text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License"
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import paddle
  import paddle.nn as nn
  import paddle.nn.functional as F
  
  
  class PointwiseMatching(nn.Layer):
      def __init__(self, pretrained_model, dropout=None):
          super().__init__()
          self.ptm = pretrained_model
          self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
  
          # num_labels = 2 (similar or dissimilar)
          self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2)
  
      def forward(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
  
          _, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids,
                                      attention_mask)
  
          cls_embedding = self.dropout(cls_embedding)
          logits = self.classifier(cls_embedding)
  
          return logits
  
  
  class PairwiseMatching(nn.Layer):
      def __init__(self, pretrained_model, dropout=None, margin=0.1):
          super().__init__()
          self.ptm = pretrained_model
          self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
          self.margin = margin
  
          # hidden_size -> 1, calculate similarity
          self.similarity = nn.Linear(self.ptm.config["hidden_size"], 1)
  
      def predict(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
  
          _, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids,
                                      attention_mask)
  
          cls_embedding = self.dropout(cls_embedding)
          sim_score = self.similarity(cls_embedding)
          sim_score = F.sigmoid(sim_score)
  
          return sim_score
  
      def forward(self,
                  pos_input_ids,
                  neg_input_ids,
                  pos_token_type_ids=None,
                  neg_token_type_ids=None,
                  pos_position_ids=None,
                  neg_position_ids=None,
                  pos_attention_mask=None,
                  neg_attention_mask=None):
  
          _, pos_cls_embedding = self.ptm(pos_input_ids, pos_token_type_ids,
                                          pos_position_ids, pos_attention_mask)
  
          _, neg_cls_embedding = self.ptm(neg_input_ids, neg_token_type_ids,
                                          neg_position_ids, neg_attention_mask)
  
          pos_embedding = self.dropout(pos_cls_embedding)
          neg_embedding = self.dropout(neg_cls_embedding)
  
          pos_sim = self.similarity(pos_embedding)
          neg_sim = self.similarity(neg_embedding)
  
          pos_sim = F.sigmoid(pos_sim)
          neg_sim = F.sigmoid(neg_sim)
  
          labels = paddle.full(
              shape=[pos_cls_embedding.shape[0]], fill_value=1.0, dtype='float32')
  
          loss = F.margin_ranking_loss(
              pos_sim, neg_sim, labels, margin=self.margin)
  
          return loss
  