






import heapq



def 					_lowerCamelCase(			lowerCAmelCase__				:		dict      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	list[list]													=   []

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCAmelCase__    ,    [-1 * len(lowerCAmelCase__      ), (key, value)]      )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE_   :	int													=   set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								SCREAMING_SNAKE_CASE_   :	Any													=   heapq.heappop(lowerCAmelCase__      )[1][0]
								chosen_vertices.add(lowerCAmelCase__      )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																SCREAMING_SNAKE_CASE_   :	Tuple													=   elem[1][1].index(lowerCAmelCase__      )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCAmelCase__      )
				return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				A        =			{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A        =			input('Enter image url: ').strip()
				print(f"""Downloading image from {url} ...""")
				A        =			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A        =			soup.find('meta', {'property': 'og:image'})['content']
				A        =			requests.get(image_url).content
				A        =			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}.""")