text: #!/usr/bin/env python3 -u
  # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  """
  Generate n-best translations using a trained model.
  """
  
  from contextlib import redirect_stdout
  import os
  import subprocess
  
  from fairseq import options
  from fairseq_cli import generate, preprocess
  
  from . import rerank_options, rerank_utils
  
  
  def gen_and_reprocess_nbest(args):
      if args.score_dict_dir is None:
          args.score_dict_dir = args.data
      if args.prefix_len is not None:
          assert args.right_to_left1 is False, "prefix length not compatible with right to left models"
          assert args.right_to_left2 is False, "prefix length not compatible with right to left models"
  
      if args.nbest_list is not None:
          assert args.score_model2 is None
  
      if args.backwards1:
          scorer1_src = args.target_lang
          scorer1_tgt = args.source_lang
      else:
          scorer1_src = args.source_lang
          scorer1_tgt = args.target_lang
  
      store_data = os.path.join(os.path.dirname(__file__))+"/rerank_data/"+args.data_dir_name
      if not os.path.exists(store_data):
          os.makedirs(store_data)
  
      pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \
          backwards_preprocessed_dir, lm_preprocessed_dir = \
          rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset,
                                       args.gen_model_name, args.shard_id, args.num_shards,
                                       args.sampling, args.prefix_len, args.target_prefix_frac,
                                       args.source_prefix_frac)
      assert not (args.right_to_left1 and args.backwards1), "backwards right to left not supported"
      assert not (args.right_to_left2 and args.backwards2), "backwards right to left not supported"
      assert not (args.prefix_len is not None and args.target_prefix_frac is not None), \
          "target prefix frac and target prefix len incompatible"
  
      # make directory to store generation results
      if not os.path.exists(pre_gen):
          os.makedirs(pre_gen)
  
      rerank1_is_gen = args.gen_model == args.score_model1 and args.source_prefix_frac is None
      rerank2_is_gen = args.gen_model == args.score_model2 and args.source_prefix_frac is None
  
      if args.nbest_list is not None:
          rerank2_is_gen = True
  
      # make directories to store preprossed nbest list for reranking
      if not os.path.exists(left_to_right_preprocessed_dir):
          os.makedirs(left_to_right_preprocessed_dir)
      if not os.path.exists(right_to_left_preprocessed_dir):
          os.makedirs(right_to_left_preprocessed_dir)
      if not os.path.exists(lm_preprocessed_dir):
          os.makedirs(lm_preprocessed_dir)
      if not os.path.exists(backwards_preprocessed_dir):
          os.makedirs(backwards_preprocessed_dir)
  
      score1_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model1_name,
                                                   target_prefix_frac=args.target_prefix_frac,
                                                   source_prefix_frac=args.source_prefix_frac,
                                                   backwards=args.backwards1)
      if args.score_model2 is not None:
          score2_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model2_name,
                                                       target_prefix_frac=args.target_prefix_frac,
                                                       source_prefix_frac=args.source_prefix_frac,
                                                       backwards=args.backwards2)
  
      predictions_bpe_file = pre_gen+"/generate_output_bpe.txt"
  
      using_nbest = args.nbest_list is not None
  
      if using_nbest:
          print("Using predefined n-best list from interactive.py")
          predictions_bpe_file = args.nbest_list
  
      else:
          if not os.path.isfile(predictions_bpe_file):
              print("STEP 1: generate predictions using the p(T|S) model with bpe")
              print(args.data)
              param1 = [args.data,
                        "--path", args.gen_model,
                        "--shard-id", str(args.shard_id),
                        "--num-shards", str(args.num_shards),
                        "--nbest", str(args.num_rescore),
                        "--batch-size", str(args.batch_size),
                        "--beam", str(args.num_rescore),
                        "--batch-size", str(args.num_rescore),
                        "--gen-subset", args.gen_subset,
                        "--source-lang", args.source_lang,
                        "--target-lang", args.target_lang]
              if args.sampling:
                  param1 += ["--sampling"]
  
              gen_parser = options.get_generation_parser()
              input_args = options.parse_args_and_arch(gen_parser, param1)
  
              print(input_args)
              with open(predictions_bpe_file, 'w') as f:
                  with redirect_stdout(f):
                      generate.main(input_args)
  
      gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe,
                                                    nbest=using_nbest, prefix_len=args.prefix_len,
                                                    target_prefix_frac=args.target_prefix_frac)
  
      if args.diff_bpe:
          rerank_utils.write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo,
                                         gen_output.no_bpe_target, pre_gen+"/source_gen_bpe."+args.source_lang,
                                         pre_gen+"/target_gen_bpe."+args.target_lang,
                                         pre_gen+"/reference_gen_bpe."+args.target_lang)
          bitext_bpe = args.rescore_bpe_code
          bpe_src_param = ["-c", bitext_bpe,
                           "--input", pre_gen+"/source_gen_bpe."+args.source_lang,
                           "--output", pre_gen+"/rescore_data."+args.source_lang]
          bpe_tgt_param = ["-c", bitext_bpe,
                           "--input", pre_gen+"/target_gen_bpe."+args.target_lang,
                           "--output", pre_gen+"/rescore_data."+args.target_lang]
  
          subprocess.call(["python",
                           os.path.join(os.path.dirname(__file__),
                                        "subword-nmt/subword_nmt/apply_bpe.py")] + bpe_src_param,
                          shell=False)
  
          subprocess.call(["python",
                           os.path.join(os.path.dirname(__file__),
                                        "subword-nmt/subword_nmt/apply_bpe.py")] + bpe_tgt_param,
                          shell=False)
  
      if (not os.path.isfile(score1_file) and not rerank1_is_gen) or \
              (args.score_model2 is not None and not os.path.isfile(score2_file) and not rerank2_is_gen):
          print("STEP 2: process the output of generate.py so we have clean text files with the translations")
  
          rescore_file = "/rescore_data"
          if args.prefix_len is not None:
              prefix_len_rescore_file = rescore_file + "prefix"+str(args.prefix_len)
          if args.target_prefix_frac is not None:
              target_prefix_frac_rescore_file = rescore_file + "target_prefix_frac"+str(args.target_prefix_frac)
          if args.source_prefix_frac is not None:
              source_prefix_frac_rescore_file = rescore_file + "source_prefix_frac"+str(args.source_prefix_frac)
  
          if not args.right_to_left1 or not args.right_to_left2:
              if not args.diff_bpe:
                  rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
                                                 pre_gen+rescore_file+"."+args.source_lang,
                                                 pre_gen+rescore_file+"."+args.target_lang,
                                                 pre_gen+"/reference_file", bpe_symbol=args.remove_bpe)
                  if args.prefix_len is not None:
                      bw_rescore_file = prefix_len_rescore_file
                      rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
                                                     pre_gen+prefix_len_rescore_file+"."+args.source_lang,
                                                     pre_gen+prefix_len_rescore_file+"."+args.target_lang,
                                                     pre_gen+"/reference_file", prefix_len=args.prefix_len,
                                                     bpe_symbol=args.remove_bpe)
                  elif args.target_prefix_frac is not None:
                      bw_rescore_file = target_prefix_frac_rescore_file
                      rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
                                                     pre_gen+target_prefix_frac_rescore_file+"."+args.source_lang,
                                                     pre_gen+target_prefix_frac_rescore_file+"."+args.target_lang,
                                                     pre_gen+"/reference_file", bpe_symbol=args.remove_bpe,
                                                     target_prefix_frac=args.target_prefix_frac)
                  else:
                      bw_rescore_file = rescore_file
  
                  if args.source_prefix_frac is not None:
                      fw_rescore_file = source_prefix_frac_rescore_file
                      rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
                                                     pre_gen+source_prefix_frac_rescore_file+"."+args.source_lang,
                                                     pre_gen+source_prefix_frac_rescore_file+"."+args.target_lang,
                                                     pre_gen+"/reference_file", bpe_symbol=args.remove_bpe,
                                                     source_prefix_frac=args.source_prefix_frac)
                  else:
                      fw_rescore_file = rescore_file
  
          if args.right_to_left1 or args.right_to_left2:
              rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
                                             pre_gen+"/right_to_left_rescore_data."+args.source_lang,
                                             pre_gen+"/right_to_left_rescore_data."+args.target_lang,
                                             pre_gen+"/right_to_left_reference_file",
                                             right_to_left=True, bpe_symbol=args.remove_bpe)
  
          print("STEP 3: binarize the translations")
          if not args.right_to_left1 or args.score_model2 is not None and not args.right_to_left2 or not rerank1_is_gen:
  
              if args.backwards1 or args.backwards2:
                  if args.backwards_score_dict_dir is not None:
                      bw_dict = args.backwards_score_dict_dir
                  else:
                      bw_dict = args.score_dict_dir
                  bw_preprocess_param = ["--source-lang", scorer1_src,
                                         "--target-lang", scorer1_tgt,
                                         "--trainpref", pre_gen+bw_rescore_file,
                                         "--srcdict", bw_dict + "/dict." + scorer1_src + ".txt",
                                         "--tgtdict", bw_dict + "/dict." + scorer1_tgt + ".txt",
                                         "--destdir", backwards_preprocessed_dir]
                  preprocess_parser = options.get_preprocessing_parser()
                  input_args = preprocess_parser.parse_args(bw_preprocess_param)
                  preprocess.main(input_args)
  
              preprocess_param = ["--source-lang", scorer1_src,
                                  "--target-lang", scorer1_tgt,
                                  "--trainpref", pre_gen+fw_rescore_file,
                                  "--srcdict", args.score_dict_dir+"/dict."+scorer1_src+".txt",
                                  "--tgtdict", args.score_dict_dir+"/dict."+scorer1_tgt+".txt",
                                  "--destdir", left_to_right_preprocessed_dir]
              preprocess_parser = options.get_preprocessing_parser()
              input_args = preprocess_parser.parse_args(preprocess_param)
              preprocess.main(input_args)
  
          if args.right_to_left1 or args.right_to_left2:
              preprocess_param = ["--source-lang", scorer1_src,
                                  "--target-lang", scorer1_tgt,
                                  "--trainpref", pre_gen+"/right_to_left_rescore_data",
                                  "--srcdict", args.score_dict_dir+"/dict."+scorer1_src+".txt",
                                  "--tgtdict", args.score_dict_dir+"/dict."+scorer1_tgt+".txt",
                                  "--destdir", right_to_left_preprocessed_dir]
              preprocess_parser = options.get_preprocessing_parser()
              input_args = preprocess_parser.parse_args(preprocess_param)
              preprocess.main(input_args)
  
      return gen_output
  
  
  def cli_main():
      parser = rerank_options.get_reranking_parser()
      args = options.parse_args_and_arch(parser)
      gen_and_reprocess_nbest(args)
  
  
  if __name__ == '__main__':
      cli_main()
  