




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase       			= {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase       			= [
         'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MegatronBertForCausalLM',
         'MegatronBertForMaskedLM',
         'MegatronBertForMultipleChoice',
         'MegatronBertForNextSentencePrediction',
         'MegatronBertForPreTraining',
         'MegatronBertForQuestionAnswering',
         'MegatronBertForSequenceClassification',
         'MegatronBertForTokenClassification',
         'MegatronBertModel',
         'MegatronBertPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_megatron_bert import (
              MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MegatronBertForCausalLM,
              MegatronBertForMaskedLM,
              MegatronBertForMultipleChoice,
              MegatronBertForNextSentencePrediction,
              MegatronBertForPreTraining,
              MegatronBertForQuestionAnswering,
              MegatronBertForSequenceClassification,
              MegatronBertForTokenClassification,
              MegatronBertModel,
              MegatronBertPreTrainedModel,
          )

else:
     import sys

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













import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





