



"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   42
   __UpperCamelCase									=   42







   def __init__(self       ,	lowerCamelCase       ,	lowerCamelCase				):




      '''simple docstring'''




      super().__init__()

      _lowerCAmelCase					     =		PNDMScheduler.from_config(scheduler.config				)

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







   @torch.no_grad()
   def __call__(self       ,	lowerCamelCase = 1       ,	lowerCamelCase = 50       ,	lowerCamelCase = None       ,	lowerCamelCase = "pil"       ,	lowerCamelCase = True       ,	**lowerCamelCase       ,	):




      '''simple docstring'''




      _lowerCAmelCase					     =		randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,	generator=lowerCamelCase       ,	device=self.device       ,	)

      self.scheduler.set_timesteps(lowerCamelCase				)
      for t in self.progress_bar(self.scheduler.timesteps				):
         _lowerCAmelCase					     =		self.unet(lowerCamelCase       ,	lowerCamelCase				).sample

         _lowerCAmelCase					     =		self.scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				).prev_sample

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

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCamelCase				)




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   _lowerCAmelCase					     =		[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   _lowerCAmelCase					     =		1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(snake_case_   ,	n + 1   ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
  import doctest

  doctest.testmod()