text: # src: https://github.com/facebookresearch/DrQA/blob/master/scripts/reader/train.py
  
  import sys
  
  sys.path.append(".")
  sys.path.append("..")
  
  import os
  import json
  import torch
  import logging
  import subprocess
  import argparse
  import numpy as np
  
  import c2nl.config as config
  import c2nl.inputters.utils as util
  import c2nl.inputters.vector as vector
  import c2nl.inputters.dataset as data
  from tqdm import tqdm
  from main.model import Code2NaturalLanguage
  from main.train import compute_eval_score
  
  from collections import OrderedDict
  from c2nl.utils.copy_utils import collapse_copy_scores, make_src_map, align
  from c2nl.inputters.timer import AverageMeter, Timer
  from c2nl.inputters import constants
  from c2nl.eval.bleu import Bleu, nltk_corpus_bleu, corpus_bleu
  from c2nl.eval.rouge import Rouge
  from c2nl.eval.meteor import Meteor
  from c2nl.translator.translator import Translator
  from c2nl.translator.beam import GNMTGlobalScorer
  from c2nl.translator.translation import TranslationBuilder
  
  logger = logging.getLogger()
  
  
  def str2bool(v):
      return v.lower() in ('yes', 'true', 't', '1', 'y')
  
  
  def add_test_args(parser):
      """Adds commandline arguments pertaining to training a model. These
      are different from the arguments dictating the model architecture.
      """
      parser.register('type', 'bool', str2bool)
  
      # Runtime environment
      runtime = parser.add_argument_group('Environment')
      runtime.add_argument('--data_workers', type=int, default=5,
                           help='Number of subprocesses for data loading')
      runtime.add_argument('--random_seed', type=int, default=1013,
                           help=('Random seed for all numpy/torch/cuda '
                                 'operations (for reproducibility)'))
      runtime.add_argument('--test_batch_size', type=int, default=128,
                           help='Batch size during validation/testing')
  
      # Files
      files = parser.add_argument_group('Filesystem')
      files.add_argument('--dataset_name', nargs='+', type=str, required=True,
                         help='Name of the experimental dataset')
      files.add_argument('--model_dir', type=str, default='/tmp/qa_models/',
                         help='Directory for saved models/checkpoints/logs')
      files.add_argument('--model_name', type=str, default='',
                         help='Unique model identifier (.mdl, .txt, .checkpoint)')
      files.add_argument('--data_dir', type=str, default='/data/',
                         help='Directory of training/validation data')
      files.add_argument('--dev_src', nargs='+', type=str, required=True,
                         help='Preprocessed dev source file')
      files.add_argument('--dev_src_tag', nargs='+', type=str,
                         help='Preprocessed dev source tag file')
      files.add_argument('--dev_tgt', nargs='+', type=str,
                         help='Preprocessed dev target file')
  
      # Data preprocessing
      preprocess = parser.add_argument_group('Preprocessing')
      preprocess.add_argument('--max_examples', type=int, default=-1,
                              help='Maximum number of examples for training')
      preprocess.add_argument('--uncase', type='bool', default=False,
                              help='Code and summary words will be lower-cased')
      preprocess.add_argument('--max_characters_per_token', type=int, default=30,
                              help='Maximum number of characters allowed per token')
  
      # General
      general = parser.add_argument_group('General')
      general.add_argument('--sort_by_len', type='bool', default=True,
                           help='Sort batches by length for speed')
      general.add_argument('--only_generate', type='bool', default=False,
                           help='Only generate code summaries')
  
      # Beam Search
      bsearch = parser.add_argument_group('Beam Search arguments')
      bsearch.add_argument('--beam_size', type=int, default=4,
                           help='Set the beam size (=1 means greedy decoding)')
      bsearch.add_argument('--n_best', type=int, default=1,
                           help="""If verbose is set, will output the n_best
                             decoded sentences""")
      bsearch.add_argument('--stepwise_penalty', type='bool', default=False,
                           help="""Apply penalty at every decoding step.
                             Helpful for summary penalty.""")
      bsearch.add_argument('--length_penalty', default='none',
                           choices=['none', 'wu', 'avg'],
                           help="""Length Penalty to use.""")
      bsearch.add_argument('--coverage_penalty', default='none',
                           choices=['none', 'wu', 'summary'],
                           help="""Coverage Penalty to use.""")
      bsearch.add_argument('--block_ngram_repeat', type=int, default=0,
                           help='Block repetition of ngrams during decoding.')
      bsearch.add_argument('--ignore_when_blocking', nargs='+', type=str,
                           default=[],
                           help="""Ignore these strings when blocking repeats.
                             You want to block sentence delimiters.""")
      bsearch.add_argument('--gamma', type=float, default=0.,
                           help="""Google NMT length penalty parameter
                              (higher = longer generation)""")
      bsearch.add_argument('--beta', type=float, default=0.,
                           help="""Coverage penalty parameter""")
      bsearch.add_argument('--replace_unk', action="store_true",
                           help="""Replace the generated UNK tokens with the
                             source token that had highest attention weight. If
                             phrase_table is provided, it will lookup the
                             identified source token and give the corresponding
                             target token. If it is not provided(or the identified
                             source token does not exist in the table) then it
                             will copy the source token""")
      bsearch.add_argument('--verbose', action="store_true",
                           help='Print scores and predictions for each sentence')
  
  
  def set_defaults(args):
      """Make sure the commandline arguments are initialized properly."""
      # Check critical files exist
      args.dev_src_files = []
      args.dev_tgt_files = []
      args.dev_src_tag_files = []
  
      num_dataset = len(args.dataset_name)
      if num_dataset > 1:
          if len(args.dev_src) == 1:
              args.dev_src = args.dev_src * num_dataset
          if len(args.dev_tgt) == 1:
              args.dev_tgt = args.dev_tgt * num_dataset
          if len(args.dev_src_tag) == 1:
              args.dev_src_tag = args.dev_src_tag * num_dataset
  
      for i in range(num_dataset):
          dataset_name = args.dataset_name[i]
          data_dir = os.path.join(args.data_dir, dataset_name)
          dev_src = os.path.join(data_dir, args.dev_src[i])
          if not os.path.isfile(dev_src):
              raise IOError('No such file: %s' % dev_src)
          if args.only_generate:
              dev_tgt = None
          else:
              dev_tgt = os.path.join(data_dir, args.dev_tgt[i])
              if not os.path.isfile(dev_tgt):
                  raise IOError('No such file: %s' % dev_tgt)
          if args.use_code_type:
              dev_src_tag = os.path.join(data_dir, args.dev_src_tag[i])
              if not os.path.isfile(dev_src_tag):
                  raise IOError('No such file: %s' % dev_src_tag)
          else:
              dev_src_tag = None
  
          args.dev_src_files.append(dev_src)
          args.dev_tgt_files.append(dev_tgt)
          args.dev_src_tag_files.append(dev_src_tag)
  
      # Set model directory
      subprocess.call(['mkdir', '-p', args.model_dir])
  
      # Set model name
      if not args.model_name:
          import uuid
          import time
          args.model_name = time.strftime("%Y%m%d-") + str(uuid.uuid4())[:8]
  
      # Set log + model file names
      args.log_file = os.path.join(args.model_dir, args.model_name + '_beam.txt')
      args.model_file = os.path.join(args.model_dir, args.model_name + '.mdl')
      args.pred_file = os.path.join(args.model_dir, args.model_name + '_beam.json')
  
  
  # ------------------------------------------------------------------------------
  # Validation loops. Includes both "unofficial" and "official" functions that
  # use different metrics and implementations.
  # ------------------------------------------------------------------------------
  
  def build_translator(model, args):
      scorer = GNMTGlobalScorer(args.gamma,
                                args.beta,
                                args.coverage_penalty,
                                args.length_penalty)
  
      translator = Translator(model,
                              args.cuda,
                              args.beam_size,
                              n_best=args.n_best,
                              max_length=args.max_tgt_len,
                              copy_attn=model.args.copy_attn,
                              global_scorer=scorer,
                              min_length=0,
                              stepwise_penalty=args.stepwise_penalty,
                              block_ngram_repeat=args.block_ngram_repeat,
                              ignore_when_blocking=args.ignore_when_blocking,
                              replace_unk=args.replace_unk)
      return translator
  
  
  def prepare_batch(batch, model):
      # To enable copy attn, collect source map and alignment info
      batch_inputs = dict()
  
      if model.args.copy_attn:
          assert 'src_map' in batch and 'alignment' in batch
          source_map = make_src_map(batch['src_map'])
          source_map = source_map.cuda(non_blocking=True) if args.cuda \
              else source_map
          alignment = None
          blank, fill = collapse_copy_scores(model.tgt_dict, batch['src_vocab'])
      else:
          source_map, alignment = None, None
          blank, fill = None, None
  
      batch_inputs['src_map'] = source_map
      batch_inputs['alignment'] = alignment
      batch_inputs['blank'] = blank
      batch_inputs['fill'] = fill
  
      code_word_rep = batch['code_word_rep']
      code_char_rep = batch['code_char_rep']
      code_type_rep = batch['code_type_rep']
      code_mask_rep = batch['code_mask_rep']
      code_len = batch['code_len']
      if args.cuda:
          code_len = batch['code_len'].cuda(non_blocking=True)
          if code_word_rep is not None:
              code_word_rep = code_word_rep.cuda(non_blocking=True)
          if code_char_rep is not None:
              code_char_rep = code_char_rep.cuda(non_blocking=True)
          if code_type_rep is not None:
              code_type_rep = code_type_rep.cuda(non_blocking=True)
          if code_mask_rep is not None:
              code_mask_rep = code_mask_rep.cuda(non_blocking=True)
  
      batch_inputs['code_word_rep'] = code_word_rep
      batch_inputs['code_char_rep'] = code_char_rep
      batch_inputs['code_type_rep'] = code_type_rep
      batch_inputs['code_mask_rep'] = code_mask_rep
      batch_inputs['code_len'] = code_len
      return batch_inputs
  
  
  def validate_official(args, data_loader, model):
      """Run one full official validation. Uses exact spans and same
      exact match/F1 score computation as in the SQuAD script.
      Extra arguments:
          offsets: The character start/end indices for the tokens in each context.
          texts: Map of qid --> raw text of examples context (matches offsets).
          answers: Map of qid --> list of accepted answers.
      """
  
      eval_time = Timer()
      translator = build_translator(model, args)
      builder = TranslationBuilder(model.tgt_dict,
                                   n_best=args.n_best,
                                   replace_unk=args.replace_unk)
  
      # Run through examples
      examples = 0
      trans_dict, sources = dict(), dict()
      with torch.no_grad():
          pbar = tqdm(data_loader)
          for batch_no, ex in enumerate(pbar):
              batch_size = ex['batch_size']
              ids = list(range(batch_no * batch_size,
                               (batch_no * batch_size) + batch_size))
              batch_inputs = prepare_batch(ex, model)
  
              ret = translator.translate_batch(batch_inputs)
              targets = [[summ] for summ in ex['summ_text']]
              translations = builder.from_batch(ret,
                                                ex['code_tokens'],
                                                targets,
                                                ex['src_vocab'])
  
              src_sequences = [code for code in ex['code_text']]
              for eid, trans, src in zip(ids, translations, src_sequences):
                  trans_dict[eid] = trans
                  sources[eid] = src
  
              examples += batch_size
  
      hypotheses, references = dict(), dict()
      for eid, trans in trans_dict.items():
          hypotheses[eid] = [' '.join(pred) for pred in trans.pred_sents]
          hypotheses[eid] = [constants.PAD_WORD if len(hyp.split()) == 0
                             else hyp for hyp in hypotheses[eid]]
          references[eid] = trans.targets
  
      if args.only_generate:
          with open(args.pred_file, 'w') as fw:
              json.dump(hypotheses, fw, indent=4)
  
      else:
          bleu, rouge_l, meteor, precision, recall, f1, ind_bleu, ind_rouge = \
              eval_accuracies(hypotheses, references)
          logger.info('beam evaluation official: '
                      'bleu = %.2f | rouge_l = %.2f | meteor = %.2f | ' %
                      (bleu, rouge_l, meteor) +
                      'Precision = %.2f | Recall = %.2f | F1 = %.2f | '
                      'examples = %d | ' %
                      (precision, recall, f1, examples) +
                      'test time = %.2f (s)' % eval_time.time())
  
          with open(args.pred_file, 'w') as fw:
              for eid, translation in trans_dict.items():
                  out_dict = OrderedDict()
                  out_dict['id'] = eid
                  out_dict['code'] = sources[eid]
                  # printing all beam search predictions
                  out_dict['predictions'] = [' '.join(pred) for pred in translation.pred_sents]
                  out_dict['references'] = references[eid]
                  out_dict['bleu'] = ind_bleu[eid]
                  out_dict['rouge_l'] = ind_rouge[eid]
                  fw.write(json.dumps(out_dict) + '\n')
  
  
  def eval_accuracies(hypotheses, references):
      """An unofficial evalutation helper.
       Arguments:
          hypotheses: A mapping from instance id to predicted sequences.
          references: A mapping from instance id to ground truth sequences.
          copy_info: Map of id --> copy information.
          sources: Map of id --> input text sequence.
          filename:
          print_copy_info:
      """
      assert sorted(references.keys()) == sorted(hypotheses.keys())
  
      # Compute BLEU scores
      # bleu_scorer = Bleu(n=4)
      # _, _, bleu = bleu_scorer.compute_score(references, hypotheses, verbose=0)
      # bleu = compute_bleu(references, hypotheses, max_order=4)['bleu']
      # _, bleu, _ = nltk_corpus_bleu(hypotheses, references)
      _, bleu, ind_bleu = corpus_bleu(hypotheses, references)
  
      # Compute ROUGE scores
      rouge_calculator = Rouge()
      rouge_l, ind_rouge = rouge_calculator.compute_score(references, hypotheses)
  
      # Compute METEOR scores
      meteor_calculator = Meteor()
      meteor, _ = meteor_calculator.compute_score(references, hypotheses)
  
      f1 = AverageMeter()
      precision = AverageMeter()
      recall = AverageMeter()
  
      for key in references.keys():
          _prec, _rec, _f1 = compute_eval_score(hypotheses[key][0], references[key])
          precision.update(_prec)
          recall.update(_rec)
          f1.update(_f1)
  
      return bleu * 100, rouge_l * 100, meteor * 100, precision.avg * 100, \
             recall.avg * 100, f1.avg * 100, ind_bleu, ind_rouge
  
  
  # ------------------------------------------------------------------------------
  # Main.
  # ------------------------------------------------------------------------------
  
  
  def main(args):
      # --------------------------------------------------------------------------
      # DATA
      logger.info('-' * 100)
      logger.info('Load and process data files')
      dev_exs = []
      for dev_src, dev_src_tag, dev_tgt, dataset_name in \
              zip(args.dev_src_files, args.dev_src_tag_files,
                  args.dev_tgt_files, args.dataset_name):
          dev_files = dict()
          dev_files['src'] = dev_src
          dev_files['src_tag'] = dev_src_tag
          dev_files['tgt'] = dev_tgt
          exs = util.load_data(args,
                               dev_files,
                               max_examples=args.max_examples,
                               dataset_name=dataset_name,
                               test_split=True)
          dev_exs.extend(exs)
      logger.info('Num dev examples = %d' % len(dev_exs))
  
      # --------------------------------------------------------------------------
      # MODEL
      logger.info('-' * 100)
      if not os.path.isfile(args.model_file):
          raise IOError('No such file: %s' % args.model_file)
      model = Code2NaturalLanguage.load(args.model_file)
  
      # Use the GPU?
      if args.cuda:
          model.cuda()
  
      # Use multiple GPUs?
      if args.parallel:
          model.parallelize()
  
      # --------------------------------------------------------------------------
      # DATA ITERATORS
      # Two datasets: train and dev. If we sort by length it's faster.
      logger.info('-' * 100)
      logger.info('Make data loaders')
      dev_dataset = data.CommentDataset(dev_exs, model)
      dev_sampler = torch.utils.data.sampler.SequentialSampler(dev_dataset)
      dev_loader = torch.utils.data.DataLoader(
          dev_dataset,
          batch_size=args.test_batch_size,
          sampler=dev_sampler,
          num_workers=args.data_workers,
          collate_fn=vector.batchify,
          pin_memory=args.cuda,
          drop_last=args.parallel
      )
  
      # -------------------------------------------------------------------------
      # PRINT CONFIG
      logger.info('-' * 100)
      logger.info('CONFIG:\n%s' %
                  json.dumps(vars(args), indent=4, sort_keys=True))
  
      # --------------------------------------------------------------------------
      # DO TEST
      validate_official(args, dev_loader, model)
  
  
  if __name__ == '__main__':
      # Parse cmdline args and setup environment
      parser = argparse.ArgumentParser(
          'Code to Natural Language Generation',
          formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
      add_test_args(parser)
      config.add_model_args(parser)
      args = parser.parse_args()
      set_defaults(args)
  
      # Set cuda
      args.cuda = torch.cuda.is_available()
      args.parallel = torch.cuda.device_count() > 1
  
      # Set random state
      np.random.seed(args.random_seed)
      torch.manual_seed(args.random_seed)
      if args.cuda:
          torch.cuda.manual_seed(args.random_seed)
  
      # Set logging
      logger.setLevel(logging.INFO)
      fmt = logging.Formatter('%(asctime)s: [ %(message)s ]',
                              '%m/%d/%Y %I:%M:%S %p')
      console = logging.StreamHandler()
      console.setFormatter(fmt)
      logger.addHandler(console)
      if args.log_file:
          logfile = logging.FileHandler(args.log_file, 'w')
          logfile.setFormatter(fmt)
          logger.addHandler(logfile)
      logger.info('COMMAND: %s' % ' '.join(sys.argv))
  
      # Run!
      main(args)
  