
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase								=					{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TapasForMaskedLM',
         'TapasForQuestionAnswering',
         'TapasForSequenceClassification',
         'TapasModel',
         'TapasPreTrainedModel',
         'load_tf_weights_in_tapas',
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFTapasForMaskedLM',
         'TFTapasForQuestionAnswering',
         'TFTapasForSequenceClassification',
         'TFTapasModel',
         'TFTapasPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

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






import random



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			[], [], []
     for element in data:
          if element < pivot:
               less.append(lowerCAmelCase_      )
          elif element > pivot:
               greater.append(lowerCAmelCase_      )
          else:
               equal.append(lowerCAmelCase_      )
     return less, equal, greater






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     if index >= len(lowerCAmelCase_      ) or index < 0:
          return None

     lowerCAmelCase__          =			items[random.randint(0       ,    len(lowerCAmelCase_      ) - 1      )]
     lowerCAmelCase__          =			0
     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			_partition(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )

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