





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 : int				,				_lowerCAmelCase : int = 1000	) ->						str:
   if (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"	)
   elif (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"	)

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   A_					:				List[str]          =			(
       4 * _subsum(_lowerCAmelCase				,				1				,				_lowerCAmelCase	)
       - 2 * _subsum(_lowerCAmelCase				,				4				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				5				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				6				,				_lowerCAmelCase	)
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16	)	)[2:]

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int	) ->						float:
   A_					:				int          =			0.0
   for sum_index in range(digit_pos_to_extract + precision	):
      A_					:				Optional[int]          =			8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         A_					:				Optional[Any]          =			pow(
             16				,				digit_pos_to_extract - 1 - sum_index				,				_lowerCAmelCase	)
      else:
         A_					:				Tuple          =			pow(16				,				digit_pos_to_extract - 1 - sum_index	)
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
 import doctest

 doctest.testmod()
