code: 
  '''simple docstring'''
  
  
  
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @register_to_config
    def __init__(self  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : float	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : str	, lowerCAmelCase_  : bool = False	, )   ->       Tuple:
          super().__init__()
  
          snake_case_						       =							nn.Linear(lowerCAmelCase_	, lowerCAmelCase_	, bias=lowerCAmelCase_    )
  
          snake_case_						       =							nn.Embedding(lowerCAmelCase_	, lowerCAmelCase_    )
          snake_case_						       =							False
  
          snake_case_						       =							nn.Dropout(p=lowerCAmelCase_    )
  
          snake_case_						       =							TaConfig(
              d_model=lowerCAmelCase_	, num_heads=lowerCAmelCase_	, d_kv=lowerCAmelCase_	, d_ff=lowerCAmelCase_	, feed_forward_proj=lowerCAmelCase_	, dropout_rate=lowerCAmelCase_	, is_decoder=lowerCAmelCase_	, is_encoder_decoder=lowerCAmelCase_	, )
          snake_case_						       =							nn.ModuleList()
          for lyr_num in range(lowerCAmelCase_    ):
                snake_case_						       =							TaBlock(lowerCAmelCase_    )
                self.encoders.append(lowerCAmelCase_    )
  
          snake_case_						       =							TaLayerNorm(lowerCAmelCase_    )
          snake_case_						       =							nn.Dropout(p=lowerCAmelCase_    )
  
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Any    )   ->       str:
          snake_case_						       =							self.input_proj(lowerCAmelCase_    )
  
          # terminal relative positional encodings
          snake_case_						       =							encoder_inputs.shape[1]
          snake_case_						       =							torch.arange(lowerCAmelCase_	, device=encoder_inputs.device    )
  
          snake_case_						       =							encoder_inputs_mask.sum(-1    )
          snake_case_						       =							torch.roll(input_positions.unsqueeze(0    )	, tuple(seq_lens.tolist()    )	, dims=0    )
          x += self.position_encoding(lowerCAmelCase_    )
  
          snake_case_						       =							self.dropout_pre(lowerCAmelCase_    )
  
          # inverted the attention mask
          snake_case_						       =							encoder_inputs.size()
          snake_case_						       =							self.get_extended_attention_mask(lowerCAmelCase_	, lowerCAmelCase_    )
  
          for lyr in self.encoders:
                snake_case_						       =							lyr(lowerCAmelCase_	, lowerCAmelCase_    )[0]
          snake_case_						       =							self.layer_norm(lowerCAmelCase_    )
  
          return self.dropout_post(lowerCAmelCase_    ), encoder_inputs_mask
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  A           =   [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Optional[int]	, lowerCAmelCase_  : int    )   ->       None:
          snake_case_						       =							-1
          snake_case_						       =							-1
          snake_case_						       =							[-1] * length
          snake_case_						       =							None
          snake_case_						       =							None
  
  
    def __repr__(self  : List[Any]    )   ->       str:
          return f'''Node(min_value={self.minn} max_value={self.maxx})'''
  
  def   snake_case_       (							_a     :							list[int]			):
        snake_case_						       =							Node(len(_a			)			)
        snake_case_       ,	snake_case_						       =							min(_a			), max(_a			)
        # Leaf node case where the node contains only one unique value
        if root.minn == root.maxx:
              return root
        snake_case_						       =							(root.minn + root.maxx) // 2
  
        snake_case_						       =							[]
        snake_case_						       =							[]
  
        for index, num in enumerate(_a			):
              if num <= pivot:
                    left_arr.append(_a			)
              else:
                    right_arr.append(_a			)
              snake_case_						       =							len(_a			)
        snake_case_						       =							build_tree(_a			)
        snake_case_						       =							build_tree(_a			)
        return root
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int			):
        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
        snake_case_						       =							(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   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if start > end:
              return 0
        snake_case_						       =							rank_till_index(_a ,					_a ,					_a			)
        snake_case_						       =							rank_till_index(_a ,					_a ,					start - 1			)
        return rank_till_end - rank_before_start
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        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]
        snake_case_						       =							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   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        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
        snake_case_						       =							range_counting(
            node.left ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					_a ,					_a ,					)
        snake_case_						       =							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()
  
  