
import bsa
import requests





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    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__      ( __lowerCAmelCase = 5							):
    snake_case__  =			int(float(SCREAMING_SNAKE_CASE_							)							)
    if num_movies < 1:
        return ()
    snake_case__  =			(
        "https://www.imdb.com/search/title?title_type="
        F"""feature&sort=num_votes,desc&count={num_movies}"""
    )
    snake_case__  =			bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_							).content      ,						"html.parser"							)
    return tuple(
        get_movie_data_from_soup(SCREAMING_SNAKE_CASE_							)
        for movie in source.find_all("div"      ,						class_="lister-item mode-advanced"							)							)


if __name__ == "__main__":
     import json

     __magic_name__           =					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)
         )
     )


from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__magic_name__           =					[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			len(cells[0]							)  # 31
    snake_case__  =			[]
    for i in range(__lowerCAmelCase							):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        snake_case__  =			0 if i == 0 else cells[time][i - 1]
        snake_case__  =			0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        snake_case__  =			7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""      ,						2							)
        next_generation.append(rule[situation]							)
    return next_generation





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


if __name__ == "__main__":
     __magic_name__           =					bin(int(input('''Rule:\n''').strip()))[2:]
     __magic_name__           =					format_ruleset(int(rule_num))
     for time in range(16):
          CELLS.append(new_generation(CELLS, rule, time))
     __magic_name__           =					generate_image(CELLS)
     # Uncomment to save the image
     # img.save(f"rule_{rule_num}.png")
     img.show()



