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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  42
  lowercase_								=  42




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




       """simple docstring"""
       super().__init__()
       self.register_modules(unet=a_  ,			scheduler=a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.unet.config.sample_size
       SCREAMING_SNAKE_CASE__					:					Tuple      =		(batch_size, 3, img_size, img_size)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.unet

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		randn_tensor(a_  ,			generator=a_					) * self.scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__					:					int      =		sample.to(self.device					)

       self.scheduler.set_timesteps(a_					)
       self.scheduler.set_sigmas(a_					)

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

            # correction step
            for _ in range(self.scheduler.config.correct_steps					):
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.unet(a_  ,			a_					).sample
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		self.scheduler.step_correct(a_  ,			a_  ,			generator=a_					).prev_sample

            # prediction step
            SCREAMING_SNAKE_CASE__					:					Any      =		model(a_  ,			a_					).sample
            SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler.step_pred(a_  ,			a_  ,			a_  ,			generator=a_					)

            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		output.prev_sample, output.prev_sample_mean

       SCREAMING_SNAKE_CASE__					:					Dict      =		sample_mean.clamp(0  ,			1					)
       SCREAMING_SNAKE_CASE__					:					int      =		sample.cpu().permute(0  ,			2  ,			3  ,			1					).numpy()
       if output_type == "pil":
            SCREAMING_SNAKE_CASE__					:					str      =		self.numpy_to_pil(a_					)

       if not return_dict:
            return (sample,)

       return ImagePipelineOutput(images=a_					)










"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def      lowercase__ (					lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__  :	Tuple		 =			prime_factors(lowerCAmelCase__			)
			if is_square_free(lowerCAmelCase__			):
						return -1 if len(lowerCAmelCase__			) % 2 else 1
			return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()