code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A :		str      							=    {
      '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
  }
  
  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __A :		Any      							=    ['''ImageGPTFeatureExtractor''']
    __A :		List[str]      							=    ['''ImageGPTImageProcessor''']
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __A :		List[str]      							=    [
        '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ImageGPTForCausalImageModeling''',
        '''ImageGPTForImageClassification''',
        '''ImageGPTModel''',
        '''ImageGPTPreTrainedModel''',
        '''load_tf_weights_in_imagegpt''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig
  
    try:
      if not is_vision_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )
  
  else:
    import sys
  
    __A :		Any      							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  
  
  