text: import torch
  from torch import nn, Tensor
  from typing import Union, Tuple, List, Iterable, Dict
  from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
  from language_bert.LanguageTransformer import LanguageTransformer
  
  class BatchHardSoftMarginTripletLoss(BatchHardTripletLoss):
      """
      BatchHardSoftMarginTripletLoss takes a batch with (label, sentence) pairs and computes the loss for all possible, valid
      triplets, i.e., anchor and positive must have the same label, anchor and negative a different label. The labels
      must be integers, with same label indicating sentences from the same class. You train dataset
      must contain at least 2 examples per label class. The margin is computed automatically.
  
      Source: https://github.com/NegatioN/OnlineMiningTripletLoss/blob/master/online_triplet_loss/losses.py
      Paper: In Defense of the Triplet Loss for Person Re-Identification, https://arxiv.org/abs/1703.07737
      Blog post: https://omoindrot.github.io/triplet-loss
  
      :param model: LanguageTransformer model
      :param distance_metric: Function that returns a distance between two emeddings. The class SiameseDistanceMetric contains pre-defined metrices that can be used
  
  
      Example::
  
         from language_bert import LanguageTransformer,  SentencesDataset, LoggingHandler, losses
         from language_bert.readers import InputExample
  
         model = LanguageTransformer('distilbert-base-nli-mean-tokens')
         train_examples = [InputExample(texts=['Sentence from class 0'], label=0), InputExample(texts=['Another sentence from class 0'], label=0),
             InputExample(texts=['Sentence from class 1'], label=1), InputExample(texts=['Sentence from class 2'], label=2)]
         train_dataset = SentencesDataset(train_examples, model)
         train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
         train_loss = losses.BatchHardSoftMarginTripletLoss(model=model)
      """
      def __init__(self, model: LanguageTransformer, distance_metric=BatchHardTripletLossDistanceFunction.eucledian_distance):
          super(BatchHardSoftMarginTripletLoss, self).__init__(model)
          self.sentence_embedder = model
          self.distance_metric = distance_metric
  
      def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor):
          rep = self.sentence_embedder(sentence_features[0])['sentence_embedding']
          return self.batch_hard_triplet_soft_margin_loss(labels, rep)
  
  
      # Hard Triplet Loss with Soft Margin
      # Paper: In Defense of the Triplet Loss for Person Re-Identification, https://arxiv.org/abs/1703.07737
      def batch_hard_triplet_soft_margin_loss(self, labels: Tensor, embeddings: Tensor) -> Tensor:
          """Build the triplet loss over a batch of embeddings.
          For each anchor, we get the hardest positive and hardest negative to form a triplet.
          Args:
              labels: labels of the batch, of size (batch_size,)
              embeddings: tensor of shape (batch_size, embed_dim)
              squared: Boolean. If true, output is the pairwise squared euclidean distance matrix.
                       If false, output is the pairwise euclidean distance matrix.
          Returns:
              Label_Sentence_Triplet: scalar tensor containing the triplet loss
          """
          # Get the pairwise distance matrix
          pairwise_dist = self.distance_metric(embeddings)
  
  
          # For each anchor, get the hardest positive
          # First, we need to get a mask for every valid positive (they should have same label)
          mask_anchor_positive = BatchHardTripletLoss.get_anchor_positive_triplet_mask(labels).float()
  
          # We put to 0 any element where (a, p) is not valid (valid if a != p and label(a) == label(p))
          anchor_positive_dist = mask_anchor_positive * pairwise_dist
  
          # shape (batch_size, 1)
          hardest_positive_dist, _ = anchor_positive_dist.max(1, keepdim=True)
  
          # For each anchor, get the hardest negative
          # First, we need to get a mask for every valid negative (they should have different labels)
          mask_anchor_negative = BatchHardTripletLoss.get_anchor_negative_triplet_mask(labels).float()
  
          # We add the maximum value in each row to the invalid negatives (label(a) == label(n))
          max_anchor_negative_dist, _ = pairwise_dist.max(1, keepdim=True)
          anchor_negative_dist = pairwise_dist + max_anchor_negative_dist * (1.0 - mask_anchor_negative)
  
          # shape (batch_size,)
          hardest_negative_dist, _ = anchor_negative_dist.min(1, keepdim=True)
  
          # Combine biggest d(a, p) and smallest d(a, n) into final triplet loss with soft margin
          #tl = hardest_positive_dist - hardest_negative_dist + margin
          #tl[tl < 0] = 0
          tl = torch.log1p(torch.exp(hardest_positive_dist - hardest_negative_dist))
          triplet_loss = tl.mean()
  
          return triplet_loss
  