





from __future__ import annotations

_snake_case				= [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class 					lowerCAmelCase							:







  def __init__(			self  :str    ,							_lowercase  :int					):






     '''simple docstring'''





     lowercase__    =  -1
     lowercase__    =  -1
     lowercase__    =  [-1] * length
     lowercase__    =  None
     lowercase__    =  None







  def __repr__(			self  :List[str]					):






     '''simple docstring'''





     return f'''Node(min_value={self.minn} max_value={self.maxx})'''


def _A   (    __magic_name__       ):
   lowercase__    =  Node(len(lowerCAmelCase__       )       )
   lowercase__   ,							lowercase__    =  min(lowerCAmelCase__       ), max(lowerCAmelCase__       )
   # Leaf node case where the node contains only one unique value
   if root.minn == root.maxx:
      return root
   lowercase__    =  (root.minn + root.maxx) // 2

   lowercase__    =  []
   lowercase__    =  []

   for index, num in enumerate(lowerCAmelCase__       ):
      if num <= pivot:
         left_arr.append(lowerCAmelCase__       )
      else:
         right_arr.append(lowerCAmelCase__       )
      lowercase__    =  len(lowerCAmelCase__       )
   lowercase__    =  build_tree(lowerCAmelCase__       )
   lowercase__    =  build_tree(lowerCAmelCase__       )
   return root


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   if index < 0 or node is None:
      return 0
   # Leaf node cases
   if node.minn == node.maxx:
      return index + 1 if node.minn == num else 0
   lowercase__    =  (node.minn + node.maxx) // 2
   if num <= pivot:
      # go the left subtree and map index to the left subtree
      return rank_till_index(node.left  ,					lowerCAmelCase__  ,					node.map_left[index] - 1       )
   else:
      # go to the right subtree and map index to the right subtree
      return rank_till_index(node.right  ,					lowerCAmelCase__  ,					index - node.map_left[index]       )


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   if start > end:
      return 0
   lowercase__    =  rank_till_index(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__       )
   lowercase__    =  rank_till_index(lowerCAmelCase__  ,					lowerCAmelCase__  ,					start - 1       )
   return rank_till_end - rank_before_start


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   if index > (end - start) or start > end or node is None:
      return -1
   # Leaf node case
   if node.minn == node.maxx:
      return node.minn
   # Number of elements in the left subtree in interval [start, end]
   lowercase__    =  node.map_left[end] - (
       node.map_left[start - 1] if start else 0
   )
   if num_elements_in_left_tree > index:
      return quantile(
          node.left  ,					lowerCAmelCase__  ,					(node.map_left[start - 1] if start else 0)  ,					node.map_left[end] - 1  ,					)
   else:
      return quantile(
          node.right  ,					index - num_elements_in_left_tree  ,					start - (node.map_left[start - 1] if start else 0)  ,					end - node.map_left[end]  ,					)


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   if (
       start > end
       or node is None
       or start_num > end_num
       or node.minn > end_num
       or node.maxx < start_num
   ):
      return 0
   if start_num <= node.minn and node.maxx <= end_num:
      return end - start + 1
   lowercase__    =  range_counting(
       node.left  ,					(node.map_left[start - 1] if start else 0)  ,					node.map_left[end] - 1  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					)
   lowercase__    =  range_counting(
       node.right  ,					start - (node.map_left[start - 1] if start else 0)  ,					end - node.map_left[end]  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					)
   return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()







from typing import TYPE_CHECKING

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


lowerCAmelCase__							= {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= ['DebertaV2TokenizerFast']

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFDebertaV2ForMaskedLM',
        'TFDebertaV2ForQuestionAnswering',
        'TFDebertaV2ForSequenceClassification',
        'TFDebertaV2ForTokenClassification',
        'TFDebertaV2Model',
        'TFDebertaV2PreTrainedModel',
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DebertaV2ForMaskedLM',
        'DebertaV2ForMultipleChoice',
        'DebertaV2ForQuestionAnswering',
        'DebertaV2ForSequenceClassification',
        'DebertaV2ForTokenClassification',
        'DebertaV2Model',
        'DebertaV2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deberta_va import (
        DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DebertaVaConfig,
        DebertaVaOnnxConfig,
    )
    from .tokenization_deberta_va import DebertaVaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta_va import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
            TFDebertaVaModel,
            TFDebertaVaPreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta_va import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaVaForMaskedLM,
            DebertaVaForMultipleChoice,
            DebertaVaForQuestionAnswering,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaModel,
            DebertaVaPreTrainedModel,
        )

else:
    import sys

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