




'''simple docstring'''







from __future__ import annotations

import math





def 		snake_case_							(_a			:							int  ):

    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(_a  ) + 1  )					,     6  ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True


A									=[num for num in range(3, 10_00_01, 2) if not is_prime(num)]





def 		snake_case_							(_a			:							int  ):
    if not isinstance(_a					,     _a  ):
        raise ValueError('''n must be an integer'''  )
    if n <= 0:
        raise ValueError('''n must be >= 0'''  )

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

    return []





def 		snake_case_							():
    return compute_nums(1  )[0]


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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

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




