



"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			UNetaDModel
 _lowerCAmelCase   :			ScoreSdeVeScheduler

 def __init__(     self		:       Dict  ,     lowercase_		:       UNetaDModel  ,     lowercase_		:       ScoreSdeVeScheduler				):
     super().__init__()
     self.register_modules(unet=lowercase_  ,     scheduler=lowercase_				)






 @torch.no_grad()
 def __call__(     self		:       Tuple  ,     lowercase_		:       int = 1  ,     lowercase_		:       int = 2000  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     **lowercase_		:       int  ,     ):
     snake_case_ :     List[str]				=	self.unet.config.sample_size
     snake_case_ :     Union[str, Any]				=	(batch_size, 3, img_size, img_size)

     snake_case_ :     List[Any]				=	self.unet

     snake_case_ :     List[Any]				=	randn_tensor(lowercase_  ,     generator=lowercase_				) * self.scheduler.init_noise_sigma
     snake_case_ :     List[Any]				=	sample.to(self.device				)

     self.scheduler.set_timesteps(lowercase_				)
     self.scheduler.set_sigmas(lowercase_				)

     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
         snake_case_ :     List[str]				=	self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

         # correction step
         for _ in range(self.scheduler.config.correct_steps				):
             snake_case_ :     Tuple				=	self.unet(lowercase_  ,     lowercase_				).sample
             snake_case_ :     Tuple				=	self.scheduler.step_correct(lowercase_  ,     lowercase_  ,     generator=lowercase_				).prev_sample

         # prediction step
         snake_case_ :     Union[str, Any]				=	model(lowercase_  ,     lowercase_				).sample
         snake_case_ :     List[Any]				=	self.scheduler.step_pred(lowercase_  ,     lowercase_  ,     lowercase_  ,     generator=lowercase_				)

         snake_case_,	snake_case_ :     List[str]				=	output.prev_sample, output.prev_sample_mean

     snake_case_ :     int				=	sample_mean.clamp(0  ,     1				)
     snake_case_ :     List[Any]				=	sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
     if output_type == "pil":
         snake_case_ :     Any				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (sample,)

     return ImagePipelineOutput(images=lowercase_				)










"""simple docstring"""

import numpy as np




def __lowercase			(							_a    ):
    return (2 / (1 + np.exp(-2 * vector    ))) - 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





