




'''simple docstring'''






import bsa
import requests






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      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 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 5		)						->      tuple:
      snake_case__					:      Tuple  			=						int(float(__SCREAMING_SNAKE_CASE		)		)
      if num_movies < 1:
            return ()
      snake_case__					:      Any  			=						(
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      snake_case__					:      Union[str, Any]  			=						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

      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)
          )
      )






'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A_ 			=		{
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		["ErnieMTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		[
          "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ErnieMForMultipleChoice",
          "ErnieMForQuestionAnswering",
          "ErnieMForSequenceClassification",
          "ErnieMForTokenClassification",
          "ErnieMModel",
          "ErnieMPreTrainedModel",
          "ErnieMForInformationExtraction",
      ]


if TYPE_CHECKING:
      from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ernie_m import ErnieMTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie_m import (
                ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieMForInformationExtraction,
                ErnieMForMultipleChoice,
                ErnieMForQuestionAnswering,
                ErnieMForSequenceClassification,
                ErnieMForTokenClassification,
                ErnieMModel,
                ErnieMPreTrainedModel,
            )


else:
      import sys

      A_ 			=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
