


import bsa
import requests

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   bsa.element.ResultSet							):
     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__     :   int = 5							):
     __UpperCamelCase       				=int(float(SCREAMING_SNAKE_CASE__							)							)
     if num_movies < 1:
          return ()
     __UpperCamelCase       				=(
         'https://www.imdb.com/search/title?title_type='
         F'feature&sort=num_votes,desc&count={num_movies}'
     )
     __UpperCamelCase       				=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)
       )
   )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

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





