# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						snake_case_ (	__A ):






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  Dict			,		lowercase_       :  Optional[Any]						)      ->		Dict:
       super().__init__()
       self.register_modules(unet=lowercase_			,		scheduler=lowercase_						)







   @torch.no_grad()
   def __call__(   self       :  Dict			,		lowercase_       :  int = 1			,		lowercase_       :  Optional[torch.Generator] = None			,		lowercase_       :  int = 50			,		lowercase_       :  Optional[str] = "pil"			,		lowercase_       :  bool = True			,		**lowercase_       :  List[Any]			,		)      ->		Union[ImagePipelineOutput, Tuple]:
       lowercase__				:     Optional[int]					      =       torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,		generator=lowercase_			,		)
       lowercase__				:     List[str]					      =       image.to(self.device						)

       # set step values
       self.scheduler.set_timesteps(lowercase_						)

       for t in self.progress_bar(self.scheduler.timesteps						):
           # 1. predict noise model_output
           lowercase__				:     Optional[int]					      =       self.unet(lowercase_			,		lowercase_						).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           lowercase__				:     int					      =       self.scheduler.step(lowercase_			,		lowercase_			,		lowercase_						).prev_sample

       lowercase__				:     Optional[int]					      =       (image / 2 + 0.5).clamp(0			,		1						)
       lowercase__				:     List[str]					      =       image.cpu().permute(0			,		2			,		3			,		1						).numpy()
       if output_type == "pil":
           lowercase__				:     Any					      =       self.numpy_to_pil(lowercase_						)

       if not return_dict:
           return (image,), "This is a local test"

       return ImagePipelineOutput(images=lowercase_						), "This is a local test"




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase													=		{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''FlavaFeatureExtractor''']
      UpperCamelCase													=		['''FlavaImageProcessor''']
      UpperCamelCase													=		['''FlavaProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''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

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



