

"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  42
  SCREAMING_SNAKE_CASE					      =  42



  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

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




  @torch.no_grad()
  def __call__(self							,       _lowerCamelCase = 1							,       _lowerCamelCase = 2000							,       _lowerCamelCase = None							,       _lowerCamelCase = "pil"							,       _lowerCamelCase = True							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    self.unet.config.sample_size
        UpperCAmelCase__			:							int							=    (batch_size, 3, img_size, img_size)

        UpperCAmelCase__			:							List[Any]							=    self.unet

        UpperCAmelCase__			:							List[Any]							=    randn_tensor(_lowerCamelCase							,       generator=_lowerCamelCase	) * self.scheduler.init_noise_sigma
        UpperCAmelCase__			:							List[Any]							=    sample.to(self.device	)

        self.scheduler.set_timesteps(_lowerCamelCase	)
        self.scheduler.set_sigmas(_lowerCamelCase	)

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
              UpperCAmelCase__			:							Optional[Any]							=    self.scheduler.sigmas[i] * torch.ones(shape[0]							,       device=self.device	)

              # correction step
              for _ in range(self.scheduler.config.correct_steps	):
                    UpperCAmelCase__			:							Tuple							=    self.unet(_lowerCamelCase							,       _lowerCamelCase	).sample
                    UpperCAmelCase__			:							Optional[Any]							=    self.scheduler.step_correct(_lowerCamelCase							,       _lowerCamelCase							,       generator=_lowerCamelCase	).prev_sample

              # prediction step
              UpperCAmelCase__			:							Optional[Any]							=    model(_lowerCamelCase							,       _lowerCamelCase	).sample
              UpperCAmelCase__			:							List[Any]							=    self.scheduler.step_pred(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       generator=_lowerCamelCase	)

              UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    output.prev_sample, output.prev_sample_mean

        UpperCAmelCase__			:							int							=    sample_mean.clamp(0							,       1	)
        UpperCAmelCase__			:							str							=    sample.cpu().permute(0							,       2							,       3							,       1	).numpy()
        if output_type == "pil":
              UpperCAmelCase__			:							Dict							=    self.numpy_to_pil(_lowerCamelCase	)

        if not return_dict:
              return (sample,)

        return ImagePipelineOutput(images=_lowerCamelCase	)




"""simple docstring"""
import math




def 					a__						(						lowerCAmelCase    )	-> list[int]:
      UpperCAmelCase__			:							Optional[int]							=    []
      UpperCAmelCase__			:							Union[str, Any]							=    2
      UpperCAmelCase__			:							List[Any]							=    int(math.sqrt(lowerCAmelCase    )    )  # Size of every segment
      UpperCAmelCase__			:							Dict							=    [True] * (end + 1)
      UpperCAmelCase__			:							str							=    []

      while start <= end:
            if temp[start] is True:
                  in_prime.append(lowerCAmelCase    )
                  for i in range(start * start			,	end + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							Optional[Any]							=    False
            start += 1
      prime += in_prime

      UpperCAmelCase__			:							Optional[Any]							=    end + 1
      UpperCAmelCase__			:							Dict							=    min(2 * end			,	lowerCAmelCase    )

      while low <= n:
            UpperCAmelCase__			:							Optional[int]							=    [True] * (high - low + 1)
            for each in in_prime:
                  UpperCAmelCase__			:							Optional[int]							=    math.floor(low / each    ) * each
                  if t < low:
                        t += each

                  for j in range(lowerCAmelCase			,	high + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							int							=    False

            for j in range(len(lowerCAmelCase    )    ):
                  if temp[j] is True:
                        prime.append(j + low    )

            UpperCAmelCase__			:							Dict							=    high + 1
            UpperCAmelCase__			:							int							=    min(high + end			,	lowerCAmelCase    )

      return prime





print(sieve(10**6))

