





"""simple docstring"""




a						: Tuple     	=			'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

a						: Optional[int]     	=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a						: Union[str, Any]     	=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




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      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

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

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

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

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )