text: # Copyright (c) 2021 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 argparse
  import os
  
  import numpy as np
  import paddle
  import paddlenlp as ppnlp
  from scipy.special import softmax
  from paddle import inference
  from paddlenlp.data import Tuple, Pad
  from paddlenlp.datasets import load_dataset
  from paddlenlp.utils.log import logger
  
  from utils import convert_example
  
  # yapf: disable
  parser = argparse.ArgumentParser()
  parser.add_argument("--model_dir", type=str, required=True, default="./export/", help="The directory to static model.")
  parser.add_argument("--max_seq_length", type=int, default=128, help="The maximum total input sequence length after tokenization. "
      "Sequences longer than this will be truncated, sequences shorter will be padded.")
  parser.add_argument("--batch_size", type=int, default=2, help="Batch size per GPU/CPU for training.")
  parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
  parser.add_argument('--use_tensorrt', type=eval, default=False, choices=[True, False], help='Enable to use tensorrt to speed up.')
  parser.add_argument("--precision", type=str, default="fp32", choices=["fp32", "fp16", "int8"], help='The tensorrt precision.')
  parser.add_argument('--cpu_threads', type=int, default=10, help='Number of threads to predict when using cpu.')
  parser.add_argument('--enable_mkldnn', type=eval, default=False, choices=[True, False], help='Enable to use mkldnn to speed up when using cpu.')
  parser.add_argument("--benchmark", type=eval, default=False, help="To log some information about environment and running.")
  parser.add_argument("--save_log_path", type=str, default="./log_output/", help="The file path to save log.")
  args = parser.parse_args()
  # yapf: enable
  
  
  class Predictor(object):
      def __init__(self,
                   model_dir,
                   device="gpu",
                   max_seq_length=128,
                   batch_size=32,
                   use_tensorrt=False,
                   precision="fp32",
                   cpu_threads=10,
                   enable_mkldnn=False):
          self.max_seq_length = max_seq_length
          self.batch_size = batch_size
  
          model_file = model_dir + "/inference.pdmodel"
          params_file = model_dir + "/inference.pdiparams"
          if not os.path.exists(model_file):
              raise ValueError("not find model file path {}".format(model_file))
          if not os.path.exists(params_file):
              raise ValueError("not find params file path {}".format(params_file))
          config = paddle.inference.Config(model_file, params_file)
  
          if device == "gpu":
              # set GPU configs accordingly
              # such as intialize the gpu memory, enable tensorrt
              config.enable_use_gpu(100, 0)
              precision_map = {
                  "fp16": inference.PrecisionType.Half,
                  "fp32": inference.PrecisionType.Float32,
                  "int8": inference.PrecisionType.Int8
              }
              precision_mode = precision_map[precision]
  
              if use_tensorrt:
                  config.enable_tensorrt_engine(
                      max_batch_size=batch_size,
                      min_subgraph_size=30,
                      precision_mode=precision_mode)
          elif device == "cpu":
              # set CPU configs accordingly,
              # such as enable_mkldnn, set_cpu_math_library_num_threads
              config.disable_gpu()
              if enable_mkldnn:
                  # cache 10 different shapes for mkldnn to avoid memory leak
                  config.set_mkldnn_cache_capacity(10)
                  config.enable_mkldnn()
              config.set_cpu_math_library_num_threads(cpu_threads)
          elif device == "xpu":
              # set XPU configs accordingly
              config.enable_xpu(100)
  
          config.switch_use_feed_fetch_ops(False)
          self.predictor = paddle.inference.create_predictor(config)
          self.input_handles = [
              self.predictor.get_input_handle(name)
              for name in self.predictor.get_input_names()
          ]
          self.output_handle = self.predictor.get_output_handle(
              self.predictor.get_output_names()[0])
  
          if args.benchmark:
              import auto_log
              pid = os.getpid()
              self.autolog = auto_log.AutoLogger(
                  model_name="ernie-tiny",
                  model_precision=precision,
                  batch_size=self.batch_size,
                  data_shape="dynamic",
                  save_path=args.save_log_path,
                  inference_config=config,
                  pids=pid,
                  process_name=None,
                  gpu_ids=0,
                  time_keys=[
                      'preprocess_time', 'inference_time', 'postprocess_time'
                  ],
                  warmup=0,
                  logger=logger)
  
      def predict(self, data, tokenizer, label_map):
          """
          Predicts the data labels.
  
          Args:
              data (obj:`List(str)`): The batch data whose each element is a raw text.
              tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer` 
                  which contains most of the methods. Users should refer to the superclass for more information regarding methods.
              label_map(obj:`dict`): The label id (key) to label str (value) map.
  
          Returns:
              results(obj:`dict`): All the predictions labels.
          """
          if args.benchmark:
              self.autolog.times.start()
  
          examples = []
          for text in data:
              example = {"text": text}
              input_ids, segment_ids = convert_example(
                  example,
                  tokenizer,
                  max_seq_length=self.max_seq_length,
                  is_test=True)
              examples.append((input_ids, segment_ids))
  
          batchify_fn = lambda samples, fn=Tuple(
              Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
              Pad(axis=0, pad_val=tokenizer.pad_token_id),  # segment
          ): fn(samples)
  
          if args.benchmark:
              self.autolog.times.stamp()
  
          input_ids, segment_ids = batchify_fn(examples)
          self.input_handles[0].copy_from_cpu(input_ids)
          self.input_handles[1].copy_from_cpu(segment_ids)
          self.predictor.run()
          logits = self.output_handle.copy_to_cpu()
          if args.benchmark:
              self.autolog.times.stamp()
  
          probs = softmax(logits, axis=1)
          idx = np.argmax(probs, axis=1)
          idx = idx.tolist()
          labels = [label_map[i] for i in idx]
  
          if args.benchmark:
              self.autolog.times.end(stamp=True)
  
          return labels
  
  
  if __name__ == "__main__":
      # Define predictor to do prediction.
      predictor = Predictor(args.model_dir, args.device, args.max_seq_length,
                            args.batch_size, args.use_tensorrt, args.precision,
                            args.cpu_threads, args.enable_mkldnn)
  
      # ErnieTinyTokenizer is special for ernie-tiny pretained model.
      tokenizer = ppnlp.transformers.ErnieTinyTokenizer.from_pretrained(
          'ernie-tiny')
      test_ds = load_dataset("chnsenticorp", splits=["test"])
      data = [d["text"] for d in test_ds]
      batches = [
          data[idx:idx + args.batch_size]
          for idx in range(0, len(data), args.batch_size)
      ]
      label_map = {0: 'negative', 1: 'positive'}
  
      results = []
      for batch_data in batches:
          results.extend(predictor.predict(batch_data, tokenizer, label_map))
      for idx, text in enumerate(data):
          print('Data: {} \t Label: {}'.format(text, results[idx]))
      if args.benchmark:
          predictor.autolog.report()
  