text: #   Copyright (c) 2018 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.fluid as fluid
  import paddle
  import unittest
  import tarfile
  import os
  import shutil
  
  proto_str = ('name: "MultiSlotDataFeed"\n'
               'batch_size: 2\n'
               'multi_slot_desc {\n'
               '   slots {\n'
               '       name: "words"\n'
               '       type: "uint64"\n'
               '       is_dense: false\n'
               '       is_used: true\n'
               '   }\n'
               '   slots {\n'
               '       name: "label"\n'
               '       type: "uint64"\n'
               '       is_dense: false\n'
               '       is_used: true\n'
               '   }\n'
               '}')
  
  URL = 'http://paddle-unittest-data.gz.bcebos.com/python_paddle_fluid_tests_demo_async-executor/train_data.tar.gz'
  MD5 = '2a405a31508969b3ab823f42c0f522ca'
  
  
  def bow_net(data,
              label,
              dict_dim=89528,
              emb_dim=128,
              hid_dim=128,
              hid_dim2=96,
              class_dim=2):
      """
      BOW net
      This model is from https://github.com/PaddlePaddle/models:
      models/fluid/PaddleNLP/text_classification/nets.py
      """
      # embedding
      emb = fluid.layers.embedding(
          input=data, size=[dict_dim, emb_dim], is_sparse=True)
      bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
      bowh = fluid.layers.tanh(bow)
      # fc layer after conv
      fc_1 = fluid.layers.fc(input=bowh, size=hid_dim, act="tanh")
      fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
      # probability of each class
      prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
      # cross entropy loss
      cost = fluid.layers.cross_entropy(input=prediction, label=label)
      # mean loss
      avg_cost = fluid.layers.mean(x=cost)
      acc = fluid.layers.accuracy(input=prediction, label=label)
      return avg_cost, acc, prediction
  
  
  class TestAsyncExecutor(unittest.TestCase):
      def setUp(self):
          with open('./data.prototxt', 'w+') as f:
              f.write(proto_str)
              f.close()
  
          with tarfile.open(paddle.dataset.common.download(URL, "imdb",
                                                           MD5)) as tarf:
              tarf.extractall(path='./')
              tarf.close()
  
  
  if __name__ == '__main__':
      unittest.main()
  