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from fairseq.models.transformer import TransformerModel

zh2en = TransformerModel.from_pretrained(
  '/path/to/checkpoints',
  checkpoint_file='checkpoint_best.pt',
  data_name_or_path='data-bin/wmt17_zh_en_full',
  bpe='subword_nmt',
  bpe_codes='data-bin/wmt17_zh_en_full/zh.code'
)
zh2en.translate('你好 世界')

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import torch

# List available models
torch.hub.list('pytorch/fairseq')  # [..., 'transformer.wmt16.en-de', ... ]

# Load a transformer trained on WMT'16 En-De
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt16.en-de', tokenizer='moses', bpe='subword_nmt')
en2de.eval()  # disable dropout

# The underlying model is available under the *models* attribute

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@register_task('classification')
class ClassificationTask(FairseqTask): (...)

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	model.train()
        model.set_num_updates(update_num)
        loss, sample_size, logging_output = criterion(model, sample)
        if ignore_grad:
            loss *= 0
        optimizer.backward(loss)

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# setup the task (e.g., load dictionaries)
task = fairseq.tasks.setup_task(args)

# build model and criterion
model = task.build_model(args)
criterion = task.build_criterion(args)

# load datasets
task.load_dataset('train')
task.load_dataset('valid')

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for epoch in range(num_epochs):
    itr = task.get_batch_iterator(task.dataset('train'))
    for num_updates, batch in enumerate(itr):
        task.train_step(batch, model, criterion, optimizer)
        average_and_clip_gradients()
        optimizer.step()
        lr_scheduler.step_update(num_updates)
    lr_scheduler.step(epoch)

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if args.joined_dictionary:
    assert not args.srcdict or not args.tgtdict, \
        "cannot use both --srcdict and --tgtdict with --joined-dictionary"

    if args.srcdict:
        src_dict = task.load_dictionary(args.srcdict)
    elif args.tgtdict:
        src_dict = task.load_dictionary(args.tgtdict)
    else:
        assert args.trainpref, "--trainpref must be set if --srcdict is not specified"

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def cli_main():
    parser = options.get_preprocessing_parser()
    args = parser.parse_args()
    main(args)

if __name__ == "__main__":
    cli_main()