

'''simple docstring'''

from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 					lowerCAmelCase       ( A				):







       def __init__(							self     :	List[Any]  , __lowercase     :	Optional[int]  , __lowercase     :	Tuple ):







            """simple docstring"""







            super().__init__()
            self.register_modules(unet=__lowercase  , scheduler=__lowercase )







       @torch.no_grad()
       def __call__(							self     :	Optional[int]  , __lowercase     :	int = 1  , __lowercase     :	Optional[torch.Generator] = None  , __lowercase     :	int = 50  , __lowercase     :	Optional[str] = "pil"  , __lowercase     :	bool = True  , **__lowercase     :	Any  , ):







            """simple docstring"""







            __lowercase     						=torch.randn(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  , generator=__lowercase  , )
            __lowercase     						=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     						=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     						=self.scheduler.step(__lowercase  , __lowercase  , __lowercase ).prev_sample

            __lowercase     						=(image / 2 + 0.5).clamp(0  , 1 )
            __lowercase     						=image.cpu().permute(0  , 2  , 3  , 1 ).numpy()
            if output_type == "pil":
                 __lowercase     						=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"





'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase							=					{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTHybridForImageClassification''',
           '''ViTHybridModel''',
           '''ViTHybridPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

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


