





'''simple docstring'''






from __future__ import annotations

A__       :     str          =[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]




class 				UpperCAmelCase  :




      def __init__(    self    :				Tuple    ,    __snake_case    :				int						)       ->   None:
            _lowerCAmelCase							  =							-1
            _lowerCAmelCase							  =							-1
            _lowerCAmelCase							  =							[-1] * length
            _lowerCAmelCase							  =							None
            _lowerCAmelCase							  =							None




      def __repr__(    self    :				Optional[int]						)       ->   str:
            return f"Node(min_value={self.minn} max_value={self.maxx})"




def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


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

      _lowerCAmelCase							  =							[]
      _lowerCAmelCase							  =							[]

      for index, num in enumerate(lowerCAmelCase						):
            if num <= pivot:
                  left_arr.append(lowerCAmelCase						)
            else:
                  right_arr.append(lowerCAmelCase						)
            _lowerCAmelCase							  =							len(lowerCAmelCase						)
      _lowerCAmelCase							  =							build_tree(lowerCAmelCase						)
      _lowerCAmelCase							  =							build_tree(lowerCAmelCase						)
      return root
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """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
      _lowerCAmelCase							  =							(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      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      if start > end:
            return 0
      _lowerCAmelCase							  =							rank_till_index(lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						)
      _lowerCAmelCase							  =							rank_till_index(lowerCAmelCase							,			lowerCAmelCase							,			start - 1						)
      return rank_till_end - rank_before_start
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """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]
      _lowerCAmelCase							  =							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      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """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
      _lowerCAmelCase							  =							range_counting(
          node.left							,			(node.map_left[start - 1] if start else 0)							,			node.map_left[end] - 1							,			lowerCAmelCase							,			lowerCAmelCase							,			)
      _lowerCAmelCase							  =							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()













'''simple docstring'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass






