


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        super().__init__()
        self.register_modules(unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_		)




   @torch.no_grad()
   def __call__(     self   ,							UpperCamelCase_ = 1   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = 50   ,							UpperCamelCase_ = "pil"   ,							UpperCamelCase_ = True   ,							**UpperCamelCase_   ,							):
        lowercase_       :Dict     =       torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,							generator=UpperCamelCase_   ,							)
        lowercase_       :List[Any]     =       image.to(self.device		)

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

        for t in self.progress_bar(self.scheduler.timesteps		):
             # 1. predict noise model_output
             lowercase_       :List[str]     =       self.unet(UpperCamelCase_   ,							UpperCamelCase_		).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_       :Dict     =       self.scheduler.step(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		).prev_sample

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

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

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





from __future__ import annotations




def     UpperCamelCase    (      _a       ,		_a = None       ,		_a = None		)					->							None:
     '''simple docstring'''





     if start is None:
          lowercase_       :str     =       0

     if end is None:
          lowercase_       :str     =       len(_a		) - 1

     if start >= end:
          return

     lowercase_       :Dict     =       (start + end) // 2

     slowsort(_a       ,		_a       ,		_a		)
     slowsort(_a       ,		mid + 1       ,		_a		)

     if sequence[end] < sequence[mid]:
          lowercase_    ,      lowercase_       :List[Any]     =       sequence[mid], sequence[end]

     slowsort(_a       ,		_a       ,		end - 1		)


if __name__ == "__main__":
   from doctest import testmod

   testmod()

