import torch


def wrapped_getattr(self, name, default=None, wrapped_member_name="model"):
    """should be called from wrappers of model classes such as ClassifierFreeSampleModel"""

    if isinstance(self, torch.nn.Module):
        # for descendants of nn.Module, name may be in self.__dict__[_parameters/_buffers/_modules]
        # so we activate nn.Module.__getattr__ first.
        # Otherwise, we might encounter an infinite loop
        try:
            attr = torch.nn.Module.__getattr__(self, name)
        except AttributeError:
            wrapped_member = torch.nn.Module.__getattr__(self, wrapped_member_name)
            attr = getattr(wrapped_member, name, default)
    else:
        # the easy case, where self is not derived from nn.Module
        wrapped_member = getattr(self, wrapped_member_name)
        attr = getattr(wrapped_member, name, default)
    return attr


def load_model_wo_clip(model, state_dict):
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    assert len(unexpected_keys) == 0
    assert all([k.startswith("clip_model.") for k in missing_keys])