#!/usr/bin/env python3
# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
#
# 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:

./prepare.sh

If you use --datatang-prob=0, then you don't need to run the above script.

export CUDA_VISIBLE_DEVICES="0,1,2,3"

./pruned_transducer_stateless7/train.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir pruned_transducer_stateless7/exp \
  --full-libri 1 \
  --max-duration 550
"""


import argparse
import copy
import logging
import random
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple, Union
import deepspeed
from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict

import k2
import optim
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from typing import List

from asr_datamodule import AishellAsrDataModule

from lhotse import CutSet, load_manifest
from lhotse.cut import Cut
from lhotse.dataset.sampling.base import CutSampler
from lhotse.utils import fix_random_seed

from optim import Eden, ScaledAdam
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.nn.functional import pad as pad_tensor
from torch.utils.tensorboard import SummaryWriter


from icefall import diagnostics

from icefall.checkpoint import load_checkpoint, remove_checkpoints
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.checkpoint import (
    save_checkpoint_with_global_batch_idx,
    update_averaged_model,
)
from icefall.dist import cleanup_dist, setup_dist, get_world_size, get_rank, get_local_rank
from icefall.env import get_env_info
from icefall.hooks import register_inf_check_hooks
from icefall.lexicon import Lexicon
from icefall.utils import (
    AttributeDict,
    MetricsTracker,
    filter_uneven_sized_batch,
    setup_logger,
    str2bool,
)

import whisper
from model import load_model
from label_smoothing import LabelSmoothingLoss

LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]


def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
    if isinstance(model, DDP):
        # get underlying nn.Module
        model = model.module
    for module in model.modules():
        if hasattr(module, "batch_count"):
            module.batch_count = batch_count

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

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

    parser.add_argument(
        "--num-epochs",
        type=int,
        default=10,
        help="Number of epochs to train.",
    )

    parser.add_argument(
        "--start-epoch",
        type=int,
        default=1,
        help="""Resume training from this epoch. It should be positive.
        If larger than 1, it will load checkpoint from
        exp-dir/epoch-{start_epoch-1}.pt
        """,
    )

    parser.add_argument(
        "--start-batch",
        type=int,
        default=0,
        help="""If positive, --start-epoch is ignored and
        it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
        """,
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="pruned_transducer_stateless7/exp",
        help="""The experiment dir.
        It specifies the directory where all training related
        files, e.g., checkpoints, log, etc, are saved
        """,
    )

    parser.add_argument(
        "--model-name",
        type=str,
        default="large-v2",
        choices=["large-v2", "large-v3", "medium", "small", "tiny"],
        help="""The model name to use.
        """,
    )

    parser.add_argument(
        "--base-lr", type=float, default=1e-5, help="The base learning rate."
    )

    parser.add_argument(
        "--lr-batches",
        type=float,
        default=5000,
        help="""Number of steps that affects how rapidly the learning rate
        decreases. We suggest not to change this.""",
    )

    parser.add_argument(
        "--lr-epochs",
        type=float,
        default=6,
        help="""Number of epochs that affects how rapidly the learning rate decreases.
        """,
    )

    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="The seed for random generators intended for reproducibility",
    )

    parser.add_argument(
        "--print-diagnostics",
        type=str2bool,
        default=False,
        help="Accumulate stats on activations, print them and exit.",
    )

    parser.add_argument(
        "--inf-check",
        type=str2bool,
        default=False,
        help="Add hooks to check for infinite module outputs and gradients.",
    )

    parser.add_argument(
        "--keep-last-k",
        type=int,
        default=30,
        help="""Only keep this number of checkpoints on disk.
        For instance, if it is 3, there are only 3 checkpoints
        in the exp-dir with filenames `checkpoint-xxx.pt`.
        It does not affect checkpoints with name `epoch-xxx.pt`.
        """,
    )

    parser.add_argument(
        "--average-period",
        type=int,
        default=200,
        help="""Update the averaged model, namely `model_avg`, after processing
        this number of batches. `model_avg` is a separate version of model,
        in which each floating-point parameter is the average of all the
        parameters from the start of training. Each time we take the average,
        we do: `model_avg = model * (average_period / batch_idx_train) +
            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
        """,
    )

    parser.add_argument(
        "--use-fp16",
        type=str2bool,
        default=False,
        help="Whether to use half precision training.",
    )

    parser = deepspeed.add_config_arguments(parser)

    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.

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

        - num_decoder_layers: Number of decoder layer of transformer decoder.

        - warm_step: The warmup period that dictates the decay of the
              scale on "simple" (un-pruned) loss.
    """
    params = AttributeDict(
        {
            "frame_shift_ms": 10.0,
            "allowed_excess_duration_ratio": 0.1,
            "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": 9999999,
            "env_info": get_env_info(),
        }
    )

    return params

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

    If params.start_batch is positive, it will load the checkpoint from
    `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
    params.start_epoch is larger than 1, it will load the checkpoint from
    `params.start_epoch - 1`.

    Apart from loading state dict for `model` and `optimizer` 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.
      model_avg:
        The stored model averaged from the start of training.
      optimizer:
        The optimizer that we are using.
      scheduler:
        The scheduler that we are using.
    Returns:
      Return a dict containing previously saved training info.
    """
    if params.start_batch > 0:
        filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
    elif params.start_epoch > 1:
        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
    else:
        return None

    assert filename.is_file(), f"{filename} does not exist!"

    saved_params = load_checkpoint(
        filename,
        model=model,
        model_avg=model_avg,
        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]

    if params.start_batch > 0:
        if "cur_epoch" in saved_params:
            params["start_epoch"] = saved_params["cur_epoch"]

    return saved_params


def save_checkpoint(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    model_avg: Optional[nn.Module] = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[LRSchedulerType] = None,
    sampler: Optional[CutSampler] = None,
    scaler: Optional[GradScaler] = 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.
      model_avg:
        The stored model averaged from the start of training.
      optimizer:
        The optimizer used in the training.
      sampler:
       The sampler for the training dataset.
      scaler:
        The scaler used for mix precision training.
    """
    if rank != 0:
        return
    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
    save_checkpoint_impl(
        filename=filename,
        model=model,
        model_avg=model_avg,
        params=params,
        optimizer=optimizer,
        scheduler=scheduler,
        sampler=sampler,
        scaler=scaler,
        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,
    tokenizer: whisper.tokenizer.Tokenizer,
    model: Union[nn.Module, DDP],
    batch: dict,
    is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute RNN-T 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 Zipformer 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.
     warmup: a floating point value which increases throughout training;
        values >= 1.0 are fully warmed up and have all modules present.
    """
    # For the uneven-sized batch, the total duration after padding would possibly
    # cause OOM. Hence, for each batch, which is sorted descendingly by length,
    # we simply drop the last few shortest samples, so that the retained total frames
    # (after padding) would not exceed `allowed_max_frames`:
    # `allowed_max_frames = int(max_frames * (1.0 + allowed_excess_duration_ratio))`,
    # where `max_frames = max_duration * 1000 // frame_shift_ms`.
    # We set allowed_excess_duration_ratio=0.1.
    if isinstance(model, DDP):
        # get underlying nn.Module
        model = model.module
    def _batch_tensors(tensors: List[Tensor], pad_value: Any) -> Tensor:
        padding_size = max(tensor.shape[0] for tensor in tensors)
        dims = len(tensors[0].shape)
        padded_tensors = []
        for tensor in tensors:
            padding = [0] * 2 * dims
            padding[-1] = padding_size - tensor.shape[0]
            padded_tensors.append(pad_tensor(tensor, padding, "constant", pad_value))
        return torch.stack([tensor for tensor in padded_tensors], dim=0)

    max_frames = params.max_duration * 1000 // params.frame_shift_ms
    allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
    batch = filter_uneven_sized_batch(batch, allowed_max_frames)

    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
    feature = batch["inputs"]

    assert feature.ndim == 3
    feature = feature.to(device)
    feature = feature.transpose(1, 2)  # (N, C, T)

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

    batch_idx_train = params.batch_idx_train

    texts = batch["supervisions"]["text"]
    # remove spaces in texts
    texts = [text.replace(" ", "") for text in texts]

    text_tokens_list = [list(tokenizer.sot_sequence_including_notimestamps) + tokenizer.encode(text) + [tokenizer.eot] for text in texts]
    # convert it to torch tensor
    text_tokens_list = [torch.LongTensor(text_tokens) for text_tokens in text_tokens_list]

    # 50256 is the index of <pad> for all whisper models
    prev_outputs_tokens = _batch_tensors(
        [tokens[:-1] for tokens in text_tokens_list], pad_value=50256
    )
    target_tokens = _batch_tensors(
        [tokens[1:] for tokens in text_tokens_list], pad_value=50256
    )
    target_lengths = torch.LongTensor(
        [tokens.shape[0] - 1 for tokens in text_tokens_list]
    )

    decoder_criterion = LabelSmoothingLoss(ignore_index=50256, label_smoothing=0.1, reduction="sum")

    # ignore the first 3 tokens, which are always <sos>, <lang_id>, <transcibe>
    ignore_prefix_size = 3
    with torch.set_grad_enabled(is_training):
        encoder_out = model.encoder(feature)
        text_logits = model.decoder(prev_outputs_tokens.to(device), encoder_out)
        loss = decoder_criterion(text_logits, target_tokens.to(device))
        text_logits = text_logits[:, ignore_prefix_size:, :]
        target_tokens = target_tokens[:, ignore_prefix_size:]

    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,
    tokenizer: whisper.tokenizer.Tokenizer,
    model: Union[nn.Module, DDP],
    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):
        with torch.cuda.amp.autocast(enabled=params.use_fp16):
            loss, loss_info = compute_loss(
                params=params,
                tokenizer=tokenizer,
                model=model,
                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,
    tokenizer: whisper.tokenizer.Tokenizer,
    model: Union[nn.Module, DDP],
    optimizer: torch.optim.Optimizer,
    scheduler: LRSchedulerType,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    scaler: GradScaler,
    model_avg: Optional[nn.Module] = None,
    tb_writer: Optional[SummaryWriter] = None,
    world_size: int = 1,
    rank: int = 0,
) -> 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.
      scheduler:
        The learning rate scheduler, we call step() every step.
      train_dl:
        Dataloader for the training dataset.
      valid_dl:
        Dataloader for the validation dataset.
      scaler:
        The scaler used for mix precision training.
      model_avg:
        The stored model averaged from the start of training.
      tb_writer:
        Writer to write log messages to tensorboard.
      world_size:
        Number of nodes in DDP training. If it is 1, DDP is disabled.
      rank:
        The rank of the node in DDP training. If no DDP is used, it should
        be set to 0.
    """
    model.train()

    tot_loss = MetricsTracker()

    for batch_idx, batch in enumerate(train_dl):
        params.batch_idx_train += 1
        batch_size = len(batch["supervisions"]["text"])
        if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
            logging.info("Computing validation loss")
            valid_info = compute_validation_loss(
                params=params,
                tokenizer=tokenizer,
                model=model,
                valid_dl=valid_dl,
                world_size=world_size,
            )
            model.train()
            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
            logging.info(
                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
            )
            if tb_writer is not None:
                valid_info.write_summary(
                    tb_writer, "train/valid_", params.batch_idx_train
                )
        
        try:
            with torch.cuda.amp.autocast(enabled=params.use_fp16):
                loss, loss_info = compute_loss(
                    params=params,
                    tokenizer=tokenizer,
                    model=model,
                    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.
            if params.deepspeed:
                # deepspeed's backward() is different from torch's backward()
                # in that it does not accept a loss tensor as input.
                # It computes the loss internally.
                model.backward(loss)
                model.step()
            else:
                scaler.scale(loss).backward()
                set_batch_count(model, params.batch_idx_train)
                scheduler.step_batch(params.batch_idx_train)

                scaler.step(optimizer)
                scaler.update()
                optimizer.zero_grad()
        except:  # noqa
            display_and_save_batch(batch, params=params)
            raise

        if params.print_diagnostics and batch_idx == 5:
            return

        if (
            rank == 0
            and params.batch_idx_train > 0
            and params.batch_idx_train % params.average_period == 0
            and not params.deepspeed
        ):
            update_averaged_model(
                params=params,
                model_cur=model,
                model_avg=model_avg,
            )

        if batch_idx % 100 == 0 and params.use_fp16 and not params.deepspeed:
            # If the grad scale was less than 1, try increasing it.    The _growth_interval
            # of the grad scaler is configurable, but we can't configure it to have different
            # behavior depending on the current grad scale.
            cur_grad_scale = scaler._scale.item()
            if cur_grad_scale < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0):
                scaler.update(cur_grad_scale * 2.0)
            if cur_grad_scale < 0.01:
                logging.warning(f"Grad scale is small: {cur_grad_scale}")
            if cur_grad_scale < 1.0e-05:
                raise RuntimeError(
                    f"grad_scale is too small, exiting: {cur_grad_scale}"
                )
        if batch_idx % params.log_interval == 0:
            try:
                cur_lr = scheduler.get_last_lr()[0]
            except:
                cur_lr = 0.0
            cur_grad_scale = scaler._scale.item() if (params.use_fp16 and not params.deepspeed) else 1.0

            logging.info(
                f"Epoch {params.cur_epoch}, "
                f"batch {batch_idx}, loss[{loss_info}], "
                f"tot_loss[{tot_loss}], batch size: {batch_size}, "
                f"lr: {cur_lr:.2e}, "
                + (f"grad_scale: {scaler._scale.item()}" if (params.use_fp16 and not params.deepspeed) else "")
            )

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

                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 params.use_fp16:
                    tb_writer.add_scalar(
                        "train/grad_scale",
                        cur_grad_scale,
                        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(params.seed)

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

    logging.info("About to create model")

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

    tokenizer = whisper.tokenizer.get_tokenizer(
        model.is_multilingual, num_languages=model.num_languages, language="zh", task="transcribe"
    )

    model_avg: Optional[nn.Module] = None
    if rank == 0:
        # model_avg is only used with rank 0
        model_avg = copy.deepcopy(model).to(torch.float64)

    assert params.start_epoch > 0, params.start_epoch
    checkpoints = load_checkpoint_if_available(
        params=params, model=model, model_avg=model_avg
    )

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

    optimizer = torch.optim.AdamW(model.parameters(), lr=params.base_lr)
    scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)

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

    if (
        checkpoints
        and "scheduler" in checkpoints
        and checkpoints["scheduler"] is not None
    ):
        logging.info("Loading scheduler state dict")
        scheduler.load_state_dict(checkpoints["scheduler"])

    if world_size > 1:
        if params.deepspeed:
            logging.info("Using DeepSpeed")
            model, optimizer, _, scheduler = deepspeed.initialize(
                args=params, model=model, model_parameters=model.parameters())
        else:
            logging.info("Using DDP")
            setup_dist(use_ddp_launch=True)
            model = DDP(model, device_ids=[rank], find_unused_parameters=True)

    if params.print_diagnostics:
        opts = diagnostics.TensorDiagnosticOptions(
            2**22
        )  # allow 4 megabytes per sub-module
        diagnostic = diagnostics.attach_diagnostics(model, opts)

    if params.inf_check:
        register_inf_check_hooks(model)

    aishell = AishellAsrDataModule(args)

    if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
        # We only load the sampler's state dict when it loads a checkpoint
        # saved in the middle of an epoch
        sampler_state_dict = checkpoints["sampler"]
    else:
        sampler_state_dict = None

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

    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
    if checkpoints and "grad_scaler" in checkpoints:
        logging.info("Loading grad scaler state dict")
        scaler.load_state_dict(checkpoints["grad_scaler"])

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

    logging.info(f"start training from epoch {params.start_epoch}")
    for epoch in range(params.start_epoch, params.num_epochs + 1):
        if not params.deepspeed:
            scheduler.step_epoch(epoch - 1)
        fix_random_seed(params.seed + epoch - 1)
        train_dl.sampler.set_epoch(epoch - 1)

        if tb_writer is not None:
            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)

        params.cur_epoch = epoch

        train_one_epoch(
            params=params,
            tokenizer=tokenizer,
            model=model,
            model_avg=model_avg,
            optimizer=optimizer,
            scheduler=scheduler,
            train_dl=train_dl,
            valid_dl=valid_dl,
            scaler=scaler,
            tb_writer=tb_writer,
            world_size=world_size,
            rank=rank,
        )

        if params.print_diagnostics:
            diagnostic.print_diagnostics()
            break

        if params.deepspeed:
            model.save_checkpoint(save_dir=params.exp_dir,
                                    tag=f"epoch-{params.cur_epoch}",
                                    client_state={})
            if rank == 0:
                convert_zero_checkpoint_to_fp32_state_dict(
                            params.exp_dir, f"{params.exp_dir}/epoch-{params.cur_epoch}.pt",
                            tag=f"epoch-{params.cur_epoch}")
        else:
            save_checkpoint(
                params=params,
                model=model,
                model_avg=model_avg,
                optimizer=optimizer,
                scheduler=scheduler,
                sampler=train_dl.sampler,
                scaler=scaler,
                rank=rank,
            )

    logging.info("Done!")

    if world_size > 1 and not params.deepspeed:
        torch.distributed.barrier()
        cleanup_dist()


def display_and_save_batch(
    batch: dict,
    params: AttributeDict,
) -> None:
    """Display the batch statistics and save the batch into disk.

    Args:
      batch:
        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
        for the content in it.
      params:
        Parameters for training. See :func:`get_params`.
    """
    from lhotse.utils import uuid4

    filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
    logging.info(f"Saving batch to {filename}")
    torch.save(batch, filename)

    supervisions = batch["supervisions"]
    features = batch["inputs"]

    logging.info(f"features shape: {features.shape}")


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

    world_size = get_world_size()
    rank = get_rank()

    torch.set_num_threads(1)
    torch.set_num_interop_threads(1)
    run(rank=rank, world_size=world_size, args=args)

if __name__ == "__main__":
    main()