import torch
import torch.nn as nn

def train_one_epoch(model, dataloader, optimizer, criterion, device):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0

    for images, labels in dataloader:
        images, labels = images.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        _, preds = outputs.max(1)
        correct += preds.eq(labels).sum().item()
        total += labels.size(0)

    epoch_loss = running_loss / len(dataloader)
    epoch_acc = correct / total
    return epoch_loss, epoch_acc

def validate_one_epoch(model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
    correct = 0
    total = 0

    with torch.no_grad():
        for images, labels in dataloader:
            images, labels = images.to(device), labels.to(device)

            outputs = model(images)
            loss = criterion(outputs, labels)

            running_loss += loss.item()
            _, preds = outputs.max(1)
            correct += preds.eq(labels).sum().item()
            total += labels.size(0)

    epoch_loss = running_loss / len(dataloader)
    epoch_acc = correct / total
    return epoch_loss, epoch_acc