
"""simple docstring"""

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Tuple:
			super().__init__()
			self.register_modules(vqvae=UpperCAmelCase_		,			unet=UpperCAmelCase_		,			scheduler=UpperCAmelCase_ )




	@torch.no_grad()
	def __call__(			self		,			UpperCAmelCase_ = 1		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = 0.0		,			UpperCAmelCase_ = 50		,			UpperCAmelCase_ = "pil"		,			UpperCAmelCase_ = True		,			**UpperCAmelCase_		,			)		->							Union[Tuple, ImagePipelineOutput]:
			lowerCamelCase					:      Dict          =   randn_tensor(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,			generator=UpperCAmelCase_		,			)
			lowerCamelCase					:      str          =   latents.to(self.device )

			# scale the initial noise by the standard deviation required by the scheduler
			lowerCamelCase					:      Optional[int]          =   latents * self.scheduler.init_noise_sigma

			self.scheduler.set_timesteps(UpperCAmelCase_ )

			# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
			lowerCamelCase					:      Any          =   'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

			lowerCamelCase					:      Tuple          =   {}
			if accepts_eta:
					lowerCamelCase					:      List[Any]          =   eta

			for t in self.progress_bar(self.scheduler.timesteps ):
					lowerCamelCase					:      Union[str, Any]          =   self.scheduler.scale_model_input(UpperCAmelCase_		,			UpperCAmelCase_ )
					# predict the noise residual
					lowerCamelCase					:      Optional[int]          =   self.unet(UpperCAmelCase_		,			UpperCAmelCase_ ).sample
					# compute the previous noisy sample x_t -> x_t-1
					lowerCamelCase					:      Tuple          =   self.scheduler.step(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ ).prev_sample

			# decode the image latents with the VAE
			lowerCamelCase					:      Optional[int]          =   self.vqvae.decode(UpperCAmelCase_ ).sample

			lowerCamelCase					:      Any          =   (image / 2 + 0.5).clamp(0		,			1 )
			lowerCamelCase					:      Tuple          =   image.cpu().permute(0		,			2		,			3		,			1 ).numpy()
			if output_type == "pil":
					lowerCamelCase					:      Any          =   self.numpy_to_pil(UpperCAmelCase_ )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_ )








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 10      ):





		'''simple docstring'''


		if not isinstance(a_,						a_      ) or n < 0:
				raise ValueError('Invalid input'      )
		lowerCamelCase					:      Union[str, Any]          =   10**n
		lowerCamelCase					:      int          =   2_8433 * (pow(2,						783_0457,						a_      )) + 1
		return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"""{solution(1_0) = }""")






