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 os
  import time
  import ast
  import math
  import argparse
  from functools import partial
  
  import numpy as np
  import paddle
  from paddle.static import InputSpec
  from paddlenlp.data import Pad, Tuple, Stack
  from paddlenlp.layers.crf import LinearChainCrfLoss, ViterbiDecoder
  from paddlenlp.metrics import ChunkEvaluator
  from paddlenlp.utils.log import logger
  import distutils.util
  
  from data import load_dataset, load_vocab, convert_example
  from model import BiGruCrf
  
  # yapf: disable
  parser = argparse.ArgumentParser(__doc__)
  parser.add_argument("--data_dir", type=str, default=None, help="The folder where the dataset is located.")
  parser.add_argument("--init_checkpoint", type=str, default=None, help="Path to init model.")
  parser.add_argument("--model_save_dir", type=str, default=None, help="The model will be saved in this path.")
  parser.add_argument("--epochs", type=int, default=10, help="Corpus iteration num.")
  parser.add_argument("--batch_size", type=int, default=300, help="The number of sequences contained in a mini-batch.")
  parser.add_argument("--max_seq_len", type=int, default=64, help="Number of words of the longest seqence.")
  parser.add_argument("--device", default="gpu", type=str, choices=["cpu", "gpu"] ,help="The device to select to train the model, is must be cpu/gpu.")
  parser.add_argument("--base_lr", type=float, default=0.001, help="The basic learning rate that affects the entire network.")
  parser.add_argument("--emb_dim", type=int, default=128, help="The dimension in which a word is embedded.")
  parser.add_argument("--hidden_size", type=int, default=128, help="The number of hidden nodes in the GRU layer.")
  parser.add_argument("--logging_steps", type=int, default=10, help="Log every X updates steps.")
  parser.add_argument("--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.")
  parser.add_argument("--do_eval", type=distutils.util.strtobool, default=True, help="To evaluate the model if True.")
  # yapf: enable
  
  
  @paddle.no_grad()
  def evaluate(model, metric, data_loader):
      model.eval()
      metric.reset()
      for batch in data_loader:
          token_ids, length, labels = batch
          preds = model(token_ids, length)
          num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute(
              length, preds, labels)
          metric.update(num_infer_chunks.numpy(),
                        num_label_chunks.numpy(), num_correct_chunks.numpy())
          precision, recall, f1_score = metric.accumulate()
      print("eval precision: %f, recall: %f, f1: %f" %
            (precision, recall, f1_score))
      model.train()
  
  
  def train(args):
      paddle.set_device(args.device)
  
      # Create dataset.
      train_ds, test_ds = load_dataset(datafiles=(os.path.join(
          args.data_dir, 'train.tsv'), os.path.join(args.data_dir, 'test.tsv')))
  
      word_vocab = load_vocab(os.path.join(args.data_dir, 'word.dic'))
      label_vocab = load_vocab(os.path.join(args.data_dir, 'tag.dic'))
      # q2b.dic is used to replace DBC case to SBC case
      normlize_vocab = load_vocab(os.path.join(args.data_dir, 'q2b.dic'))
  
      trans_func = partial(
          convert_example,
          max_seq_len=args.max_seq_len,
          word_vocab=word_vocab,
          label_vocab=label_vocab,
          normlize_vocab=normlize_vocab)
      train_ds.map(trans_func)
      test_ds.map(trans_func)
  
      batchify_fn = lambda samples, fn=Tuple(
          Pad(axis=0, pad_val=word_vocab.get("[PAD]", 0), dtype='int64'),  # word_ids
          Stack(dtype='int64'),  # length
          Pad(axis=0, pad_val=label_vocab.get("O", 0), dtype='int64'),  # label_ids
      ): fn(samples)
  
      # Create sampler for dataloader
      train_sampler = paddle.io.DistributedBatchSampler(
          dataset=train_ds,
          batch_size=args.batch_size,
          shuffle=True,
          drop_last=True)
      train_loader = paddle.io.DataLoader(
          dataset=train_ds,
          batch_sampler=train_sampler,
          return_list=True,
          collate_fn=batchify_fn)
  
      test_sampler = paddle.io.BatchSampler(
          dataset=test_ds,
          batch_size=args.batch_size,
          shuffle=False,
          drop_last=False)
      test_loader = paddle.io.DataLoader(
          dataset=test_ds,
          batch_sampler=test_sampler,
          return_list=True,
          collate_fn=batchify_fn)
  
      # Define the model netword and its loss
      model = BiGruCrf(args.emb_dim, args.hidden_size,
                       len(word_vocab), len(label_vocab))
      # Prepare optimizer, loss and metric evaluator
      optimizer = paddle.optimizer.Adam(
          learning_rate=args.base_lr, parameters=model.parameters())
      chunk_evaluator = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)
  
      if args.init_checkpoint:
          model_dict = paddle.load(args.init_checkpoint)
          model.load_dict(model_dict)
  
      # Start training
      global_step = 0
      last_step = args.epochs * len(train_loader)
      train_reader_cost = 0.0
      train_run_cost = 0.0
      total_samples = 0
      reader_start = time.time()
      for epoch in range(args.epochs):
          for step, batch in enumerate(train_loader):
              train_reader_cost += time.time() - reader_start
              global_step += 1
              token_ids, length, label_ids = batch
              train_start = time.time()
              loss = model(token_ids, length, label_ids)
              avg_loss = paddle.mean(loss)
              train_run_cost += time.time() - train_start
              total_samples += args.batch_size
              if global_step % args.logging_steps == 0:
                  print(
                      "global step %d / %d, loss: %f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, avg_samples: %.5f, ips: %.5f sequences/sec"
                      % (global_step, last_step, avg_loss, train_reader_cost /
                         args.logging_steps, (train_reader_cost + train_run_cost)
                         / args.logging_steps, total_samples / args.logging_steps,
                         total_samples / (train_reader_cost + train_run_cost)))
                  train_reader_cost = 0.0
                  train_run_cost = 0.0
                  total_samples = 0
              avg_loss.backward()
              optimizer.step()
              optimizer.clear_grad()
              if global_step % args.save_steps == 0 or global_step == last_step:
                  if paddle.distributed.get_rank() == 0:
                      if args.do_eval:
                          evaluate(model, chunk_evaluator, test_loader)
                      paddle.save(model.state_dict(),
                                  os.path.join(args.model_save_dir,
                                               "model_%d.pdparams" % global_step))
              reader_start = time.time()
  
  
  if __name__ == "__main__":
      args = parser.parse_args()
      train(args)
  