






"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

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

class 	a					(       a_     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase	)







    @torch.no_grad()
    def __call__(      self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 1_0_0_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		):
      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size							,		_lowerCamelCase	):
        lowercase							   =	(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
      else:
        lowercase							   =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

        # 2. compute previous image: x_t -> x_t-1
        lowercase							   =	self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		generator=_lowerCamelCase	).prev_sample

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=_lowerCamelCase	)










"""simple docstring"""



_UpperCamelCase		:  str						=		1_0**8
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int   ):





  '''simple docstring'''





  if n % 10 == 0:
    return False
  lowercase							   =	str(__snake_case   )
  return s == s[::-1]
def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





  lowercase							   =	set()
  lowercase							   =	1
  lowercase							   =	5
  while sum_squares < LIMIT:
    lowercase							   =	first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(__snake_case   ):
        answer.add(__snake_case   )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    lowercase							   =	first_square**2 + (first_square + 1) ** 2

  return sum(__snake_case   )


if __name__ == "__main__":
    print(solution())


