code: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  __lowerCAmelCase							:			Optional[Any]				={
      "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
  }
  
  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Optional[Any]				=["SegformerFeatureExtractor"]
    __lowerCAmelCase							:			Optional[int]				=["SegformerImageProcessor"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Optional[int]				=[
        "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SegformerDecodeHead",
        "SegformerForImageClassification",
        "SegformerForSemanticSegmentation",
        "SegformerLayer",
        "SegformerModel",
        "SegformerPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "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							:			Optional[Any]				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  __lowerCAmelCase							:			Dict				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
       warnings.warn(
           "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use ViltImageProcessor instead."		, lowercase_		, )
       super().__init__(*lowercase_		, **lowercase_							)
  
  
  
  