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

from basics.base_pe import BasePE
from utils.infer_utils import resample_align_curve
from utils.pitch_utils import interp_f0
from .constants import *
from .model import E2E0
from .spec import MelSpectrogram
from .utils import to_local_average_f0, to_viterbi_f0


class RMVPE(BasePE):
    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.hop_length = hop_length
        self.seg_length = 32 * hop_length
        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='reflect')
        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)
        B, T = audio_res.shape
        n_frames = T // self.hop_length + 1 
        T1 = T + self.hop_length
        T_pad = self.seg_length * ((T1 - 1) // self.seg_length + 1) - T1
        audio_res = F.pad(audio_res, (0, T_pad))
        mel = self.mel_extractor(audio_res, center=True)
        with torch.no_grad():
            hidden = self.model(mel)
        f0 = self.decode(hidden[:, :n_frames], thred=thred, use_viterbi=use_viterbi)
        return f0

    def get_pitch(
            self, waveform, samplerate, length,
            *, hop_size, f0_min=65, f0_max=1100,
            speed=1, interp_uv=False
    ):
        f0 = self.infer_from_audio(waveform, sample_rate=samplerate)
        uv = f0 == 0
        f0, uv = interp_f0(f0, uv)

        hop_size = int(np.round(hop_size * speed))
        time_step = hop_size / samplerate
        f0_res = resample_align_curve(f0, 0.01, time_step, length)
        uv_res = resample_align_curve(uv.astype(np.float32), 0.01, time_step, length) > 0.5
        if not interp_uv:
            f0_res[uv_res] = 0
        return f0_res, uv_res