





"""simple docstring"""


lowerCamelCase__				  =   {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		list[str]:




      """simple docstring"""

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

            # mark node as explored
                  explored.add(lowercase_			)

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




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		int:




      """simple docstring"""

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







"""simple docstring"""


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
  lowerCamelCase__				  =   input("Enter image url: ").strip()
  print(f"""Downloading image from {url} ...""")
  lowerCamelCase__				  =   BeautifulSoup(requests.get(url).content, "html.parser")
  # The image URL is in the content field of the first meta tag with property og:image
  lowerCamelCase__				  =   soup.find("meta", {"property": "og:image"})["content"]
  lowerCamelCase__				  =   requests.get(image_url).content
  lowerCamelCase__				  =   f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
  with open(file_name, "wb") as fp:
    fp.write(image_data)
  print(f"""Done. Image saved to disk as {file_name}.""")
