code: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __SCREAMING_SNAKE_CASE					=						{
      """configuration_big_bird""": ["""BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BigBirdConfig""", """BigBirdOnnxConfig"""],
  }
  
  try:
     if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __SCREAMING_SNAKE_CASE					=						["""BigBirdTokenizer"""]
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __SCREAMING_SNAKE_CASE					=						["""BigBirdTokenizerFast"""]
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __SCREAMING_SNAKE_CASE					=						[
         """BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BigBirdForCausalLM""",
         """BigBirdForMaskedLM""",
         """BigBirdForMultipleChoice""",
         """BigBirdForPreTraining""",
         """BigBirdForQuestionAnswering""",
         """BigBirdForSequenceClassification""",
         """BigBirdForTokenClassification""",
         """BigBirdLayer""",
         """BigBirdModel""",
         """BigBirdPreTrainedModel""",
         """load_tf_weights_in_big_bird""",
     ]
  
  try:
     if not is_flax_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __SCREAMING_SNAKE_CASE					=						[
         """FlaxBigBirdForCausalLM""",
         """FlaxBigBirdForMaskedLM""",
         """FlaxBigBirdForMultipleChoice""",
         """FlaxBigBirdForPreTraining""",
         """FlaxBigBirdForQuestionAnswering""",
         """FlaxBigBirdForSequenceClassification""",
         """FlaxBigBirdForTokenClassification""",
         """FlaxBigBirdModel""",
         """FlaxBigBirdPreTrainedModel""",
     ]
  
  if TYPE_CHECKING:
     from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
     try:
        if not is_sentencepiece_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_big_bird import BigBirdTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_big_bird import (
            BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdForCausalLM,
            BigBirdForMaskedLM,
            BigBirdForMultipleChoice,
            BigBirdForPreTraining,
            BigBirdForQuestionAnswering,
            BigBirdForSequenceClassification,
            BigBirdForTokenClassification,
            BigBirdLayer,
            BigBirdModel,
            BigBirdPreTrainedModel,
            load_tf_weights_in_big_bird,
        )
  
     try:
        if not is_flax_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_flax_big_bird import (
            FlaxBigBirdForCausalLM,
            FlaxBigBirdForMaskedLM,
            FlaxBigBirdForMultipleChoice,
            FlaxBigBirdForPreTraining,
            FlaxBigBirdForQuestionAnswering,
            FlaxBigBirdForSequenceClassification,
            FlaxBigBirdForTokenClassification,
            FlaxBigBirdModel,
            FlaxBigBirdPreTrainedModel,
        )
  
  else:
     import sys
  
     __SCREAMING_SNAKE_CASE					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  __SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
            _a          =				-1
            _a          =				-1
            _a          =				[-1] * length
            _a          =				None
            _a          =				None
  
  
  
  
     def __repr__(		self							:Optional[Any]       ):
            return f'Node(min_value={self.minn} max_value={self.maxx})'
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				Node(len(a					)					)
         _a   , _a          =				min(a					), max(a					)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         _a          =				(root.minn + root.maxx) // 2
  
         _a          =				[]
         _a          =				[]
  
         for index, num in enumerate(a					):
                if num <= pivot:
                       left_arr.append(a					)
                else:
                       right_arr.append(a					)
                _a          =				len(a					)
         _a          =				build_tree(a					)
         _a          =				build_tree(a					)
         return root
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         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
         _a          =				(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,		a,		node.map_left[index] - 1					)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right,		a,		index - node.map_left[index]					)
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if start > end:
                return 0
         _a          =				rank_till_index(a,		a,		a					)
         _a          =				rank_till_index(a,		a,		start - 1					)
         return rank_till_end - rank_before_start
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         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]
         _a          =				node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left,		a,		(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      (  a,		a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         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
         _a          =				range_counting(
             node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
         _a          =				range_counting(
             node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  