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 sys
  import os
  import numpy as np
  from attrdict import AttrDict
  import argparse
  import time
  
  import paddle
  
  import yaml
  from pprint import pprint
  
  from paddlenlp.ops import FasterGPT
  from paddlenlp.transformers import GPTLMHeadModel
  from paddlenlp.transformers import GPTChineseTokenizer, GPTTokenizer
  
  from paddlenlp.utils.log import logger
  
  MODEL_CLASSES = {
      "gpt-cpm-large-cn": (GPTLMHeadModel, GPTChineseTokenizer),
      "gpt2-medium-en": (GPTLMHeadModel, GPTTokenizer),
  }
  
  
  def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model_name_or_path",
          default="gpt2-medium-en",
          type=str,
          help="The model name to specify the gpt to use. Can be one of ['gpt2-en', 'gpt2-medium-en', 'gpt-cpm-large-cn']. "
      )
      parser.add_argument(
          "--decoding_lib",
          default="../build/lib/libdecoding_op.so",
          type=str,
          help="Path of libdecoding_op.so. ")
      parser.add_argument(
          "--batch_size", default=4, type=int, help="Batch size. ")
      parser.add_argument(
          "--topk",
          default=4,
          type=int,
          help="The number of candidate to procedure beam search. ")
      parser.add_argument(
          "--topp",
          default=1.0,
          type=float,
          help="The probability threshold to procedure topp sampling. ")
      parser.add_argument(
          "--max_length", default=32, type=int, help="Maximum output length. ")
      parser.add_argument(
          "--start_token",
          default="<|endoftext|>",
          type=str,
          help="The start token. Defaults to <|endoftext|>. ")
      parser.add_argument(
          "--end_token",
          default="<|endoftext|>",
          type=str,
          help="The end token. Defaults to <|endoftext|>. ")
      parser.add_argument(
          "--temperature",
          default=1.0,
          type=float,
          help="The temperature to set. ")
      parser.add_argument(
          "--use_fp16_decoding",
          action="store_true",
          help="Whether to use fp16 decoding to predict. ")
      args = parser.parse_args()
      return args
  
  
  def do_predict(args):
      place = "gpu"
      place = paddle.set_device(place)
  
      model_class, tokenizer_class = MODEL_CLASSES[args.model_name_or_path]
      tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
      logger.info('Loading the model parameters, please wait...')
      model = model_class.from_pretrained(args.model_name_or_path)
      model.eval()
  
      bos_id = tokenizer.convert_tokens_to_ids(args.start_token)
      eos_id = tokenizer.convert_tokens_to_ids(args.end_token)
  
      # Define model
      gpt = model
  
      # Set evaluate mode
      gpt.eval()
      input_ids = np.array(
          [[bos_id] for i in range(args.batch_size * 1)]).astype("int64").reshape(
              [args.batch_size, 1])
      input_ids = paddle.to_tensor(input_ids)
  
      with paddle.no_grad():
          for i in range(100):
              # For warmup. 
              if 50 == i:
                  paddle.device.cuda.synchronize(place)
                  start = time.time()
              out_seq, _ = gpt.generate(
                  input_ids,
                  top_k=args.topk,
                  top_p=args.topp,
                  max_length=args.max_length,
                  temperature=args.temperature,
                  bos_token_id=bos_id,
                  eos_token_id=eos_id,
                  decode_strategy="sampling",
                  use_fp16_decoding=args.use_fp16_decoding,
                  use_faster=True)
              output_sequence = out_seq.numpy()
  
          paddle.device.cuda.synchronize(place)
          logger.info("Average test time for decoding is %f ms" % (
              (time.time() - start) / 50 * 1000))
          output_sequence = out_seq.numpy().tolist()
      for i in range(args.batch_size):
          print("========== Sample-%d ==========" % i)
          print(tokenizer.convert_ids_to_string(output_sequence[i]))
  
  
  if __name__ == "__main__":
      args = parse_args()
      pprint(args)
      do_predict(args)
  