from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE :	str							     =      {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE :	Any							     =      ["""LongformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE :	str							     =      [
      """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LongformerForMaskedLM""",
      """LongformerForMultipleChoice""",
      """LongformerForQuestionAnswering""",
      """LongformerForSequenceClassification""",
      """LongformerForTokenClassification""",
      """LongformerModel""",
      """LongformerPreTrainedModel""",
      """LongformerSelfAttention""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE :	int							     =      [
      """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFLongformerForMaskedLM""",
      """TFLongformerForMultipleChoice""",
      """TFLongformerForQuestionAnswering""",
      """TFLongformerForSequenceClassification""",
      """TFLongformerForTokenClassification""",
      """TFLongformerModel""",
      """TFLongformerPreTrainedModel""",
      """TFLongformerSelfAttention""",
  ]


if TYPE_CHECKING:
  from .configuration_longformer import (
      LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      LongformerConfig,
      LongformerOnnxConfig,
  )
  from .tokenization_longformer import LongformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_longformer_fast import LongformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_longformer import (
        LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        LongformerForMaskedLM,
        LongformerForMultipleChoice,
        LongformerForQuestionAnswering,
        LongformerForSequenceClassification,
        LongformerForTokenClassification,
        LongformerModel,
        LongformerPreTrainedModel,
        LongformerSelfAttention,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_longformer import (
        TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLongformerForMaskedLM,
        TFLongformerForMultipleChoice,
        TFLongformerForQuestionAnswering,
        TFLongformerForSequenceClassification,
        TFLongformerForTokenClassification,
        TFLongformerModel,
        TFLongformerPreTrainedModel,
        TFLongformerSelfAttention,
    )

else:
  import sys

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



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

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

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

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

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


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


