text: # -*- coding: utf-8 -*-
  """
  # 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.
  """
  
  import os
  import argparse
  import numpy as np
  import argparse
  import paddle.fluid as fluid
  from paddle.fluid.core import PaddleTensor
  from paddle.fluid.core import AnalysisConfig
  from paddle.fluid.core import create_paddle_predictor
  from dataset_generator import TDMDataset
  from infer_network import TdmInferNet
  from args import print_arguments, parse_args
  import logging
  
  logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
  logger = logging.getLogger("fluid")
  logger.setLevel(logging.INFO)
  
  
  def to_tensor(data):
      """
      Convert data to paddle tensor
      """
      flattened_data = np.concatenate(data, axis=0).astype("float32")
      flattened_data = flattened_data.reshape([-1, 768])
      return flattened_data
  
  
  def data2tensor(data):
      """
      Dataset prepare
      """
      input_emb = to_tensor([x[0] for x in data])
  
      return input_emb
  
  
  def tdm_input(input_emb, first_layer_node, first_layer_mask):
      """
      Create input of tdm pred
      """
      input_emb = PaddleTensor(input_emb)
      first_layer_node = PaddleTensor(first_layer_node)
      first_layer_mask = PaddleTensor(first_layer_mask)
      return [input_emb, first_layer_node, first_layer_mask]
  
  
  def main():
      """Predictor main"""
      args = parse_args()
  
      config = AnalysisConfig(args.model_files_path)
      config.disable_gpu()
      config.enable_profile()
      # config.enable_mkldnn()
      config.set_cpu_math_library_num_threads(args.cpu_num)
  
      predictor = create_paddle_predictor(config)
      tdm_model = TdmInferNet(args)
      first_layer_node = tdm_model.first_layer_node
      first_layer_nums = len(first_layer_node)
      first_layer_node = np.array(first_layer_node)
      first_layer_node = first_layer_node.reshape((1, -1)).astype('int64')
      first_layer_node = first_layer_node.repeat(args.batch_size, axis=0)
      first_layer_mask = (
          np.zeros((args.batch_size, first_layer_nums))).astype('int64')
  
      file_list = [
          str(args.test_files_path) + "/%s" % x
          for x in os.listdir(args.test_files_path)
      ]
      test_reader = TDMDataset().infer_reader(file_list, args.batch_size)
  
      for batch_id, data in enumerate(test_reader()):
          input_emb = data2tensor(data)
  
          inputs = tdm_input(input_emb, first_layer_node, first_layer_mask)
          outputs = predictor.run(inputs)
          output = outputs[0]
          output_data = output.as_ndarray()
  
          logger.info("TEST --> batch: {} infer_item {}".format(
              batch_id, output_data))
  
  
  if __name__ == "__main__":
      import paddle
      paddle.enable_static()
      main()
  