import matplotlib
matplotlib.use('Agg')

import argparse
import tensorflow as tf 
from tacotron.train import tacotron_train
from wavenet_vocoder.train import wavenet_train
from tacotron.synthesize import tacotron_synthesize
from infolog import log
from hparams import hparams
import os
import infolog
from time import sleep

log = infolog.log


def save_seq(file, sequence, input_path):
	'''Save Tacotron-2 training state to disk. (To skip for future runs)
	'''
	sequence = [str(int(s)) for s in sequence] + [input_path]
	with open(file, 'w') as f:
		f.write('|'.join(sequence))

def read_seq(file):
	'''Load Tacotron-2 training state from disk. (To skip if not first run)
	'''
	if os.path.isfile(file):
		with open(file, 'r') as f:
			sequence = f.read().split('|')

		return [bool(int(s)) for s in sequence[:-1]], sequence[-1]
	else:
		return [0, 0, 0], ''

def prepare_run(args):
	modified_hp = hparams.parse(args.hparams)
	os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
	run_name = args.name or args.model
	log_dir = os.path.join(args.base_dir, 'logs-{}'.format(run_name))
	os.makedirs(log_dir, exist_ok=True)
	infolog.init(os.path.join(log_dir, 'Terminal_train_log'), run_name)
	return log_dir, modified_hp

def train(args, log_dir, hparams):
	state_file = os.path.join(log_dir, 'state_log')
	#Get training states
	(taco_state, GTA_state, wave_state), input_path = read_seq(state_file)

	if not taco_state:
		log('\n#############################################################\n')
		log('Tacotron Train\n')
		log('###########################################################\n')
		checkpoint = tacotron_train(args, log_dir, hparams)
		tf.reset_default_graph()
		#Sleep 1 second to let previous graph close and avoid error messages while synthesis
		sleep(1)
		if checkpoint is None:
			raise('Error occured while training Tacotron, Exiting!')
		taco_state = 1
		save_seq(state_file, [taco_state, GTA_state, wave_state], input_path)

	if not GTA_state:
		log('\n#############################################################\n')
		log('Tacotron GTA Synthesis\n')
		log('###########################################################\n')
		input_path = tacotron_synthesize(args, hparams, checkpoint)
		GTA_state = 1
		save_seq(state_file, [taco_state, GTA_state, wave_state], input_path)

	if input_path == '' or input_path is None:
		raise RuntimeError('input_path has an unpleasant value -> {}'.format(input_path))

	if not wave_state:
		log('\n#############################################################\n')
		log('Wavenet Train\n')
		log('###########################################################\n')
		checkpoint = wavenet_train(args, log_dir, hparams, input_path)
		if checkpoint is None:
			raise ('Error occured while training Wavenet, Exiting!')
		wave_state = 1
		save_seq(state_file, [taco_state, GTA_state, wave_state], input_path)

	if wave_state and GTA_state and taco_state:
		log('TRAINING IS ALREADY COMPLETE!!')

def main():
	import os
	os.environ["CUDA_VISIBLE_DEVICES"] = "0"
	parser = argparse.ArgumentParser()
	parser.add_argument('--base_dir', default='')
	parser.add_argument('--hparams', default='',
		help='Hyperparameter overrides as a comma-separated list of name=value pairs')
	parser.add_argument('--tacotron_input', default='training_data/train.txt')
	parser.add_argument('--wavenet_input', default='tacotron_output/gta/map.txt')
	parser.add_argument('--name', help='Name of logging directory.')
	parser.add_argument('--model', default='Tacotron-2')
	parser.add_argument('--input_dir', default='training_data/', help='folder to contain inputs sentences/targets')
	parser.add_argument('--output_dir', default='output/', help='folder to contain synthesized mel spectrograms')
	parser.add_argument('--mode', default='synthesis', help='mode for synthesis of tacotron after training')
	parser.add_argument('--GTA', default='True', help='Ground truth aligned synthesis, defaults to True, only considered in Tacotron synthesis mode')
	parser.add_argument('--restore', type=bool, default=True, help='Set this to False to do a fresh training')
	parser.add_argument('--summary_interval', type=int, default=250,
		help='Steps between running summary ops')
	parser.add_argument('--checkpoint_interval', type=int, default=5000,
		help='Steps between writing checkpoints')
	parser.add_argument('--eval_interval', type=int, default=2000,
		help='Steps between eval on test data')
	parser.add_argument('--tacotron_train_steps', type=int, default=200000, help='total number of tacotron training steps')
	parser.add_argument('--wavenet_train_steps', type=int, default=3600000, help='total number of wavenet training steps')
	parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
	args = parser.parse_args()

	accepted_models = ['Tacotron', 'WaveNet', 'Both', 'Tacotron-2']

	if args.model not in accepted_models:
		raise ValueError('please enter a valid model to train: {}'.format(accepted_models))

	log_dir, hparams = prepare_run(args)

	if args.model == 'Tacotron':
		tacotron_train(args, log_dir, hparams)
	elif args.model == 'WaveNet':
		wavenet_train(args, log_dir, hparams, args.wavenet_input)
	elif args.model in ('Both', 'Tacotron-2'):
		train(args, log_dir, hparams)
	else:
		raise ValueError('Model provided {} unknown! {}'.format(args.model, accepted_models))


if __name__ == '__main__':
	main()
matplotlib.use