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.
  
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import logging
  import math
  
  import torch
  import torch.nn.functional as F
  from fairseq_mod import utils
  from fairseq_mod.criterions import FairseqCriterion, register_criterion
  
  
  @register_criterion("cross_entropy_acc")
  class CrossEntropyWithAccCriterion(FairseqCriterion):
      def __init__(self, task, sentence_avg):
          super().__init__(task)
          self.sentence_avg = sentence_avg
  
      def compute_loss(self, model, net_output, target, reduction, log_probs):
          # N, T -> N * T
          target = target.view(-1)
          lprobs = model.get_normalized_probs(net_output, log_probs=log_probs)
          if not hasattr(lprobs, "batch_first"):
              logging.warning(
                  "ERROR: we need to know whether "
                  "batch first for the net output; "
                  "you need to set batch_first attribute for the return value of "
                  "model.get_normalized_probs. Now, we assume this is true, but "
                  "in the future, we will raise exception instead. "
              )
          batch_first = getattr(lprobs, "batch_first", True)
          if not batch_first:
              lprobs = lprobs.transpose(0, 1)
  
          # N, T, D -> N * T, D
          lprobs = lprobs.view(-1, lprobs.size(-1))
          loss = F.nll_loss(
              lprobs, target, ignore_index=self.padding_idx, reduction=reduction
          )
          return lprobs, loss
  
      def get_logging_output(self, sample, target, lprobs, loss):
          target = target.view(-1)
          mask = target != self.padding_idx
          correct = torch.sum(
              lprobs.argmax(1).masked_select(mask) == target.masked_select(mask)
          )
          total = torch.sum(mask)
          sample_size = (
              sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
          )
  
          logging_output = {
              "loss": utils.item(loss.data),  # * sample['ntokens'],
              "ntokens": sample["ntokens"],
              "nsentences": sample["target"].size(0),
              "sample_size": sample_size,
              "correct": utils.item(correct.data),
              "total": utils.item(total.data),
              "nframes": torch.sum(sample["net_input"]["src_lengths"]).item(),
          }
  
          return sample_size, logging_output
  
      def forward(self, model, sample, reduction="sum", log_probs=True):
          """Computes the cross entropy with accuracy metric for the given sample.
  
          This is similar to CrossEntropyCriterion in fairseq, but also
          computes accuracy metrics as part of logging
  
          Args:
              logprobs (Torch.tensor) of shape N, T, D i.e.
                  batchsize, timesteps, dimensions
              targets (Torch.tensor) of shape N, T  i.e batchsize, timesteps
  
          Returns:
          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
  
          TODO:
              * Currently this Criterion will only work with LSTMEncoderModels or
              FairseqModels which have decoder, or Models which return TorchTensor
              as net_output.
              We need to make a change to support all FairseqEncoder models.
          """
          net_output = model(**sample["net_input"])
          target = model.get_targets(sample, net_output)
          lprobs, loss = self.compute_loss(
              model, net_output, target, reduction, log_probs
          )
          sample_size, logging_output = self.get_logging_output(
              sample, target, lprobs, loss
          )
          return loss, sample_size, logging_output
  
      @staticmethod
      def aggregate_logging_outputs(logging_outputs):
          """Aggregate logging outputs from data parallel training."""
          correct_sum = sum(log.get("correct", 0) for log in logging_outputs)
          total_sum = sum(log.get("total", 0) for log in logging_outputs)
          loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
          ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
          nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
          sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
          nframes = sum(log.get("nframes", 0) for log in logging_outputs)
          agg_output = {
              "loss": loss_sum / sample_size / math.log(2) if sample_size > 0 else 0.0,
              # if args.sentence_avg, then sample_size is nsentences, then loss
              # is per-sentence loss; else sample_size is ntokens, the loss
              # becomes per-output token loss
              "ntokens": ntokens,
              "nsentences": nsentences,
              "nframes": nframes,
              "sample_size": sample_size,
              "acc": correct_sum * 100.0 / total_sum if total_sum > 0 else 0.0,
              "correct": correct_sum,
              "total": total_sum,
              # total is the number of validate tokens
          }
          if sample_size != ntokens:
              agg_output["nll_loss"] = loss_sum / ntokens / math.log(2)
          # loss: per output token loss
          # nll_loss: per sentence loss
          return agg_output
  