def METHOD_NAME(model_raw: Model, model_convert: Model, tokenizer_raw,
                    tokenizer_convert):
    if model_raw.__class__ != model_convert.__class__:
        logger.error(
            f'weight diff: These two models should be of the same class. model_raw:'
            f'{model_raw.__class__} vs model_convert: {model_convert.__class__}.'
        )
    special_tokens = {}
    for k, v in tokenizer_convert.special_tokens_map_extended.items():
        if k not in tokenizer_raw.special_tokens_map_extended:
            special_tokens[k] = v
    smart_tokenizer_and_embedding_resize(
        special_tokens_dict=special_tokens,
        model=model_raw,
        tokenizer=tokenizer_raw,
    )
    state_dict_tuned = model_convert.state_dict()
    state_dict_raw = model_raw.state_dict()
    for key in tqdm.tqdm(state_dict_tuned):
        if state_dict_tuned[key].shape != state_dict_raw[key].shape:
            logger.error(
                f'weight diff: shape mismatch. {key}, model_raw shape: {state_dict_raw[key].shape}'
                f' vs model_convert shape: {state_dict_tuned[key].shape}.')