old_contents: # Copyright (c) 2017-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the LICENSE file in
  # the root directory of this source tree. An additional grant of patent rights
  # can be found in the PATENTS file in the same directory.
  
  from torch.nn.modules.loss import _Loss
  
  
  class FairseqCriterion(_Loss):
  
      def __init__(self, args, task):
          super().__init__()
          self.args = args
          self.padding_idx = task.target_dictionary.pad() if task.target_dictionary is not None else -100
  
      @staticmethod
      def add_args(parser):
          """Add criterion-specific arguments to the parser."""
          pass
  
      @classmethod
      def build_criterion(cls, args, task):
          return cls(args, task)
  
      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
          """
          raise NotImplementedError
  
      @staticmethod
      def aggregate_logging_outputs(logging_outputs):
          """Aggregate logging outputs from data parallel training."""
          raise NotImplementedError
  
      @staticmethod
      def grad_denom(sample_sizes):
          """Compute the gradient denominator for a set of sample sizes."""
          return sum(sample_sizes)
  
new_contents: # Copyright (c) 2017-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the LICENSE file in
  # the root directory of this source tree. An additional grant of patent rights
  # can be found in the PATENTS file in the same directory.
  
  from torch.nn.modules.loss import _Loss
  
  
  class FairseqCriterion(_Loss):
  
      def __init__(self, args, task):
          super().__init__()
          self.args = args
          self.task = task
          self.padding_idx = task.target_dictionary.pad() if task.target_dictionary is not None else -100
  
      @staticmethod
      def add_args(parser):
          """Add criterion-specific arguments to the parser."""
          pass
  
      @classmethod
      def build_criterion(cls, args, task):
          return cls(args, task)
  
      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
          """
          raise NotImplementedError
  
      @staticmethod
      def aggregate_logging_outputs(logging_outputs):
          """Aggregate logging outputs from data parallel training."""
          raise NotImplementedError
  
      @staticmethod
      def grad_denom(sample_sizes):
          """Compute the gradient denominator for a set of sample sizes."""
          return sum(sample_sizes)
  
subject: Store task in the criterion base class