




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   ["image_processor", "tokenizer"]
				lowerCAmelCase__			=   "EfficientNetImageProcessor"
				lowerCAmelCase__			=   ("BertTokenizer", "BertTokenizerFast")



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple						,		_UpperCAmelCase					:						Optional[int]					)     ->			List[str]:





								'''simple docstring'''




								super().__init__(_UpperCAmelCase						,		_UpperCAmelCase					)



				def __call__(					self					:						Any						,		_UpperCAmelCase					:						Tuple=None						,		_UpperCAmelCase					:						List[Any]=None						,		_UpperCAmelCase					:						List[Any]="max_length"						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=None						,		**_UpperCAmelCase					:						Any					)     ->			Union[str, Any]:





								'''simple docstring'''




								if text is None and images is None:
												raise ValueError("""You have to specify either text or images. Both cannot be none."""					)

								if text is not None:
												_lowerCAmelCase			:			int        =					self.tokenizer(
												    _UpperCAmelCase						,		padding=_UpperCAmelCase						,		max_length=_UpperCAmelCase						,		return_tensors=_UpperCAmelCase						,		**_UpperCAmelCase					)

								if images is not None:
												_lowerCAmelCase			:			Tuple        =					self.image_processor(_UpperCAmelCase						,		return_tensors=_UpperCAmelCase						,		**_UpperCAmelCase					)

								if text is not None and images is not None:
												_lowerCAmelCase			:			List[Any]        =					image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_UpperCAmelCase					)						,		tensor_type=_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]						,		*_UpperCAmelCase					:						Dict						,		**_UpperCAmelCase					:						str					)     ->			Optional[Any]:





								'''simple docstring'''




								return self.tokenizer.batch_decode(*_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		*_UpperCAmelCase					:						Dict						,		**_UpperCAmelCase					:						List[Any]					)     ->			List[str]:





								'''simple docstring'''




								return self.tokenizer.decode(*_UpperCAmelCase						,		**_UpperCAmelCase					)



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					self.tokenizer.model_input_names
								_lowerCAmelCase			:			int        =					self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









"""simple docstring"""
import bsa
import requests






def        lowerCamelCase_  (						_lowerCamelCase				):
 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        lowerCamelCase_  (						_lowerCamelCase = 5				):
 lowerCamelCase__     :			Optional[int]     				=						int(float(_lowerCamelCase				)				)
 if num_movies < 1:
  return ()
 lowerCamelCase__     :			Any     				=						(
     'https://www.imdb.com/search/title?title_type='
     f'''feature&sort=num_votes,desc&count={num_movies}'''
 )
 lowerCamelCase__     :			Union[str, Any]     				=						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

      A_			: 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)
          )
      )

