






"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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






class __a ( snake_case_					):



  def __init__(self	:		int					,      lowercase_	:		str					,      lowercase_	:		Optional[int] )		->				Dict:
   super().__init__()
   self.register_modules(unet=lowercase_					,      scheduler=lowercase_ )



  @torch.no_grad()
  def __call__(self	:		Tuple					,      lowercase_	:		int = 1					,      lowercase_	:		Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_	:		int = 1_000					,      lowercase_	:		Optional[str] = "pil"					,      lowercase_	:		bool = True					,      )		->				Union[ImagePipelineOutput, Tuple]:
   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size					,      lowercase_ ):
    __A						:							List[str]      =						(
        batch_size,
        self.unet.config.in_channels,
        self.unet.config.sample_size,
        self.unet.config.sample_size,
    )
   else:
    __A						:							Optional[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						:							Dict      =						randn_tensor(lowercase_					,      generator=lowercase_ )
    __A						:							Union[str, Any]      =						image.to(self.device )
   else:
    __A						:							Union[str, Any]      =						randn_tensor(lowercase_					,      generator=lowercase_					,      device=self.device )

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

   for t in self.progress_bar(self.scheduler.timesteps ):
    # 1. predict noise model_output
    __A						:							Union[str, Any]      =						self.unet(lowercase_					,      lowercase_ ).sample

    # 2. compute previous image: x_t -> x_t-1
    __A						:							Optional[Any]      =						self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      generator=lowercase_ ).prev_sample

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

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=lowercase_ )













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					float		)	->		int:
 return int(UpperCamelCase_		) if x - int(UpperCamelCase_		) <= 0 else int(UpperCamelCase_		) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()





