
from __future__ import annotations

import math




def  lowerCamelCase__ (  a__     :     int   )						->		list[int]:

       if num <= 0:
              UpperCamelCase_											=				f'''{num}: Invalid input, please enter a positive integer.'''
              raise ValueError(__UpperCAmelCase   )

       UpperCamelCase_											=				[True] * (num + 1)
       UpperCamelCase_											=				[]
       UpperCamelCase_											=				2
       UpperCamelCase_											=				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:
                                   UpperCamelCase_											=				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_     							= {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= ['ConvBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ConvBertForMaskedLM',
      'ConvBertForMultipleChoice',
      'ConvBertForQuestionAnswering',
      'ConvBertForSequenceClassification',
      'ConvBertForTokenClassification',
      'ConvBertLayer',
      'ConvBertModel',
      'ConvBertPreTrainedModel',
      'load_tf_weights_in_convbert',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFConvBertForMaskedLM',
      'TFConvBertForMultipleChoice',
      'TFConvBertForQuestionAnswering',
      'TFConvBertForSequenceClassification',
      'TFConvBertForTokenClassification',
      'TFConvBertLayer',
      'TFConvBertModel',
      'TFConvBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

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

