import math
import numpy as np
import librosa
import models.vocoder.wavernn.hparams as hp
from scipy.signal import lfilter
import soundfile as sf


def label_2_float(x, bits) :
    return 2 * x / (2**bits - 1.) - 1.


def float_2_label(x, bits) :
    assert abs(x).max() <= 1.0
    x = (x + 1.) * (2**bits - 1) / 2
    return x.clip(0, 2**bits - 1)


def load_wav(path) :
    return librosa.load(str(path), sr=hp.sample_rate)[0]


def save_wav(x, path) :
    sf.write(path, x.astype(np.float32), hp.sample_rate)


def split_signal(x) :
    unsigned = x + 2**15
    coarse = unsigned // 256
    fine = unsigned % 256
    return coarse, fine


def combine_signal(coarse, fine) :
    return coarse * 256 + fine - 2**15


def encode_16bits(x) :
    return np.clip(x * 2**15, -2**15, 2**15 - 1).astype(np.int16)


mel_basis = None


def linear_to_mel(spectrogram):
    global mel_basis
    if mel_basis is None:
        mel_basis = build_mel_basis()
    return np.dot(mel_basis, spectrogram)


def build_mel_basis():
    return librosa.filters.mel(sr = hp.sample_rate, n_fft = hp.n_fft, n_mels=hp.num_mels, fmin=hp.fmin)


def normalize(S):
    return np.clip((S - hp.min_level_db) / -hp.min_level_db, 0, 1)


def denormalize(S):
    return (np.clip(S, 0, 1) * -hp.min_level_db) + hp.min_level_db


def amp_to_db(x):
    return 20 * np.log10(np.maximum(1e-5, x))


def db_to_amp(x):
    return np.power(10.0, x * 0.05)


def spectrogram(y):
    D = stft(y)
    S = amp_to_db(np.abs(D)) - hp.ref_level_db
    return normalize(S)


def melspectrogram(y):
    D = stft(y)
    S = amp_to_db(linear_to_mel(np.abs(D)))
    return normalize(S)


def stft(y):
    return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=hp.hop_length, win_length=hp.win_length)


def pre_emphasis(x):
    return lfilter([1, -hp.preemphasis], [1], x)


def de_emphasis(x):
    return lfilter([1], [1, -hp.preemphasis], x)


def encode_mu_law(x, mu) :
    mu = mu - 1
    fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
    return np.floor((fx + 1) / 2 * mu + 0.5)


def decode_mu_law(y, mu, from_labels=True) :
    if from_labels: 
        y = label_2_float(y, math.log2(mu))
    mu = mu - 1
    x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
    return x