code: 
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				["image_processor", "tokenizer"]
       __SCREAMING_SNAKE_CASE						    =				"AutoImageProcessor"
       __SCREAMING_SNAKE_CASE						    =				"AutoTokenizer"
  
  
       def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       List[str]:
              _A   : int       					=				None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                         " instead."				,		__lowerCamelCase				,		)
                     _A   : Dict       					=				kwargs.pop("feature_extractor")
  
              _A   : str       					=				image_processor if image_processor is not None else feature_extractor
              if image_processor is None:
                     raise ValueError("You need to specify an `image_processor`.")
              if tokenizer is None:
                     raise ValueError("You need to specify a `tokenizer`.")
  
              super().__init__(__lowerCamelCase				,		__lowerCamelCase)
              _A   : Tuple       					=				self.image_processor
              _A   : Dict       					=				False
  
  
       def __call__(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       str:
              # For backward compatibility
              if self._in_target_context_manager:
                     return self.current_processor(*__lowerCamelCase				,		**__lowerCamelCase)
  
              _A   : Optional[Any]       					=				kwargs.pop("images"				,		__lowerCamelCase)
              _A   : Optional[Any]       					=				kwargs.pop("text"				,		__lowerCamelCase)
              if len(__lowerCamelCase) > 0:
                     _A   : Dict       					=				args[0]
                     _A   : str       					=				args[1:]
  
              if images is None and text is None:
                     raise ValueError("You need to specify either an `images` or `text` input to process.")
  
              if images is not None:
                     _A   : Union[str, Any]       					=				self.image_processor(__lowerCamelCase				,		*__lowerCamelCase				,		**__lowerCamelCase)
              if text is not None:
                     _A   : str       					=				self.tokenizer(__lowerCamelCase				,		**__lowerCamelCase)
  
              if text is None:
                     return inputs
              elif images is None:
                     return encodings
              else:
                     _A   : List[Any]       					=				encodings["input_ids"]
                     return inputs
  
  
       def 				_lowerCamelCase				(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       str:
              return self.tokenizer.batch_decode(*__lowerCamelCase				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       Optional[Any]:
              return self.tokenizer.decode(*__lowerCamelCase				,		**__lowerCamelCase)
  
  
       @contextmanager
       def 				_lowerCamelCase				(		self)    ->       int:
              warnings.warn(
                  "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                  "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                  "your images inputs, or in a separate call.")
              _A   : List[str]       					=				True
              _A   : Union[str, Any]       					=				self.tokenizer
              yield
              _A   : List[Any]       					=				self.image_processor
              _A   : Optional[Any]       					=				False
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       int:
              warnings.warn(
                  "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,		__lowerCamelCase				,		)
              return self.image_processor_class
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       int:
              warnings.warn(
                  "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,		__lowerCamelCase				,		)
              return self.image_processor
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  