


'''simple docstring'''


from typing import TYPE_CHECKING

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


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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_		:       Any							       =		[
				    '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:
				UpperCAmelCase_		:       Dict							       =		[
				    '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

				UpperCAmelCase_		:       Any							       =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



'''simple docstring'''


import bsa
import requests







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""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       snake_case_       (  SCREAMING_SNAKE_CASE__ = 5					):







				"""simple docstring"""





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

				UpperCAmelCase_		:       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)
				    )
				)




