dev(narugo): add documentation

@lru_cache()

def _open_anime_portrait_model(model_name):

    """

    Open the anime portrait model.

    :param model_name: The model name.

    :type model_name: str

    :return: The ONNX model.

    """

    return open_onnx_model(hf_hub_download(

        f'deepghs/anime_portrait',

        f'{model_name}/model.onnx',

@lru_cache()

def _get_anime_portrait_labels(model_name) -> List[str]:

    """

    Get the labels for the anime portrait model.

    :param model_name: The model name.

    :type model_name: str

    :return: The list of labels.

    :rtype: List[str]

    """

    with open(hf_hub_download(

            f'deepghs/anime_portrait',

            f'{model_name}/meta.json',

def _img_encode(image: Image.Image, size: Tuple[int, int] = (384, 384),

                normalize: Optional[Tuple[float, float]] = (0.5, 0.5)):

    """

    Encode the input image.

    :param image: The input image.

    :type image: Image.Image

    :param size: The desired size of the image.

    :type size: Tuple[int, int]

    :param normalize: Mean and standard deviation for normalization. Default is (0.5, 0.5).

    :type normalize: Optional[Tuple[float, float]]

    :return: The encoded image data.

    :rtype: np.ndarray

    """

    image = image.resize(size, Image.BILINEAR)

    data = rgb_encode(image, order_='CHW')

def _raw_anime_portrait(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME):

    """

    Perform raw anime portrait processing on the input image.

    :param image: The input image.

    :type image: ImageTyping

    :param model_name: The model name. Default is 'mobilenetv3_v0_dist'.

    :type model_name: str

    :return: The processed image data.

    :rtype: np.ndarray

    """

    image = load_image(image, force_background='white', mode='RGB')

    input_ = _img_encode(image)[None, ...]

    output, = _open_anime_portrait_model(model_name).run(['output'], {'input': input_})

    return output

def anime_portrait_score(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> Dict[str, float]:

    """

    Get the scores for different types in an anime portrait.

    :param image: The input image.

    :type image: ImageTyping

    :param model_name: The model name. Default is 'mobilenetv3_v0_dist'.

    :type model_name: str

    :return: A dictionary with type scores.

    :rtype: Dict[str, float]

    Examples::

        >>> from imgutils.validate import anime_portrait_score

        >>>

        >>> anime_portrait_score('person/1.jpg')

        {'person': 0.999900221824646, 'halfbody': 8.645313209854066e-05, 'head': 1.3387104445428122e-05}

        >>> anime_portrait_score('person/2.jpg')

        {'person': 0.9999704360961914, 'halfbody': 2.4465465685352683e-05, 'head': 5.071506166132167e-06}

        >>> anime_portrait_score('person/3.jpg')

        {'person': 0.9999785423278809, 'halfbody': 1.512719154561637e-05, 'head': 6.292278612818336e-06}

        >>> anime_portrait_score('halfbody/4.jpg')

        {'person': 4.919455750496127e-05, 'halfbody': 0.9999444484710693, 'head': 6.3647335082350764e-06}

        >>> anime_portrait_score('halfbody/5.jpg')

        {'person': 1.0555699191172607e-05, 'halfbody': 0.9999880790710449, 'head': 1.3210242286731955e-06}

        >>> anime_portrait_score('halfbody/6.jpg')

        {'person': 1.7451418898417614e-05, 'halfbody': 0.9999822378158569, 'head': 3.2084267331811134e-07}

        >>> anime_portrait_score('head/7.jpg')

        {'person': 2.7460413321023225e-07, 'halfbody': 1.1532473820352607e-07, 'head': 0.9999996423721313}

        >>> anime_portrait_score('head/8.jpg')

        {'person': 1.0316136922483565e-07, 'halfbody': 5.840229633236049e-08, 'head': 0.9999998807907104}

        >>> anime_portrait_score('head/9.jpg')

        {'person': 5.736660568800289e-07, 'halfbody': 7.199210472208506e-08, 'head': 0.9999992847442627}

    """

    output = _raw_anime_portrait(image, model_name)

    values = dict(zip(_get_anime_portrait_labels(model_name), map(lambda x: x.item(), output[0])))

    return values

def anime_portrait(image: ImageTyping, model_name: str = _DEFAULT_MODEL_NAME) -> Tuple[str, float]:

    """

    Get the primary anime portrait type and its score.

    :param image: The input image.

    :type image: ImageTyping

    :param model_name: The model name. Default is 'mobilenetv3_v0_dist'.

    :type model_name: str

    :return: A tuple with the primary type and its score.

    :rtype: Tuple[str, float]

    Examples::

        >>> from imgutils.validate import anime_portrait

        >>>

        >>> anime_portrait('person/1.jpg')

        ('person', 0.999900221824646)

        >>> anime_portrait('person/2.jpg')

        ('person', 0.9999704360961914)

        >>> anime_portrait('person/3.jpg')

        ('person', 0.9999785423278809)

        >>> anime_portrait('halfbody/4.jpg')

        ('halfbody', 0.9999444484710693)

        >>> anime_portrait('halfbody/5.jpg')

        ('halfbody', 0.9999880790710449)

        >>> anime_portrait('halfbody/6.jpg')

        ('halfbody', 0.9999822378158569)

        >>> anime_portrait('head/7.jpg')

        ('head', 0.9999996423721313)

        >>> anime_portrait('head/8.jpg')

        ('head', 0.9999998807907104)

        >>> anime_portrait('head/9.jpg')

        ('head', 0.9999992847442627)

    """

    output = _raw_anime_portrait(image, model_name)[0]

    max_id = np.argmax(output)

    return _get_anime_portrait_labels(model_name)[max_id], output[max_id].item()