import librosa
import librosa.filters
import numpy as np
# import tensorflow as tf
from scipy import signal
from scipy.io import wavfile
# from hparams import hparams as hp

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("'HParams' object has no attribute %s" % key)
		return self.data[key]

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


# Default hyperparameters
hp = HParams(
	num_mels=80,  # Number of mel-spectrogram channels and local conditioning dimensionality
	#  network
	rescale=True,  # Whether to rescale audio prior to preprocessing
	rescaling_max=0.9,  # Rescaling value
	
	# Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction
	# It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder
	# Does not work if n_ffit is not multiple of hop_size!!
	use_lws=False,
	
	n_fft=800,  # Extra window size is filled with 0 paddings to match this parameter
	hop_size=200,  # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate)
	win_size=800,  # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate)
	sample_rate=16000,  # 16000Hz (corresponding to librispeech) (sox --i <filename>)
	
	frame_shift_ms=None,  # Can replace hop_size parameter. (Recommended: 12.5)
	
	# Mel and Linear spectrograms normalization/scaling and clipping
	signal_normalization=True,
	# Whether to normalize mel spectrograms to some predefined range (following below parameters)
	allow_clipping_in_normalization=True,  # Only relevant if mel_normalization = True
	symmetric_mels=True,
	# Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, 
	# faster and cleaner convergence)
	max_abs_value=4.,
	# max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not 
	# be too big to avoid gradient explosion, 
	# not too small for fast convergence)
	# Contribution by @begeekmyfriend
	# Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude 
	# levels. Also allows for better G&L phase reconstruction)
	preemphasize=True,  # whether to apply filter
	preemphasis=0.97,  # filter coefficient.
	
	# Limits
	min_level_db=-100,
	ref_level_db=20,
	fmin=55,
	# Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To 
	# test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
	fmax=7600,  # To be increased/reduced depending on data.

	###################### Our training parameters #################################
	img_size=96,
	fps=25,
	
	batch_size=16,
	initial_learning_rate=1e-4,
	nepochs=200000000000000000,  ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs
	num_workers=16,
	checkpoint_interval=3000,
	eval_interval=3000,
    save_optimizer_state=True,

    syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. 
	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 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)))
    #proposed by @dsmiller
    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, preemphasize=True):
    if preemphasize:
        return signal.lfilter([1, -k], [1], wav)
    return wav

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

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

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

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

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

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

##########################################################
#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
def num_frames(length, fsize, fshift):
    """Compute number of time frames of spectrogram
    """
    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):
    """Compute left and right padding
    """
    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
##########################################################
#Librosa correct padding
def librosa_pad_lr(x, fsize, fshift):
    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]

# Conversions
_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 hp.fmax <= hp.sample_rate // 2
    # return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels,
    #                            fmin=hp.fmin, fmax=hp.fmax)
    return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft)

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

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