src/transformers/models/xlm/convert_xlm_original_pytorch_checkpoint_to_pytorch.py/convert_xlm_checkpoint_to_pytorch
def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
    # Load checkpoint
    chkpt = torch.load(xlm_checkpoint_path, map_location="cpu")

    state_dict = chkpt["model"]

    # We have the base model one level deeper than the original XLM repository
    two_levels_state_dict = {}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            two_levels_state_dict[k] = v
        else:
            two_levels_state_dict["transformer." + k] = v

    config = chkpt["params"]
    config = {n: v for n, v in config.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray))}

    vocab = chkpt["dico_word2id"]
    vocab = {s + "</w>" if s.find("@@") == -1 and i > 13 else s.replace("@@", ""): i for s, i in vocab.items()}

    # Save pytorch-model
    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
    pytorch_vocab_dump_path = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

    print(f"Save PyTorch model to {pytorch_weights_dump_path}")
    torch.save(two_levels_state_dict, pytorch_weights_dump_path)

    print(f"Save configuration file to {pytorch_config_dump_path}")
    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
        f.write(json.dumps(config, indent=2) + "\n")

    print(f"Save vocab file to {pytorch_config_dump_path}")
    with open(pytorch_vocab_dump_path, "w", encoding="utf-8") as f:
        f.write(json.dumps(vocab, indent=2) + "\n")