





'''simple docstring'''




__SCREAMING_SNAKE_CASE     :int       =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__SCREAMING_SNAKE_CASE     :Any       =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def 	UpperCAmelCase_							(					__lowercase							:      dict[int, list[int]]			,     __lowercase							:      int			,     __lowercase							:      list[bool]       )		->       list[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   True
     _UpperCAmelCase     							=   []

     for neighbour in graph[vert]:
          if not visited[neighbour]:
               order += topology_sort(__lowercase			,     __lowercase			,     __lowercase       )

     order.append(__lowercase       )

     return order
def 	UpperCAmelCase_							(					__lowercase							:      dict[int, list[int]]			,     __lowercase							:      int			,     __lowercase							:      list[bool]       )		->       list[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   True
     _UpperCAmelCase     							=   [vert]

     for neighbour in reversed_graph[vert]:
          if not visited[neighbour]:
               component += find_components(__lowercase			,     __lowercase			,     __lowercase       )

     return component






def 	UpperCAmelCase_							(					__lowercase							:      dict[int, list[int]]       )		->       list[list[int]]:


     '''simple docstring'''



     _UpperCAmelCase     							=   len(__lowercase       ) * [False]
     _UpperCAmelCase     							=   {vert: [] for vert in range(len(__lowercase       )       )}

     for vert, neighbours in graph.items():
          for neighbour in neighbours:
               reversed_graph[neighbour].append(__lowercase       )

     _UpperCAmelCase     							=   []
     for i, was_visited in enumerate(__lowercase       ):
          if not was_visited:
               order += topology_sort(__lowercase			,     __lowercase			,     __lowercase       )

     _UpperCAmelCase     							=   []
     _UpperCAmelCase     							=   len(__lowercase       ) * [False]

     for i in range(len(__lowercase       )       ):
          _UpperCAmelCase     							=   order[len(__lowercase       ) - i - 1]
          if not visited[vert]:
               _UpperCAmelCase     							=   find_components(__lowercase			,     __lowercase			,     __lowercase       )
               components_list.append(__lowercase       )

     return components_list









'''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 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

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

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

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

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





