code: 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
  
  
  a     :			Union[str, Any]					    =						logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  a     :			Optional[Any]					    =						direct_transformers_import(Path(__file__).parent)
  
  
  a     :			Union[str, Any]					    =						{
      '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
      '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
      '''AutoImageProcessor''': '''ImageProcessingMixin''',
  }
  
  class 				_lowercase						(    lowercase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						["""feature_extractor""", """tokenizer"""]
      # Names need to be attr_class for attr in attributes
      SCREAMING_SNAKE_CASE:							int        =						None
      SCREAMING_SNAKE_CASE:							List[str]        =						None
      SCREAMING_SNAKE_CASE:							List[Any]        =						None
  
  
  
  
      def __init__(						self					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
          # Sanitize args and kwargs
          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:
                  lowerCAmelCase_:       Any         =  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():
              lowerCAmelCase_:       List[Any]         =  getattr(self					,	F'''{attribute_name}_class'''   )
              # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
              lowerCAmelCase_:       int         =  AUTO_TO_BASE_CLASS_MAPPING.get(_A					,	_A   )
              if isinstance(_A					,	_A   ):
                  lowerCAmelCase_:       List[Any]         =  tuple(getattr(_A					,	_A   ) for n in class_name if n is not None   )
              else:
                  lowerCAmelCase_:       Optional[int]         =  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   ):
          lowerCAmelCase_:       int         =  [F'''- {name}: {repr(getattr(self					,	_A   )   )}''' for name in self.attributes]
          lowerCAmelCase_:       Union[str, Any]         =  '\n'.join(_A   )
          return F'''{self.__class__.__name__}:\n{attributes_repr}'''
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = False					,	**lowerCamelCase__   ):
          os.makedirs(_A					,	exist_ok=_A   )
  
          if push_to_hub:
              lowerCAmelCase_:       Dict         =  kwargs.pop("commit_message"					,	_A   )
              lowerCAmelCase_:       Optional[Any]         =  kwargs.pop("repo_id"					,	save_directory.split(os.path.sep   )[-1]   )
              lowerCAmelCase_:       List[str]         =  self._create_repo(_A					,	**_A   )
              lowerCAmelCase_:       Optional[Any]         =  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:
              lowerCAmelCase_:       Optional[Any]         =  [getattr(self					,	_A   ) for attribute_name in self.attributes]
              lowerCAmelCase_:       Dict         =  [(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:
              lowerCAmelCase_:       Union[str, Any]         =  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:
                  lowerCAmelCase_:       Optional[int]         =  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     _a						(						cls					,	lowerCamelCase__					,	lowerCamelCase__ = None					,	lowerCamelCase__ = False					,	lowerCamelCase__ = False					,	lowerCamelCase__ = None					,	lowerCamelCase__ = "main"					,	**lowerCamelCase__					,	):
          lowerCAmelCase_:       str         =  cache_dir
          lowerCAmelCase_:       Any         =  force_download
          lowerCAmelCase_:       List[str]         =  local_files_only
          lowerCAmelCase_:       Optional[int]         =  revision
  
          lowerCAmelCase_:       Optional[Any]         =  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`."   )
              lowerCAmelCase_:       str         =  use_auth_token
  
          if token is not None:
              # change to `token` in a follow-up PR
              lowerCAmelCase_:       Any         =  token
  
          lowerCAmelCase_:       Dict         =  cls._get_arguments_from_pretrained(_A					,	**_A   )
          return cls(*_A   )
  
  
  
  
      @classmethod
      def     _a						(						cls					,	lowerCamelCase__="AutoProcessor"   ):
          if not isinstance(_A					,	_A   ):
              lowerCAmelCase_:       Dict         =  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.'''   )
  
          lowerCAmelCase_:       Tuple         =  auto_class
  
  
  
  
      @classmethod
      def     _a						(						cls					,	lowerCamelCase__					,	**lowerCamelCase__   ):
          lowerCAmelCase_:       List[str]         =  []
          for attribute_name in cls.attributes:
              lowerCAmelCase_:       List[str]         =  getattr(cls					,	F'''{attribute_name}_class'''   )
              if isinstance(_A					,	_A   ):
                  lowerCAmelCase_:       int         =  tuple(getattr(_A					,	_A   ) if n is not None else None for n in class_name   )
                  lowerCAmelCase_:       Tuple         =  kwargs.get("use_fast"					,	_A   )
                  if use_fast and classes[1] is not None:
                      lowerCAmelCase_:       Tuple         =  classes[1]
                  else:
                      lowerCAmelCase_:       Optional[Any]         =  classes[0]
              else:
                  lowerCAmelCase_:       List[str]         =  getattr(_A					,	_A   )
  
              args.append(attribute_class.from_pretrained(_A					,	**_A   )   )
          return args
  
  
  
  
      @property
      def     _a						(						self   ):
          lowerCAmelCase_:       Optional[Any]         =  getattr(self					,	self.attributes[0]   )
          return getattr(_A					,	"model_input_names"					,	_A   )
  
  
  
  
  
  a     :			str					    =						copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
   a     :			str					    =						ProcessorMixin.push_to_hub.__doc__.format(
       object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
   )
style_context: def 		snake_case__  (		lowercase = 998001				):
  
     # fetches the next number
     for number in range(n - 1       ,							9999       ,							-1				):
        lowerCAmelCase_:       str         =  str(lowercase				)
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           lowerCAmelCase_:       List[str]         =  999
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
                 return number
              divisor -= 1
     raise ValueError("That number is larger than our acceptable range."				)
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')