# Copyright (c) 2025, NVIDIA CORPORATION.  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.

from collections import OrderedDict

import lightning.pytorch as pl
import torch
from omegaconf import OmegaConf

from nemo.collections.asr.models import EncDecSpeakerLabelModel, SpeechEncDecSelfSupervisedModel
from nemo.core.classes.common import typecheck
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager

typecheck.set_typecheck_enabled(enabled=False)

"""
Example script for training a speech classification model with a self-supervised pre-trained encoder, and 
use the SSL encoder for multi-layer feature extraction.

# Example of training a speaker classification model with a self-supervised pre-trained encoder
```sh
python speech_classification_mfa_train.py \
    # (Optional: --config-path=<path to dir of configs> --config-name=<name of config without .yaml>) \
    ++init_from_nemo_model=<path to pre-trained SSL .nemo file> \
    # or use ++init_from_pretrained_model=<model_name> \
    model.train_ds.manifest_filepath=<path to train manifest> \
    model.validation_ds.manifest_filepath=<path to val manifest> \
    trainer.devices=-1 \
    trainer.accelerator="gpu" \
    strategy="ddp"  \
    trainer.max_epochs=100 \
    model.optim.name="adamw" \
    model.optim.lr=0.001 \
    model.optim.betas=[0.9,0.999] \
    model.optim.weight_decay=0.0001 \
    model.optim.sched.warmup_steps=2000
    exp_manager.create_wandb_logger=True \
    exp_manager.wandb_logger_kwargs.name="<Name of experiment>" \
    exp_manager.wandb_logger_kwargs.project="<Namex of project>"
```
    
"""


def load_ssl_encoder(model, cfg):
    if cfg.get("init_from_ptl_ckpt", None) is not None:
        state_dict = torch.load(cfg.init_from_ptl_ckpt, map_location='cpu')['state_dict']
        logging.info(f"Loading encoder from PyTorch Lightning checkpoint: {cfg.init_from_ptl_ckpt}")
    elif cfg.get("init_from_nemo_model", None) is not None:
        ssl_model = SpeechEncDecSelfSupervisedModel.restore_from(cfg.init_from_nemo_model, map_location='cpu')
        state_dict = ssl_model.state_dict()
        logging.info(f"Loading encoder from NeMo model: {cfg.init_from_nemo_model}")
    elif cfg.get("init_from_pretrained_model", None) is not None:
        ssl_model = SpeechEncDecSelfSupervisedModel.from_pretrained(cfg.init_from_pretrained_model, map_location='cpu')
        state_dict = ssl_model.state_dict()
        logging.info(f"Loading encoder from pretrained model: {cfg.init_from_pretrained_model}")
    else:
        logging.info("No model checkpoint or pretrained model specified for encoder initialization.")
        return model

    encoder_state_dict = OrderedDict()
    for key, value in state_dict.items():
        if key.startswith('encoder.'):
            encoder_state_dict[f'preprocessor.feature_extractor.{key}'] = value

    model.load_state_dict(encoder_state_dict, strict=False)
    logging.info("Loaded ssl encoder state dict.")

    return model


@hydra_runner(config_path="../conf/ssl/nest/multi_layer_feat", config_name="nest_ecapa_tdnn_small")
def main(cfg):

    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
    trainer = pl.Trainer(**cfg.trainer)
    exp_manager(trainer, cfg.get("exp_manager", None))

    speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)

    if cfg.model.preprocessor.get("encoder", None) is not None:
        # multi-layer feature extractor
        speaker_model = load_ssl_encoder(speaker_model, cfg)
    else:
        speaker_model.maybe_init_from_pretrained_checkpoint(cfg)

    trainer.fit(speaker_model)

    if hasattr(cfg.model, 'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
        if speaker_model.prepare_test(trainer):
            trainer.test(speaker_model)


if __name__ == '__main__':
    main()