




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     ):
		__snake_case									=   42
		__snake_case									=   42



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



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







		@torch.no_grad()
		def __call__(     self,	UpperCamelCase__ = 1,	UpperCamelCase__ = 2000,	UpperCamelCase__ = None,	UpperCamelCase__ = "pil",	UpperCamelCase__ = True,	**UpperCamelCase__,	):







								"""simple docstring"""



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

								lowerCAmelCase_      			=  self.unet

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

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

														# prediction step
														lowerCAmelCase_      			=  model(UpperCamelCase__,	UpperCamelCase__   ).sample
														lowerCAmelCase_      			=  self.scheduler.step_pred(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	generator=UpperCamelCase__   )

														lowerCAmelCase_			,					lowerCAmelCase_      			=  output.prev_sample, output.prev_sample_mean

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

								if not return_dict:
														return (sample,)

								return ImagePipelineOutput(images=UpperCamelCase__   )










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     =			'''scheduler_config.json'''


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   1
		__snake_case									=   2
		__snake_case									=   3
		__snake_case									=   4
		__snake_case									=   5
		__snake_case									=   6
		__snake_case									=   7
		__snake_case									=   8
		__snake_case									=   9
		__snake_case									=   10
		__snake_case									=   11
		__snake_case									=   12
		__snake_case									=   13
		__snake_case									=   14







@dataclass
class  A		(  __UpperCAmelCase     ):
		__snake_case									=   42







class  A		:
		__snake_case									=   SCHEDULER_CONFIG_NAME
		__snake_case									=   []
		__snake_case									=   True



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls,	UpperCamelCase__ = None,	UpperCamelCase__ = None,	UpperCamelCase__=False,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_      			=  cls.load_config(
								    pretrained_model_name_or_path=UpperCamelCase__,	subfolder=UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	return_commit_hash=UpperCamelCase__,	**UpperCamelCase__,	)
								return cls.from_config(UpperCamelCase__,	return_unused_kwargs=UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = False,	**UpperCamelCase__   ):







								"""simple docstring"""



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



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._get_compatibles()







		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



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




