






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    :		UNetaDModel
       __lowerCAmelCase    :		ScoreSdeVeScheduler





       def __init__(				self	:Dict,      snake_case	:UNetaDModel,      snake_case	:ScoreSdeVeScheduler):



          """simple docstring"""

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





       @torch.no_grad()
       def __call__(				self	:Optional[Any],      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	:int,      ):



          """simple docstring"""

          _lowercase              =self.unet.config.sample_size
          _lowercase              =(batch_size, 3, img_size, img_size)

          _lowercase              =self.unet

          _lowercase              =randn_tensor(snake_case,      generator=snake_case) * self.scheduler.init_noise_sigma
          _lowercase              =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)):
             _lowercase              =self.scheduler.sigmas[i] * torch.ones(shape[0],      device=self.device)

             # correction step
             for _ in range(self.scheduler.config.correct_steps):
                _lowercase              =self.unet(snake_case,      snake_case).sample
                _lowercase              =self.scheduler.step_correct(snake_case,      snake_case,      generator=snake_case).prev_sample

             # prediction step
             _lowercase              =model(snake_case,      snake_case).sample
             _lowercase              =self.scheduler.step_pred(snake_case,      snake_case,      snake_case,      generator=snake_case)

             _lowercase       ,       _lowercase              =output.prev_sample, output.prev_sample_mean

          _lowercase              =sample_mean.clamp(0,      1)
          _lowercase              =sample.cpu().permute(0,      2,      3,      1).numpy()
          if output_type == "pil":
             _lowercase              =self.numpy_to_pil(snake_case)

          if not return_dict:
             return (sample,)

          return ImagePipelineOutput(images=snake_case)










class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


