





from typing import TYPE_CHECKING

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


UpperCAmelCase_     							= {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AdaptiveEmbedding',
      'TransfoXLForSequenceClassification',
      'TransfoXLLMHeadModel',
      'TransfoXLModel',
      'TransfoXLPreTrainedModel',
      'load_tf_weights_in_transfo_xl',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFAdaptiveEmbedding',
      'TFTransfoXLForSequenceClassification',
      'TFTransfoXLLMHeadModel',
      'TFTransfoXLMainLayer',
      'TFTransfoXLModel',
      'TFTransfoXLPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

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








import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    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 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # 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

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      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)      )

