text: # -*- coding: UTF-8 -*-
  #   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 math
  import argparse
  
  import numpy as np
  import paddle
  from paddle.static import InputSpec
  from paddlenlp.data import Pad, Tuple, Stack
  from paddlenlp.metrics import ChunkEvaluator
  
  from data import LacDataset
  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("--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('--use_gpu', action='store_true', help='If set, use gpu to excute')
  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.")
  args = parser.parse_args()
  # yapf: enable
  
  
  def evaluate(args):
      place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
      paddle.set_device("gpu" if args.use_gpu else "cpu")
  
      # create dataset.
      test_dataset = LacDataset(args.data_dir, mode='test')
      batchify_fn = lambda samples, fn=Tuple(
          Pad(axis=0, pad_val=0),  # word_ids
          Stack(),  # length
          Pad(axis=0, pad_val=0),  # label_ids
      ): fn(samples)
  
      # Create sampler for dataloader
      test_sampler = paddle.io.BatchSampler(
          dataset=test_dataset,
          batch_size=args.batch_size,
          shuffle=False,
          drop_last=False)
      test_loader = paddle.io.DataLoader(
          dataset=test_dataset,
          batch_sampler=test_sampler,
          places=place,
          return_list=True,
          collate_fn=batchify_fn)
  
      # Define the model network and metric evaluator
      network = BiGruCrf(args.emb_dim, args.hidden_size, test_dataset.vocab_size,
                         test_dataset.num_labels)
      inputs = InputSpec(shape=(-1, ), dtype="int16", name='inputs')
      lengths = InputSpec(shape=(-1, ), dtype="int16", name='lengths')
      model = paddle.Model(network, inputs=[inputs, lengths])
      chunk_evaluator = ChunkEvaluator(
          label_list=test_dataset.label_vocab.keys(), suffix=True)
      model.prepare(None, None, chunk_evaluator)
  
      # Load the model and start predicting
      model.load(args.init_checkpoint)
      model.evaluate(
          eval_data=test_loader,
          batch_size=args.batch_size,
          log_freq=100,
          verbose=2, )
  
  
  if __name__ == '__main__':
      args = parser.parse_args()
      evaluate(args)
  