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  """
  Modified from
  https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/train.py
  """
  
  import argparse
  import logging
  import os
  import random
  from datetime import datetime
  from functools import partial
  from time import time
  
  import matplotlib.pyplot as plt
  import torch
  import torch.distributed as dist
  import torch.multiprocessing as mp
  import torchaudio
  from torch.optim import Adam
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from torchaudio.models import Tacotron2
  from tqdm import tqdm
  
  plt.switch_backend("agg")
  
  from datasets import text_mel_collate_fn, split_process_dataset, SpectralNormalization
  from loss import Tacotron2Loss
  from text.text_preprocessing import (
      available_symbol_set,
      available_phonemizers,
      get_symbol_list,
      text_to_sequence,
  )
  from utils import save_checkpoint
  
  
  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("--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")
  
      preprocessor = parser.add_argument_group("text preprocessor setup")
      preprocessor.add_argument(
          "--text-preprocessor",
          default="english_characters",
          type=str,
          choices=available_symbol_set,
          help="select text preprocessor to use.",
      )
      preprocessor.add_argument(
          "--phonemizer",
          type=str,
          choices=available_phonemizers,
          help='select phonemizer to use, only used when text-preprocessor is "english_phonemes"',
      )
      preprocessor.add_argument(
          "--phonemizer-checkpoint",
          type=str,
          help="the path or name of the checkpoint for the phonemizer, "
          'only used when text-preprocessor is "english_phonemes"',
      )
      preprocessor.add_argument(
          "--cmudict-root", default="./", type=str, help="the root directory for storing cmudictionary files"
      )
  
      # 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 get_datasets(args):
      text_preprocessor = partial(
          text_to_sequence,
          symbol_list=args.text_preprocessor,
          phonemizer=args.phonemizer,
          checkpoint=args.phonemizer_checkpoint,
          cmudict_root=args.cmudict_root,
      )
  
      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
      )
      return trainset, valset
  
  
  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 = get_symbol_list(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']}")
  
      trainset, valset = get_datasets(args)
  
      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,
          "prefetch_factor": 1024,
          "persistent_workers": True,
          "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)
  