import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
import lws

class HParams:
    def __init__(self, **kwargs):
        self.data = {}
        for key, value in kwargs.items():
            self.data[key] = value

    def __getattr__(self, key):
        if key not in self.data:
            raise AttributeError(f"'HParams' object has no attribute {key}")
        return self.data[key]

    def set_hparam(self, key, value):
        self.data[key] = value

# Default hyperparameters
hparams = HParams(
    num_mels=80,
    rescale=True,
    rescaling_max=0.9,
    use_lws=False,
    n_fft=800,
    hop_size=200,
    win_size=800,
    sample_rate=16000,
    frame_shift_ms=None,
    signal_normalization=True,
    allow_clipping_in_normalization=True,
    symmetric_mels=True,
    max_abs_value=4.0,
    preemphasize=True,
    preemphasis=0.97,
    min_level_db=-100,
    ref_level_db=20,
    fmin=55,
    fmax=7600,
    img_size=96,
    fps=25,
    batch_size=16,
    initial_learning_rate=1e-4,
    nepochs=200000000000000000,
    num_workers=16,
    checkpoint_interval=3000,
    eval_interval=3000,
    save_optimizer_state=True,
    syncnet_wt=0.0,
    syncnet_batch_size=64,
    syncnet_lr=1e-4,
    syncnet_eval_interval=10000,
    syncnet_checkpoint_interval=10000,
    disc_wt=0.07,
    disc_initial_learning_rate=1e-4,
)

def hparams_debug_string():
    values = hparams.data
    hp = [f"  {name}: {values[name]}" for name in sorted(values) if name != "sentences"]
    return "Hyperparameters:\n" + "\n".join(hp)

# Audio processing functions
def load_wav(path, sr):
    return librosa.core.load(path, sr=sr)[0]

def save_wav(wav, path, sr):
    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
    wavfile.write(path, sr, wav.astype(np.int16))

def save_wavenet_wav(wav, path, sr):
    librosa.output.write_wav(path, wav, sr=sr)

def preemphasis(wav, k=0.97, preemphasize=True):
    if preemphasize:
        return signal.lfilter([1, -k], [1], wav)
    return wav

def inv_preemphasis(wav, k=0.97, inv_preemphasize=True):
    if inv_preemphasize:
        return signal.lfilter([1], [1, -k], wav)
    return wav

def get_hop_size():
    hop_size = hparams.hop_size
    if hop_size is None:
        assert hparams.frame_shift_ms is not None
        hop_size = int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)
    return hop_size

def linearspectrogram(wav):
    D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize))
    S = _amp_to_db(np.abs(D)) - hparams.ref_level_db
    
    if hparams.signal_normalization:
        return _normalize(S)
    return S

def melspectrogram(wav):
    D = _stft(preemphasis(wav, hparams.preemphasis, hparams.preemphasize))
    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hparams.ref_level_db
    
    if hparams.signal_normalization:
        return _normalize(S)
    return S

def _lws_processor():
    return lws.lws(hparams.n_fft, get_hop_size(), fftsize=hparams.win_size, mode="speech")

def _stft(y):
    if hparams.use_lws:
        return _lws_processor().stft(y).T
    else:
        return librosa.stft(y=y, n_fft=hparams.n_fft, hop_length=get_hop_size(), win_length=hparams.win_size)

def num_frames(length, fsize, fshift):
    pad = (fsize - fshift)
    if length % fshift == 0:
        M = (length + pad * 2 - fsize) // fshift + 1
    else:
        M = (length + pad * 2 - fsize) // fshift + 2
    return M

def pad_lr(x, fsize, fshift):
    M = num_frames(len(x), fsize, fshift)
    pad = (fsize - fshift)
    T = len(x) + 2 * pad
    r = (M - 1) * fshift + fsize - T
    return pad, pad + r

def librosa_pad_lr(x, fsize, fshift):
    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]

_mel_basis = None

def _linear_to_mel(spectogram):
    global _mel_basis
    if _mel_basis is None:
        _mel_basis = _build_mel_basis()
    return np.dot(_mel_basis, spectogram)

def _build_mel_basis():
    assert hparams.fmax <= hparams.sample_rate // 2
    return librosa.filters.mel(hparams.sample_rate, hparams.n_fft, n_mels=hparams.num_mels,
                               fmin=hparams.fmin, fmax=hparams.fmax)

def _amp_to_db(x):
    min_level = np.exp(hparams.min_level_db / 20 * np.log(10))
    return 20 * np.log10(np.maximum(min_level, x))

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

def _normalize(S):
    if hparams.allow_clipping_in_normalization:
        if hparams.symmetric_mels:
            return np.clip((2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value,
                           -hparams.max_abs_value, hparams.max_abs_value)
        else:
            return np.clip(hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db)), 0, hparams.max_abs_value)
    
    assert S.max() <= 0 and S.min() - hparams.min_level_db >= 0
    if hparams.symmetric_mels:
        return (2 * hparams.max_abs_value) * ((S - hparams.min_level_db) / (-hparams.min_level_db)) - hparams.max_abs_value
    else:
        return hparams.max_abs_value * ((S - hparams.min_level_db) / (-hparams.min_level_db))

def _denormalize(D):
    if hparams.allow_clipping_in_normalization:
        if hparams.symmetric_mels:
            return (((np.clip(D, -hparams.max_abs_value,
                              hparams.max_abs_value) + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value))
                    + hparams.min_level_db)
        else:
            return ((np.clip(D, 0, hparams.max_abs_value) * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)
    
    if hparams.symmetric_mels:
        return (((D + hparams.max_abs_value) * -hparams.min_level_db / (2 * hparams.max_abs_value)) + hparams.min_level_db)
    else:
        return ((D * -hparams.min_level_db / hparams.max_abs_value) + hparams.min_level_db)