text: # coding=utf-8
  # Copyright (c) 2020, NVIDIA CORPORATION.  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.
  
  """Sample Generate GPT2"""
  
  import os
  import sys
  sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                               os.path.pardir)))
  
  from megatron import get_args
  from megatron import print_rank_0
  from megatron import get_tokenizer
  from megatron.checkpointing import load_checkpoint
  from megatron.initialize import initialize_megatron
  from megatron.model import GPT2Model
  from megatron.training import get_model
  from megatron.text_generation_utils import generate_and_write_samples_unconditional
  from megatron.text_generation_utils import generate_samples_input_from_file
  from megatron.text_generation_utils import generate_samples_interactive
  
  
  def model_provider():
      """Build the model."""
  
      print_rank_0('building GPT2 model ...')
      model = GPT2Model(num_tokentypes=0, parallel_output=False)
  
      return model
  
  
  def add_text_generate_args(parser):
      """Text generation arguments."""
      group = parser.add_argument_group(title='text generation')
  
      group.add_argument("--temperature", type=float, default=1.0,
                         help='Sampling temperature.')
      group.add_argument("--greedy", action='store_true', default=False,
                         help='Use greedy sampling.')
      group.add_argument("--top_p", type=float, default=0.0,
                         help='Top p sampling.')
      group.add_argument("--top_k", type=int, default=0,
                         help='Top k sampling.')
      group.add_argument("--out-seq-length", type=int, default=1024,
                         help='Size of the output generated text.')
      group.add_argument("--sample-input-file", type=str, default=None,
                         help='Get input from file instead of interactive mode, '
                         'each line is an input.')
      group.add_argument("--sample-output-file", type=str, default=None,
                         help='Output file got from --sample-input-file')
      group.add_argument("--num-samples", type=int, default=0,
                         help='Number of samples to generate unconditionally, '
                         'defaults to 0 and interactive conditional sampling')
      group.add_argument("--genfile", type=str,
                         help='Output file when generating unconditionally')
      group.add_argument("--recompute", action='store_true',
                         help='During generation recompute all attention '
                         'instead of using previously computed keys/values.')
  
      return parser
  
  
  def main():
      """Main program."""
  
      initialize_megatron(extra_args_provider=add_text_generate_args,
                          args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})
  
      # Set up model and load checkpoint.
      model = get_model(model_provider)
      args = get_args()
      if args.load is not None:
          _ = load_checkpoint(model, None, None)
  
      # Generate samples.
      if args.num_samples == 0:
          args.batch_size = 1
          if args.sample_input_file != "":
              generate_samples_input_from_file(model)
          else:
              generate_samples_interactive(model)
      else:
          generate_and_write_samples_unconditional(model)
  
  
  if __name__ == "__main__":
  
      main()
  