"""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   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a	,    a							) ->      Union[str, Any]:
             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	,    ) ->      Union[Tuple, ImagePipelineOutput]:
             lowercase__    :	List[Any]     					=			randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,    generator=a	,    )
             lowercase__    :	int     					=			latents.to(self.device							)

             # scale the initial noise by the standard deviation required by the scheduler
             lowercase__    :	Optional[int]     					=			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
             lowercase__    :	str     					=			'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)

             lowercase__    :	Any     					=			{}
             if accepts_eta:
                    lowercase__    :	str     					=			eta

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

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

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

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a							)


"""simple docstring"""





from collections.abc import Sequence





def        a_  (   _lowerCAmelCase						:				Sequence[float]     ,				_lowerCAmelCase						:				float  ):





       '''simple docstring'''

       return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase  )  )





def        a_  (   _lowerCAmelCase						:				Sequence[float]     ,				_lowerCAmelCase						:				float  ):





       '''simple docstring'''

       lowercase__    :	int     					=			0.0
       for coeff in reversed(_lowerCAmelCase  ):
              lowercase__    :	List[Any]     					=			result * x + coeff
       return result


if __name__ == "__main__":
       _UpperCamelCase     :		int											=					(0.0, 0.0, 5.0, 9.3, 7.0)
       _UpperCamelCase     :		Dict											=					1_0.0
       print(evaluate_poly(poly, x))
       print(horner(poly, x))

