




'''simple docstring'''


UpperCAmelCase_ 		=      {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
UpperCAmelCase_ 		=      ["a", "b", "c", "d", "e"]


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]				,					a_	:							int				,					a_	:							Union[str, Any]			):
  __a			=					start
  # add current to visited
  visited.append(a_			)
  __a			=					edges[current]
  for neighbor in neighbors:
    # if neighbor not in visited, visit
    if neighbor not in visited:
      __a			=					topological_sort(a_				,					a_				,					a_			)
    # if all neighbors visited add current to sort
  sort.append(a_			)
  # if all vertices haven't been visited select a new one to visit
  if len(a_			) != len(a_			):
    for vertice in vertices:
      if vertice not in visited:
        __a			=					topological_sort(a_				,					a_				,					a_			)
    # return sort
  return sort


if __name__ == "__main__":
  UpperCAmelCase_ 		=      topological_sort("a", [], [])
  print(sort)









'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_ 		=      "path-to-your-trained-model"
UpperCAmelCase_ 		=      StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

UpperCAmelCase_ 		=      "A photo of sks dog in a bucket"
UpperCAmelCase_ 		=      pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")



