import torch
import torch.nn.functional as F
from torchaudio.transforms import Resample

from .constants import *
from .model import E2E0
from .spec import MelSpectrogram
from .utils import to_local_average_f0, to_viterbi_f0


class RMVPE:
    def __init__(self, model_path, hop_length=160):
        self.resample_kernel = {}
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.model = E2E0(4, 1, (2, 2)).eval().to(self.device)
        ckpt = torch.load(model_path, map_location=self.device)
        self.model.load_state_dict(ckpt['model'], strict=False)
        self.mel_extractor = MelSpectrogram(
            N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX
        ).to(self.device)

    @torch.no_grad()
    def mel2hidden(self, mel):
        n_frames = mel.shape[-1]
        mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='constant')
        hidden = self.model(mel)
        return hidden[:, :n_frames]

    def decode(self, hidden, thred=0.03, use_viterbi=False):
        if use_viterbi:
            f0 = to_viterbi_f0(hidden, thred=thred)
        else:
            f0 = to_local_average_f0(hidden, thred=thred)
        return f0

    def infer_from_audio(self, audio, sample_rate=16000, thred=0.03, use_viterbi=False):
        audio = torch.from_numpy(audio).float().unsqueeze(0).to(self.device)
        if sample_rate == 16000:
            audio_res = audio
        else:
            key_str = str(sample_rate)
            if key_str not in self.resample_kernel:
                self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128)
            self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.device)
            audio_res = self.resample_kernel[key_str](audio)
        mel = self.mel_extractor(audio_res, center=True)
        hidden = self.mel2hidden(mel)
        f0 = self.decode(hidden, thred=thred, use_viterbi=use_viterbi)
        return f0