

"""simple docstring"""






import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__lowerCAmelCase				      =   logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__lowerCAmelCase				      =   direct_transformers_import(Path(__file__).parent)


__lowerCAmelCase				      =   {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}
class 	_lowerCAmelCase    (      UpperCamelCase_       ):
							__lowerCAmelCase					:    Union[str, Any]     =  ['''feature_extractor''', '''tokenizer''']
							# Names need to be attr_class for attr in attributes
							__lowerCAmelCase					:    List[Any]     =  None
							__lowerCAmelCase					:    int     =  None
							__lowerCAmelCase					:    Any     =  None

							def __init__( self       :    Any ,		*a       :    Optional[int] ,		**a       :    List[Any]    )   ->    Any:

									"""simple docstring"""






									for key in kwargs:
											if key not in self.attributes:
													raise TypeError(f"""Unexpected keyword argument {key}."""    )
									for arg, attribute_name in zip(__a ,		self.attributes    ):
											if attribute_name in kwargs:
													raise TypeError(f"""Got multiple values for argument {attribute_name}."""    )
											else:
													lowercase										=							arg

									if len(__a    ) != len(self.attributes    ):
											raise ValueError(
											    f"""This processor requires {len(self.attributes    )} arguments: {", ".join(self.attributes    )}. Got """
											    f"""{len(__a    )} arguments instead."""    )

									# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
									for attribute_name, arg in kwargs.items():
											lowercase										=							getattr(self ,		f"""{attribute_name}_class"""    )
											# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
											lowercase										=							AUTO_TO_BASE_CLASS_MAPPING.get(__a ,		__a    )
											if isinstance(__a ,		__a    ):
													lowercase										=							tuple(getattr(__a ,		__a    ) for n in class_name if n is not None    )
											else:
													lowercase										=							getattr(__a ,		__a    )

											if not isinstance(__a ,		__a    ):
													raise ValueError(
													    f"""Received a {type(__a    ).__name__} for argument {attribute_name}, but a {class_name} was expected."""    )

											setattr(self ,		__a ,		__a    )

							def __repr__( self       :    List[str]    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							[f"""- {name}: {repr(getattr(self ,		__a    )    )}""" for name in self.attributes]
									lowercase										=							'\n'.join(__a    )
									return f"""{self.__class__.__name__}:\n{attributes_repr}"""

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    Dict ,		a       :    List[Any] = False ,		**a       :    Optional[Any]    )   ->    Tuple:

									"""simple docstring"""
									os.makedirs(__a ,		exist_ok=__a    )

									if push_to_hub:
											lowercase										=							kwargs.pop('''commit_message''' ,		__a    )
											lowercase										=							kwargs.pop('''repo_id''' ,		save_directory.split(os.path.sep    )[-1]    )
											lowercase										=							self._create_repo(__a ,		**__a    )
											lowercase										=							self._get_files_timestamps(__a    )
									# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
									# loaded from the Hub.
									if self._auto_class is not None:
											lowercase										=							[getattr(self ,		__a    ) for attribute_name in self.attributes]
											lowercase										=							[(a.init_kwargs if isinstance(__a ,		__a    ) else a) for a in attrs]
											custom_object_save(self ,		__a ,		config=__a    )

									for attribute_name in self.attributes:
											lowercase										=							getattr(self ,		__a    )
											# Include the processor class in the attribute config so this processor can then be reloaded with the
											# `AutoProcessor` API.
											if hasattr(__a ,		'''_set_processor_class'''    ):
													attribute._set_processor_class(self.__class__.__name__    )
											attribute.save_pretrained(__a    )

									if self._auto_class is not None:
											# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
											for attribute_name in self.attributes:
													lowercase										=							getattr(self ,		__a    )
													if isinstance(__a ,		__a    ):
															del attribute.init_kwargs["auto_map"]

									if push_to_hub:
											self._upload_modified_files(
											    __a ,		__a ,		__a ,		commit_message=__a ,		token=kwargs.get('''use_auth_token'''    ) ,		)

							@classmethod
							def 					_lowerCAmelCase  ( cls       :    Any ,		a       :    str ,		a       :    Tuple = None ,		a       :    int = False ,		a       :    Optional[int] = False ,		a       :    Optional[int] = None ,		a       :    Dict = "main" ,		**a       :    int ,		)   ->    Optional[int]:

									"""simple docstring"""
									lowercase										=							cache_dir
									lowercase										=							force_download
									lowercase										=							local_files_only
									lowercase										=							revision

									lowercase										=							kwargs.pop('''use_auth_token''' ,		__a    )
									if use_auth_token is not None:
											warnings.warn(
											    '''The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.''' ,		__a    )
											if token is not None:
													raise ValueError(
													    '''`token` and `use_auth_token` are both specified. Please set only the argument `token`.'''    )
											lowercase										=							use_auth_token

									if token is not None:
											# change to `token` in a follow-up PR
											lowercase										=							token

									lowercase										=							cls._get_arguments_from_pretrained(__a ,		**__a    )
									return cls(*__a    )

							@classmethod
							def 					_lowerCAmelCase  ( cls       :    Tuple ,		a       :    Tuple="AutoProcessor"    )   ->    Optional[int]:

									"""simple docstring"""






									if not isinstance(__a ,		__a    ):
											lowercase										=							auto_class.__name__

									import transformers.models.auto as auto_module

									if not hasattr(__a ,		__a    ):
											raise ValueError(f"""{auto_class} is not a valid auto class."""    )

									lowercase										=							auto_class

							@classmethod
							def 					_lowerCAmelCase  ( cls       :    Any ,		a       :    Any ,		**a       :    Union[str, Any]    )   ->    str:

									"""simple docstring"""
									lowercase										=							[]
									for attribute_name in cls.attributes:
											lowercase										=							getattr(cls ,		f"""{attribute_name}_class"""    )
											if isinstance(__a ,		__a    ):
													lowercase										=							tuple(getattr(__a ,		__a    ) if n is not None else None for n in class_name    )
													lowercase										=							kwargs.get('''use_fast''' ,		__a    )
													if use_fast and classes[1] is not None:
															lowercase										=							classes[1]
													else:
															lowercase										=							classes[0]
											else:
													lowercase										=							getattr(__a ,		__a    )

											args.append(attribute_class.from_pretrained(__a ,		**__a    )    )
									return args

							@property
							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    str:

									"""simple docstring"""
									lowercase										=							getattr(self ,		self.attributes[0]    )
									return getattr(__a ,		'''model_input_names''' ,		__a    )




__lowerCAmelCase				      =   copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
							__lowerCAmelCase				      =   ProcessorMixin.push_to_hub.__doc__.format(
							    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
							)







'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			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 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       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)
          )
      )