


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

UpperCAmelCase__   	=							{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =True
 _lowercase							      =[]

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

 order.append(__snake_case						)

 return order
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =True
 _lowercase							      =[vert]

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

 return component



def 				UpperCAmelCase_					(							__snake_case						)     ->			list[list[int]]:







 """simple docstring"""


 _lowercase							      =len(__snake_case						) * [False]
 _lowercase							      ={vert: [] for vert in range(len(__snake_case						)						)}

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

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

 _lowercase							      =[]
 _lowercase							      =len(__snake_case						) * [False]

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

 return components_list





"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

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



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


