text: # Copyright (c) 2020 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 math
  import paddle
  from net import DSSMLayer
  
  
  class StaticModel():
      def __init__(self, config):
          self.cost = None
          self.config = config
          self._init_hyper_parameters()
  
      def _init_hyper_parameters(self):
          self.trigram_d = self.config.get("hyper_parameters.trigram_d")
          self.neg_num = self.config.get("hyper_parameters.neg_num")
          self.hidden_layers = self.config.get("hyper_parameters.fc_sizes")
          self.hidden_acts = self.config.get("hyper_parameters.fc_acts")
          self.learning_rate = self.config.get("hyper_parameters.learning_rate")
          self.slice_end = self.config.get("hyper_parameters.slice_end")
          self.learning_rate = self.config.get(
              "hyper_parameters.optimizer.learning_rate")
  
      def create_feeds(self, is_infer=False):
          query = paddle.static.data(
              name="query", shape=[-1, self.trigram_d], dtype='float32')
          self.prune_feed_vars = [query]
  
          doc_pos = paddle.static.data(
              name="doc_pos", shape=[-1, self.trigram_d], dtype='float32')
  
          if is_infer:
              return [query, doc_pos]
  
          doc_negs = [
              paddle.static.data(
                  name="doc_neg_" + str(i),
                  shape=[-1, self.trigram_d],
                  dtype="float32") for i in range(self.neg_num)
          ]
          feeds_list = [query, doc_pos] + doc_negs
          return feeds_list
  
      def net(self, input, is_infer=False):
          dssm_model = DSSMLayer(self.trigram_d, self.neg_num, self.slice_end,
                                 self.hidden_layers, self.hidden_acts)
          R_Q_D_p, hit_prob = dssm_model.forward(input, is_infer)
  
          self.inference_target_var = R_Q_D_p
          self.prune_target_var = dssm_model.query_fc
          self.train_dump_fields = [dssm_model.query_fc, R_Q_D_p]
          self.train_dump_params = dssm_model.params
          self.infer_dump_fields = [dssm_model.doc_pos_fc]
          if is_infer:
              fetch_dict = {'query_doc_sim': R_Q_D_p}
              return fetch_dict
          loss = -paddle.sum(paddle.log(hit_prob), axis=-1)
          avg_cost = paddle.mean(x=loss)
          # print(avg_cost)
          self._cost = avg_cost
          fetch_dict = {'Loss': avg_cost}
          return fetch_dict
  
      def create_optimizer(self, strategy=None):
          optimizer = paddle.optimizer.Adam(
              learning_rate=self.learning_rate, lazy_mode=True)
          if strategy != None:
              import paddle.distributed.fleet as fleet
              optimizer = fleet.distributed_optimizer(optimizer, strategy)
          optimizer.minimize(self._cost)
  
      def infer_net(self, input):
          return self.net(input, is_infer=True)
  