#!/usr/bin/env python3
# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
#              2024  Yuekai Zhang
#              2025  Yifan  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:
# fine-tuning with whisper and Qwen2
pip install huggingface_hub['cli']
mkdir -p models/whisper models/qwen

# For aishell fine-tuned whisper model
huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_aishell_whisper exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt
# For multi-hans fine-tuned whisper model
# huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt

# huggingface-clie download  --local-dir models/qwen     Qwen/Qwen2-7B-Instruct
huggingface-clie download  --local-dir models/qwen     Qwen/Qwen2-1.5B-Instruct

torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
  --max-duration 200 \
  --exp-dir ./whisper_llm_zh/exp_test \
  --speech-encoder-path-or-name models/whisper/exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt \
  --llm-path-or-name Qwen/Qwen2-1.5B-Instruct \
  --manifest-dir data/fbank \
  --deepspeed \
  --deepspeed_config ./whisper_llm_zh/ds_config_zero1.json \
  --use-flash-attn True \
  --use-lora True --unfreeze-llm True
"""

import argparse
import logging
import os
import warnings
from pathlib import Path
from typing import Dict, Optional, Tuple

import deepspeed
import torch
import torch.nn as nn
import transformers
import whisper
from asr_datamodule import AsrDataModule
from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
from lhotse.cut import Cut
from lhotse.utils import fix_random_seed
from model import IGNORE_TOKEN_ID, SPEECH_LLM, EncoderProjector
from multi_dataset import MultiDataset
from peft import LoraConfig, get_peft_model
from torch import Tensor
from torch.utils.tensorboard import SummaryWriter
from transformers import AutoModelForCausalLM, AutoTokenizer
from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward

from icefall.dist import get_rank, get_world_size
from icefall.env import get_env_info
from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool

DEFAULT_SPEECH_TOKEN = "<speech>"


def set_batch_count(model: nn.Module, batch_count: float) -> None:
    for module in model.modules():
        if hasattr(module, "batch_count"):
            module.batch_count = batch_count


def add_model_arguments(parser: argparse.ArgumentParser):
    parser.add_argument(
        "--llm-path-or-name",
        type=str,
        default="/workspace/asr/Qwen1.5-0.5B-Chat",
        help="Path or name of the large language model.",
    )

    parser.add_argument(
        "--speech-encoder-path-or-name",
        type=str,
        default="whisper-large-v2",
        help="Path or name of the speech encoder.",
    )

    parser.add_argument(
        "--encoder-projector-ds-rate",
        type=int,
        default=8,
        help="Downsample rate for the encoder projector.",
    )
    parser.add_argument(
        "--use-flash-attn",
        type=str2bool,
        default=True,
        help="Whether to use flash attention.",
    )

    parser.add_argument(
        "--use-lora",
        type=str2bool,
        default=False,
        help="Whether to use lora to fine-tune llm.",
    )

    parser.add_argument(
        "--unfreeze-llm",
        type=str2bool,
        default=False,
        help="Whether to unfreeze llm during training.",
    )


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

    parser.add_argument(
        "--pretrained-model-path",
        type=str,
        default=None,
        help="""The path to the pretrained model if it is not None. Training will
        start from this model. e.g. ./wenetspeech/ASR/whisper/exp_large_v2/epoch-4-avg-3.pt
        """,
    )

    parser.add_argument(
        "--sampler-state-dict-path",
        type=str,
        default=None,
        help="""The path to the sampler state dict if it is not None. Training will start from this sampler state dict.
        """,
    )

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

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

    parser.add_argument(
        "--use-aishell",
        type=str2bool,
        default=True,
        help="Whether to only use aishell1 dataset for training.",
    )

    parser = deepspeed.add_config_arguments(parser)
    add_model_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`:

        - frame_shift_ms: The frame shift in milliseconds.
        - allowed_excess_duration_ratio: The allowed excess duration ratio.
        - best_train_loss: The best training loss so far.
        - best_valid_loss: The best validation loss so far.
        - best_train_epoch: The epoch where the best training loss is achieved.
        - best_valid_epoch: The epoch where the best validation loss is achieved.
        - batch_idx_train: The batch index of the current batch.
        - log_interval: Log training stats every `log_interval` batches.
        - reset_interval: Reset the stats every `reset_interval` batches.
        - valid_interval: Run validation every `valid_interval` batches.
        - env_info: The environment information.
    """
    params = AttributeDict(
        {
            "allowed_excess_duration_ratio": 0.1,
            "subsampling_factor": 2,
            "frame_shift_ms": 10,
            "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": 5000,
            "env_info": get_env_info(),
        }
    )

    return params


def compute_loss(
    params: AttributeDict,
    tokenizer: AutoTokenizer,
    model: nn.Module,
    batch: dict,
    is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute the loss for the given batch.
    Args:
        params:
            It is returned by :func:`get_params`.
        tokenizer:
            The tokenizer used to encode the text.
        model:
            The model for training.
        batch:
            A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
            for the content in it.
        is_training:
            Whether it is training.
    Returns:
        Return a tuple of two elements. The first element is the loss tensor.
    """

    def preprocess(
        messages,
        tokenizer: transformers.PreTrainedTokenizer,
        max_len: int,
    ) -> Dict:
        """Preprocesses the data for supervised fine-tuning."""
        texts = []
        TEMPLATE = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if loop.last %}{{ '<|im_end|>'}}{% else %}{{ '<|im_end|>\n' }}{% endif %}{% endfor %}"
        for i, msg in enumerate(messages):
            texts.append(
                tokenizer.apply_chat_template(
                    msg,
                    tokenize=True,
                    chat_template=TEMPLATE,
                    add_generation_prompt=False,
                    padding="longest",  # FIX me change padding to longest
                    max_length=max_len,
                    truncation=True,
                )
            )
        # padding texts to the same length, texts is a list of list, padding with tokenzier.pad_token_id
        max_len_texts = max([len(text) for text in texts])
        if tokenizer.padding_side == "right":
            texts = [
                text + [tokenizer.pad_token_id] * (max_len_texts - len(text))
                for text in texts
            ]
        else:
            texts = [
                [tokenizer.pad_token_id] * (max_len_texts - len(text)) + text
                for text in texts
            ]
        input_ids = torch.tensor(texts, dtype=torch.int)
        # response = tokenizer.batch_decode(input_ids, skip_special_tokens=True)[0]
        target_ids = input_ids.clone()
        target_ids[target_ids == tokenizer.pad_token_id] = IGNORE_TOKEN_ID
        # mask all tokens before token_id 151646 with IGNORE_TOKEN_ID
        # first get the indices of the tokens
        mask_prompt = True
        if mask_prompt:
            mask_indices = torch.where(
                input_ids == tokenizer.convert_tokens_to_ids("assistant")
            )
            for i in range(mask_indices[0].size(0)):
                row = mask_indices[0][i]
                col = mask_indices[1][i]
                # + 2 to  skip: 'assistant', '\n'
                target_ids[row, : col + 2] = IGNORE_TOKEN_ID

        attention_mask = input_ids.ne(tokenizer.pad_token_id)

        return input_ids, attention_mask, target_ids

    device = next(model.parameters()).device
    feature = batch["inputs"]

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

    batch_idx_train = params.batch_idx_train
    supervisions = batch["supervisions"]
    texts = batch["supervisions"]["text"]

    messages = []
    for i, text in enumerate(texts):
        text = text.replace(" ", "")
        message = [
            {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
            {"role": "assistant", "content": text},
        ]
        messages.append(message)

    input_ids, attention_mask, target_ids = preprocess(messages, tokenizer, max_len=128)

    target_ids = target_ids.type(torch.LongTensor)
    input_ids = input_ids.type(torch.LongTensor)

    with torch.set_grad_enabled(is_training):
        model_outputs, acc = model(
            fbank=feature,
            input_ids=input_ids.to(device),
            attention_mask=attention_mask.to(device),
            labels=target_ids.to(device),
        )
        loss = model_outputs.loss
    assert loss.requires_grad == is_training

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

    # Note: We use reduction=sum while computing the loss.
    info["loss"] = loss.detach().cpu().item()
    info["acc"] = (
        acc * info["frames"]
    )  # WAR: to avoid normalization by the number of frames

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    tokenizer: whisper.tokenizer.Tokenizer,
    model: nn.Module,
    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: AutoTokenizer,
    model: nn.Module,
    optimizer: torch.optim.Optimizer,
    scheduler: torch.optim.lr_scheduler,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    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()
    model.encoder.eval()
    if not params.unfreeze_llm:
        model.llm.eval()

    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:
            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()
            model.encoder.eval()
            if not params.unfreeze_llm:
                model.llm.eval()
            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
                )
            if batch_idx != 0:
                model.save_checkpoint(
                    save_dir=params.exp_dir,
                    tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
                    client_state={},
                    exclude_frozen_parameters=True,
                )

                if rank == 0:
                    convert_zero_checkpoint_to_fp32_state_dict(
                        params.exp_dir,
                        f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}.pt",
                        tag=f"epoch-{params.cur_epoch}-checkpoint-{batch_idx}",
                        exclude_frozen_parameters=True,
                    )
                    # save sampler state dict into checkpoint
                    sampler_state_dict = train_dl.sampler.state_dict()
                    torch.save(
                        sampler_state_dict,
                        f"{params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}-sampler.pt",
                    )
                    os.system(
                        f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}-checkpoint-{batch_idx}"
                    )
        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.

            # 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()

        except:  # noqa
            display_and_save_batch(batch, params=params)
            raise

        if batch_idx % params.log_interval == 0:
            try:
                cur_lr = scheduler.get_last_lr()[0]
            except:  # noqa
                cur_lr = 0.0

            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}, "
            )

            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)

    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")

    replace_whisper_encoder_forward()
    whisper_model = whisper.load_model(params.speech_encoder_path_or_name, "cpu")
    speech_encoder = whisper_model.encoder
    speech_encoder_dim = whisper_model.dims.n_audio_state
    for name, param in speech_encoder.named_parameters():
        param.requires_grad = False

    tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name)
    if params.use_flash_attn:
        attn_implementation = "flash_attention_2"
        # torch_dtype=torch.bfloat16 FIX ME
        torch_dtype = torch.float16
        tokenizer.padding_side = "left"

    else:
        attn_implementation = "eager"
        torch_dtype = torch.float16
        tokenizer.padding_side = "right"

    llm = AutoModelForCausalLM.from_pretrained(
        params.llm_path_or_name,
        attn_implementation=attn_implementation,
        torch_dtype=torch_dtype,
    )

    if not params.unfreeze_llm:
        for name, param in llm.named_parameters():
            param.requires_grad = False
    else:
        if params.use_lora:
            lora_config = LoraConfig(
                r=64,
                lora_alpha=16,
                target_modules=[
                    "q_proj",
                    "k_proj",
                    "v_proj",
                    "o_proj",
                    "up_proj",
                    "gate_proj",
                    "down_proj",
                ],
                lora_dropout=0.05,
                task_type="CAUSAL_LM",
            )
            llm = get_peft_model(llm, lora_config)
            llm.print_trainable_parameters()

    special_tokens_dict = {"additional_special_tokens": [DEFAULT_SPEECH_TOKEN]}
    tokenizer.add_special_tokens(special_tokens_dict)
    llm.config.pad_token_id = tokenizer.pad_token_id
    llm.config.default_speech_token_id = tokenizer.convert_tokens_to_ids(
        DEFAULT_SPEECH_TOKEN
    )

    encoder_projector = EncoderProjector(
        speech_encoder_dim, llm.config.hidden_size, params.encoder_projector_ds_rate
    )

    model = SPEECH_LLM(
        speech_encoder,
        llm,
        encoder_projector,
    )

    if params.pretrained_model_path:
        checkpoint = torch.load(
            params.pretrained_model_path, map_location="cpu", weights_only=False
        )
        missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)

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

    logging.info("Trainable parameters (excluding model.eval modules):")
    for name, param in model.named_parameters():
        if param.requires_grad:
            logging.info(f"{name}: {param.shape}")

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

    assert params.deepspeed
    logging.info("Using DeepSpeed")
    model, optimizer, _, scheduler = deepspeed.initialize(
        args=params, model=model, model_parameters=model.parameters()
    )

    data_module = AsrDataModule(args)
    multi_dataset = MultiDataset(args.manifest_dir)

    def remove_short_and_long_utt(c: Cut):
        # Keep only utterances with duration between 1 second and 20 seconds
        #
        # Caution: There is a reason to select 20.0 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
        if c.duration < 1.0 or c.duration > 20.0:
            # logging.warning(
            #    f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
            # )
            return False
        return True

    if params.use_aishell:
        train_cuts = multi_dataset.aishell_train_cuts()
    else:
        train_cuts = multi_dataset.train_cuts()

    train_cuts = train_cuts.filter(remove_short_and_long_utt)

    sampler_state_dict = None
    if params.sampler_state_dict_path:
        sampler_state_dict = torch.load(
            params.sampler_state_dict_path, weights_only=False
        )
        sampler_state_dict["max_duration"] = params.max_duration

    train_dl = data_module.train_dataloaders(
        train_cuts, sampler_state_dict=sampler_state_dict
    )

    if params.use_aishell:
        valid_cuts = multi_dataset.aishell_dev_cuts()
    else:
        valid_cuts = multi_dataset.dev_cuts()
    valid_dl = data_module.valid_dataloaders(valid_cuts)

    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):

        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,
            optimizer=optimizer,
            scheduler=scheduler,
            train_dl=train_dl,
            valid_dl=valid_dl,
            tb_writer=tb_writer,
            world_size=world_size,
            rank=rank,
        )

        model.save_checkpoint(
            save_dir=params.exp_dir,
            tag=f"zero-epoch-{params.cur_epoch}",
            client_state={},
            exclude_frozen_parameters=True,
        )
        if rank == 0:
            convert_zero_checkpoint_to_fp32_state_dict(
                params.exp_dir,
                f"{params.exp_dir}/epoch-{params.cur_epoch}",
                tag=f"zero-epoch-{params.cur_epoch}",
                exclude_frozen_parameters=True,
            )
            # save sampler state dict into checkpoint
            sampler_state_dict = train_dl.sampler.state_dict()
            torch.save(
                sampler_state_dict,
                f"{params.exp_dir}/epoch-{params.cur_epoch}-sampler.pt",
            )

            os.system(f"rm -rf {params.exp_dir}/zero-epoch-{params.cur_epoch}")

    logging.info("Done!")


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()
    AsrDataModule.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)
    warnings.filterwarnings("ignore", category=FutureWarning)
    run(rank=rank, world_size=world_size, args=args)


if __name__ == "__main__":
    main()