
'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         super().__init__()

         # make sure scheduler can always be converted to DDIM
         A       :   Dict					=						DDIMScheduler.from_config(scheduler.config	)

         self.register_modules(unet=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE	)





     @torch.no_grad()
     def __call__(							self						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0.0						,			SCREAMING_SNAKE_CASE = 50						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = "pil"						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[ImagePipelineOutput, Tuple]:






         """simple docstring"""






         if isinstance(self.unet.config.sample_size						,			SCREAMING_SNAKE_CASE	):
             A       :   List[Any]					=						(
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
         else:
             A       :   List[Any]					=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) and len(SCREAMING_SNAKE_CASE	) != batch_size:
             raise ValueError(
                 F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE	)}, but requested an effective batch'
                 F' size of {batch_size}. Make sure the batch size matches the length of the generators.'	)

         A       :   Tuple					=						randn_tensor(SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			device=self.device						,			dtype=self.unet.dtype	)

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE	)

         for t in self.progress_bar(self.scheduler.timesteps	):
             # 1. predict noise model_output
             A       :   Optional[int]					=						self.unet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             A       :   Union[str, Any]					=						self.scheduler.step(
                 SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			eta=SCREAMING_SNAKE_CASE						,			use_clipped_model_output=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE	).prev_sample

         A       :   List[str]					=						(image / 2 + 0.5).clamp(0						,			1	)
         A       :   Union[str, Any]					=						image.cpu().permute(0						,			2						,			3						,			1	).numpy()
         if output_type == "pil":
             A       :   Optional[Any]					=						self.numpy_to_pil(SCREAMING_SNAKE_CASE	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE	)









'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


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






