code: 
  '''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
  
  
  A           =   logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  A           =   direct_transformers_import(Path(__file__).parent)
  
  
  A           =   {
      "AutoTokenizer": "PreTrainedTokenizerBase",
      "AutoFeatureExtractor": "FeatureExtractionMixin",
      "AutoImageProcessor": "ImageProcessingMixin",
  }
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= ['feature_extractor', 'tokenizer']
    # Names need to be attr_class for attr in attributes
    _UpperCamelCase						= None
    _UpperCamelCase						= None
    _UpperCamelCase						= None
  
    def __init__(self  : str	, *lowerCAmelCase_  : List[str]	, **lowerCAmelCase_  : Any    )   ->       Any:
          # 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(lowerCAmelCase_	, self.attributes    ):
                if attribute_name in kwargs:
                      raise TypeError(f'''Got multiple values for argument {attribute_name}.'''    )
                else:
                      snake_case_						       =							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():
                snake_case_						       =							getattr(self	, f'''{attribute_name}_class'''    )
                # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
                snake_case_						       =							AUTO_TO_BASE_CLASS_MAPPING.get(lowerCAmelCase_	, lowerCAmelCase_    )
                if isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                      snake_case_						       =							tuple(getattr(lowerCAmelCase_	, lowerCAmelCase_    ) for n in class_name if n is not None    )
                else:
                      snake_case_						       =							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  : List[str]    )   ->       Optional[int]:
          snake_case_						       =							[f'''- {name}: {repr(getattr(self	, lowerCAmelCase_    )    )}''' for name in self.attributes]
          snake_case_						       =							"\n".join(lowerCAmelCase_    )
          return f'''{self.__class__.__name__}:\n{attributes_repr}'''
  
    def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : bool = False	, **lowerCAmelCase_  : List[Any]    )   ->       List[str]:
          os.makedirs(lowerCAmelCase_	, exist_ok=lowerCAmelCase_    )
  
          if push_to_hub:
                snake_case_						       =							kwargs.pop("commit_message"	, lowerCAmelCase_    )
                snake_case_						       =							kwargs.pop("repo_id"	, save_directory.split(os.path.sep    )[-1]    )
                snake_case_						       =							self._create_repo(lowerCAmelCase_	, **lowerCAmelCase_    )
                snake_case_						       =							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:
                snake_case_						       =							[getattr(self	, lowerCAmelCase_    ) for attribute_name in self.attributes]
                snake_case_						       =							[(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:
                snake_case_						       =							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:
                      snake_case_						       =							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 		_lowerCamelCase	(cls  : Union[str, Any]	, lowerCAmelCase_  : Union[str, os.PathLike]	, lowerCAmelCase_  : Optional[Union[str, os.PathLike]] = None	, lowerCAmelCase_  : bool = False	, lowerCAmelCase_  : bool = False	, lowerCAmelCase_  : Optional[Union[str, bool]] = None	, lowerCAmelCase_  : str = "main"	, **lowerCAmelCase_  : Tuple	, )   ->       Dict:
          snake_case_						       =							cache_dir
          snake_case_						       =							force_download
          snake_case_						       =							local_files_only
          snake_case_						       =							revision
  
          snake_case_						       =							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`."    )
                snake_case_						       =							use_auth_token
  
          if token is not None:
                # change to `token` in a follow-up PR
                snake_case_						       =							token
  
          snake_case_						       =							cls._get_arguments_from_pretrained(lowerCAmelCase_	, **lowerCAmelCase_    )
          return cls(*lowerCAmelCase_    )
  
    @classmethod
    def 		_lowerCamelCase	(cls  : Dict	, lowerCAmelCase_  : Dict="AutoProcessor"    )   ->       Optional[Any]:
          if not isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                snake_case_						       =							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.'''    )
  
          snake_case_						       =							auto_class
  
    @classmethod
    def 		_lowerCamelCase	(cls  : Dict	, lowerCAmelCase_  : int	, **lowerCAmelCase_  : int    )   ->       List[Any]:
          snake_case_						       =							[]
          for attribute_name in cls.attributes:
                snake_case_						       =							getattr(cls	, f'''{attribute_name}_class'''    )
                if isinstance(lowerCAmelCase_	, lowerCAmelCase_    ):
                      snake_case_						       =							tuple(getattr(lowerCAmelCase_	, lowerCAmelCase_    ) if n is not None else None for n in class_name    )
                      snake_case_						       =							kwargs.get("use_fast"	, lowerCAmelCase_    )
                      if use_fast and classes[1] is not None:
                            snake_case_						       =							classes[1]
                      else:
                            snake_case_						       =							classes[0]
                else:
                      snake_case_						       =							getattr(lowerCAmelCase_	, lowerCAmelCase_    )
  
                args.append(attribute_class.from_pretrained(lowerCAmelCase_	, **lowerCAmelCase_    )    )
          return args
  
  
    @property
    def 		_lowerCamelCase	(self  : Any    )   ->       List[Any]:
          snake_case_						       =							getattr(self	, self.attributes[0]    )
          return getattr(lowerCAmelCase_	, "model_input_names"	, lowerCAmelCase_    )
  
  A           =   copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
       A           =   ProcessorMixin.push_to_hub.__doc__.format(
           object="processor", object_class="AutoProcessor", object_files="processor files"
       )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  