from functools import lru_cache

import numpy as np
import torch
from sentence_transformers import SentenceTransformer

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

list_models = [
    'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2',
    'sentence-transformers/paraphrase-multilingual-mpnet-base-v2',
    'cyclone/simcse-chinese-roberta-wwm-ext'
]


class SBert:
    def __init__(self, path):
        print(f'Loading model from {path} ...')
        self.model = SentenceTransformer(path, device=DEVICE)

    @lru_cache(maxsize=10000)
    def __call__(self, x) -> np.ndarray:
        y = self.model.encode(x)
        return y