




"""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__)










"""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




