code: 
  '''simple docstring'''
  
  
  
  
  
  import math
  from typing import Optional
  
  import torch
  import torch.utils.checkpoint as checkpoint
  from torch import nn
  
  from ....modeling_utils import PreTrainedModel
  from ....utils import add_start_docstrings, logging
  from ...bert.modeling_bert import BertModel
  from .configuration_retribert import RetriBertConfig
  
  
  A     :   Optional[Any]           =   logging.get_logger(__name__)
  
  A     :   List[str]           =   [
      "yjernite/retribert-base-uncased",
      # See all RetriBert models at https://huggingface.co/models?filter=retribert
  ]
  
  
  
  class  lowerCAmelCase__   (       lowerCAmelCase__       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= RetriBertConfig
    _UpperCamelCase						= None
    _UpperCamelCase						= 'retribert'
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Union[str, Any]    )   ->       List[str]:
  
          if isinstance(_lowerCamelCase	, nn.Linear    ):
                module.weight.data.normal_(mean=0.0	, std=self.config.initializer_range    )
                if module.bias is not None:
                      module.bias.data.zero_()
          elif isinstance(_lowerCamelCase	, nn.Embedding    ):
                module.weight.data.normal_(mean=0.0	, std=self.config.initializer_range    )
                if module.padding_idx is not None:
                      module.weight.data[module.padding_idx].zero_()
          elif isinstance(_lowerCamelCase	, nn.LayerNorm    ):
                module.bias.data.zero_()
                module.weight.data.fill_(1.0    )
  
  A     :   Optional[int]           =   r"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
  
  
  
  @add_start_docstrings(
      'Bert Based model to embed queries or document for document retrieval.'					,							lowerCAmelCase__					,							)
  class  lowerCAmelCase__   (       lowerCAmelCase__       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : List[Any]	, lowerCAmelCase_  : List[Any]    )   ->       Tuple:
          super().__init__(_lowerCamelCase    )
          snake_case_						       =							config.projection_dim
  
          snake_case_						       =							BertModel(_lowerCamelCase    )
          snake_case_						       =							None if config.share_encoders else BertModel(_lowerCamelCase    )
          snake_case_						       =							nn.Dropout(config.hidden_dropout_prob    )
          snake_case_						       =							nn.Linear(config.hidden_size	, config.projection_dim	, bias=_lowerCamelCase    )
          snake_case_						       =							nn.Linear(config.hidden_size	, config.projection_dim	, bias=_lowerCamelCase    )
  
          snake_case_						       =							nn.CrossEntropyLoss(reduction="mean"    )
  
          # Initialize weights and apply final processing
          self.post_init()
  
    def 		_lowerCamelCase	(self  : Any	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Optional[int]=-1	, )   ->       int:
  
          if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
                return sent_encoder(_lowerCamelCase	, attention_mask=_lowerCamelCase    )[1]
          else:
                # prepare implicit variables
                snake_case_						       =							input_ids.device
                snake_case_						       =							input_ids.size()
                snake_case_						       =							torch.zeros(_lowerCamelCase	, dtype=torch.long	, device=_lowerCamelCase    )
                snake_case_						       =							[None] * sent_encoder.config.num_hidden_layers
                snake_case_						       =							sent_encoder.get_extended_attention_mask(
                    _lowerCamelCase	, _lowerCamelCase    )
  
                # define function for checkpointing
                def partial_encode(*lowerCAmelCase_  : str    ):
                      snake_case_						       =							sent_encoder.encoder(
                          inputs[0]	, attention_mask=inputs[1]	, head_mask=_lowerCamelCase	, )
                      snake_case_						       =							encoder_outputs[0]
                      snake_case_						       =							sent_encoder.pooler(_lowerCamelCase    )
                      return pooled_output
  
                # run embedding layer on everything at once
                snake_case_						       =							sent_encoder.embeddings(
                    input_ids=_lowerCamelCase	, position_ids=_lowerCamelCase	, token_type_ids=_lowerCamelCase	, inputs_embeds=_lowerCamelCase    )
                # run encoding and pooling on one mini-batch at a time
                snake_case_						       =							[]
                for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size    )    ):
                      snake_case_						       =							embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                      snake_case_						       =							extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                      snake_case_						       =							checkpoint.checkpoint(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase    )
                      pooled_output_list.append(_lowerCamelCase    )
                return torch.cat(_lowerCamelCase	, dim=0    )
  
    def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Tuple=None	, lowerCAmelCase_  : Optional[int]=-1	, )   ->       str:
          snake_case_						       =							self.embed_sentences_checkpointed(
              _lowerCamelCase	, _lowerCamelCase	, self.bert_query	, _lowerCamelCase	, )
          return self.project_query(_lowerCamelCase    )
  
    def 		_lowerCamelCase	(self  : Any	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Optional[int]=None	, lowerCAmelCase_  : Optional[int]=-1	, )   ->       Any:
          snake_case_						       =							self.embed_sentences_checkpointed(
              _lowerCamelCase	, _lowerCamelCase	, self.bert_query if self.bert_doc is None else self.bert_doc	, _lowerCamelCase	, )
          return self.project_doc(_lowerCamelCase    )
  
  
    def 		_lowerCamelCase	(self  : List[str]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Tuple = -1	, )   ->       Optional[Any]:
          snake_case_						       =							input_ids_query.device
          snake_case_						       =							self.embed_questions(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase    )
          snake_case_						       =							self.embed_answers(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase    )
          snake_case_						       =							torch.mm(_lowerCamelCase	, a_reps.t()    )
          snake_case_						       =							self.ce_loss(_lowerCamelCase	, torch.arange(compare_scores.shape[1]    ).to(_lowerCamelCase    )    )
          snake_case_						       =							self.ce_loss(compare_scores.t()	, torch.arange(compare_scores.shape[0]    ).to(_lowerCamelCase    )    )
          snake_case_						       =							(loss_qa + loss_aq) / 2
          return loss
  
  
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()
  
  