



'''simple docstring'''






from __future__ import annotations

import math
def  lowerCamelCase__			(		_A							):

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


lowerCAmelCase:   Union[str, Any]					=		[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]
def  lowerCamelCase__			(		_A							):
  if not isinstance(lowerCAmelCase_					,     lowerCAmelCase_							):
    raise ValueError('n must be an integer'							)
  if n <= 0:
    raise ValueError('n must be >= 0'							)

  a		:   List[Any]	  =	[]
  for num in range(len(lowerCAmelCase_							)							):
    a		:   List[Any]	  =	0
    while 2 * i * i <= odd_composites[num]:
      a		:   Union[str, Any]	  =	odd_composites[num] - 2 * i * i
      if is_prime(lowerCAmelCase_							):
        break
      i += 1
    else:
      list_nums.append(odd_composites[num]							)
      if len(lowerCAmelCase_							) == n:
        return list_nums

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


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



from typing import TYPE_CHECKING

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


__a	 =      {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      ["""ReformerTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ReformerAttention""",
     """ReformerForMaskedLM""",
     """ReformerForQuestionAnswering""",
     """ReformerForSequenceClassification""",
     """ReformerLayer""",
     """ReformerModel""",
     """ReformerModelWithLMHead""",
     """ReformerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



