




from __future__ import annotations
class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   Dict          =							vertices
       UpperCamelCase				:   Optional[int]          =							[[0] * vertices for _ in range(SCREAMING_SNAKE_CASE_ )]







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       print("""Vertex \t Distance from Source""" )
       for vertex in range(self.vertices ):
           print(SCREAMING_SNAKE_CASE_    ,      """\t\t"""    ,      distances_from_source[vertex] )







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   int          =							1e7
       UpperCamelCase				:   str          =							0

       # Search not nearest vertex not in the shortest path tree
       for vertex in range(self.vertices ):
           if distances_from_source[vertex] < minimum and visited[vertex] is False:
               UpperCamelCase				:   Optional[int]          =							distances_from_source[vertex]
               UpperCamelCase				:   List[str]          =							vertex
       return min_index







   def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
       UpperCamelCase				:   int          =							[int(1e7 )] * self.vertices  # distances from the source
       UpperCamelCase				:   Dict          =							0
       UpperCamelCase				:   List[str]          =							[False] * self.vertices

       for _ in range(self.vertices ):
           UpperCamelCase				:   List[Any]          =							self.minimum_distance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
           UpperCamelCase				:   List[Any]          =							True

           # Update dist value of the adjacent vertices
           # of the picked vertex only if the current
           # distance is greater than new distance and
           # the vertex in not in the shortest path tree
           for v in range(self.vertices ):
               if (
                   self.graph[u][v] > 0
                   and visited[v] is False
                   and distances[v] > distances[u] + self.graph[u][v]
               ):
                   UpperCamelCase				:   List[Any]          =							distances[u] + self.graph[u][v]

       self.print_solution(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
  __A :		List[str]      							=    Graph(9)
  __A :		Optional[int]      							=    [
      [0, 4, 0, 0, 0, 0, 0, 8, 0],
      [4, 0, 8, 0, 0, 0, 0, 11, 0],
      [0, 8, 0, 7, 0, 4, 0, 0, 2],
      [0, 0, 7, 0, 9, 14, 0, 0, 0],
      [0, 0, 0, 9, 0, 10, 0, 0, 0],
      [0, 0, 4, 14, 10, 0, 2, 0, 0],
      [0, 0, 0, 0, 0, 2, 0, 1, 6],
      [8, 11, 0, 0, 0, 0, 1, 0, 7],
      [0, 0, 2, 0, 0, 0, 6, 7, 0],
  ]
  graph.dijkstra(0)










"""simple docstring"""




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[int]          =							min(img.size    )

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

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







    '''simple docstring'''
    UpperCamelCase				:   Any          =							2.0 * x - 1.0
    return x
def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							x.detach().cpu()
    if process:
        UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
    UpperCamelCase				:   List[Any]          =							x.numpy()
    if process:
        UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
    UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
    if not x.mode == mode:
        UpperCamelCase				:   Any          =							x.convert(snake_case_    )
    return x
def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
    UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
    UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
    return x


def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
    return x.permute(2						,0						,1    ).unsqueeze(0    )




