



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       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					42
			UpperCamelCase        =					42



			def __init__(						self       :  List[str]			,		A       :  UNetaDModel			,		A       :  ScoreSdeVeScheduler)       ->   Dict:



						"""simple docstring"""






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



			@torch.no_grad()
			def __call__(						self       :  Any			,		A       :  int = 1			,		A       :  int = 20_00			,		A       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		A       :  Optional[str] = "pil"			,		A       :  bool = True			,		**A       :  Union[str, Any]			,		)       ->   Union[ImagePipelineOutput, Tuple]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.unet.config.sample_size
						_UpperCAmelCase			=       (batch_size, 3, img_size, img_size)

						_UpperCAmelCase			=       self.unet

						_UpperCAmelCase			=       randn_tensor(A			,		generator=A) * self.scheduler.init_noise_sigma
						_UpperCAmelCase			=       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)):
									_UpperCAmelCase			=       self.scheduler.sigmas[i] * torch.ones(shape[0]			,		device=self.device)

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

									# prediction step
									_UpperCAmelCase			=       model(A			,		A).sample
									_UpperCAmelCase			=       self.scheduler.step_pred(A			,		A			,		A			,		generator=A)

									_UpperCAmelCase	,					_UpperCAmelCase			=       output.prev_sample, output.prev_sample_mean

						_UpperCAmelCase			=       sample_mean.clamp(0			,		1)
						_UpperCAmelCase			=       sample.cpu().permute(0			,		2			,		3			,		1).numpy()
						if output_type == "pil":
									_UpperCAmelCase			=       self.numpy_to_pil(A)

						if not return_dict:
									return (sample,)

						return ImagePipelineOutput(images=A)





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase__       =	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
							from .tokenization_bertweet import BertweetTokenizer

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
