
from PIL import Image






def   lowerCamelCase__							(					a		,		a      )      ->   Image:
   _A:  int					=       (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))

   def contrast(a      ) -> int:
      return int(1_28 + factor * (c - 1_28)      )

   return img.point(a      )


if __name__ == "__main__":
   # Load image
   with Image.open('image_data/lena.jpg') as img:
      # Change contrast to 170
      UpperCAmelCase__		:     List[Any]     			=      change_contrast(img, 170)
      cont_img.save('image_data/lena_high_contrast.png', format='png')





UpperCAmelCase__		:     Optional[Any]     			=      {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   list[str]:
   _A:  Union[str, Any]					=       set()
   # keep track of all the paths to be checked
   _A:  Union[str, Any]					=       [[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
      _A:  Any					=       queue.pop(0      )
      # get the last node from the path
      _A:  Union[str, Any]					=       path[-1]
      if node not in explored:
         _A:  str					=       graph[node]
         # go through all neighbour nodes, construct a new path and
         # push it into the queue
         for neighbour in neighbours:
            _A:  Optional[int]					=       list(a      )
            new_path.append(a      )
            queue.append(a      )
            # return path if neighbour is goal
            if neighbour == goal:
               return new_path

            # mark node as explored
         explored.add(a      )

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






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



