





from typing import List, Optional, Tuple, Union

import torch

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



class        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        super().__init__()
        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 = 1000				,   __SCREAMING_SNAKE_CASE = "pil"				,   __SCREAMING_SNAKE_CASE = True				,   ):






        '''simple docstring'''

        if isinstance(self.unet.config.sample_size				,   __SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
        else:
         snake_case__      							=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

        if self.device.type == "mps":
         # randn does not work reproducibly on mps
         snake_case__      							=			randn_tensor(__SCREAMING_SNAKE_CASE				,   generator=__SCREAMING_SNAKE_CASE       )
         snake_case__      							=			image.to(self.device       )
        else:
         snake_case__      							=			randn_tensor(__SCREAMING_SNAKE_CASE				,   generator=__SCREAMING_SNAKE_CASE				,   device=self.device       )

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

        for t in self.progress_bar(self.scheduler.timesteps       ):
         # 1. predict noise model_output
         snake_case__      							=			self.unet(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).sample

         # 2. compute previous image: x_t -> x_t-1
         snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   generator=__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"""


import math





def 							a__     (					_UpperCAmelCase		)      ->					list:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive integers have prime factors"		)
 snake_case__      							=			[]
 while n % 2 == 0:
  pf.append(2		)
  snake_case__      							=			int(n / 2		)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  while n % i == 0:
   pf.append(_UpperCAmelCase		)
   snake_case__      							=			int(n / i		)
 if n > 2:
  pf.append(_UpperCAmelCase		)
 return pf





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 div *= temp
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  div *= temp
 if n > 1:
  div *= 2
 return div





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 if temp > 1:
  s *= (2**temp - 1) / (2 - 1)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  if temp > 1:
   s *= (i**temp - 1) / (i - 1)
 return int(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			n
 for x in set(prime_factors(_UpperCAmelCase		)		):
  s *= (x - 1) / x
 return int(_UpperCAmelCase		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

