




import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



      lowerCamelCase_							=   []
      lowerCamelCase_							=   2
      lowerCamelCase_							=   int(math.sqrt(lowercase					)					)  # Size of every segment
      lowerCamelCase_							=   [True] * (end + 1)
      lowerCamelCase_							=   []

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

      lowerCamelCase_							=   end + 1
      lowerCamelCase_							=   min(2 * end     ,							lowercase					)

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

                  for j in range(lowercase     ,							high + 1     ,							lowercase					):
                        lowerCamelCase_							=   False

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

            lowerCamelCase_							=   high + 1
            lowerCamelCase_							=   min(high + end     ,							lowercase					)

      return prime






print(sieve(10**6))









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



