






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A     						= {
    '''configuration_bark''': [
        '''BARK_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BarkCoarseConfig''',
        '''BarkConfig''',
        '''BarkFineConfig''',
        '''BarkSemanticConfig''',
    ],
    '''processing_bark''': ['''BarkProcessor'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''BARK_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BarkFineModel''',
          '''BarkSemanticModel''',
          '''BarkCoarseModel''',
          '''BarkModel''',
          '''BarkPreTrainedModel''',
          '''BarkCausalModel''',
      ]

if TYPE_CHECKING:
      from .configuration_bark import (
          BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BarkCoarseConfig,
          BarkConfig,
          BarkFineConfig,
          BarkSemanticConfig,
      )
      from .processing_bark import BarkProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bark import (
                BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
                BarkCausalModel,
                BarkCoarseModel,
                BarkFineModel,
                BarkModel,
                BarkPreTrainedModel,
                BarkSemanticModel,
            )

else:
      import sys

      __A     						= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''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)
          )
      )