import torch

from vencoder.encoder import SpeechEncoder
from vencoder.wavlm.WavLM import WavLM, WavLMConfig


class WavLMBasePlus(SpeechEncoder):
    def __init__(self, vec_path="pretrain/WavLM-Base+.pt", device=None):
        super().__init__()
        print("load model(s) from {}".format(vec_path))
        checkpoint = torch.load(vec_path)
        self.cfg = WavLMConfig(checkpoint['cfg'])
        if device is None:
            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        else:
            self.dev = torch.device(device)
        self.hidden_dim = self.cfg.encoder_embed_dim
        self.model = WavLM(self.cfg)
        self.model.load_state_dict(checkpoint['model'])
        self.model.to(self.dev).eval()

    def encoder(self, wav):
        feats = wav
        if feats.dim() == 2:  # double channels
            feats = feats.mean(-1)
        assert feats.dim() == 1, feats.dim()
        if self.cfg.normalize:
            feats = torch.nn.functional.layer_norm(feats, feats.shape)
        with torch.no_grad():
            with torch.inference_mode():
                units = self.model.extract_features(feats[None, :])[0]
                return units.transpose(1, 2)