NUM_EPOCHS = 50
AUG_PROB = 0.5
GRAD_CLIP = 1.0
SAVE_EVERY = 5
CHECKPOINT_DIR = "./checkpoints"
BEST_MODEL_DIR = "./nula-best-model"

MEAN = pt.tensor([0.5,0.5,0.5], device=DEVICE).view(1,3,1,1)
STD = pt.tensor([0.5,0.5,0.5], device=DEVICE).view(1,3,1,1) 

GLOBAL_POOL_BLUR = BlurPool2d(channels=cfg.in_channels, stride=2).to(DEVICE)

def train_one_epoch(model, loader, optimizer, device, grad_clip=1.0):
    model.train()
    
    total_loss = 0.0
    total_correct = 0
    total_examples = 0

    pbar = tqdm(loader, desc="training...", leave=False)

    for batch in pbar:
        x = batch["pixel_values"].to(device, non_blocking=True)
        y = batch["labels"].to(device, non_blocking=True)
        optimizer.zero_grad(set_to_none=True)
 
        B = x.size(0) 
        mask_aug = pt.rand(B, device=x.device) < AUG_PROB

        if mask_aug.any():
            with pt.no_grad():
                x_image = x * STD + MEAN
                choices = pt.randint(0, 3, (B,), device=x.device)
                mask_resize = mask_aug & (choices == 0)
                if mask_resize.any():
                    scales = pt.empty(mask_resize.sum(), device=x.device).uniform_(0.2, 0.6)
                    x_subset = x_image[mask_resize]

                    resize_out = []
                    for i in range(x_subset.size(0)):
                        resize_out.append(resize_down_up(x_subset[i:i+1], scale=scales[i].item()))
                    x_image[mask_resize] = pt.cat(resize_out, dim=0)
                mask_decimate = mask_aug & (choices == 1)
                if mask_decimate.any():
                    factors = pt.randint(2, 5, (mask_decimate.sum(),), device=x.device)
                    x_subset = x_image[mask_decimate]

                    decimate_out = []
                    for i in range(x_subset.size(0)):
                        decimate_out.append(decimate(x_subset[i:i+1], factor=int(factors[i].item())))
                    x_image[mask_decimate] = pt.cat(decimate_out, dim=0)
                
                mask_blur = mask_aug & (choices == 2)
                if mask_blur.any():
                    x_subset = x_image[mask_blur]
                    x_down = GLOBAL_POOL_BLUR(x_subset)
                    x_up = F.interpolate(x_down, size=x_subset.shape[-2:], mode="bilinear", align_corners=False)
                    x_image[mask_blur] = x_up

                x = (x_image - MEAN) / STD

        out = model(pixel_values=x, labels=y)
        loss = out.loss
        logits = out.logits
        preds = logits.argmax(dim=1)
        loss.backward()
        pt.nn.utils.clip_grad_norm_(model.parameters(), max_norm=GRAD_CLIP)
        optimizer.step()

        total_loss += loss.item() * y.size(0)
        total_correct += (preds == y).sum().item()
        total_examples += y.size(0)

        pbar.set_postfix(loss=f"{loss.item():.4f}", acc=f"{100 * total_correct / total_examples:.2f}%")

    return total_loss / total_examples, total_correct / total_examples

@pt.no_grad()
def evaluate(model, loader, device):
    model.eval()
    total_loss = 0.0
    total_correct = 0
    total_examples = 0
    for batch in loader:
        x = batch["pixel_values"].to(device, non_blocking=True)
        y = batch["labels"].to(device, non_blocking=True)
        out = model(pixel_values=x, labels=y)
        loss = out.loss
        logits = out.logits 

        total_loss += loss.item() * y.size(0)
        total_correct += (logits.argmax(dim=1) == y).sum().item()
        total_examples += y.size(0)
    return total_loss / total_examples, total_correct / total_examples

best_val_acc = 0.0

for epoch in range(1, NUM_EPOCHS + 1):
    train_loss, train_acc = train_one_epoch(model, train_loader, optimizer, DEVICE)
    val_loss, val_acc = evaluate(model, test_loader, DEVICE)
    scheduler.step()

    if epoch % SAVE_EVERY == 0:
        model.save_pretrained(f"{CHECKPOINT_DIR}/epoch{epoch}")

    if val_acc > best_val_acc:
        best_val_acc = val_acc
        model.save_pretrained(BEST_MODEL_DIR)
        print(f"new best: {100 * best_val_acc:.2f}%")

    current_lr = optimizer.param_groups[0]["lr"]
    print(f"{'|'}{'-'*60}{'|'}")
    print(f"Epoch [{epoch}/{NUM_EPOCHS}]")
    print(f"lr : {current_lr:.6f}")
    print(f"train_loss : {train_loss:.4f}")
    print(f"train_acc : {train_acc * 100:.2f}%")
    print(f"val_loss : {val_loss:4f}")
    print(f"val_acc : {val_acc * 100:.2f}%")
    print(f"best_val_acc : {100 * best_val_acc:.2f}%")
    print()