def flatten_omega_conf()

in training/train_muse.py [0:0]

• 25 lines of code
• 11 McCabe index (conditional complexity)

def flatten_omega_conf(cfg: Any, resolve: bool = False) -> List[Tuple[str, Any]]:
    ret = []

    def handle_dict(key: Any, value: Any, resolve: bool) -> List[Tuple[str, Any]]:
        return [(f"{key}.{k1}", v1) for k1, v1 in flatten_omega_conf(value, resolve=resolve)]

    def handle_list(key: Any, value: Any, resolve: bool) -> List[Tuple[str, Any]]:
        return [(f"{key}.{idx}", v1) for idx, v1 in flatten_omega_conf(value, resolve=resolve)]

    if isinstance(cfg, DictConfig):
        for k, v in cfg.items_ex(resolve=resolve):
            if isinstance(v, DictConfig):
                ret.extend(handle_dict(k, v, resolve=resolve))
            elif isinstance(v, ListConfig):
                ret.extend(handle_list(k, v, resolve=resolve))
            else:
                ret.append((str(k), v))
    elif isinstance(cfg, ListConfig):
        for idx, v in enumerate(cfg._iter_ex(resolve=resolve)):
            if isinstance(v, DictConfig):
                ret.extend(handle_dict(idx, v, resolve=resolve))
            elif isinstance(v, ListConfig):
                ret.extend(handle_list(idx, v, resolve=resolve))
            else:
                ret.append((str(idx), v))
    else:
        assert False

    return ret