import argparse
import random

from lightning import Trainer
import lightning as L
from lightning.pytorch.loggers import MLFlowLogger
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader

from data import (
    FootballSegDataset,
    calculate_mean_std,
    get_data,
)
from model import UNetLightning


def main(train=True, test=True, save_model=False, load_model=None):
    all_images, all_paths = get_data()
    image_train_paths, image_test_paths, label_train_paths, label_test_paths = (
        train_test_split(all_images, all_paths, test_size=0.2, random_state=42)
    )

    mean, std = calculate_mean_std(image_train_paths)
    label_mean, label_std = calculate_mean_std(label_train_paths)
    test_mean, test_std = calculate_mean_std(image_test_paths)
    test_label_mean, test_label_std = calculate_mean_std(label_test_paths)

    train_dataset = FootballSegDataset(
        image_train_paths, label_train_paths, mean, std, label_mean, label_std)
    test_dataset = FootballSegDataset(
        image_test_paths, label_test_paths, test_mean, test_std, test_label_mean, test_label_std)

    train_loader = DataLoader(
        train_dataset, batch_size=32, shuffle=True, num_workers=7, persistent_workers=True)
    test_loader = DataLoader(test_dataset, batch_size=32,
                             shuffle=False, num_workers=7, persistent_workers=True)

    # train_indices = random.sample(range(len(train_loader.dataset)), 5)
    # test_indices = random.sample(range(len(test_loader.dataset)), 5)

    # plot_random_images(train_indices, train_loader, "train")
    # plot_random_images(test_indices, test_loader, "test")
    # statistics = SegmentationStatistics(all_paths, train_loader, test_loader, mean, std)
    # statistics.count_colors()
    # statistics.print_statistics()

    mlFlowLogger = MLFlowLogger(
        experiment_name="football-segmentation", tracking_uri="http://127.0.0.1:8080")
    model = UNetLightning(3, 3, learning_rate=1e-3)
    trainer = Trainer(max_epochs=2, logger=mlFlowLogger, log_every_n_steps=1)
    if train:
        trainer.fit(model, train_loader, test_loader)

    if save_model:
        model.save_hyperparameters()

    if load_model:
        model = UNetLightning.load_from_checkpoint(load_model)
        model.eval()
        model.freeze()

    if test:
        trainer.test(model, test_loader)


if __name__ == "__main__":
    args = argparse.ArgumentParser()
    args.add_argument("--seed", type=int, default=42)
    args.add_argument("--train", type=bool, default=False)
    args.add_argument("--test", type=bool, default=False)
    args.add_argument("--save_model", type=bool, default=False)
    args.add_argument("--load_model", type=str, default=None)
    args = args.parse_args()
    L.seed_everything(args.seed)
    main(args.train, args.test, args.save_model, args.load_model)