from prefigure.prefigure import get_all_args, push_wandb_config
import json
import os
import torch
import pytorch_lightning as pl
import random

from stable_audio_tools.data.dataset import create_dataloader_from_config
from stable_audio_tools.models import create_model_from_config
from stable_audio_tools.models.utils import (
    load_ckpt_state_dict,
    remove_weight_norm_from_model,
)
from stable_audio_tools.training import (
    create_training_wrapper_from_config,
    create_demo_callback_from_config,
)
from stable_audio_tools.training.utils import copy_state_dict


class ExceptionCallback(pl.Callback):
    def on_exception(self, trainer, module, err):
        print(f"{type(err).__name__}: {err}")


class ModelConfigEmbedderCallback(pl.Callback):
    def __init__(self, model_config):
        self.model_config = model_config

    def on_save_checkpoint(self, trainer, pl_module, checkpoint):
        checkpoint["model_config"] = self.model_config


def main():

    args = get_all_args()

    seed = args.seed

    # Set a different seed for each process if using SLURM
    if os.environ.get("SLURM_PROCID") is not None:
        seed += int(os.environ.get("SLURM_PROCID"))

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

    # Get JSON config from args.model_config
    with open(args.model_config) as f:
        model_config = json.load(f)

    with open(args.dataset_config) as f:
        dataset_config = json.load(f)

    train_dl, val_dl = create_dataloader_from_config(
        dataset_config,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        sample_rate=model_config["sample_rate"],
        sample_size=model_config["sample_size"],
        audio_channels=model_config.get("audio_channels", 2),
    )

    model = create_model_from_config(model_config)

    if args.pretrained_ckpt_path:
        copy_state_dict(model, load_ckpt_state_dict(args.pretrained_ckpt_path))

    if args.remove_pretransform_weight_norm == "pre_load":
        remove_weight_norm_from_model(model.pretransform)

    if args.pretransform_ckpt_path:
        model.pretransform.load_state_dict(
            load_ckpt_state_dict(args.pretransform_ckpt_path)
        )

    # Remove weight_norm from the pretransform if specified
    if args.remove_pretransform_weight_norm == "post_load":
        remove_weight_norm_from_model(model.pretransform)

    training_wrapper = create_training_wrapper_from_config(model_config, model)

    wandb_logger = pl.loggers.WandbLogger(project=args.name)
    wandb_logger.watch(training_wrapper)

    exc_callback = ExceptionCallback()

    if args.save_dir and isinstance(wandb_logger.experiment.id, str):
        checkpoint_dir = os.path.join(
            args.save_dir,
            wandb_logger.experiment.project,
            wandb_logger.experiment.id,
            "checkpoints",
        )
    else:
        checkpoint_dir = None

    ckpt_callback = pl.callbacks.ModelCheckpoint(
        every_n_train_steps=args.checkpoint_every, dirpath=checkpoint_dir, save_top_k=-1
    )
    save_model_config_callback = ModelConfigEmbedderCallback(model_config)

    # demo_callback = create_demo_callback_from_config(model_config, demo_dl=train_dl)

    # Combine args and config dicts
    args_dict = vars(args)
    args_dict.update({"model_config": model_config})
    args_dict.update({"dataset_config": dataset_config})
    push_wandb_config(wandb_logger, args_dict)

    # Set multi-GPU strategy if specified
    if args.strategy:
        if args.strategy == "deepspeed":
            from pytorch_lightning.strategies import DeepSpeedStrategy

            strategy = DeepSpeedStrategy(
                stage=2,
                contiguous_gradients=True,
                overlap_comm=True,
                reduce_scatter=True,
                reduce_bucket_size=5e8,
                allgather_bucket_size=5e8,
                load_full_weights=True,
            )
        else:
            strategy = args.strategy
    else:
        strategy = "ddp_find_unused_parameters_true" if args.num_gpus > 1 else "auto"

    trainer = pl.Trainer(
        devices=args.num_gpus,
        accelerator="gpu",
        num_nodes=args.num_nodes,
        strategy=strategy,
        precision=args.precision,
        accumulate_grad_batches=args.accum_batches,
        callbacks=[ckpt_callback, exc_callback, save_model_config_callback],
        logger=wandb_logger,
        log_every_n_steps=100,
        max_epochs=1,
        # val_check_interval=10000,
        default_root_dir=args.save_dir,
        gradient_clip_val=args.gradient_clip_val,
        reload_dataloaders_every_n_epochs=0,
    )

    trainer.fit(
        training_wrapper,
        train_dl,
        val_dl,
        ckpt_path=args.ckpt_path if args.ckpt_path else None,
    )


if __name__ == "__main__":
    main()