dev(narugo): update them, ci skip

            paths=[model.model_version_map[model_name]['ckpt_name']],

            expand=True

        )[0].last_commit.date.timestamp()

        model_repo_id = model.model_version_map[model_name]['repo_id']

        model_file = model.model_version_map[model_name]['ckpt_name']

    except (KeyError, ValueError):

        alt_model_name = "tagger_v_2_2_7"

            repo_type='model',

            filename=model.model_version_map[alt_model_name]['ckpt_name'],

        ), map_location="cpu")

        model_repo_id = model.model_version_map[model_name]['repo_id']

        model_file = f'{model_name}.pth'

        state_dicts_head = torch.load(hf_client.hf_hub_download(

            repo_id=model.model_version_map[alt_model_name]['repo_id'],

            repo_type='model',

            filename=f'{model_name}.pth',

            filename=model_file,

        ), map_location="cpu")

        state_dicts['head.weight'] = state_dicts_head['head.0.weight']

        state_dicts['head.bias'] = state_dicts_head['head.0.bias']

    infer_model = model.model

    transforms = model.transform

    return model, infer_model, transforms, created_at

    return model, infer_model, transforms, (model_repo_id, model_file), created_at

def extract(export_dir: str, model_name: str = "tagger_v_2_2_7", no_optimize: bool = False):

    os.makedirs(export_dir, exist_ok=True)

    raw_model, model, transforms, created_at = load_model(model_name)

    raw_model, model, transforms, (model_repo_id, model_filename), created_at = load_model(model_name)

    raw_model: PixAITaggerInference

    image = Image.open(get_testfile('genshin_post.jpg'))

    dummy_input = transforms(image).unsqueeze(0)

    s_macs, s_params = clever_format([macs, params], "%.1f")

    logging.info(f'Params: {s_params}, FLOPs: {s_macs}')

    src_repo_id = raw_model.model_version_map[raw_model.model_version]['repo_id']

    src_model_filename = raw_model.model_version_map[raw_model.model_version]['ckpt_name']

    with open(os.path.join(export_dir, 'meta.json'), 'w') as f:

        json.dump({

            'num_classes': conv_preds.shape[-1],

            'name': model_name,

            'model_cls': type(model).__name__,

            'input_size': dummy_input.shape[2],

            'repo_id': src_repo_id,

            'model_filename': src_model_filename,

            'repo_id': model_repo_id,

            'model_filename': model_filename,

            'created_at': created_at,

        }, f, indent=4, sort_keys=True)