code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  SCREAMING_SNAKE_CASE : str          =    {
      "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
  }
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : int          =    ["SegformerFeatureExtractor"]
  	SCREAMING_SNAKE_CASE : Optional[Any]          =    ["SegformerImageProcessor"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    [
  	    "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "SegformerDecodeHead",
  	    "SegformerForImageClassification",
  	    "SegformerForSemanticSegmentation",
  	    "SegformerLayer",
  	    "SegformerModel",
  	    "SegformerPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[str]          =    [
  	    "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
  
  	SCREAMING_SNAKE_CASE : Optional[int]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
  	from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  