
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		    =	{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__		    =	[
						    "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "QDQBertForMaskedLM",
						    "QDQBertForMultipleChoice",
						    "QDQBertForNextSentencePrediction",
						    "QDQBertForQuestionAnswering",
						    "QDQBertForSequenceClassification",
						    "QDQBertForTokenClassification",
						    "QDQBertLayer",
						    "QDQBertLMHeadModel",
						    "QDQBertModel",
						    "QDQBertPreTrainedModel",
						    "load_tf_weights_in_qdqbert",
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

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




'''simple docstring'''
import bsa
import requests




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__ = 5       )   ->				tuple:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		int(float(SCREAMING_SNAKE_CASE__       )       )
		if num_movies < 1:
				return ()
		snake_case   :    int			       =		(
		    '''https://www.imdb.com/search/title?title_type='''
		    F'feature&sort=num_votes,desc&count={num_movies}'
		)
		snake_case   :    Optional[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

						lowercase__		    =	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)
						    )
						)


