import argparse

import pytorch_lightning as pl

from dataloader import create_named_dataloader
from model import ColorTransformerModel
from pytorch_lightning.callbacks import EarlyStopping


def parse_args():
    # Define argument parser
    parser = argparse.ArgumentParser(description="Color Transformer Training Script")

    # Add arguments
    parser.add_argument(
        "--bs",
        type=int,
        default=64,
        help="Input batch size for training",
    )
    parser.add_argument(
        "-a", "--alpha", type=float, default=0.5, help="Alpha value for loss function"
    )
    parser.add_argument("--lr", type=float, default=1e-5, help="Learning rate")
    parser.add_argument(
        "-e", "--max_epochs", type=int, default=1000, help="Number of epochs to train"
    )
    parser.add_argument(
        "-L", "--log_every_n_steps", type=int, default=5, help="Logging frequency"
    )
    parser.add_argument(
        "-w",
        "--num_workers",
        type=int,
        default=3,
        help="Number of workers for data loading",
    )

    # Parse arguments
    args = parser.parse_args()
    return args


if __name__ == "__main__":
    args = parse_args()
    early_stop_callback = EarlyStopping(
        monitor='hp_metric',  # Metric to monitor
        min_delta=0,      # Minimum change in the monitored quantity to qualify as an improvement
        patience=50,          # Number of epochs with no improvement after which training will be stopped
        mode='min',           # Mode can be either 'min' for minimizing the monitored quantity or 'max' for maximizing it.
        verbose=True,
    )

    # Initialize data loader with parsed arguments
    # named_data_loader also has grayscale extras. TODO: remove unnamed
    train_dataloader = create_named_dataloader(
        N=0,
        batch_size=args.bs,
        shuffle=True,
        num_workers=args.num_workers,
    )

    params = argparse.Namespace(
        alpha=args.alpha,
        learning_rate=args.lr,
        batch_size=args.bs,
    )

    # Initialize model with parsed arguments
    model = ColorTransformerModel(params)

    # Initialize trainer with parsed arguments
    trainer = pl.Trainer(
        callbacks=[early_stop_callback],
        max_epochs=args.max_epochs,
        log_every_n_steps=args.log_every_n_steps,
    )

    # Train the model
    trainer.fit(model, train_dataloader)