






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 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     42
     __UpperCamelCase					    =     42



     def __init__(							self       :str			,      snake_case       :UNetaDModel			,      snake_case       :PNDMScheduler  ):





        '''simple docstring'''




        super().__init__()

        A_					:				Any          =			PNDMScheduler.from_config(scheduler.config  )

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





     @torch.no_grad()
     def __call__(							self       :Optional[Any]			,      snake_case       :int = 1			,      snake_case       :int = 50			,      snake_case       :Optional[Union[torch.Generator, List[torch.Generator]]] = None			,      snake_case       :Optional[str] = "pil"			,      snake_case       :bool = True			,      **snake_case       :Any			,      ):





        '''simple docstring'''




        A_					:				Tuple          =			randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,      generator=snake_case			,      device=self.device			,      )

        self.scheduler.set_timesteps(snake_case  )
        for t in self.progress_bar(self.scheduler.timesteps  ):
           A_					:				str          =			self.unet(snake_case			,      snake_case  ).sample

           A_					:				Optional[int]          =			self.scheduler.step(snake_case			,      snake_case			,      snake_case  ).prev_sample

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

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=snake_case  )







def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

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