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
  
  
  snake_case				:	Tuple							   =						logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  snake_case				:	Dict							   =						direct_transformers_import(Path(__file__).parent)
  
  
  snake_case				:	Optional[int]							   =						{
      "AutoTokenizer": "PreTrainedTokenizerBase",
      "AutoFeatureExtractor": "FeatureExtractionMixin",
      "AutoImageProcessor": "ImageProcessingMixin",
  }
  
  
  
  
  
  class    _snake_case      (  __UpperCAmelCase       ):
  						UpperCamelCase__					   =					["""feature_extractor""", """tokenizer"""]
  						# Names need to be attr_class for attr in attributes
  						UpperCamelCase__					   =					None
  						UpperCamelCase__					   =					None
  						UpperCamelCase__					   =					None
  
  
  
  
  
  
  
  						def __init__(       self		,     *_a		,     **_a	):
  
  
  
  							for key in kwargs:
  								if key not in self.attributes:
  									raise TypeError(f'''Unexpected keyword argument {key}.'''	)
  							for arg, attribute_name in zip(lowerCamelCase__		,     self.attributes	):
  								if attribute_name in kwargs:
  									raise TypeError(f'''Got multiple values for argument {attribute_name}.'''	)
  								else:
  									__magic_name__	:	Optional[Any]			     =  arg
  
  							if len(lowerCamelCase__	) != len(self.attributes	):
  								raise ValueError(
  								    f'''This processor requires {len(self.attributes	)} arguments: {", ".join(self.attributes	)}. Got '''
  								    f'''{len(lowerCamelCase__	)} 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():
  								__magic_name__	:	Optional[int]			     =  getattr(self		,     f'''{attribute_name}_class'''	)
  								# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  								__magic_name__	:	Optional[int]			     =  AUTO_TO_BASE_CLASS_MAPPING.get(lowerCamelCase__		,     lowerCamelCase__	)
  								if isinstance(lowerCamelCase__		,     lowerCamelCase__	):
  									__magic_name__	:	Union[str, Any]			     =  tuple(getattr(lowerCamelCase__		,     lowerCamelCase__	) for n in class_name if n is not None	)
  								else:
  									__magic_name__	:	List[str]			     =  getattr(lowerCamelCase__		,     lowerCamelCase__	)
  
  								if not isinstance(lowerCamelCase__		,     lowerCamelCase__	):
  									raise ValueError(
  									    f'''Received a {type(lowerCamelCase__	).__name__} for argument {attribute_name}, but a {class_name} was expected.'''	)
  
  								setattr(self		,     lowerCamelCase__		,     lowerCamelCase__	)
  
  
  
  
  
  
  
  						def __repr__(       self	):
  							__magic_name__	:	Optional[int]			     =  [f'''- {name}: {repr(getattr(self		,     lowerCamelCase__	)	)}''' for name in self.attributes]
  							__magic_name__	:	Union[str, Any]			     =  "\n".join(lowerCamelCase__	)
  							return f'''{self.__class__.__name__}:\n{attributes_repr}'''
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = False		,     **_a	):
  							os.makedirs(lowerCamelCase__		,     exist_ok=lowerCamelCase__	)
  
  							if push_to_hub:
  								__magic_name__	:	Optional[Any]			     =  kwargs.pop("commit_message"		,     lowerCamelCase__	)
  								__magic_name__	:	Union[str, Any]			     =  kwargs.pop("repo_id"		,     save_directory.split(os.path.sep	)[-1]	)
  								__magic_name__	:	Tuple			     =  self._create_repo(lowerCamelCase__		,     **lowerCamelCase__	)
  								__magic_name__	:	Optional[Any]			     =  self._get_files_timestamps(lowerCamelCase__	)
  							# 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:
  								__magic_name__	:	List[Any]			     =  [getattr(self		,     lowerCamelCase__	) for attribute_name in self.attributes]
  								__magic_name__	:	str			     =  [(a.init_kwargs if isinstance(lowerCamelCase__		,     lowerCamelCase__	) else a) for a in attrs]
  								custom_object_save(self		,     lowerCamelCase__		,     config=lowerCamelCase__	)
  
  							for attribute_name in self.attributes:
  								__magic_name__	:	Tuple			     =  getattr(self		,     lowerCamelCase__	)
  								# Include the processor class in the attribute config so this processor can then be reloaded with the
  								# `AutoProcessor` API.
  								if hasattr(lowerCamelCase__		,     "_set_processor_class"	):
  									attribute._set_processor_class(self.__class__.__name__	)
  								attribute.save_pretrained(lowerCamelCase__	)
  
  							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:
  									__magic_name__	:	Optional[Any]			     =  getattr(self		,     lowerCamelCase__	)
  									if isinstance(lowerCamelCase__		,     lowerCamelCase__	):
  										del attribute.init_kwargs["auto_map"]
  
  							if push_to_hub:
  								self._upload_modified_files(
  								    lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     commit_message=lowerCamelCase__		,     token=kwargs.get("use_auth_token"	)		,     )
  
  
  
  
  
  
  
  						@classmethod
  						def     SCREAMING_SNAKE_CASE					(       cls		,     _a		,     _a = None		,     _a = False		,     _a = False		,     _a = None		,     _a = "main"		,     **_a		,     ):
  							__magic_name__	:	int			     =  cache_dir
  							__magic_name__	:	List[str]			     =  force_download
  							__magic_name__	:	List[Any]			     =  local_files_only
  							__magic_name__	:	Dict			     =  revision
  
  							__magic_name__	:	Optional[int]			     =  kwargs.pop("use_auth_token"		,     lowerCamelCase__	)
  							if use_auth_token is not None:
  								warnings.warn(
  								    "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."		,     lowerCamelCase__	)
  								if token is not None:
  									raise ValueError(
  									    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."	)
  								__magic_name__	:	List[Any]			     =  use_auth_token
  
  							if token is not None:
  								# change to `token` in a follow-up PR
  								__magic_name__	:	List[str]			     =  token
  
  							__magic_name__	:	Optional[Any]			     =  cls._get_arguments_from_pretrained(lowerCamelCase__		,     **lowerCamelCase__	)
  							return cls(*lowerCamelCase__	)
  
  
  
  
  
  
  
  						@classmethod
  						def     SCREAMING_SNAKE_CASE					(       cls		,     _a="AutoProcessor"	):
  
  
  
  							if not isinstance(lowerCamelCase__		,     lowerCamelCase__	):
  								__magic_name__	:	int			     =  auto_class.__name__
  
  							import transformers.models.auto as auto_module
  
  							if not hasattr(lowerCamelCase__		,     lowerCamelCase__	):
  								raise ValueError(f'''{auto_class} is not a valid auto class.'''	)
  
  							__magic_name__	:	Union[str, Any]			     =  auto_class
  
  
  
  
  
  
  
  						@classmethod
  						def     SCREAMING_SNAKE_CASE					(       cls		,     _a		,     **_a	):
  							__magic_name__	:	Union[str, Any]			     =  []
  							for attribute_name in cls.attributes:
  								__magic_name__	:	int			     =  getattr(cls		,     f'''{attribute_name}_class'''	)
  								if isinstance(lowerCamelCase__		,     lowerCamelCase__	):
  									__magic_name__	:	Tuple			     =  tuple(getattr(lowerCamelCase__		,     lowerCamelCase__	) if n is not None else None for n in class_name	)
  									__magic_name__	:	Optional[Any]			     =  kwargs.get("use_fast"		,     lowerCamelCase__	)
  									if use_fast and classes[1] is not None:
  										__magic_name__	:	Union[str, Any]			     =  classes[1]
  									else:
  										__magic_name__	:	List[Any]			     =  classes[0]
  								else:
  									__magic_name__	:	str			     =  getattr(lowerCamelCase__		,     lowerCamelCase__	)
  
  								args.append(attribute_class.from_pretrained(lowerCamelCase__		,     **lowerCamelCase__	)	)
  							return args
  
  
  
  
  
  
  
  						@property
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	Dict			     =  getattr(self		,     self.attributes[0]	)
  							return getattr(lowerCamelCase__		,     "model_input_names"		,     lowerCamelCase__	)
  
  
  
  
  
  
  
  snake_case				:	Any							   =						copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  				snake_case				:	Optional[int]							   =						ProcessorMixin.push_to_hub.__doc__.format(
  				    object="processor", object_class="AutoProcessor", object_files="processor files"
  				)
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  