
import heapq



def 							lowerCamelCase_	( __UpperCamelCase  ):
				A_													=	[]

				# 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(_SCREAMING_SNAKE_CASE			,    [-1 * len(_SCREAMING_SNAKE_CASE  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				A_													=	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
								A_													=	heapq.heappop(_SCREAMING_SNAKE_CASE  )[1][0]
								chosen_vertices.add(_SCREAMING_SNAKE_CASE  )

								# 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]:
																A_													=	elem[1][1].index(_SCREAMING_SNAKE_CASE  )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(_SCREAMING_SNAKE_CASE  )
				return chosen_vertices


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						SCREAMING_SNAKE_CASE :		Optional[Any]       			=							{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)}''')



'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text,'html.parser' )
			__A=	soup.find_all('td',attrs='titleColumn' )
			__A=	soup.find_all('td',class_='ratingColumn imdbRating' )
			return {
			    title.a.text: float(rating.strong.text )
			    for title, rating in zip(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	csv.writer(_SCREAMING_SNAKE_CASE )
						writer.writerow(['Movie title', 'IMDb rating'] )
						for title, rating in movies.items():
									writer.writerow([title, rating] )


if __name__ == "__main__":
					write_movies()



