
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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				42
	_lowerCAmelCase							       =				42





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					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__				,       ):
					A__  :	Tuple         =							self.unet.config.sample_size
					A__  :	List[Any]         =							(batch_size, 3, img_size, img_size)

					A__  :	Tuple         =							self.unet

					A__  :	Union[str, Any]         =							randn_tensor(UpperCamelCase__				,       generator=UpperCamelCase__		) * self.scheduler.init_noise_sigma
					A__  :	Tuple         =							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		)		):
									A__  :	Dict         =							self.scheduler.sigmas[i] * torch.ones(shape[0]				,       device=self.device		)

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

									# prediction step
									A__  :	Any         =							model(UpperCamelCase__				,       UpperCamelCase__		).sample
									A__  :	Dict         =							self.scheduler.step_pred(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       generator=UpperCamelCase__		)

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

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

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=UpperCamelCase__		)

from numpy import exp, pi, sqrt

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						float = 0.0							,						__UpperCamelCase :						float = 1.0       ) ->       int:

				"""simple docstring"""







				return 1 / sqrt(2 * pi * sigma**2       ) * exp(-((x - mu) ** 2) / (2 * sigma**2)       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()