

def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Any:

     """simple docstring"""







     print("\nVertex Distance"					)
     for i in range(SCREAMING_SNAKE_CASE					):
          if dist[i] != float("inf"					):
               print(SCREAMING_SNAKE_CASE		,				"\t"		,				int(dist[i]					)		,				end="\t"					)
          else:
               print(SCREAMING_SNAKE_CASE		,				"\t"		,				"INF"		,				end="\t"					)
          print()







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     float("inf"					)
     _lowerCAmelCase :       Optional[Any]	   =     -1
     for i in range(SCREAMING_SNAKE_CASE					):
          if (not vset[i]) and mdist[i] < min_val:
               _lowerCAmelCase :       Any	   =     i
               _lowerCAmelCase :       Optional[int]	   =     mdist[i]
     return min_ind







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     [float("inf"					) for _ in range(SCREAMING_SNAKE_CASE					)]
     _lowerCAmelCase :       Optional[int]	   =     [False for _ in range(SCREAMING_SNAKE_CASE					)]
     _lowerCAmelCase :       Tuple	   =     0.0

     for _ in range(v - 1					):
          _lowerCAmelCase :       Any	   =     min_dist(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
          _lowerCAmelCase :       Tuple	   =     True

          for i in range(SCREAMING_SNAKE_CASE					):
               if (
                   (not vset[i])
                   and graph[u][i] != float("inf"					)
                   and mdist[u] + graph[u][i] < mdist[i]
               ):
                    _lowerCAmelCase :       int	   =     mdist[u] + graph[u][i]

     print_dist(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  __UpperCAmelCase     					=  int(input('Enter number of vertices: ').strip())
  __UpperCAmelCase     					=  int(input('Enter number of edges: ').strip())

  __UpperCAmelCase     					=  [[float('inf') for i in range(V)] for j in range(V)]

  for i in range(V):
    __UpperCAmelCase     					=  0.0

  for i in range(E):
    print('\nEdge ', i + 1)
    __UpperCAmelCase     					=  int(input('Enter source:').strip())
    __UpperCAmelCase     					=  int(input('Enter destination:').strip())
    __UpperCAmelCase     					=  float(input('Enter weight:').strip())
    __UpperCAmelCase     					=  weight

  __UpperCAmelCase     					=  int(input('\nEnter shortest path source:').strip())
  dijkstra(graph, V, gsrc)









import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






