


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Any     ):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_   ,				scheduler=lowerCamelCase_     )

       @torch.no_grad()
       def __call__( self   :  int   ,				lowerCamelCase_   :  int = 1   ,				lowerCamelCase_   :  Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,				lowerCamelCase_   :  int = 1_0_0_0   ,				lowerCamelCase_   :  Optional[str] = "pil"   ,				lowerCamelCase_   :  bool = True   ,				):
            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size   ,				lowerCamelCase_     ):
                 a_     :  Dict				      =     (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
            else:
                 a_     :  int				      =     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 a_     :  Tuple				      =     randn_tensor(lowerCamelCase_   ,				generator=lowerCamelCase_     )
                 a_     :  Dict				      =     image.to(self.device     )
            else:
                 a_     :  Dict				      =     randn_tensor(lowerCamelCase_   ,				generator=lowerCamelCase_   ,				device=self.device     )

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

            for t in self.progress_bar(self.scheduler.timesteps     ):
                 # 1. predict noise model_output
                 a_     :  int				      =     self.unet(lowerCamelCase_   ,				lowerCamelCase_     ).sample

                 # 2. compute previous image: x_t -> x_t-1
                 a_     :  Optional[int]				      =     self.scheduler.step(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_   ,				generator=lowerCamelCase_     ).prev_sample

            a_     :  str				      =     (image / 2 + 0.5).clamp(0   ,				1     )
            a_     :  Union[str, Any]				      =     image.cpu().permute(0   ,				2   ,				3   ,				1     ).numpy()
            if output_type == "pil":
                 a_     :  Tuple				      =     self.numpy_to_pil(lowerCamelCase_     )

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=lowerCamelCase_     )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase   	=  {'''configuration_visual_bert''': ['''VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VisualBertConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''VisualBertForMultipleChoice''',
           '''VisualBertForPreTraining''',
           '''VisualBertForQuestionAnswering''',
           '''VisualBertForRegionToPhraseAlignment''',
           '''VisualBertForVisualReasoning''',
           '''VisualBertLayer''',
           '''VisualBertModel''',
           '''VisualBertPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

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



