


'''simple docstring'''


from typing import TYPE_CHECKING

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


__magic_name__			=      {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     pass

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RoCBertForCausalLM''',
         '''RoCBertForMaskedLM''',
         '''RoCBertForMultipleChoice''',
         '''RoCBertForPreTraining''',
         '''RoCBertForQuestionAnswering''',
         '''RoCBertForSequenceClassification''',
         '''RoCBertForTokenClassification''',
         '''RoCBertLayer''',
         '''RoCBertModel''',
         '''RoCBertPreTrainedModel''',
         '''load_tf_weights_in_roc_bert''',
     ]

if TYPE_CHECKING:
     from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
     from .tokenization_roc_bert import RoCBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          raise OptionalDependencyNotAvailable()

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_roc_bert import (
              ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RoCBertForCausalLM,
              RoCBertForMaskedLM,
              RoCBertForMultipleChoice,
              RoCBertForPreTraining,
              RoCBertForQuestionAnswering,
              RoCBertForSequenceClassification,
              RoCBertForTokenClassification,
              RoCBertLayer,
              RoCBertModel,
              RoCBertPreTrainedModel,
              load_tf_weights_in_roc_bert,
          )


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''


from __future__ import annotations

import math




def      lowerCamelCase					(      lowerCamelCase  :      int):

      if num <= 0:
            A_						:  List[Any]          =				F'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCamelCase)

      A_						:  str          =				[True] * (num + 1)
      A_						:  Tuple          =				[]
      A_						:  str          =				2
      A_						:  Any          =				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:
                              A_						:  Union[str, Any]          =				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())))
