

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__										=      logging.get_logger(__name__)

A__										=      [
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]







class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   RetriBertConfig
  SCREAMING_SNAKE_CASE__					:			Dict					    =   None
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   """retribert"""



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					Tuple						)       ->			Optional[Any]:
      if isinstance(A_  ,	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(A_  ,	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(A_  ,	nn.LayerNorm						):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0						)




A__										=      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."""		,  _snake_case		,  )
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""



  def __init__(						self     :					List[str]  ,	A_     :					RetriBertConfig						)       ->			None:
      super().__init__(A_						)
      SCREAMING_SNAKE_CASE__         =    config.projection_dim

      SCREAMING_SNAKE_CASE__         =    BertModel(A_						)
      SCREAMING_SNAKE_CASE__         =    None if config.share_encoders else BertModel(A_						)
      SCREAMING_SNAKE_CASE__         =    nn.Dropout(config.hidden_dropout_prob						)
      SCREAMING_SNAKE_CASE__         =    nn.Linear(config.hidden_size  ,	config.projection_dim  ,	bias=A_						)
      SCREAMING_SNAKE_CASE__         =    nn.Linear(config.hidden_size  ,	config.projection_dim  ,	bias=A_						)

      SCREAMING_SNAKE_CASE__         =    nn.CrossEntropyLoss(reduction="mean"						)

      # Initialize weights and apply final processing
      self.post_init()



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					Any  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					int=-1  ,	)       ->			List[str]:
      # reproduces BERT forward pass with checkpointing
      if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
          return sent_encoder(A_  ,	attention_mask=A_						)[1]
      else:
          # prepare implicit variables
          SCREAMING_SNAKE_CASE__         =    input_ids.device
          SCREAMING_SNAKE_CASE__         =    input_ids.size()
          SCREAMING_SNAKE_CASE__         =    torch.zeros(A_  ,	dtype=torch.long  ,	device=A_						)
          SCREAMING_SNAKE_CASE__         =    [None] * sent_encoder.config.num_hidden_layers
          SCREAMING_SNAKE_CASE__         =    sent_encoder.get_extended_attention_mask(
              A_  ,	A_						)

          # define function for checkpointing
          def partial_encode(*A_     :					List[Any]						):
              SCREAMING_SNAKE_CASE__         =    sent_encoder.encoder(
                  inputs[0]  ,	attention_mask=inputs[1]  ,	head_mask=A_  ,	)
              SCREAMING_SNAKE_CASE__         =    encoder_outputs[0]
              SCREAMING_SNAKE_CASE__         =    sent_encoder.pooler(A_						)
              return pooled_output

          # run embedding layer on everything at once
          SCREAMING_SNAKE_CASE__         =    sent_encoder.embeddings(
              input_ids=A_  ,	position_ids=A_  ,	token_type_ids=A_  ,	inputs_embeds=A_						)
          # run encoding and pooling on one mini-batch at a time
          SCREAMING_SNAKE_CASE__         =    []
          for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
              SCREAMING_SNAKE_CASE__         =    embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              SCREAMING_SNAKE_CASE__         =    extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
              SCREAMING_SNAKE_CASE__         =    checkpoint.checkpoint(A_  ,	A_  ,	A_						)
              pooled_output_list.append(A_						)
          return torch.cat(A_  ,	dim=0						)



  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					Optional[Any]  ,	A_     :					Optional[Any]=None  ,	A_     :					List[Any]=-1  ,	)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.embed_sentences_checkpointed(
          A_  ,	A_  ,	self.bert_query  ,	A_  ,	)
      return self.project_query(A_						)



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	A_     :					Optional[Any]  ,	A_     :					Any=None  ,	A_     :					List[str]=-1  ,	)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.embed_sentences_checkpointed(
          A_  ,	A_  ,	self.bert_query if self.bert_doc is None else self.bert_doc  ,	A_  ,	)
      return self.project_doc(A_						)







  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					torch.LongTensor  ,	A_     :					Optional[torch.FloatTensor]  ,	A_     :					torch.LongTensor  ,	A_     :					Optional[torch.FloatTensor]  ,	A_     :					int = -1  ,	)       ->			torch.FloatTensor:
      SCREAMING_SNAKE_CASE__         =    input_ids_query.device
      SCREAMING_SNAKE_CASE__         =    self.embed_questions(A_  ,	A_  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    self.embed_answers(A_  ,	A_  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    torch.mm(A_  ,	a_reps.t()						)
      SCREAMING_SNAKE_CASE__         =    self.ce_loss(A_  ,	torch.arange(compare_scores.shape[1]						).to(A_						)						)
      SCREAMING_SNAKE_CASE__         =    self.ce_loss(compare_scores.t()  ,	torch.arange(compare_scores.shape[0]						).to(A_						)						)
      SCREAMING_SNAKE_CASE__         =    (loss_qa + loss_aq) / 2
      return loss





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Any  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    key
      SCREAMING_SNAKE_CASE__         =    freq







  def __str__(						self     :					Union[str, Any]						)       ->			List[str]:
      return f"Node(key={self.key}, freq={self.freq})"





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A						):
    if i > j or i < 0 or j > len(__A						) - 1:
        return

    SCREAMING_SNAKE_CASE__         =    root[i][j]
    if parent == -1:  # root does not have a parent
        print(f"{key[node]} is the root of the binary search tree."						)
    elif is_left:
        print(f"{key[node]} is the left child of key {parent}."						)
    else:
        print(f"{key[node]} is the right child of key {parent}."						)

    print_binary_search_tree(__A			,							__A			,							__A			,							node - 1			,							key[node]			,							__A						)
    print_binary_search_tree(__A			,							__A			,							node + 1			,							__A			,							key[node]			,							__A						)





def 				__a							(       __A						):
    nodes.sort(key=lambda __A						: node.key						)

    SCREAMING_SNAKE_CASE__         =    len(__A						)

    SCREAMING_SNAKE_CASE__         =    [nodes[i].key for i in range(__A						)]
    SCREAMING_SNAKE_CASE__         =    [nodes[i].freq for i in range(__A						)]

    # This 2D array stores the overall tree cost (which's as minimized as possible);
    # for a single key, cost is equal to frequency of the key.
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
    # array
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # stores tree roots that will be used later for constructing binary search tree
    SCREAMING_SNAKE_CASE__         =    [[i if i == j else 0 for j in range(__A						)] for i in range(__A						)]

    for interval_length in range(2			,							n + 1						):
        for i in range(n - interval_length + 1						):
            SCREAMING_SNAKE_CASE__         =    i + interval_length - 1

            SCREAMING_SNAKE_CASE__         =    sys.maxsize  # set the value to "infinity"
            SCREAMING_SNAKE_CASE__         =    total[i][j - 1] + freqs[j]

            # Apply Knuth's optimization
            # Loop without optimization: for r in range(i, j + 1):
            for r in range(root[i][j - 1]			,							root[i + 1][j] + 1						):  # r is a temporal root
                SCREAMING_SNAKE_CASE__         =    dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                SCREAMING_SNAKE_CASE__         =    dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                SCREAMING_SNAKE_CASE__         =    left + total[i][j] + right

                if dp[i][j] > cost:
                    SCREAMING_SNAKE_CASE__         =    cost
                    SCREAMING_SNAKE_CASE__         =    r

    print("Binary search tree nodes:"						)
    for node in nodes:
        print(__A						)

    print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."						)
    print_binary_search_tree(__A			,							__A			,							0			,							n - 1			,							-1			,							__A						)





def 				__a							(       ):
    # A sample binary search tree
    SCREAMING_SNAKE_CASE__         =    [Node(__A			,							randint(1			,							50						)						) for i in range(10			,							0			,							-1						)]
    find_optimal_binary_search_tree(__A						)


if __name__ == "__main__":
  main()


