text: #   Copyright (c) 2019 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.
  """
  bow class
  """
  
  import paddle_layers as layers
  from paddle import fluid
  from paddle.fluid.dygraph.base import to_variable
  from paddle.fluid.dygraph import Layer, Embedding, Linear
  import paddle.fluid.param_attr as attr
  uniform_initializer = lambda x: fluid.initializer.UniformInitializer(low=-x, high=x)
  
  class BOW(Layer):
      """
      BOW
      """
  
      def __init__(self, conf_dict):
          """
          initialize
          """
          super(BOW, self).__init__()
          self.dict_size = conf_dict["dict_size"]
          self.task_mode = conf_dict["task_mode"]
          self.emb_dim = conf_dict["net"]["emb_dim"]
          self.bow_dim = conf_dict["net"]["bow_dim"]
          self.seq_len = conf_dict["seq_len"]
          self.emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb").ops()
          self.bow_layer = Linear(self.bow_dim, self.bow_dim)
          self.bow_layer_po = layers.FCLayer(self.bow_dim, None, "fc").ops()
          self.softmax_layer = layers.FCLayer(2, "softmax", "cos_sim").ops()
      
  
      def forward(self, left, right):
          """
          Forward network
          """
          
          # embedding layer
          left_emb = self.emb_layer(left)
          right_emb = self.emb_layer(right)
          left_emb = fluid.layers.reshape(
              left_emb, shape=[-1, self.seq_len, self.bow_dim])
          right_emb = fluid.layers.reshape(
              right_emb, shape=[-1, self.seq_len, self.bow_dim])
         
          bow_left = fluid.layers.reduce_sum(left_emb, dim=1)
          bow_right = fluid.layers.reduce_sum(right_emb, dim=1)
          softsign_layer = layers.SoftsignLayer()
          left_soft = softsign_layer.ops(bow_left)
          right_soft = softsign_layer.ops(bow_right)
        
          # matching layer
          if self.task_mode == "pairwise":
              left_bow = self.bow_layer(left_soft)
              right_bow = self.bow_layer(right_soft)
              cos_sim_layer = layers.CosSimLayer()
              pred = cos_sim_layer.ops(left_bow, right_bow)
              return left_bow, pred
          else:
              concat_layer = layers.ConcatLayer(1)
              concat = concat_layer.ops([left_soft, right_soft])
              concat_fc = self.bow_layer_po(concat)
              pred = self.softmax_layer(concat_fc)
              return left_soft, pred
  