import torch
import torchvision
import torchvision.transforms as transforms
import os

def get_cifar10_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None):
    """获取CIFAR10数据集的数据加载器
    
    Args:
        batch_size: 批次大小
        num_workers: 数据加载的工作进程数
        local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载
        
    Returns:
        trainloader: 训练数据加载器
        testloader: 测试数据加载器
    """
    # 数据预处理
    transform_train = transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    # 设置数据集路径
    if local_dataset_path:
        print(f"使用本地数据集: {local_dataset_path}")
        download = False
        dataset_path = local_dataset_path
    else:
        print("未指定本地数据集路径，将下载数据集")
        download = True
        dataset_path = '../dataset'

    # 创建数据集路径
    if not os.path.exists(dataset_path):
        os.makedirs(dataset_path)

    trainset = torchvision.datasets.CIFAR10(
        root=dataset_path, train=True, download=download, transform=transform_train)
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

    testset = torchvision.datasets.CIFAR10(
        root=dataset_path, train=False, download=download, transform=transform_test)
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=100, shuffle=False, num_workers=num_workers)

    return trainloader, testloader

def get_mnist_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None):
    """获取MNIST数据集的数据加载器
    
    Args:
        batch_size: 批次大小
        num_workers: 数据加载的工作进程数
        local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载
        
    Returns:
        trainloader: 训练数据加载器
        testloader: 测试数据加载器
    """
    # 数据预处理
    transform_train = transforms.Compose([
        transforms.RandomRotation(10),     # 随机旋转±10度
        transforms.RandomAffine(          # 随机仿射变换
            degrees=0,                    # 不进行旋转
            translate=(0.1, 0.1),         # 平移范围
            scale=(0.9, 1.1)             # 缩放范围
        ),
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))  # MNIST数据集的均值和标准差
    ])

    transform_test = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])

    # 设置数据集路径
    if local_dataset_path:
        print(f"使用本地数据集: {local_dataset_path}")
        download = False
        dataset_path = local_dataset_path
    else:
        print("未指定本地数据集路径，将下载数据集")
        download = True
        dataset_path = '../dataset'

    # 创建数据集路径
    if not os.path.exists(dataset_path):
        os.makedirs(dataset_path)

    trainset = torchvision.datasets.MNIST(
        root=dataset_path, train=True, download=download, transform=transform_train)
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

    testset = torchvision.datasets.MNIST(
        root=dataset_path, train=False, download=download, transform=transform_test)
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=100, shuffle=False, num_workers=num_workers)

    return trainloader, testloader