import numpy as np
import torch
import torch.nn.functional as F
from torchaudio.transforms import Resample
from .constants import *
from .model import E2E0, E2E
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 = {}
        model = E2E0(4, 1, (2, 2))
        ckpt = torch.load(model_path)
        model.load_state_dict(ckpt['model'], strict=False)
        model.eval()
        self.model = model
        self.mel_extractor = MelSpectrogram(N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX)
        self.resample_kernel = {}

    def mel2hidden(self, mel):
        with torch.no_grad():
            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, device=None, thred=0.03, use_viterbi=False):
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        try:
            audio = audio.float().unsqueeze(0).to(device) # for torch.tensor [T]
        except:
            audio = torch.from_numpy(audio).float().unsqueeze(0).to(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(device)
            audio_res = self.resample_kernel[key_str](audio)
        mel_extractor = self.mel_extractor.to(device)
        self.model = self.model.to(device)
        mel = mel_extractor(audio_res, center=True)
        hidden = self.mel2hidden(mel)
        f0 = self.decode(hidden, thred=thred, use_viterbi=use_viterbi)
        return f0