# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# 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.

import lightning.pytorch as pl
import torch.multiprocessing as mp
from omegaconf import OmegaConf, open_dict

from nemo.collections.tts.models import (
    MagpieTTSModel,
    MagpieTTSModelOfflinePO,
    MagpieTTSModelOfflinePODataGen,
    MagpieTTSModelOnlinePO,
)
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager


@hydra_runner(config_path="conf/magpietts", config_name="magpietts_lhotse")
def main(cfg):
    logging.info('\nConfig Params:\n%s', OmegaConf.to_yaml(cfg, resolve=True))

    # forcing "spawn" method for multiprocessing over "fork" when choosing multiple
    # worker processes for dataloaders. By default, multiprocessing uses "fork" to create
    # worker processes, which inherit the memory state of the main process, including its
    # already initialized CUDA state. When the worker processes trieds to use
    # CUDA, it runs into conflicts with the inherited, now potentially invalid,
    # CUDA context, resuling in the CUDA initialization error. When
    # num_workers=0, all dataloading happens in the main process, so there is no
    # process forking and no CUDA context conflict. When num_workers>0, the standard way
    # to fix this is to use "spawn" to create a completely new and clean python process for
    # each worker, avoding the problematic CUDA state inheritance.
    mp.set_start_method("spawn", force=True)

    trainer = pl.Trainer(**cfg.trainer)
    trainer.callbacks.append(pl.callbacks.LearningRateMonitor(logging_interval='step', log_weight_decay=True))
    exp_manager(trainer, cfg.get("exp_manager", None))

    seed = cfg.get('seed', None)
    if seed is not None:
        # Option to seed for debugging
        logging.info(f"Setting seed to {seed}")
        pl.seed_everything(seed, workers=True)

    mode = cfg.get('mode', 'train')
    if mode == 'train':
        model = MagpieTTSModel(cfg=cfg.model, trainer=trainer)
    elif mode == 'dpo_train':
        model_cfg = cfg.model
        with open_dict(model_cfg):
            model_cfg.reference_model_ckpt_path = cfg.init_from_ptl_ckpt
        model = MagpieTTSModelOfflinePO(cfg=model_cfg, trainer=trainer)
    elif mode == 'onlinepo_train':
        model_cfg = cfg.model
        with open_dict(model_cfg):
            model_cfg.reference_model_ckpt_path = cfg.init_from_ptl_ckpt
        model = MagpieTTSModelOnlinePO(cfg=model_cfg, trainer=trainer)
    elif mode == 'test':
        model = MagpieTTSModelOfflinePODataGen(cfg=cfg.model, trainer=trainer)
    else:
        raise NotImplementedError(f"Only train, dpo_train, onlinepo_train and test modes are supported. Got {mode}")

    model.maybe_init_from_pretrained_checkpoint(cfg=cfg)

    try:
        if mode in ['train', 'dpo_train', 'onlinepo_train']:
            logging.info("Starting training...")
            trainer.fit(model)
        elif mode == 'test':
            logging.info("Starting testing...")
            trainer.test(model)
        else:
            raise NotImplementedError(
                f"Only train, dpo_train, onlinepo_train and test modes are supported. Got {mode}"
            )
        logging.info("Training/testing completed successfully.")
    finally:
        # Ensure WandB completes all uploads before Python thread shutdown
        # Critical when num_workers=0 during debugging - the main process can become
        # overwhelmed and fail to properly coordinate with WandB's background threads
        try:
            import wandb

            if wandb.run is not None:
                logging.info("Finishing WandB run to prevent threading shutdown hang...")
                wandb.finish()
        except Exception as e:
            logging.warning(f"Error finishing WandB: {e}")


if __name__ == '__main__':
    main()  # noqa pylint: disable=no-value-for-parameter