






import bsa
import requests


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


if __name__ == "__main__":
       import json

       __A      :       Union[str, Any]      =	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)
           )
       )










'''simple docstring'''







from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__A      :       Tuple      =	datasets.utils.logging.get_logger(__name__)






class 					a__    (			folder_based_builder.FolderBasedBuilderConfig						):
     '''simple docstring'''







     _snake_case             =  None
     _snake_case             =  None







class 					a__    (			folder_based_builder.FolderBasedBuilder						):
     '''simple docstring'''







     _snake_case             =  datasets.Image()
     _snake_case             =  'image'
     _snake_case             =  ImageFolderConfig
     _snake_case             =  42  # definition at the bottom of the script
     _snake_case             =  ImageClassification(image_column='image' ,						label_column='label'						)







__A      :       Any      =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__A      :       List[Any]      =	IMAGE_EXTENSIONS


