code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase					 =	{
      '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''VideoMAEForPreTraining''',
  	    '''VideoMAEModel''',
  	    '''VideoMAEPreTrainedModel''',
  	    '''VideoMAEForVideoClassification''',
  	]
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	['''VideoMAEFeatureExtractor''']
  	lowerCAmelCase					 =	['''VideoMAEImageProcessor''']
  
  if TYPE_CHECKING:
  	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_videomae import (
  		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    VideoMAEForPreTraining,
  		    VideoMAEForVideoClassification,
  		    VideoMAEModel,
  		    VideoMAEPreTrainedModel,
  		)
  
  	try:
  		if not is_vision_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .feature_extraction_videomae import VideoMAEFeatureExtractor
  		from .image_processing_videomae import VideoMAEImageProcessor
  
  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
  
  
  
  
  
  
  