

'''simple docstring'''



from __future__ import annotations

import math


def       snake_case   (	snake_case      : int			) ->  bool:


    """simple docstring"""


    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5					,		int(math.sqrt(snake_case			) + 1			)					,		6			):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


_UpperCamelCase						:  List[Any]							       =				[num for num in range(3, 10_0001, 2) if not is_prime(num)]


def       snake_case   (	snake_case      : int			) ->  list[int]:


    """simple docstring"""


    if not isinstance(snake_case					,		snake_case			):
        raise ValueError('n must be an integer'			)
    if n <= 0:
        raise ValueError('n must be >= 0'			)

    lowerCAmelCase 						=							[]
    for num in range(len(snake_case			)			):
        lowerCAmelCase 						=							0
        while 2 * i * i <= odd_composites[num]:
            lowerCAmelCase 						=							odd_composites[num] - 2 * i * i
            if is_prime(snake_case			):
                break
            i += 1
        else:
            list_nums.append(odd_composites[num]			)
            if len(snake_case			) == n:
                return list_nums

    return []


def       snake_case   (	) ->  int:


    """simple docstring"""


    return compute_nums(1			)[0]


if __name__ == "__main__":
     print(F"""{solution() = }""")








'''simple docstring'''



from typing import TYPE_CHECKING

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


_UpperCamelCase						:  str							       =				{
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				[
         "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXJapaneseForCausalLM",
         "GPTNeoXJapaneseLayer",
         "GPTNeoXJapaneseModel",
         "GPTNeoXJapanesePreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
     from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neox_japanese import (
              GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoXJapaneseForCausalLM,
              GPTNeoXJapaneseLayer,
              GPTNeoXJapaneseModel,
              GPTNeoXJapanesePreTrainedModel,
          )


else:
     import sys

     _UpperCamelCase						:  List[Any]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





