






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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    UNetaDModel
		_snake_case    :    ScoreSdeVeScheduler



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							super().__init__()
							self.register_modules(unet=_UpperCamelCase				,		scheduler=_UpperCamelCase   )



		@torch.no_grad()
		def __call__(							self				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_0_0_0				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		**_UpperCamelCase				,		)	->							Union[ImagePipelineOutput, Tuple]:
							UpperCAmelCase_    :      List[Any]            =						self.unet.config.sample_size
							UpperCAmelCase_    :      Optional[Any]            =						(batch_size, 3, img_size, img_size)

							UpperCAmelCase_    :      Dict            =						self.unet

							UpperCAmelCase_    :      Optional[Any]            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase   ) * self.scheduler.init_noise_sigma
							UpperCAmelCase_    :      List[Any]            =						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   )   ):
												UpperCAmelCase_    :      str            =						self.scheduler.sigmas[i] * torch.ones(shape[0]				,		device=self.device   )

												# correction step
												for _ in range(self.scheduler.config.correct_steps   ):
																	UpperCAmelCase_    :      str            =						self.unet(_UpperCamelCase				,		_UpperCamelCase   ).sample
																	UpperCAmelCase_    :      Tuple            =						self.scheduler.step_correct(_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase   ).prev_sample

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

												UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						output.prev_sample, output.prev_sample_mean

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

							if not return_dict:
												return (sample,)

							return ImagePipelineOutput(images=_UpperCamelCase   )














from __future__ import annotations




def      lowercase__      (     __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
					UpperCAmelCase_    :      str            =						[
					    (y + 1, x + 2),
					    (y - 1, x + 2),
					    (y + 1, x - 2),
					    (y - 1, x - 2),
					    (y + 2, x + 1),
					    (y + 2, x - 1),
					    (y - 2, x + 1),
					    (y - 2, x - 1),
					]
					UpperCAmelCase_    :      Optional[Any]            =						[]

					for position in positions:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
										if 0 <= y_test < n and 0 <= x_test < n:
															permissible_positions.append(__snake_case  )

					return permissible_positions




def      lowercase__      (     __snake_case    :							list[list[int]]  ):


					'''simple docstring'''


					return not any(elem == 0 for row in board for elem in row  )




def      lowercase__      (     __snake_case    :							list[list[int]]     ,       __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if is_complete(__snake_case  ):
										return True

					for position in get_valid_pos(__snake_case     ,       len(__snake_case  )  ):
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						position

										if board[y][x] == 0:
															UpperCAmelCase_    :      Optional[Any]            =						curr + 1
															if open_knight_tour_helper(__snake_case     ,       __snake_case     ,       curr + 1  ):
																				return True
															UpperCAmelCase_    :      List[Any]            =						0

					return False




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						[[0 for i in range(__snake_case  )] for j in range(__snake_case  )]

					for i in range(__snake_case  ):
										for j in range(__snake_case  ):
															UpperCAmelCase_    :      Optional[Any]            =						1
															if open_knight_tour_helper(__snake_case     ,       (i, j)     ,       1  ):
																				return board
															UpperCAmelCase_    :      List[Any]            =						0

					UpperCAmelCase_    :      List[str]            =						F"Open Kight Tour cannot be performed on a board of size {n}"
					raise ValueError(__snake_case  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






