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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       42
      _snake_case    		=       42



      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				UNetaDModel    ,  _UpperCamelCase     :				ScoreSdeVeScheduler)				->Tuple:


          """simple docstring"""


          super().__init__()
          self.register_modules(unet=_UpperCamelCase    ,  scheduler=_UpperCamelCase)




      @torch.no_grad()
      def __call__(					self     :				Dict    ,  _UpperCamelCase     :				int = 1    ,  _UpperCamelCase     :				int = 2000    ,  _UpperCamelCase     :				Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,  _UpperCamelCase     :				Optional[str] = "pil"    ,  _UpperCamelCase     :				bool = True    ,  **_UpperCamelCase     :				Optional[Any]    ,  )				->Union[ImagePipelineOutput, Tuple]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.unet.config.sample_size
          _lowerCamelCase     :		Optional[int] 						=						(batch_size, 3, img_size, img_size)

          _lowerCamelCase     :		int 						=						self.unet

          _lowerCamelCase     :		Union[str, Any] 						=						randn_tensor(_UpperCamelCase    ,  generator=_UpperCamelCase) * self.scheduler.init_noise_sigma
          _lowerCamelCase     :		List[str] 						=						sample.to(self.device)

          self.scheduler.set_timesteps(_UpperCamelCase)
          self.scheduler.set_sigmas(_UpperCamelCase)

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

              # correction step
              for _ in range(self.scheduler.config.correct_steps):
                  _lowerCamelCase     :		List[str] 						=						self.unet(_UpperCamelCase    ,  _UpperCamelCase).sample
                  _lowerCamelCase     :		Optional[Any] 						=						self.scheduler.step_correct(_UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase).prev_sample

              # prediction step
              _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  _UpperCamelCase).sample
              _lowerCamelCase     :		Optional[Any] 						=						self.scheduler.step_pred(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)

              _lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						output.prev_sample, output.prev_sample_mean

          _lowerCamelCase     :		Union[str, Any] 						=						sample_mean.clamp(0    ,  1)
          _lowerCamelCase     :		Tuple 						=						sample.cpu().permute(0    ,  2    ,  3    ,  1).numpy()
          if output_type == "pil":
              _lowerCamelCase     :		Any 						=						self.numpy_to_pil(_UpperCamelCase)

          if not return_dict:
              return (sample,)

          return ImagePipelineOutput(images=_UpperCamelCase)





from __future__ import annotations

lowerCAmelCase			:							int     =[]







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        if board[row][i] == 1:
            return False
    for i in range(len(__A	)	):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , -1   , -1	)	):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , len(__A	)	)	):
        if board[i][j] == 1:
            return False
    return True







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if row >= len(__A	):
        solution.append(__A	)
        printboard(__A	)
        print()
        return True
    for i in range(len(__A	)	):
        if is_safe(__A   , __A   , __A	):
            _lowerCamelCase     :		int 						=						1
            solve(__A   , row + 1	)
            _lowerCamelCase     :		List[str] 						=						0
    return False







def 						A__  (					__A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        for j in range(len(__A	)	):
            if board[i][j] == 1:
                print("""Q"""   , end=""" """	)
            else:
                print("""."""   , end=""" """	)
        print()


# n=int(input("The no. of queens"))
lowerCAmelCase			:							int     =8
lowerCAmelCase			:							Union[str, Any]     =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))




