


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    UpperCamelCase    (						UpperCamelCase__   ):


   '''simple docstring'''







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




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

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

        for t in self.progress_bar(self.scheduler.timesteps						):
             # 1. predict noise model_output
             lowercase_				:str       			=				self.unet(SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__						).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(SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__						).prev_sample

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

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

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







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

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


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

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

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

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

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

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