#!/usr/bin/env python3
# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
#                                                  Wei Kang
#                                                  Mingshuang Luo)
# Copyright    2021                      (Pingfeng Luo)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Usage:

export CUDA_VISIBLE_DEVICES="0,1,2"

./transducer_stateless_modified/train.py \
  --world-size 3 \
  --num-epochs 65 \
  --start-epoch 0 \
  --exp-dir transducer_stateless_modified/exp \
  --max-duration 250 \
  --lr-factor 2.0 \
  --context-size 2 \
  --modified-transducer-prob 0.25
"""


import argparse
import logging
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Optional, Tuple

import k2
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from asr_datamodule import AishellAsrDataModule
from conformer import Conformer
from decoder import Decoder
from joiner import Joiner
from lhotse.cut import Cut
from lhotse.utils import fix_random_seed
from model import Transducer
from torch import Tensor
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.nn.utils import clip_grad_norm_
from torch.utils.tensorboard import SummaryWriter
from transformer import Noam

from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
from icefall.checkpoint import load_checkpoint
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.dist import cleanup_dist, setup_dist
from icefall.env import get_env_info
from icefall.lexicon import Lexicon
from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool


def get_parser():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--world-size",
        type=int,
        default=1,
        help="Number of GPUs for DDP training.",
    )

    parser.add_argument(
        "--master-port",
        type=int,
        default=12354,
        help="Master port to use for DDP training.",
    )

    parser.add_argument(
        "--tensorboard",
        type=str2bool,
        default=True,
        help="Should various information be logged in tensorboard.",
    )

    parser.add_argument(
        "--num-epochs",
        type=int,
        default=30,
        help="Number of epochs to train.",
    )

    parser.add_argument(
        "--start-epoch",
        type=int,
        default=0,
        help="""Resume training from from this epoch.
        If it is positive, it will load checkpoint from
        transducer_stateless/exp/epoch-{start_epoch-1}.pt
        """,
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="transducer_stateless_modified/exp",
        help="""The experiment dir.
        It specifies the directory where all training related
        files, e.g., checkpoints, log, etc, are saved
        """,
    )

    parser.add_argument(
        "--lang-dir",
        type=str,
        default="data/lang_char",
        help="""The lang dir
        It contains language related input files such as
        "lexicon.txt"
        """,
    )

    parser.add_argument(
        "--lr-factor",
        type=float,
        default=5.0,
        help="The lr_factor for Noam optimizer",
    )

    parser.add_argument(
        "--context-size",
        type=int,
        default=2,
        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
    )

    parser.add_argument(
        "--modified-transducer-prob",
        type=float,
        default=0.25,
        help="""The probability to use modified transducer loss.
        In modified transduer, it limits the maximum number of symbols
        per frame to 1. See also the option --max-sym-per-frame in
        transducer_stateless/decode.py
        """,
    )

    return parser


def get_params() -> AttributeDict:
    """Return a dict containing training parameters.

    All training related parameters that are not passed from the commandline
    are saved in the variable `params`.

    Commandline options are merged into `params` after they are parsed, so
    you can also access them via `params`.

    Explanation of options saved in `params`:

        - best_train_loss: Best training loss so far. It is used to select
                           the model that has the lowest training loss. It is
                           updated during the training.

        - best_valid_loss: Best validation loss so far. It is used to select
                           the model that has the lowest validation loss. It is
                           updated during the training.

        - best_train_epoch: It is the epoch that has the best training loss.

        - best_valid_epoch: It is the epoch that has the best validation loss.

        - batch_idx_train: Used to writing statistics to tensorboard. It
                           contains number of batches trained so far across
                           epochs.

        - log_interval:  Print training loss if batch_idx % log_interval` is 0

        - reset_interval: Reset statistics if batch_idx % reset_interval is 0

        - valid_interval:  Run validation if batch_idx % valid_interval is 0

        - feature_dim: The model input dim. It has to match the one used
                       in computing features.

        - subsampling_factor:  The subsampling factor for the model.

        - attention_dim: Hidden dim for multi-head attention model.

        - num_decoder_layers: Number of decoder layer of transformer decoder.

        - warm_step: The warm_step for Noam optimizer.
    """
    params = AttributeDict(
        {
            "best_train_loss": float("inf"),
            "best_valid_loss": float("inf"),
            "best_train_epoch": -1,
            "best_valid_epoch": -1,
            "batch_idx_train": 0,
            "log_interval": 50,
            "reset_interval": 200,
            "valid_interval": 800,
            # parameters for conformer
            "feature_dim": 80,
            "encoder_out_dim": 512,
            "subsampling_factor": 4,
            "attention_dim": 512,
            "nhead": 8,
            "dim_feedforward": 2048,
            "num_encoder_layers": 12,
            "vgg_frontend": False,
            # parameters for Noam
            "warm_step": 80000,  # For the 100h subset, use 8k
            "env_info": get_env_info(),
        }
    )

    return params


def get_encoder_model(params: AttributeDict) -> nn.Module:
    # TODO: We can add an option to switch between Conformer and Transformer
    encoder = Conformer(
        num_features=params.feature_dim,
        output_dim=params.encoder_out_dim,
        subsampling_factor=params.subsampling_factor,
        d_model=params.attention_dim,
        nhead=params.nhead,
        dim_feedforward=params.dim_feedforward,
        num_encoder_layers=params.num_encoder_layers,
        vgg_frontend=params.vgg_frontend,
    )
    return encoder


def get_decoder_model(params: AttributeDict) -> nn.Module:
    decoder = Decoder(
        vocab_size=params.vocab_size,
        embedding_dim=params.encoder_out_dim,
        blank_id=params.blank_id,
        context_size=params.context_size,
    )
    return decoder


def get_joiner_model(params: AttributeDict) -> nn.Module:
    joiner = Joiner(
        input_dim=params.encoder_out_dim,
        output_dim=params.vocab_size,
    )
    return joiner


def get_transducer_model(params: AttributeDict) -> nn.Module:
    encoder = get_encoder_model(params)
    decoder = get_decoder_model(params)
    joiner = get_joiner_model(params)

    model = Transducer(
        encoder=encoder,
        decoder=decoder,
        joiner=joiner,
    )
    return model


def load_checkpoint_if_available(
    params: AttributeDict,
    model: nn.Module,
    optimizer: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
) -> None:
    """Load checkpoint from file.

    If params.start_epoch is positive, it will load the checkpoint from
    `params.start_epoch - 1`. Otherwise, this function does nothing.

    Apart from loading state dict for `model`, `optimizer` and `scheduler`,
    it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
    and `best_valid_loss` in `params`.

    Args:
      params:
        The return value of :func:`get_params`.
      model:
        The training model.
      optimizer:
        The optimizer that we are using.
      scheduler:
        The learning rate scheduler we are using.
    Returns:
      Return None.
    """
    if params.start_epoch <= 0:
        return

    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
    saved_params = load_checkpoint(
        filename,
        model=model,
        optimizer=optimizer,
        scheduler=scheduler,
    )

    keys = [
        "best_train_epoch",
        "best_valid_epoch",
        "batch_idx_train",
        "best_train_loss",
        "best_valid_loss",
    ]
    for k in keys:
        params[k] = saved_params[k]

    return saved_params


def save_checkpoint(
    params: AttributeDict,
    model: nn.Module,
    optimizer: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
    rank: int = 0,
) -> None:
    """Save model, optimizer, scheduler and training stats to file.

    Args:
      params:
        It is returned by :func:`get_params`.
      model:
        The training model.
    """
    if rank != 0:
        return
    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
    save_checkpoint_impl(
        filename=filename,
        model=model,
        params=params,
        optimizer=optimizer,
        scheduler=scheduler,
        rank=rank,
    )

    if params.best_train_epoch == params.cur_epoch:
        best_train_filename = params.exp_dir / "best-train-loss.pt"
        copyfile(src=filename, dst=best_train_filename)

    if params.best_valid_epoch == params.cur_epoch:
        best_valid_filename = params.exp_dir / "best-valid-loss.pt"
        copyfile(src=filename, dst=best_valid_filename)


def compute_loss(
    params: AttributeDict,
    model: nn.Module,
    graph_compiler: CharCtcTrainingGraphCompiler,
    batch: dict,
    is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute CTC loss given the model and its inputs.

    Args:
      params:
        Parameters for training. See :func:`get_params`.
      model:
        The model for training. It is an instance of Conformer in our case.
      batch:
        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
        for the content in it.
      is_training:
        True for training. False for validation. When it is True, this
        function enables autograd during computation; when it is False, it
        disables autograd.
    """
    device = model.device
    feature = batch["inputs"]
    # at entry, feature is (N, T, C)
    assert feature.ndim == 3
    feature = feature.to(device)

    supervisions = batch["supervisions"]
    feature_lens = supervisions["num_frames"].to(device)

    texts = batch["supervisions"]["text"]
    y = graph_compiler.texts_to_ids(texts)
    y = k2.RaggedTensor(y).to(device)

    with torch.set_grad_enabled(is_training):
        loss = model(
            x=feature,
            x_lens=feature_lens,
            y=y,
            modified_transducer_prob=params.modified_transducer_prob,
        )

    assert loss.requires_grad == is_training

    info = MetricsTracker()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()

    # Note: We use reduction=sum while computing the loss.
    info["loss"] = loss.detach().cpu().item()

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: nn.Module,
    graph_compiler: CharCtcTrainingGraphCompiler,
    valid_dl: torch.utils.data.DataLoader,
    world_size: int = 1,
) -> MetricsTracker:
    """Run the validation process."""
    model.eval()

    tot_loss = MetricsTracker()

    for batch_idx, batch in enumerate(valid_dl):
        loss, loss_info = compute_loss(
            params=params,
            model=model,
            graph_compiler=graph_compiler,
            batch=batch,
            is_training=False,
        )
        assert loss.requires_grad is False
        tot_loss = tot_loss + loss_info

    if world_size > 1:
        tot_loss.reduce(loss.device)

    loss_value = tot_loss["loss"] / tot_loss["frames"]
    if loss_value < params.best_valid_loss:
        params.best_valid_epoch = params.cur_epoch
        params.best_valid_loss = loss_value

    return tot_loss


def train_one_epoch(
    params: AttributeDict,
    model: nn.Module,
    optimizer: torch.optim.Optimizer,
    graph_compiler: CharCtcTrainingGraphCompiler,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    tb_writer: Optional[SummaryWriter] = None,
    world_size: int = 1,
) -> None:
    """Train the model for one epoch.

    The training loss from the mean of all frames is saved in
    `params.train_loss`. It runs the validation process every
    `params.valid_interval` batches.

    Args:
      params:
        It is returned by :func:`get_params`.
      model:
        The model for training.
      optimizer:
        The optimizer we are using.
      train_dl:
        Dataloader for the training dataset.
      valid_dl:
        Dataloader for the validation dataset.
      tb_writer:
        Writer to write log messages to tensorboard.
      world_size:
        Number of nodes in DDP training. If it is 1, DDP is disabled.
    """
    model.train()

    tot_loss = MetricsTracker()

    for batch_idx, batch in enumerate(train_dl):
        params.batch_idx_train += 1
        batch_size = len(batch["supervisions"]["text"])

        loss, loss_info = compute_loss(
            params=params,
            model=model,
            graph_compiler=graph_compiler,
            batch=batch,
            is_training=True,
        )
        # summary stats
        tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info

        # NOTE: We use reduction==sum and loss is computed over utterances
        # in the batch and there is no normalization to it so far.

        optimizer.zero_grad()
        loss.backward()
        clip_grad_norm_(model.parameters(), 5.0, 2.0)
        optimizer.step()

        if batch_idx % params.log_interval == 0:
            logging.info(
                f"Epoch {params.cur_epoch}, "
                f"batch {batch_idx}, loss[{loss_info}], "
                f"tot_loss[{tot_loss}], batch size: {batch_size}"
            )

        if batch_idx % params.log_interval == 0:

            if tb_writer is not None:
                loss_info.write_summary(
                    tb_writer, "train/current_", params.batch_idx_train
                )
                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)

        if batch_idx > 0 and batch_idx % params.valid_interval == 0:
            logging.info("Computing validation loss")
            valid_info = compute_validation_loss(
                params=params,
                model=model,
                graph_compiler=graph_compiler,
                valid_dl=valid_dl,
                world_size=world_size,
            )
            model.train()
            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
            if tb_writer is not None:
                valid_info.write_summary(
                    tb_writer, "train/valid_", params.batch_idx_train
                )

    loss_value = tot_loss["loss"] / tot_loss["frames"]
    params.train_loss = loss_value
    if params.train_loss < params.best_train_loss:
        params.best_train_epoch = params.cur_epoch
        params.best_train_loss = params.train_loss


def run(rank, world_size, args):
    """
    Args:
      rank:
        It is a value between 0 and `world_size-1`, which is
        passed automatically by `mp.spawn()` in :func:`main`.
        The node with rank 0 is responsible for saving checkpoint.
      world_size:
        Number of GPUs for DDP training.
      args:
        The return value of get_parser().parse_args()
    """
    params = get_params()
    params.update(vars(args))

    fix_random_seed(42)
    if world_size > 1:
        setup_dist(rank, world_size, params.master_port)

    setup_logger(f"{params.exp_dir}/log/log-train")
    logging.info("Training started")

    if args.tensorboard and rank == 0:
        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
    else:
        tb_writer = None

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", rank)
    logging.info(f"Device: {device}")

    lexicon = Lexicon(params.lang_dir)
    graph_compiler = CharCtcTrainingGraphCompiler(
        lexicon=lexicon,
        device=device,
        oov="<unk>",
    )

    params.blank_id = 0
    params.vocab_size = max(lexicon.tokens) + 1

    logging.info(params)

    logging.info("About to create model")
    model = get_transducer_model(params)

    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

    checkpoints = load_checkpoint_if_available(params=params, model=model)

    model.to(device)
    if world_size > 1:
        logging.info("Using DDP")
        model = DDP(model, device_ids=[rank])
    model.device = device

    optimizer = Noam(
        model.parameters(),
        model_size=params.attention_dim,
        factor=params.lr_factor,
        warm_step=params.warm_step,
    )

    if checkpoints and "optimizer" in checkpoints:
        logging.info("Loading optimizer state dict")
        optimizer.load_state_dict(checkpoints["optimizer"])

    aishell = AishellAsrDataModule(args)
    train_cuts = aishell.train_cuts()

    def remove_short_and_long_utt(c: Cut):
        # Keep only utterances with duration between 1 second and 12 seconds
        #
        # Caution: There is a reason to select 12.0 here. Please see
        # ../local/display_manifest_statistics.py
        #
        # You should use ../local/display_manifest_statistics.py to get
        # an utterance duration distribution for your dataset to select
        # the threshold
        return 1.0 <= c.duration <= 12.0

    train_cuts = train_cuts.filter(remove_short_and_long_utt)

    train_dl = aishell.train_dataloaders(train_cuts)
    valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())

    scan_pessimistic_batches_for_oom(
        model=model,
        train_dl=train_dl,
        optimizer=optimizer,
        graph_compiler=graph_compiler,
        params=params,
    )

    for epoch in range(params.start_epoch, params.num_epochs):
        train_dl.sampler.set_epoch(epoch)

        cur_lr = optimizer._rate
        if tb_writer is not None:
            tb_writer.add_scalar("train/learning_rate", cur_lr, params.batch_idx_train)
            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)

        if rank == 0:
            logging.info("epoch {}, learning rate {}".format(epoch, cur_lr))

        params.cur_epoch = epoch

        train_one_epoch(
            params=params,
            model=model,
            optimizer=optimizer,
            graph_compiler=graph_compiler,
            train_dl=train_dl,
            valid_dl=valid_dl,
            tb_writer=tb_writer,
            world_size=world_size,
        )

        save_checkpoint(
            params=params,
            model=model,
            optimizer=optimizer,
            rank=rank,
        )

    logging.info("Done!")

    if world_size > 1:
        torch.distributed.barrier()
        cleanup_dist()


def scan_pessimistic_batches_for_oom(
    model: nn.Module,
    train_dl: torch.utils.data.DataLoader,
    optimizer: torch.optim.Optimizer,
    graph_compiler: CharCtcTrainingGraphCompiler,
    params: AttributeDict,
):
    from lhotse.dataset import find_pessimistic_batches

    logging.info(
        "Sanity check -- see if any of the batches in epoch 0 would cause OOM."
    )
    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
    for criterion, cuts in batches.items():
        batch = train_dl.dataset[cuts]
        try:
            optimizer.zero_grad()
            loss, _ = compute_loss(
                params=params,
                model=model,
                graph_compiler=graph_compiler,
                batch=batch,
                is_training=True,
            )
            loss.backward()
            clip_grad_norm_(model.parameters(), 5.0, 2.0)
            optimizer.step()
        except RuntimeError as e:
            if "CUDA out of memory" in str(e):
                logging.error(
                    "Your GPU ran out of memory with the current "
                    "max_duration setting. We recommend decreasing "
                    "max_duration and trying again.\n"
                    f"Failing criterion: {criterion} "
                    f"(={crit_values[criterion]}) ..."
                )
            raise


def main():
    parser = get_parser()
    AishellAsrDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)
    args.lang_dir = Path(args.lang_dir)

    world_size = args.world_size
    assert world_size >= 1
    if world_size > 1:
        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
    else:
        run(rank=0, world_size=1, args=args)


torch.set_num_threads(1)
torch.set_num_interop_threads(1)

if __name__ == "__main__":
    main()
