




'''simple docstring'''


import math





class    __lowercase						:







   def __init__(	self      ,							UpperCamelCase=0      )				->    Dict:  # a graph with Node 0,1,...,N-1
     __a			=					n
     __a			=					[
         [math.inf for j in range(0      ,							a_      )] for i in range(0      ,							a_      )
     ]  # adjacency matrix for weight
     __a			=					[
         [math.inf for j in range(0      ,							a_      )] for i in range(0      ,							a_      )
     ]  # dp[i][j] stores minimum distance from i to j







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    int:
     __a			=					w







   def 		UpperCamelCase__			(	self      )				->    List[Any]:



     for k in range(0      ,							self.n      ):
       for i in range(0      ,							self.n      ):
         for j in range(0      ,							self.n      ):
           __a			=					min(self.dp[i][j]      ,							self.dp[i][k] + self.dp[k][j]      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[Any]:
     return self.dp[u][v]


if __name__ == "__main__":
  UpperCAmelCase_ 		=      Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)






'''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")



