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"""
Modified from
https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/train.py
"""

import argparse
from datetime import datetime
from functools import partial
import logging
import random
import os
from time import time

import torch
import torchaudio
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from torch.optim import Adam
from torchaudio.prototype.tacotron2 import Tacotron2
from tqdm import tqdm
import matplotlib.pyplot as plt
plt.switch_backend('agg')

from datasets import text_mel_collate_fn, split_process_dataset, SpectralNormalization
from utils import save_checkpoint, get_text_preprocessor
from loss import Tacotron2Loss


logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s',
                    level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')
logger = logging.getLogger(os.path.basename(__file__))


def parse_args(parser):
    """Parse commandline arguments."""

    parser.add_argument("--dataset", default="ljspeech", choices=["ljspeech"], type=str,
                        help="select dataset to train with")
    parser.add_argument('--logging-dir', type=str, default=None,
                        help='directory to save the log files')
    parser.add_argument('--dataset-path', type=str, default='./',
                        help='path to dataset')
    parser.add_argument("--val-ratio", default=0.1, type=float,
                        help="the ratio of waveforms for validation")

    parser.add_argument('--anneal-steps', nargs='*',
                        help='epochs after which decrease learning rate')
    parser.add_argument('--anneal-factor', type=float, choices=[0.1, 0.3], default=0.1,
                        help='factor for annealing learning rate')

    parser.add_argument('--text-preprocessor', default='character', type=str,
                        choices=['character'], help='[string] Select text preprocessor to use.')

    parser.add_argument('--master-addr', default=None, type=str,
                        help='The address to use for distributed training.')
    parser.add_argument('--master-port', default=None, type=str,
                        help='The port to use for distributed training.')

    # training
    training = parser.add_argument_group('training setup')
    training.add_argument('--epochs', type=int, required=True,
                          help='number of total epochs to run')
    training.add_argument('--checkpoint-path', type=str, default='',
                          help='checkpoint path. If a file exists, '
                               'the program will load it and resume training.')
    training.add_argument('--workers', default=8, type=int,
                          help="number of data loading workers")
    training.add_argument("--validate-and-checkpoint-freq", default=10, type=int, metavar="N",
                          help="validation and saving checkpoint frequency in epochs",)
    training.add_argument("--logging-freq", default=10, type=int, metavar="N",
                          help="logging frequency in epochs")

    optimization = parser.add_argument_group('optimization setup')
    optimization.add_argument('--learning-rate', default=1e-3, type=float,
                              help='initial learing rate')
    optimization.add_argument('--weight-decay', default=1e-6, type=float,
                              help='weight decay')
    optimization.add_argument('--batch-size', default=32, type=int,
                              help='batch size per GPU')
    optimization.add_argument('--grad-clip', default=5.0, type=float,
                              help='clipping gradient with maximum gradient norm value')

    # model parameters
    model = parser.add_argument_group('model parameters')
    model.add_argument('--mask-padding', action='store_true', default=False,
                       help='use mask padding')
    model.add_argument('--symbols-embedding-dim', default=512, type=int,
                       help='input embedding dimension')

    # encoder
    model.add_argument('--encoder-embedding-dim', default=512, type=int,
                       help='encoder embedding dimension')
    model.add_argument('--encoder-n-convolution', default=3, type=int,
                       help='number of encoder convolutions')
    model.add_argument('--encoder-kernel-size', default=5, type=int,
                       help='encoder kernel size')
    # decoder
    model.add_argument('--n-frames-per-step', default=1, type=int,
                       help='number of frames processed per step (currently only 1 is supported)')
    model.add_argument('--decoder-rnn-dim', default=1024, type=int,
                       help='number of units in decoder LSTM')
    model.add_argument('--decoder-dropout', default=0.1, type=float,
                       help='dropout probability for decoder LSTM')
    model.add_argument('--decoder-max-step', default=2000, type=int,
                       help='maximum number of output mel spectrograms')
    model.add_argument('--decoder-no-early-stopping', action='store_true', default=False,
                       help='stop decoding only when all samples are finished')

    # attention model
    model.add_argument('--attention-hidden-dim', default=128, type=int,
                       help='dimension of attention hidden representation')
    model.add_argument('--attention-rnn-dim', default=1024, type=int,
                       help='number of units in attention LSTM')
    model.add_argument('--attention-location-n-filter', default=32, type=int,
                       help='number of filters for location-sensitive attention')
    model.add_argument('--attention-location-kernel-size', default=31, type=int,
                       help='kernel size for location-sensitive attention')
    model.add_argument('--attention-dropout', default=0.1, type=float,
                       help='dropout probability for attention LSTM')

    model.add_argument('--prenet-dim', default=256, type=int,
                       help='number of ReLU units in prenet layers')

    # mel-post processing network parameters
    model.add_argument('--postnet-n-convolution', default=5, type=float,
                       help='number of postnet convolutions')
    model.add_argument('--postnet-kernel-size', default=5, type=float,
                       help='postnet kernel size')
    model.add_argument('--postnet-embedding-dim', default=512, type=float,
                       help='postnet embedding dimension')

    model.add_argument('--gate-threshold', default=0.5, type=float,
                       help='probability threshold for stop token')

    # audio parameters
    audio = parser.add_argument_group('audio parameters')
    audio.add_argument('--sample-rate', default=22050, type=int,
                       help='Sampling rate')
    audio.add_argument('--n-fft', default=1024, type=int,
                       help='Filter length for STFT')
    audio.add_argument('--hop-length', default=256, type=int,
                       help='Hop (stride) length')
    audio.add_argument('--win-length', default=1024, type=int,
                       help='Window length')
    audio.add_argument('--n-mels', default=80, type=int,
                       help='')
    audio.add_argument('--mel-fmin', default=0.0, type=float,
                       help='Minimum mel frequency')
    audio.add_argument('--mel-fmax', default=8000.0, type=float,
                       help='Maximum mel frequency')

    return parser


def adjust_learning_rate(epoch, optimizer, learning_rate,
                         anneal_steps, anneal_factor):
    """Adjust learning rate base on the initial setting."""
    p = 0
    if anneal_steps is not None:
        for _, a_step in enumerate(anneal_steps):
            if epoch >= int(a_step):
                p = p + 1

    if anneal_factor == 0.3:
        lr = learning_rate * ((0.1 ** (p // 2)) * (1.0 if p % 2 == 0 else 0.3))
    else:
        lr = learning_rate * (anneal_factor ** p)

    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def to_gpu(x):
    x = x.contiguous()
    if torch.cuda.is_available():
        x = x.cuda(non_blocking=True)
    return x


def batch_to_gpu(batch):
    text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths, gate_padded = batch
    text_padded = to_gpu(text_padded).long()
    text_lengths = to_gpu(text_lengths).long()
    mel_specgram_padded = to_gpu(mel_specgram_padded).float()
    gate_padded = to_gpu(gate_padded).float()
    mel_specgram_lengths = to_gpu(mel_specgram_lengths).long()
    x = (text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths)
    y = (mel_specgram_padded, gate_padded)
    return x, y


def training_step(model, train_batch, batch_idx):
    (text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths), y = batch_to_gpu(train_batch)
    y_pred = model(text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths)
    y[0].requires_grad = False
    y[1].requires_grad = False
    losses = Tacotron2Loss()(y_pred[:3], y)
    return losses[0] + losses[1] + losses[2], losses


def validation_step(model, val_batch, batch_idx):
    (text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths), y = batch_to_gpu(val_batch)
    y_pred = model(text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths)
    losses = Tacotron2Loss()(y_pred[:3], y)
    return losses[0] + losses[1] + losses[2], losses


def reduce_tensor(tensor, world_size):
    rt = tensor.clone()
    dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    if rt.is_floating_point():
        rt = rt / world_size
    else:
        rt = rt // world_size
    return rt


def log_additional_info(writer, model, loader, epoch):
    model.eval()
    data = next(iter(loader))
    with torch.no_grad():
        (text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths), _ = batch_to_gpu(data)
        y_pred = model(text_padded, text_lengths, mel_specgram_padded, mel_specgram_lengths)
        mel_out, mel_out_postnet, gate_out, alignment = y_pred

    fig = plt.figure()
    ax = plt.gca()
    ax.imshow(mel_out[0].cpu().numpy())
    writer.add_figure("trn/mel_out", fig, epoch)
    fig = plt.figure()
    ax = plt.gca()
    ax.imshow(mel_out_postnet[0].cpu().numpy())
    writer.add_figure("trn/mel_out_postnet", fig, epoch)
    writer.add_image("trn/gate_out", torch.tile(gate_out[:1], (10, 1)), epoch, dataformats="HW")
    writer.add_image("trn/alignment", alignment[0], epoch, dataformats="HW")


def train(rank, world_size, args):
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

    if rank == 0 and args.logging_dir:
        if not os.path.isdir(args.logging_dir):
            os.makedirs(args.logging_dir)
        filehandler = logging.FileHandler(os.path.join(args.logging_dir, 'train.log'))
        filehandler.setLevel(logging.INFO)
        logger.addHandler(filehandler)

        writer = SummaryWriter(log_dir=args.logging_dir)
    else:
        writer = None

    torch.manual_seed(0)

    torch.cuda.set_device(rank)

    symbols, text_preprocessor = get_text_preprocessor(args.text_preprocessor)

    model = Tacotron2(
        mask_padding=args.mask_padding,
        n_mels=args.n_mels,
        n_symbol=len(symbols),
        n_frames_per_step=args.n_frames_per_step,
        symbol_embedding_dim=args.symbols_embedding_dim,
        encoder_embedding_dim=args.encoder_embedding_dim,
        encoder_n_convolution=args.encoder_n_convolution,
        encoder_kernel_size=args.encoder_kernel_size,
        decoder_rnn_dim=args.decoder_rnn_dim,
        decoder_max_step=args.decoder_max_step,
        decoder_dropout=args.decoder_dropout,
        decoder_early_stopping=(not args.decoder_no_early_stopping),
        attention_rnn_dim=args.attention_rnn_dim,
        attention_hidden_dim=args.attention_hidden_dim,
        attention_location_n_filter=args.attention_location_n_filter,
        attention_location_kernel_size=args.attention_location_kernel_size,
        attention_dropout=args.attention_dropout,
        prenet_dim=args.prenet_dim,
        postnet_n_convolution=args.postnet_n_convolution,
        postnet_kernel_size=args.postnet_kernel_size,
        postnet_embedding_dim=args.postnet_embedding_dim,
        gate_threshold=args.gate_threshold,
    ).cuda(rank)
    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[rank])

    optimizer = Adam(model.parameters(), lr=args.learning_rate)

    best_loss = float("inf")
    start_epoch = 0

    if args.checkpoint_path and os.path.isfile(args.checkpoint_path):
        logger.info(f"Checkpoint: loading '{args.checkpoint_path}'")
        map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
        checkpoint = torch.load(args.checkpoint_path, map_location=map_location)

        start_epoch = checkpoint["epoch"]
        best_loss = checkpoint["best_loss"]

        model.load_state_dict(checkpoint["state_dict"])
        optimizer.load_state_dict(checkpoint["optimizer"])

        logger.info(
            f"Checkpoint: loaded '{args.checkpoint_path}' at epoch {checkpoint['epoch']}"
        )

    transforms = torch.nn.Sequential(
        torchaudio.transforms.MelSpectrogram(
            sample_rate=args.sample_rate,
            n_fft=args.n_fft,
            win_length=args.win_length,
            hop_length=args.hop_length,
            f_min=args.mel_fmin,
            f_max=args.mel_fmax,
            n_mels=args.n_mels,
            mel_scale='slaney',
            normalized=False,
            power=1,
            norm='slaney',
        ),
        SpectralNormalization()
    )
    trainset, valset = split_process_dataset(
        args.dataset, args.dataset_path, args.val_ratio, transforms, text_preprocessor)

    train_sampler = torch.utils.data.distributed.DistributedSampler(
        trainset,
        shuffle=True,
        num_replicas=world_size,
        rank=rank,
    )
    val_sampler = torch.utils.data.distributed.DistributedSampler(
        valset,
        shuffle=False,
        num_replicas=world_size,
        rank=rank,
    )

    loader_params = {
        "batch_size": args.batch_size,
        "num_workers": args.workers,
        "shuffle": False,
        "pin_memory": True,
        "drop_last": False,
        "collate_fn": partial(text_mel_collate_fn, n_frames_per_step=args.n_frames_per_step),
    }

    train_loader = DataLoader(trainset, sampler=train_sampler, **loader_params)
    val_loader = DataLoader(valset, sampler=val_sampler, **loader_params)
    dist.barrier()

    for epoch in range(start_epoch, args.epochs):
        start = time()

        model.train()
        trn_loss, counts = 0, 0

        if rank == 0:
            iterator = tqdm(enumerate(train_loader), desc=f"Epoch {epoch}", total=len(train_loader))
        else:
            iterator = enumerate(train_loader)

        for i, batch in iterator:
            adjust_learning_rate(epoch, optimizer, args.learning_rate,
                                 args.anneal_steps, args.anneal_factor)

            model.zero_grad()

            loss, losses = training_step(model, batch, i)

            loss.backward()
            torch.nn.utils.clip_grad_norm_(
                model.parameters(), args.grad_clip)

            optimizer.step()

            if rank == 0 and writer:
                global_iters = epoch * len(train_loader)
                writer.add_scalar("trn/mel_loss", losses[0], global_iters)
                writer.add_scalar("trn/mel_postnet_loss", losses[1], global_iters)
                writer.add_scalar("trn/gate_loss", losses[2], global_iters)

            trn_loss += loss * len(batch[0])
            counts += len(batch[0])

        trn_loss = trn_loss / counts

        trn_loss = reduce_tensor(trn_loss, world_size)
        if rank == 0:
            logger.info(f"[Epoch: {epoch}] time: {time()-start}; trn_loss: {trn_loss}")
            if writer:
                writer.add_scalar("trn_loss", trn_loss, epoch)

        if ((epoch + 1) % args.validate_and_checkpoint_freq == 0) or (epoch == args.epochs - 1):

            val_start_time = time()
            model.eval()

            val_loss, counts = 0, 0
            iterator = tqdm(enumerate(val_loader), desc=f"[Rank: {rank}; Epoch: {epoch}; Eval]", total=len(val_loader))

            with torch.no_grad():
                for val_batch_idx, val_batch in iterator:
                    val_loss = val_loss + validation_step(model, val_batch, val_batch_idx)[0] * len(val_batch[0])
                    counts = counts + len(val_batch[0])
                val_loss = val_loss / counts

            val_loss = reduce_tensor(val_loss, world_size)
            if rank == 0 and writer:
                writer.add_scalar("val_loss", val_loss, epoch)
                log_additional_info(writer, model, val_loader, epoch)

            if rank == 0:
                is_best = val_loss < best_loss
                best_loss = min(val_loss, best_loss)
                logger.info(f"[Rank: {rank}, Epoch: {epoch}; Eval] time: {time()-val_start_time}; val_loss: {val_loss}")
                logger.info(f"[Epoch: {epoch}] Saving checkpoint to {args.checkpoint_path}")
                save_checkpoint(
                    {
                        "epoch": epoch + 1,
                        "state_dict": model.state_dict(),
                        "best_loss": best_loss,
                        "optimizer": optimizer.state_dict(),
                    },
                    is_best,
                    args.checkpoint_path,
                )

    dist.destroy_process_group()


def main(args):
    logger.info("Start time: {}".format(str(datetime.now())))

    torch.manual_seed(0)
    random.seed(0)

    if args.master_addr is not None:
        os.environ['MASTER_ADDR'] = args.master_addr
    elif 'MASTER_ADDR' not in os.environ:
        os.environ['MASTER_ADDR'] = 'localhost'

    if args.master_port is not None:
        os.environ['MASTER_PORT'] = args.master_port
    elif 'MASTER_PORT' not in os.environ:
        os.environ['MASTER_PORT'] = '17778'

    device_counts = torch.cuda.device_count()

    logger.info(f"# available GPUs: {device_counts}")

    # download dataset is not already downloaded
    if args.dataset == 'ljspeech':
        if not os.path.exists(os.path.join(args.dataset_path, 'LJSpeech-1.1')):
            from torchaudio.datasets import LJSPEECH
            LJSPEECH(root=args.dataset_path, download=True)

    if device_counts == 1:
        train(0, 1, args)
    else:
        mp.spawn(train, args=(device_counts, args, ), nprocs=device_counts, join=True)

    logger.info(f"End time: {datetime.now()}")


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Training')
    parser = parse_args(parser)
    args, _ = parser.parse_known_args()

    main(args)
