



import safs



class        __snake_case		(  safs.SaFileSystem		):
							pass










from typing import TYPE_CHECKING

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


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						int          =      ['''SegformerFeatureExtractor''']
						A__						:						Dict          =      ['''SegformerImageProcessor''']

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''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

						A__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





