





"""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   lowercase_					(  __lowerCAmelCase				):
       snake_case__					:				UNetaDModel
       snake_case__					:				PNDMScheduler

       def __init__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        super().__init__()

        snake_case__     				=					PNDMScheduler.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 = 50 ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = "pil" ,       __SCREAMING_SNAKE_CASE = True ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,       generator=__SCREAMING_SNAKE_CASE ,       device=self.device ,       )

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

         snake_case__     				=					self.scheduler.step(__SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ).prev_sample

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

        if not return_dict:
         return (image,)

        return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE  )








"""simple docstring"""


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)     ->	float:

 '''simple docstring'''






 snake_case__     				=					[1 / first_term]
 snake_case__     				=					1 / first_term
 for _ in range(number_of_terms - 1						):
  first_term += common_difference
  arithmetic_progression.append(_UpperCAmelCase						)
 snake_case__     				=					[1 / step for step in arithmetic_progression]
 return sum(_UpperCAmelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

