






"""simple docstring"""





import bsa
import requests







def 					lowerCamelCase__	(							_lowerCamelCase	:		bsa.element.ResultSet			)				->    dict[str, str]:
  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 					lowerCamelCase__	(							_lowerCamelCase	:		int = 5			)				->    tuple:
  lowerCamelCase_   					=						int(float(_lowerCamelCase			)			)
  if num_movies < 1:
    return ()
  lowerCamelCase_   					=						(
      'https://www.imdb.com/search/title?title_type='
      F'''feature&sort=num_votes,desc&count={num_movies}'''
  )
  lowerCamelCase_   					=						bsa.BeautifulSoup(requests.get(_lowerCamelCase			).content		,    'html.parser'			)
  return tuple(
      get_movie_data_from_soup(_lowerCamelCase			)
      for movie in source.find_all('div'		,    class_='lister-item mode-advanced'			)			)


if __name__ == "__main__":
  import json

  _SCREAMING_SNAKE_CASE :    List[str]    =						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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE :    Optional[Any]    =						[
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE :    List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




