dev(narugo): embedding inverse

    `SmilingWolf/wd-v1-4-tags <https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags>`_ .

"""

from functools import lru_cache

from typing import List, Tuple

from typing import List, Tuple, Optional

import numpy as np

import onnxruntime

            'prediction': preds[0].astype(np.float32),

        }

    )

@lru_cache()

def _inv_data(model_name: str = _DEFAULT_MODEL_NAME):

    data = np.load(hf_hub_download(

        repo_id='deepghs/wd14_tagger_with_embeddings',

        repo_type='model',

        filename=f'{MODEL_NAMES[model_name]}/inv.npz',

    ))

    return data['best_epi'], data['inv_weights'], data['bias']

def _inv_sigmoid(x):

    return np.log(x) - np.log(1 - x)

def inv_wd14_by_predictions(predictions: np.ndarray, model_name: str = _DEFAULT_MODEL_NAME,

                            epi: Optional[float] = None, norm: bool = False) -> np.ndarray:

    best_epi, inv_weights, bias = _inv_data(model_name)

    eps = 10 ** -(epi if epi is not None else best_epi)

    pred_input = np.clip(predictions, a_min=eps, a_max=1.0 - eps)

    inv_emb_output = (_inv_sigmoid(pred_input) - bias) @ inv_weights

    if norm:

        inv_emb_output = inv_emb_output / np.linalg.norm(inv_emb_output, axis=-1)[..., None]

    return inv_emb_output