"""

This code is orginally from ***Wav2Lip*** repository (moslty copy&paste from theirs repo with few modification) 

Link : https://github.com/Rudrabha/Wav2Lip

"""



from glob import glob
import os



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
hparams = HParams(

	###### Audio preprocessing  #############
	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,  # Extr 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 #################################
	save_optimizer_state=True,
	########### Dataset  & Dataloder  #########
	fps = 25, 
	num_workers =8,
	# window_size = 18,
	# window_step = 5,
	########## SyncNet hyperparameters ###############
	syncnet_batch_size = 128,
	syncnet_lr = 1e-4,
	syncnet_nepochs = 500,
	######### Generator hyperparameters ################
	gen_batch_size = 32,
	gen_gen_lr = 1e-4,
	gen_disc_lr = 1e-5, # refer to syncnet_lr for training discrimiantor together with generator ** only for adversarial training**
	gen_nepochs = 200,
	gen_recon_coeff = 0.8, # only use when trainig with pretrain or end2end
	gen_sync_coeff = 0.2 # only use when trainig with pretrain or end2end

)


def hparams_debug_string():
	values = hparams.values()
	hp = ["  %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"]
	return "Hyperparameters:\n" + "\n".join(hp)