


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


A	:		Optional[int]    =       'scheduler_config.json'



class 							A   (	A_							):
					'''simple docstring'''






					A__      =			1
					A__      =			2
					A__      =			3
					A__      =			4
					A__      =			5
					A__      =			6
					A__      =			7
					A__      =			8
					A__      =			9
					A__      =			10
					A__      =			11
					A__      =			12
					A__      =			13
					A__      =			14






@dataclass
class 							A   (	A_							):
					'''simple docstring'''






					A__      =			42






class 							A   :
					'''simple docstring'''






					A__      =			SCHEDULER_CONFIG_NAME
					A__      =			[]
					A__      =			True

					@classmethod
					def   lowerCamelCase__					(cls   :						Union[str, Any]				, _UpperCAmelCase   :						Dict[str, Any] = None				, _UpperCAmelCase   :						Optional[str] = None				, _UpperCAmelCase   :						Optional[int]=False				, **_UpperCAmelCase   :						List[str]				, )						->       int:




							"""simple docstring"""


							lowercase__      ,     lowercase__      ,     lowercase__					      =		cls.load_config(
							    pretrained_model_name_or_path=_lowerCamelCase				, subfolder=_lowerCamelCase				, return_unused_kwargs=_lowerCamelCase				, return_commit_hash=_lowerCamelCase				, **_lowerCamelCase				, )
							return cls.from_config(_lowerCamelCase				, return_unused_kwargs=_lowerCamelCase				, **_lowerCamelCase		)

					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						Union[str, os.PathLike]				, _UpperCAmelCase   :						bool = False				, **_UpperCAmelCase   :						int		)						->       List[str]:




							"""simple docstring"""


							self.save_config(save_directory=_lowerCamelCase				, push_to_hub=_lowerCamelCase				, **_lowerCamelCase		)

					@property
					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Union[str, Any]:




							"""simple docstring"""


							return self._get_compatibles()


					@classmethod
					def   lowerCamelCase__					(cls   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		list(set([cls.__name__] + cls._compatibles		)		)
							lowercase__					      =		importlib.import_module(__name__.split("""."""		)[0]		)
							lowercase__					      =		[
							    getattr(_lowerCamelCase				, _lowerCamelCase		) for c in compatible_classes_str if hasattr(_lowerCamelCase				, _lowerCamelCase		)
							]
							return compatible_classes





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   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


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


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


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

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

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

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

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

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

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

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

