
'''simple docstring'''



UpperCamelCase__					:						str											=     {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

UpperCamelCase__					:						int											=     {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   list:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = True
   _SCREAMING_SNAKE_CASE          = []

   for neighbour in graph[vert]:
      if not visited[neighbour]:
         connected_verts += dfs(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )

   return [vert, *connected_verts]
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   list:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = len(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = graph_size * [False]
   _SCREAMING_SNAKE_CASE          = []

   for i in range(SCREAMING_SNAKE_CASE_  ):
      if not visited[i]:
         _SCREAMING_SNAKE_CASE          = dfs(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
         components_list.append(SCREAMING_SNAKE_CASE_  )

   return components_list


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       _a (_lowerCamelCase):





       """simple docstring"""




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






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

          # set step values
          self.scheduler.set_timesteps(A__					)

          for t in self.progress_bar(self.scheduler.timesteps					):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE          = self.unet(A__				,							A__					).sample

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

          _SCREAMING_SNAKE_CASE          = (image / 2 + 0.5).clamp(0				,							1					)
          _SCREAMING_SNAKE_CASE          = image.cpu().permute(0				,							2				,							3				,							1					).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE          = self.numpy_to_pil(A__					)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






