from utils.argutils import print_args
from vocoder.wavernn.train import train
from vocoder.hifigan.train import train as train_hifigan
from vocoder.fregan.train import train as train_fregan
from utils.util import AttrDict
from pathlib import Path
import argparse
import json
import torch
import torch.multiprocessing as mp

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Trains the vocoder from the synthesizer audios and the GTA synthesized mels, "
                    "or ground truth mels.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    
    parser.add_argument("run_id", type=str, help= \
        "Name for this model instance. If a model state from the same run ID was previously "
        "saved, the training will restart from there. Pass -f to overwrite saved states and "
        "restart from scratch.")
    parser.add_argument("datasets_root", type=str, help= \
        "Path to the directory containing your SV2TTS directory. Specifying --syn_dir or --voc_dir "
        "will take priority over this argument.")
    parser.add_argument("vocoder_type", type=str, default="wavernn", help= \
        "Choose the vocoder type for train. Defaults to wavernn"
        "Now, Support <hifigan> and <wavernn> for choose")
    parser.add_argument("--syn_dir", type=str, default=argparse.SUPPRESS, help= \
        "Path to the synthesizer directory that contains the ground truth mel spectrograms, "
        "the wavs and the embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/.")
    parser.add_argument("--voc_dir", type=str, default=argparse.SUPPRESS, help= \
        "Path to the vocoder directory that contains the GTA synthesized mel spectrograms. "
        "Defaults to <datasets_root>/SV2TTS/vocoder/. Unused if --ground_truth is passed.")
    parser.add_argument("-m", "--models_dir", type=str, default="vocoder/saved_models/", help=\
        "Path to the directory that will contain the saved model weights, as well as backups "
        "of those weights and wavs generated during training.")
    parser.add_argument("-g", "--ground_truth", action="store_true", help= \
        "Train on ground truth spectrograms (<datasets_root>/SV2TTS/synthesizer/mels).")
    parser.add_argument("-s", "--save_every", type=int, default=1000, help= \
        "Number of steps between updates of the model on the disk. Set to 0 to never save the "
        "model.")
    parser.add_argument("-b", "--backup_every", type=int, default=25000, help= \
        "Number of steps between backups of the model. Set to 0 to never make backups of the "
        "model.")
    parser.add_argument("-f", "--force_restart", action="store_true", help= \
        "Do not load any saved model and restart from scratch.")
    parser.add_argument("--config", type=str, default="vocoder/hifigan/config_16k_.json")
    args = parser.parse_args()

    if not hasattr(args, "syn_dir"):
        args.syn_dir = Path(args.datasets_root, "SV2TTS", "synthesizer")
    args.syn_dir = Path(args.syn_dir)
    if not hasattr(args, "voc_dir"):
        args.voc_dir = Path(args.datasets_root, "SV2TTS", "vocoder")
    args.voc_dir = Path(args.voc_dir)
    del args.datasets_root
    args.models_dir = Path(args.models_dir)
    args.models_dir.mkdir(exist_ok=True)

    print_args(args, parser)

    # Process the arguments
    if args.vocoder_type == "wavernn":
        # Run the training wavernn
        delattr(args, 'vocoder_type')
        delattr(args, 'config')
        train(**vars(args))
    elif args.vocoder_type == "hifigan":
        with open(args.config) as f:
            json_config = json.load(f)
        h = AttrDict(json_config)
        if h.num_gpus > 1:
            h.num_gpus = torch.cuda.device_count()
            h.batch_size = int(h.batch_size / h.num_gpus)
            print('Batch size per GPU :', h.batch_size)
            mp.spawn(train_hifigan, nprocs=h.num_gpus, args=(args, h,))
        else:
            train_hifigan(0, args, h)
    elif args.vocoder_type == "fregan":
        with open('vocoder/fregan/config.json') as f:
            json_config = json.load(f)
        h = AttrDict(json_config)
        if h.num_gpus > 1:
            h.num_gpus = torch.cuda.device_count()
            h.batch_size = int(h.batch_size / h.num_gpus)
            print('Batch size per GPU :', h.batch_size)
            mp.spawn(train_fregan, nprocs=h.num_gpus, args=(args, h,))
        else:
            train_fregan(0, args, h)