Small fixes

from itertools import islice

from fairseq import criterions, models, options, progress_bar

from fairseq import criterions, models, options, progress_bar, utils

from fairseq.data import data_utils, data_loaders

from fairseq.fp16_trainer import FP16Trainer

from fairseq.trainer import Trainer

from fairseq.meters import AverageMeter, StopwatchMeter

from fairseq.utils import checkpoint_paths

def main(args):

    max_epoch = args.max_epoch or math.inf

    max_update = args.max_update or math.inf

    lr = trainer.get_lr()

    first_val_loss = None

    train_meter = StopwatchMeter()

    train_meter.start()

    valid_losses = [None]

    valid_subsets = args.valid_subset.split(',')

    while lr > args.min_lr and epoch <= max_epoch and trainer.get_num_updates() < max_update:

        # train for one epoch

        updates % args.save_interval_updates == 0

    )

    checkpoint_conds['checkpoint_best.pt'] = (

        not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best

        val_loss is not None and

        (not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best)

    )

    checkpoint_conds['checkpoint_last.pt'] = True  # keep this last so that it's a symlink

    save_checkpoint.best = min(val_loss, getattr(save_checkpoint, 'best', val_loss))

    prev_best = getattr(save_checkpoint, 'best', val_loss)

    if val_loss is not None:

        save_checkpoint.best = min(val_loss, prev_best)

    extra_state = {

        'best': save_checkpoint.best,

        'best': prev_best,

        'end_of_epoch': end_of_epoch,

        'epoch': epoch,

        'val_loss': val_loss,