
'''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


SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					{
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     :    Tuple      							=					[
					    "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

					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import bsa
import requests




def 				__magic_name__				(     __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 				__magic_name__				(     __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__     :    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)
					    )
					)



