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 torch.optim
  
  from . import FairseqOptimizer, register_optimizer
  
  
  @register_optimizer('adagrad')
  class Adagrad(FairseqOptimizer):
      def __init__(self, args, params):
          super().__init__(args)
          self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config)
  
      @staticmethod
      def add_args(parser):
          """Add optimizer-specific arguments to the parser."""
          # fmt: off
          parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
                              help='weight decay')
          # fmt: on
  
      @property
      def optimizer_config(self):
          """
          Return a kwarg dictionary that will be used to override optimizer
          args stored in checkpoints. This allows us to load a checkpoint and
          resume training using a different set of optimizer args, e.g., with a
          different learning rate.
          """
          return {
              'lr': self.args.lr[0],
              'weight_decay': self.args.weight_decay,
          }
  
      @property
      def supports_flat_params(self):
          return True
  