 1     )
        _A    		=  sample.cpu().permute(0, 2, 3, 1     ).numpy()
        if output_type == "pil":
             _A    		=  self.numpy_to_pil(__lowerCAmelCase     )

        if not return_dict:
             return (sample,)

        return ImagePipelineOutput(images=__lowerCAmelCase     )











'''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)






