


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowercase          = {
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase          = ["""SegformerFeatureExtractor"""]
 __lowercase          = ["""SegformerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase          = [
     """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SegformerDecodeHead""",
     """SegformerForImageClassification""",
     """SegformerForSemanticSegmentation""",
     """SegformerLayer""",
     """SegformerModel""",
     """SegformerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase          = [
     """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

 __lowercase          = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






