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.
  """predict auto dialogue evaluation task"""
  
  import os
  import sys
  import six
  import time
  import numpy as np
  import multiprocessing
  
  import paddle
  import paddle.fluid as fluid
  
  import ade.reader as reader
  from ade_net import create_net
  
  from ade.utils.configure import PDConfig
  from ade.utils.input_field import InputField
  from ade.utils.model_check import check_cuda
  import ade.utils.save_load_io as save_load_io
  
  
  def do_predict(args): 
      """
      predict function
      """
      test_prog = fluid.default_main_program()
      startup_prog = fluid.default_startup_program()
  
      with fluid.program_guard(test_prog, startup_prog):
          test_prog.random_seed = args.random_seed
          startup_prog.random_seed = args.random_seed
  
          with fluid.unique_name.guard():
  
              context_wordseq = fluid.data(
                      name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
              response_wordseq = fluid.data(
                      name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)
              labels = fluid.data(
                      name='labels', shape=[-1, 1], dtype='int64')
  
              input_inst = [context_wordseq, response_wordseq, labels]
              input_field = InputField(input_inst)
              data_reader = fluid.io.PyReader(feed_list=input_inst, 
                          capacity=4, iterable=False)
  
              logits = create_net(
                      is_training=False,
                      model_input=input_field, 
                      args=args
                  )
              logits.persistable = True
  
              fetch_list = [logits.name]
      #for_test is True if change the is_test attribute of operators to True
      test_prog = test_prog.clone(for_test=True)
      if args.use_cuda: 
          place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
      else: 
          place = fluid.CPUPlace()
  
      exe = fluid.Executor(place)
      exe.run(startup_prog)
  
      assert (args.init_from_params) or (args.init_from_pretrain_model)
      if args.init_from_params:
          save_load_io.init_from_params(args, exe, test_prog)
      if args.init_from_pretrain_model:
          save_load_io.init_from_pretrain_model(args, exe, test_prog)
  
      compiled_test_prog = fluid.CompiledProgram(test_prog)
  
      processor = reader.DataProcessor(
          data_path=args.predict_file,
          max_seq_length=args.max_seq_len, 
          batch_size=args.batch_size)
  
      batch_generator = processor.data_generator(
          place=place,
          phase="test",
          shuffle=False, 
          sample_pro=1)
      num_test_examples = processor.get_num_examples(phase='test')
  
      data_reader.decorate_batch_generator(batch_generator)
      data_reader.start()
  
      scores = []
      while True: 
          try: 
              results = exe.run(compiled_test_prog, fetch_list=fetch_list)
              scores.extend(results[0])
          except fluid.core.EOFException:
              data_reader.reset()
              break
  
      scores = scores[: num_test_examples]
      print("Write the predicted results into the output_prediction_file")
      with open(args.output_prediction_file, 'w') as fw: 
          for index, score in enumerate(scores): 
              fw.write("%s\t%s\n" % (index, score))
      print("finish........................................")
  
  
  if __name__ == "__main__": 
      
      args = PDConfig(yaml_file="./data/config/ade.yaml")
      args.build()
      args.Print()
  
      check_cuda(args.use_cuda)
  
      do_predict(args) 
  