




from __future__ import annotations

import math



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int	)     ->   Optional[Any]:




 """simple docstring"""







 if num <= 0:
  UpperCAmelCase__							 =						F'''{num}: Invalid input, please enter a positive integer.'''
  raise ValueError(_lowerCAmelCase	)

 UpperCAmelCase__							 =						[True] * (num + 1)
 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						2
 UpperCAmelCase__							 =						int(math.sqrt(_lowerCAmelCase	)	)

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

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

  start += 1

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

 return prime


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





