


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class   __a			:
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        if dst_width < 0 or dst_height < 0:
               raise ValueError('Destination width/height should be > 0' )

        lowercase__:   Dict  			=   img
        lowercase__:   Any  			=   img.shape[1]
        lowercase__:   int  			=   img.shape[0]
        lowercase__:   int  			=   dst_width
        lowercase__:   Optional[int]  			=   dst_height

        lowercase__:   Optional[Any]  			=   self.src_w / self.dst_w
        lowercase__:   Union[str, Any]  			=   self.src_h / self.dst_h

        lowercase__:   Optional[Any]  			=   (
            np.ones((self.dst_h, self.dst_w, 3)				, np.uinta ) * 255
        )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        for i in range(self.dst_h ):
               for j in range(self.dst_w ):
                      lowercase__:   Any  			=   self.img[self.get_y(lowerCAmelCase__ )][self.get_x(lowerCAmelCase__ )]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        return int(self.ratio_x * x )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        return int(self.ratio_y * y )


if __name__ == "__main__":
 __lowerCAmelCase       ,__lowerCAmelCase		       =							8_00, 6_00
 __lowerCAmelCase		       =							imread('''image_data/lena.jpg''', 1)
 __lowerCAmelCase		       =							NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     F'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






__lowerCAmelCase		       =							{
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

    # in case there's no path between the 2 nodes
       return []




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   List[str]  			=   dist[node] + 1
       return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4


