

from __future__ import annotations

import math


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							list[int]:
 '''simple docstring'''


 if num <= 0:
  SCREAMING_SNAKE_CASE								=			f"""{num}: Invalid input, please enter a positive integer."""
  raise ValueError(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			[True] * (num + 1)
 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			2
 SCREAMING_SNAKE_CASE								=			int(math.sqrt(_UpperCamelCase	)	)

 while start <= end:
  # If start is a prime
  if sieve[start] is True:
   prime.append(_UpperCamelCase	)

   # Set multiples of start be False
   for i in range(start * start		,							num + 1		,							_UpperCamelCase	):
    if sieve[i] is True:
     SCREAMING_SNAKE_CASE								=			False

  start += 1

 for j in range(end + 1		,							num + 1	):
  if sieve[j] is True:
   prime.append(_UpperCamelCase	)

 return prime


if __name__ == "__main__":
      print(prime_sieve(int(input("Enter a positive integer: ").strip())))




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

