"""simple docstring"""


import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	VQModel ,					__lowerCAmelCase						:	UNetaDModel ,					__lowerCAmelCase						:	DDIMScheduler       ):
      super().__init__()
      self.register_modules(vqvae=__lowerCAmelCase ,					unet=__lowerCAmelCase ,					scheduler=__lowerCAmelCase       )


    @torch.no_grad()
    def __call__(       self						:	Optional[int] ,					__lowerCAmelCase						:	int = 1 ,					__lowerCAmelCase						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__lowerCAmelCase						:	float = 0.0 ,					__lowerCAmelCase						:	int = 50 ,					__lowerCAmelCase						:	Optional[str] = "pil" ,					__lowerCAmelCase						:	bool = True ,					**__lowerCAmelCase						:	Optional[int] ,					):
      _UpperCAmelCase    			=   randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,					generator=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   latents.to(self.device       )

      # scale the initial noise by the standard deviation required by the scheduler
      _UpperCAmelCase    			=   latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(__lowerCAmelCase       )

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      _UpperCAmelCase    			=   """eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

      _UpperCAmelCase    			=   {}
      if accepts_eta:
        _UpperCAmelCase    			=   eta

      for t in self.progress_bar(self.scheduler.timesteps       ):
        _UpperCAmelCase    			=   self.scheduler.scale_model_input(__lowerCAmelCase ,					__lowerCAmelCase       )
        # predict the noise residual
        _UpperCAmelCase    			=   self.unet(__lowerCAmelCase ,					__lowerCAmelCase       ).sample
        # compute the previous noisy sample x_t -> x_t-1
        _UpperCAmelCase    			=   self.scheduler.step(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					**__lowerCAmelCase       ).prev_sample

      # decode the image latents with the VAE
      _UpperCAmelCase    			=   self.vqvae.decode(__lowerCAmelCase       ).sample

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

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__lowerCAmelCase       )

"""simple docstring"""


from math import isqrt






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [True] * max_number
  for i in range(2 ,isqrt(max_number - 1		) + 1		):
    if is_prime[i]:
      for j in range(i**2 ,lowercase ,lowercase		):
        _UpperCAmelCase    			=   False

  return [i for i in range(2 ,lowercase		) if is_prime[i]]






def      __UpperCAmelCase       (      lowercase = 10**8		):







  """simple docstring"""
  _UpperCAmelCase    			=   calculate_prime_numbers(max_number // 2		)

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   len(lowercase		) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


if __name__ == "__main__":
    print(F'''{solution() = }''')
