



'''simple docstring'''





from __future__ import annotations

A					:       Dict									=     []
def 						_a       (    lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_				):
   for i in range(len(snake_case__				)				):
      if board[row][i] == 1:
         return False
   for i in range(len(snake_case__				)				):
      if board[i][column] == 1:
         return False
   for i, j in zip(range(snake_case__	,		-1	,		-1				)	,		range(snake_case__	,		-1	,		-1				)				):
      if board[i][j] == 1:
         return False
   for i, j in zip(range(snake_case__	,		-1	,		-1				)	,		range(snake_case__	,		len(snake_case__				)				)				):
      if board[i][j] == 1:
         return False
   return True
def 						_a       (    lowerCamelCase_	,		lowerCamelCase_				):
   if row >= len(snake_case__				):
      solution.append(snake_case__				)
      printboard(snake_case__				)
      print()
      return True
   for i in range(len(snake_case__				)				):
      if is_safe(snake_case__	,		snake_case__	,		snake_case__				):
         snake_case       :				Optional[Any]		    =1
         solve(snake_case__	,		row + 1				)
         snake_case       :				int		    =0
   return False
def 						_a       (    lowerCamelCase_				):
   for i in range(len(snake_case__				)				):
      for j in range(len(snake_case__				)				):
         if board[i][j] == 1:
            print('''Q'''	,		end=''' '''				)
         else:
            print('''.'''	,		end=''' '''				)
      print()


# n=int(input("The no. of queens"))
A					:       Tuple									=     8
A					:       int									=     [[0 for i in range(n)] for j in range(n)]
solve(board, 0)


print("""The total no. of solutions are :""", len(solution))









"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_UpperCamelCase					:int					     =	DDIMScheduler.from_config(scheduler.config   )

					self.register_modules(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
								raise ValueError(
								    f"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch"
								    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=self.unet.dtype   )

					# set step values
					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

					_UpperCamelCase					:str					     =	(image / 2 + 0.5).clamp(0					,				1   )
					_UpperCamelCase					:int					     =	image.cpu().permute(0					,				2					,				3					,				1   ).numpy()
					if output_type == "pil":
								_UpperCamelCase					:Union[str, Any]					     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

