






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			int       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Tuple							)						->	Tuple:


         """simple docstring"""
         super().__init__()
         self.register_modules(unet=lowerCamelCase       ,				scheduler=lowerCamelCase							)





  @torch.no_grad()
  def __call__(      self		:			Dict       ,				lowerCamelCase		:			int = 1       ,				lowerCamelCase		:			Optional[torch.Generator] = None       ,				lowerCamelCase		:			int = 50       ,				lowerCamelCase		:			Optional[str] = "pil"       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			List[Any]       ,				)						->	Union[ImagePipelineOutput, Tuple]:


         """simple docstring"""
         _UpperCAmelCase							=    torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,				generator=lowerCamelCase       ,				)
         _UpperCAmelCase							=    image.to(self.device							)

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

         for t in self.progress_bar(self.scheduler.timesteps							):
                # 1. predict noise model_output
                _UpperCAmelCase							=    self.unet(lowerCamelCase       ,				lowerCamelCase							).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
                _UpperCAmelCase							=    self.scheduler.step(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							).prev_sample

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

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

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







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)