






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   (   _A						):
      def __init__(     self				,			a				,			a				,			a   ):
       super().__init__()
       self.register_modules(vqvae=a				,			unet=a				,			scheduler=a   )



      @torch.no_grad()
      def __call__(     self				,			a = 1				,			a = None				,			a = 0.0				,			a = 5_0				,			a = "pil"				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,			generator=a				,			)
       snake_case__  :    int							       =latents.to(self.device   )

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case__  :    str							       =latents * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(a   )

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

       snake_case__  :    List[Any]							       ={}
       if accepts_eta:
        snake_case__  :    List[Any]							       =eta

       for t in self.progress_bar(self.scheduler.timesteps   ):
        snake_case__  :    Dict							       =self.scheduler.scale_model_input(a				,			a   )
        # predict the noise residual
        snake_case__  :    List[Any]							       =self.unet(a				,			a   ).sample
        # compute the previous noisy sample x_t -> x_t-1
        snake_case__  :    List[str]							       =self.scheduler.step(a				,			a				,			a				,			**a   ).prev_sample

       # decode the image latents with the VAE
       snake_case__  :    Any							       =self.vqvae.decode(a   ).sample

       snake_case__  :    Dict							       =(image / 2 + 0.5).clamp(0				,			1   )
       snake_case__  :    Optional[Any]							       =image.cpu().permute(0				,			2				,			3				,			1   ).numpy()
       if output_type == "pil":
        snake_case__  :    Dict							       =self.numpy_to_pil(a   )

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=a   )













def 			A__							(      _a  :					Any		,  _a  :					Optional[Any]		,  _a  :					int		,  _a  :					Any	):





 '''simple docstring'''







 if height >= 1:
  move_tower(height - 1		,  _a		,  _a		,  _a	)
  move_disk(_a		,  _a	)
  move_tower(height - 1		,  _a		,  _a		,  _a	)







def 			A__							(      _a  :					Optional[int]		,  _a  :					str	):





 '''simple docstring'''







 print("""moving disk from"""		,  _a		,  """to"""		,  _a	)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Dict							       =int(input("""Height of hanoi: """	).strip()	)
 move_tower(_a		,  """A"""		,  """B"""		,  """C"""	)


if __name__ == "__main__":
       main()





