






'''simple docstring'''






from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

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




def 							_SCREAMING_SNAKE_CASE	(					A	:							dict	,		A	:							str	)     ->			list[str]:







   """simple docstring"""
   __snake_case					:   Dict  				=			{start}
   __snake_case					:   int  				=			[start]
   __snake_case					:   Queue  				=			Queue()
   queue.put(A	)
   while not queue.empty():
      __snake_case					:   Optional[Any]  				=			queue.get()
      for w in graph[v]:
         if w not in explored:
            explored.add(A	)
            result.append(A	)
            queue.put(A	)
   return result




def 							_SCREAMING_SNAKE_CASE	(					A	:							dict	,		A	:							str	)     ->			list[str]:







   """simple docstring"""
   __snake_case					:   List[str]  				=			{start}
   __snake_case					:   Optional[Any]  				=			[start]
   __snake_case					:   Tuple  				=			deque([start]	)
   while queue:
      __snake_case					:   Optional[Any]  				=			queue.popleft()
      for child in graph[v]:
         if child not in visited:
            visited.add(A	)
            result.append(A	)
            queue.append(A	)
   return result




def 							_SCREAMING_SNAKE_CASE	(					A	:							str	)     ->			None:







   """simple docstring"""
   __snake_case					:   List[Any]  				=			F"""from __main__ import G, {name}"""
   __snake_case					:   Optional[int]  				=			1_00_00
   __snake_case					:   List[Any]  				=			timeit(F"""{name}(G, 'A')"""	,		setup=A	,		number=A	)
   print(F"""{name:<35} finished {number} runs in {res:.5f} seconds"""	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      benchmark_function('''breadth_first_search''')
      benchmark_function('''breadth_first_search_with_deque''')
      # breadth_first_search                finished 10000 runs in 0.20999 seconds
      # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds







'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )