

from __future__ import annotations







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      list[int]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    [True] * limit
     _lowerCAmelCase   :					Optional[Any]        =    False
     _lowerCAmelCase   :					Optional[Any]        =    False
     _lowerCAmelCase   :					Tuple        =    True

     for i in range(3		,							int(limit**0.5 + 1				)		,							2				):
          _lowerCAmelCase   :					Dict        =    i * 2
          while index < limit:
               _lowerCAmelCase   :					Dict        =    False
               _lowerCAmelCase   :					Optional[int]        =    index + i

     _lowerCAmelCase   :					List[Any]        =    [2]

     for i in range(3		,							SCREAMING_SNAKE_CASE		,							2				):
          if is_prime[i]:
               primes.append(SCREAMING_SNAKE_CASE				)

     return primes







def 	_snake_case		(    SCREAMING_SNAKE_CASE = 1000000				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    prime_sieve(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[int]        =    0
     _lowerCAmelCase   :					Union[str, Any]        =    0

     for i in range(len(SCREAMING_SNAKE_CASE				)				):
          for j in range(i + length		,							len(SCREAMING_SNAKE_CASE				)				):
               _lowerCAmelCase   :					Any        =    sum(primes[i:j]				)
               if sol >= ceiling:
                    break

               if sol in primes:
                    _lowerCAmelCase   :					int        =    j - i
                    _lowerCAmelCase   :					Any        =    sol

     return largest


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









from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

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






