import os
import numpy
import librosa
import torch
import torch.nn.functional as F
from ssl_ecapa_model import SSL_ECAPA_TDNN
from huggingface_hub import hf_hub_download


def loadWav(filename, max_frames: int = 400, num_eval: int = 10):

    # Maximum audio length
    max_audio = max_frames * 160 + 240

    # Read wav file and convert to torch tensor
    audio, sr = librosa.load(filename, sr=16000)
    audio_org = audio.copy()
    
    audiosize = audio.shape[0]

    if audiosize <= max_audio:
        shortage = max_audio - audiosize + 1
        audio       = numpy.pad(audio, (0, shortage), 'wrap')
        audiosize   = audio.shape[0]

    startframe = numpy.linspace(0,audiosize-max_audio, num=num_eval)
    
    feats = []
    if max_frames == 0:
        feats.append(audio)
        feat = numpy.stack(feats,axis=0).astype(numpy.float32)
        return torch.FloatTensor(feat)
    else:
        for asf in startframe:
            feats.append(audio[int(asf):int(asf)+max_audio])
        feat = numpy.stack(feats,axis=0).astype(numpy.float32)
        return torch.FloatTensor(feat), torch.FloatTensor(numpy.stack([audio_org],axis=0).astype(numpy.float32))


def loadModel(ckpt_path):
    model = SSL_ECAPA_TDNN(feat_dim=1024, emb_dim=256, feat_type='wavlm_large')
    if not os.path.isfile(ckpt_path):
        print("Downloading model from Hugging Face Hub...")
        ckpt_path = hf_hub_download(repo_id="junseok520/voxsim-models", filename=ckpt_path, local_dir="./")
    model.load_state_dict(torch.load(ckpt_path, map_location='cpu', weights_only=True))
    return model


class Score:
    """Predicting score for each audio clip."""

    def __init__(
        self,
        ckpt_path: str = "voxsim_wavlm_ecapa.model",
        device: str = "gpu"):
        """
        Args:
            ckpt_path: path to pretrained checkpoint of voxsim evaluator.
            input_sample_rate: sampling rate of input audio tensor. The input audio tensor
                is automatically downsampled to 16kHz.
        """
        print(f"Using device: {device}")
        self.device = device
        self.model = loadModel(ckpt_path).to(self.device)
        self.model.eval()
    
    def score(self, inp_wavs: torch.tensor, inp_wav: torch.tensor, ref_wavs: torch.tensor, ref_wav: torch.tensor) -> torch.tensor:

        inp_wavs = inp_wavs.reshape(-1, inp_wavs.shape[-1]).to(self.device)
        inp_wav = inp_wav.reshape(-1, inp_wav.shape[-1]).to(self.device)
        ref_wavs = ref_wavs.reshape(-1, ref_wavs.shape[-1]).to(self.device)
        ref_wav = ref_wav.reshape(-1, ref_wav.shape[-1]).to(self.device)

        with torch.no_grad():
            input_emb_1 = F.normalize(self.model.forward(inp_wavs), p=2, dim=1).detach()
            input_emb_2 = F.normalize(self.model.forward(inp_wav), p=2, dim=1).detach()
            ref_emb_1 = F.normalize(self.model.forward(ref_wavs), p=2, dim=1).detach()
            ref_emb_2 = F.normalize(self.model.forward(ref_wav), p=2, dim=1).detach()

            emb_size = input_emb_1.shape[-1]
            input_emb_1 = input_emb_1.reshape(-1, emb_size)
            input_emb_2 = input_emb_2.reshape(-1, emb_size)
            ref_emb_1 = ref_emb_1.reshape(-1, emb_size)
            ref_emb_2 = ref_emb_2.reshape(-1, emb_size)

            score_1 = torch.mean(torch.matmul(input_emb_1, ref_emb_1.T))
            score_2 = torch.mean(torch.matmul(input_emb_2, ref_emb_2.T))
            score = (score_1 + score_2) / 2
            score = score.detach().cpu().item()

            return score