text: # 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.
  
  import math
  
  from fairseq import metrics, utils
  from fairseq.criterions import FairseqCriterion, register_criterion
  
  
  def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
      if target.dim() == lprobs.dim() - 1:
          target = target.unsqueeze(-1)
      nll_loss = -lprobs.gather(dim=-1, index=target)
      smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
      if ignore_index is not None:
          pad_mask = target.eq(ignore_index)
          nll_loss.masked_fill_(pad_mask, 0.)
          smooth_loss.masked_fill_(pad_mask, 0.)
      else:
          nll_loss = nll_loss.squeeze(-1)
          smooth_loss = smooth_loss.squeeze(-1)
      if reduce:
          nll_loss = nll_loss.sum()
          smooth_loss = smooth_loss.sum()
      eps_i = epsilon / lprobs.size(-1)
      loss = (1. - epsilon) * nll_loss + eps_i * smooth_loss
      return loss, nll_loss
  
  
  @register_criterion('label_smoothed_cross_entropy')
  class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
  
      def __init__(self, task, sentence_avg, label_smoothing):
          super().__init__(task)
          self.sentence_avg = sentence_avg
          self.eps = label_smoothing
  
      @staticmethod
      def add_args(parser):
          """Add criterion-specific arguments to the parser."""
          # fmt: off
          parser.add_argument('--label-smoothing', default=0., type=float, metavar='D',
                              help='epsilon for label smoothing, 0 means no label smoothing')
          # fmt: on
  
      def forward(self, model, sample, reduce=True):
          """Compute the loss for the given sample.
  
          Returns a tuple with three elements:
          1) the loss
          2) the sample size, which is used as the denominator for the gradient
          3) logging outputs to display while training
          """
          
          # print("model", model)
          # print("**sample['feature']", **sample['feature'])
          
          # exit()
          net_output = model(**sample['net_input'])
          loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
          sample_size = sample['target'].size(0) if self.sentence_avg else sample['ntokens']
          logging_output = {
              'loss': loss.data,
              'nll_loss': nll_loss.data,
              'ntokens': sample['ntokens'],
              'nsentences': sample['target'].size(0),
              'sample_size': sample_size,
          }
          return loss, sample_size, logging_output
  
      def compute_loss(self, model, net_output, sample, reduce=True):
          lprobs = model.get_normalized_probs(net_output, log_probs=True)
          lprobs = lprobs.view(-1, lprobs.size(-1))
          target = model.get_targets(sample, net_output).view(-1, 1)
          loss, nll_loss = label_smoothed_nll_loss(
              lprobs, target, self.eps, ignore_index=self.padding_idx, reduce=reduce,
          )
          return loss, nll_loss
  
      @staticmethod
      def reduce_metrics(logging_outputs) -> None:
          """Aggregate logging outputs from data parallel training."""
          loss_sum = sum(log.get('loss', 0) for log in logging_outputs)
          nll_loss_sum = sum(log.get('nll_loss', 0) for log in logging_outputs)
          ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
          sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
  
          metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)
          metrics.log_scalar('nll_loss', nll_loss_sum / ntokens / math.log(2), ntokens, round=3)
          metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg))
  
      @staticmethod
      def logging_outputs_can_be_summed() -> bool:
          """
          Whether the logging outputs returned by `forward` can be summed
          across workers prior to calling `reduce_metrics`. Setting this
          to True will improves distributed training speed.
          """
          return True
  