def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our BERT structure.
    """
    bart = torch.hub.load("pytorch/fairseq", checkpoint_path)
    bart.eval()  # disable dropout
    bart.model.upgrade_state_dict(bart.model.state_dict())
    hf_model_name = checkpoint_path.replace(".", "-")
    config = BartConfig.from_pretrained(hf_model_name)
    tokens = bart.encode(SAMPLE_TEXT).unsqueeze(0)
    tokens2 = BartTokenizer.from_pretrained(hf_model_name).encode(SAMPLE_TEXT, return_tensors="pt").unsqueeze(0)
    assert torch.eq(tokens, tokens2).all()

    if checkpoint_path in ["bart.large", "bart.large.cnn"]:
        state_dict = bart.model.state_dict()
        for k in IGNORE_KEYS:
            state_dict.pop(k, None)
        state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
        model = BartModel(config)
        their_output = bart.extract_features(tokens)
    else:  # MNLI Case
        state_dict = bart.state_dict()
        for k in IGNORE_KEYS:
            state_dict.pop(k, None)
        state_dict["model.shared.weight"] = state_dict["model.decoder.embed_tokens.weight"]
        for src, dest in rename_keys:
            rename_key(state_dict, src, dest)
        model = BartForSequenceClassification(config)
        their_output = bart.predict("mnli", tokens, return_logits=True)

    # Load state dict
    model.load_state_dict(state_dict)
    model.eval()
    # Check results

    if checkpoint_path == "bart.large.cnn":  # generate doesnt work yet
        model = BartForMaskedLM(config, base_model=model)
        assert "lm_head.weight" in model.state_dict()
        assert model.lm_head.out_features == config.max_position_embeddings
        model.eval()
        our_outputs = model.model.forward(tokens)[0]
    else:
        our_outputs = model.forward(tokens)[0]
    assert their_output.shape == our_outputs.shape
    assert (their_output == our_outputs).all().item()
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    model.save_pretrained(pytorch_dump_folder_path)

    Copy/paste/tweak model's weights to our BERT structure.
    
xtune/src/transformers/convert_bart_original_pytorch_checkpoint_to_pytorch.py