code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCamelCase     =      {
      '''configuration_flava''': [
          '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''FlavaConfig''',
          '''FlavaImageCodebookConfig''',
          '''FlavaImageConfig''',
          '''FlavaMultimodalConfig''',
          '''FlavaTextConfig''',
      ],
  }
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''FlavaFeatureExtractor''']
         lowerCamelCase     =      ['''FlavaImageProcessor''']
         lowerCamelCase     =      ['''FlavaProcessor''']
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''FlavaForPreTraining''',
             '''FlavaImageCodebook''',
             '''FlavaImageModel''',
             '''FlavaModel''',
             '''FlavaMultimodalModel''',
             '''FlavaPreTrainedModel''',
             '''FlavaTextModel''',
         ]
  
  if TYPE_CHECKING:
         from .configuration_flava import (
             FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
             FlavaConfig,
             FlavaImageCodebookConfig,
             FlavaImageConfig,
             FlavaMultimodalConfig,
             FlavaTextConfig,
         )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_flava import FlavaFeatureExtractor
                from .image_processing_flava import FlavaImageProcessor
                from .processing_flava import FlavaProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flava import (
                    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
                    FlavaForPreTraining,
                    FlavaImageCodebook,
                    FlavaImageModel,
                    FlavaModel,
                    FlavaMultimodalModel,
                    FlavaPreTrainedModel,
                    FlavaTextModel,
                )
  
  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 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
     lowerCamelCase__				:			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
  