





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


lowerCAmelCase_        =			logging.get_logger(__name__)

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



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				Dict					    =RetriBertConfig
  a_							:				List[str]					    =None
  a_							:				int					    ="""retribert"""







  def UpperCamelCase_       (					self					:    Tuple					, UpperCamelCase					:    Optional[int]							):


    '''simple docstring'''





    if isinstance(UpperCamelCase					, 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(UpperCamelCase					, 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(UpperCamelCase					, nn.LayerNorm							):
      module.bias.data.zero_()
      module.weight.data.fill_(1.0							)




lowerCAmelCase_        =			r"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""



@add_start_docstrings(
    """Bert Based model to embed queries or document for document retrieval."""							,			UpperCAmelCase_							,			)
class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def __init__(					self					:    Any					, UpperCamelCase					:    RetriBertConfig							):


    '''simple docstring'''





    super().__init__(UpperCamelCase							)
    _snake_case      : int														=		config.projection_dim

    _snake_case      : Optional[Any]														=		BertModel(UpperCamelCase							)
    _snake_case      : int														=		None if config.share_encoders else BertModel(UpperCamelCase							)
    _snake_case      : int														=		nn.Dropout(config.hidden_dropout_prob							)
    _snake_case      : Tuple														=		nn.Linear(config.hidden_size					, config.projection_dim					, bias=UpperCamelCase							)
    _snake_case      : str														=		nn.Linear(config.hidden_size					, config.projection_dim					, bias=UpperCamelCase							)

    _snake_case      : Any														=		nn.CrossEntropyLoss(reduction='mean'							)

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







  def UpperCamelCase_       (					self					:    Optional[Any]					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Optional[Any]					, UpperCamelCase					:    Any=-1					, ):


    '''simple docstring'''





    if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
      return sent_encoder(UpperCamelCase					, attention_mask=UpperCamelCase							)[1]
    else:
      # prepare implicit variables
      _snake_case      : Optional[int]														=		input_ids.device
      _snake_case      : Dict														=		input_ids.size()
      _snake_case      : List[str]														=		torch.zeros(UpperCamelCase					, dtype=torch.long					, device=UpperCamelCase							)
      _snake_case      : Any														=		[None] * sent_encoder.config.num_hidden_layers
      _snake_case      : torch.Tensor														=		sent_encoder.get_extended_attention_mask(
          UpperCamelCase					, UpperCamelCase							)

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

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







  def UpperCamelCase_       (					self					:    Union[str, Any]					, UpperCamelCase					:    int					, UpperCamelCase					:    Any=None					, UpperCamelCase					:    str=-1					, ):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		self.embed_sentences_checkpointed(
        UpperCamelCase					, UpperCamelCase					, self.bert_query					, UpperCamelCase					, )
    return self.project_query(UpperCamelCase							)







  def UpperCamelCase_       (					self					:    int					, UpperCamelCase					:    Union[str, Any]					, UpperCamelCase					:    int=None					, UpperCamelCase					:    Tuple=-1					, ):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		self.embed_sentences_checkpointed(
        UpperCamelCase					, UpperCamelCase					, self.bert_query if self.bert_doc is None else self.bert_doc					, UpperCamelCase					, )
    return self.project_doc(UpperCamelCase							)







  def UpperCamelCase_       (					self					:    str					, UpperCamelCase					:    torch.LongTensor					, UpperCamelCase					:    Optional[torch.FloatTensor]					, UpperCamelCase					:    torch.LongTensor					, UpperCamelCase					:    Optional[torch.FloatTensor]					, UpperCamelCase					:    int = -1					, ):


    '''simple docstring'''





    _snake_case      : Optional[int]														=		input_ids_query.device
    _snake_case      : int														=		self.embed_questions(UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Union[str, Any]														=		self.embed_answers(UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)
    _snake_case      : Dict														=		torch.mm(UpperCamelCase					, a_reps.t()							)
    _snake_case      : List[str]														=		self.ce_loss(UpperCamelCase					, torch.arange(compare_scores.shape[1]							).to(UpperCamelCase							)							)
    _snake_case      : Union[str, Any]														=		self.ce_loss(compare_scores.t()					, torch.arange(compare_scores.shape[0]							).to(UpperCamelCase							)							)
    _snake_case      : Union[str, Any]														=		(loss_qa + loss_aq) / 2
    return loss











def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						int:
  # The longer word should come first
  if len(lowerCAmelCase ) < len(lowerCAmelCase ):
    return levenshtein_distance(lowerCAmelCase			,							lowerCAmelCase )

  if len(lowerCAmelCase ) == 0:
    return len(lowerCAmelCase )

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

    for j, ca in enumerate(lowerCAmelCase ):
      # Calculate insertions, deletions and substitutions
      _snake_case      : Optional[Any]														=		previous_row[j + 1] + 1
      _snake_case      : Optional[int]														=		current_row[j] + 1
      _snake_case      : Union[str, Any]														=		previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase ) )

    # Store the previous row
    _snake_case      : str														=		current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
      lowerCAmelCase_        =			input("""Enter the first word:\n""").strip()
      lowerCAmelCase_        =			input("""Enter the second word:\n""").strip()

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




