


'''simple docstring'''


from typing import TYPE_CHECKING

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


__magic_name__			=      {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BlenderbotForCausalLM',
         'BlenderbotForConditionalGeneration',
         'BlenderbotModel',
         'BlenderbotPreTrainedModel',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFBlenderbotForConditionalGeneration',
         'TFBlenderbotModel',
         'TFBlenderbotPreTrainedModel',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'FlaxBlenderbotForConditionalGeneration',
         'FlaxBlenderbotModel',
         'FlaxBlenderbotPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

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())))
