




from __future__ import annotations

from random import choice



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  return choice(UpperCamelCase      )



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  int		       =     random_pivot(UpperCamelCase      )

  # partition based on pivot
  # linear time
  lowerCAmelCase__				:  Any		       =     [e for e in lst if e < pivot]
  lowerCAmelCase__				:  List[Any]		       =     [e for e in lst if e > pivot]

  # if we get lucky, pivot might be the element we want.
  # we can easily see this:
  # small (elements smaller than k)
  # + pivot (kth element)
  # + big (elements larger than k)
  if len(UpperCamelCase      ) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(UpperCamelCase      ) < k - 1:
    return kth_number(UpperCamelCase	,      k - len(UpperCamelCase      ) - 1      )
  # pivot is in elements smaller than k
  else:
    return kth_number(UpperCamelCase	,      UpperCamelCase      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_					    = {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LiltForQuestionAnswering""",
         """LiltForSequenceClassification""",
         """LiltForTokenClassification""",
         """LiltModel""",
         """LiltPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lilt import (
              LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
              LiltForQuestionAnswering,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltModel,
              LiltPreTrainedModel,
          )

else:
     import sys

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

