code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase					 =	{'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	['''LevitFeatureExtractor''']
  	lowerCAmelCase					 =	['''LevitImageProcessor''']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''LevitForImageClassification''',
  	    '''LevitForImageClassificationWithTeacher''',
  	    '''LevitModel''',
  	    '''LevitPreTrainedModel''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig
  
  	try:
  		if not is_vision_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .feature_extraction_levit import LevitFeatureExtractor
  		from .image_processing_levit import LevitImageProcessor
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_levit import (
  		    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    LevitForImageClassification,
  		    LevitForImageClassificationWithTeacher,
  		    LevitModel,
  		    LevitPreTrainedModel,
  		)
  else:
  	import sys
  
  	lowerCAmelCase					 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  