

"""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  lowerCamelCase    (A__      ):

			def __init__(       self					:    str     ,					__UpperCAmelCase					:    VQModel     ,					__UpperCAmelCase					:    UNetaDModel     ,					__UpperCAmelCase					:    DDIMScheduler							) ->     List[Any]:
						super().__init__()
						self.register_modules(vqvae=__UpperCAmelCase     ,					unet=__UpperCAmelCase     ,					scheduler=__UpperCAmelCase							)





			@torch.no_grad()
			def __call__(       self					:    Optional[int]     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,					__UpperCAmelCase					:    float = 0.0     ,					__UpperCAmelCase					:    int = 5_0     ,					__UpperCAmelCase					:    Optional[str] = "pil"     ,					__UpperCAmelCase					:    bool = True     ,					**__UpperCAmelCase					:    List[Any]     ,					) ->     Union[Tuple, ImagePipelineOutput]:
						SCREAMING_SNAKE_CASE__										=   randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,					generator=__UpperCAmelCase     ,					)
						SCREAMING_SNAKE_CASE__										=   latents.to(self.device							)

						# scale the initial noise by the standard deviation required by the scheduler
						SCREAMING_SNAKE_CASE__										=   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
						SCREAMING_SNAKE_CASE__										=   """eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)

						SCREAMING_SNAKE_CASE__										=   {}
						if accepts_eta:
									SCREAMING_SNAKE_CASE__										=   eta

						for t in self.progress_bar(self.scheduler.timesteps							):
									SCREAMING_SNAKE_CASE__										=   self.scheduler.scale_model_input(__UpperCAmelCase     ,					__UpperCAmelCase							)
									# predict the noise residual
									SCREAMING_SNAKE_CASE__										=   self.unet(__UpperCAmelCase     ,					__UpperCAmelCase							).sample
									# compute the previous noisy sample x_t -> x_t-1
									SCREAMING_SNAKE_CASE__										=   self.scheduler.step(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase							).prev_sample

						# decode the image latents with the VAE
						SCREAMING_SNAKE_CASE__										=   self.vqvae.decode(__UpperCAmelCase							).sample

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__UpperCAmelCase							)





"""simple docstring"""




def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   2
			while i * i <= n:
						SCREAMING_SNAKE_CASE__										=   0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   1

			while True:
						i += 1
						t_num += i

						if count_divisors(snake_case__     ) > 5_00:
									break

			return t_num


if __name__ == "__main__":
						print(solution())


