from argparse import ArgumentParser
from math import ceil

import torch
from torch.utils.data import DataLoader

from dataset_loaders import EnWik9DataSet, LoremIpsumDataset, Dataset
from trainers import OptunaTrainer, Trainer, FullTrainer

BATCH_SIZE = 64
DEVICE = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"

# hyper parameters
context_length = 128

if __name__ == "__main__":
    print(f"Running on device: {DEVICE}...")
    parser = ArgumentParser()
    parser.add_argument("--method", choices=["optuna", "train"], required=True)
    parser.add_argument("--model-path", type=str, required=False)
    args = parser.parse_args()

    if args.method == "train":
        dataset: Dataset = EnWik9DataSet(transform=lambda x: x.to(DEVICE))
    elif args.method == "optuna":
        dataset: Dataset = LoremIpsumDataset(transform=lambda x: x.to(DEVICE))
    else:
        raise ValueError(f"Unknown method: {args.method}")

    dataset_length = len(dataset)
    training_size = ceil(0.8 * dataset_length)

    print(f"training set size = {training_size}, validation set size {dataset_length - training_size}")

    train_set, validate_set = torch.utils.data.random_split(dataset,
                                                            [training_size, dataset_length - training_size])
    training_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
    validation_loader = DataLoader(validate_set, batch_size=BATCH_SIZE, shuffle=False)
    loss_fn = torch.nn.CrossEntropyLoss()

    model = None
    if args.model_path is not None:
        model = torch.load(args.model_path)

    trainer: Trainer = OptunaTrainer() if args.method == "optuna" else FullTrainer()

    trainer.execute(
        model=model,
        train_loader=training_loader,
        validation_loader=validation_loader,
        loss_fn=loss_fn,
        n_epochs=200,
        device=DEVICE
    )