# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import argparse

import torch
from models.vc.Noro.noro_trainer import NoroTrainer
from utils.util import load_config


def build_trainer(args, cfg):
    supported_trainer = {
        "VC": NoroTrainer,
    }
    trainer_class = supported_trainer[cfg.model_type]
    trainer = trainer_class(args, cfg)
    return trainer


def cuda_relevant(deterministic=False):
    torch.cuda.empty_cache()
    # TF32 on Ampere and above
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.allow_tf32 = True
    # Deterministic
    torch.backends.cudnn.deterministic = deterministic
    torch.backends.cudnn.benchmark = not deterministic
    torch.use_deterministic_algorithms(deterministic)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--config",
        default="config.json",
        help="json files for configurations.",
        required=True,
    )
    parser.add_argument(
        "--exp_name",
        type=str,
        default="exp_name",
        help="A specific name to note the experiment",
        required=True,
    )
    parser.add_argument(
        "--resume", action="store_true", help="The model name to restore"
    )
    parser.add_argument(
        "--log_level", default="warning", help="logging level (debug, info, warning)"
    )
    parser.add_argument(
        "--resume_type",
        type=str,
        default="resume",
        help="Resume training or finetuning.",
    )
    parser.add_argument(
        "--checkpoint_path",
        type=str,
        default=None,
        help="Checkpoint for resume training or finetuning.",
    )
    args = parser.parse_args()
    cfg = load_config(args.config)
    print("experiment name: ", args.exp_name)
    # # CUDA settings
    cuda_relevant()
    # Build trainer
    print(f"Building {cfg.model_type} trainer")
    trainer = build_trainer(args, cfg)
    torch.set_num_threads(1)
    torch.set_num_interop_threads(1)
    print(f"Start training {cfg.model_type} model")
    trainer.train_loop()


if __name__ == "__main__":
    main()