






'''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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42







	def __init__(		self    :  List[str]	,    __snake_case    :  UNetaDModel	,    __snake_case    :  ScoreSdeVeScheduler	)    ->	Tuple:
								super().__init__()
								self.register_modules(unet=__snake_case	,    scheduler=__snake_case	)







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

								UpperCAmelCase				: Optional[int]											=	self.unet

								UpperCAmelCase				: int											=	randn_tensor(__snake_case	,    generator=__snake_case	) * self.scheduler.init_noise_sigma
								UpperCAmelCase				: int											=	sample.to(self.device	)

								self.scheduler.set_timesteps(__snake_case	)
								self.scheduler.set_sigmas(__snake_case	)

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

															# correction step
															for _ in range(self.scheduler.config.correct_steps	):
																						UpperCAmelCase				: Dict											=	self.unet(__snake_case	,    __snake_case	).sample
																						UpperCAmelCase				: int											=	self.scheduler.step_correct(__snake_case	,    __snake_case	,    generator=__snake_case	).prev_sample

															# prediction step
															UpperCAmelCase				: List[str]											=	model(__snake_case	,    __snake_case	).sample
															UpperCAmelCase				: Optional[Any]											=	self.scheduler.step_pred(__snake_case	,    __snake_case	,    __snake_case	,    generator=__snake_case	)

															UpperCAmelCase				: Dict											=	output.prev_sample, output.prev_sample_mean

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

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case	)










'''simple docstring'''






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





