from pathlib import Path
import numpy
import torch
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import v2 as tvtrans
from torchvision import datasets
import torch.nn.functional as F


class CNN(torch.nn.Module):
    """卷积神经网络模型"""

    def __init__(self):
        super(CNN, self).__init__()

        self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=(3, 3))
        self.pool1 = torch.nn.MaxPool2d(kernel_size=(2, 2))
        self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=(3, 3))
        self.pool2 = torch.nn.MaxPool2d(kernel_size=(2, 2))
        self.conv3 = torch.nn.Conv2d(64, 64, kernel_size=(3, 3))
        self.flatten = torch.nn.Flatten()
        # 28x28过第一轮卷积后变为26x26，过第一轮池化后变为13x13
        # 过第二轮卷积后变为11x11，过第二轮池化后变为5x5
        # 过第三轮卷积后变为3x3。
        # 最后一轮卷积核个数为64。
        self.fc1 = torch.nn.Linear(64 * 3 * 3, 64)
        torch.nn.init.xavier_normal_(self.fc1.weight)
        torch.nn.init.zeros_(self.fc1.bias)
        self.fc2 = torch.nn.Linear(64, 10)
        torch.nn.init.xavier_normal_(self.fc2.weight)
        torch.nn.init.zeros_(self.fc2.bias)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = F.relu(self.conv3(x))
        x = self.flatten(x)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.softmax(x, dim=1)


class MnistDataset(Dataset):
    """用于加载Mnist的自定义数据集"""

    shape: int
    transform: tvtrans.Transform
    images_data: numpy.ndarray
    labels_data: torch.Tensor

    def __init__(self, images: numpy.ndarray, labels: numpy.ndarray, transform: tvtrans.Transform):
        images_len: int = images.shape[0]
        labels_len: int = labels.shape[0]
        assert (images_len == labels_len)
        self.shape = images_len

        self.images_data = images
        self.labels_data = torch.from_numpy(labels)
        self.transform = transform

    def __getitem__(self, index):
        return self.transform(self.images_data[index]), self.labels_data[index]

    def __len__(self):
        return self.shape


class MnistDataSource:
    """用于读取MNIST数据的数据读取器"""

    train_loader: DataLoader
    test_loader: DataLoader

    def __init__(self, batch_size: int):
        dataset_path = Path(__file__).resolve().parent.parent / 'datasets' / 'mnist.npz'
        dataset = numpy.load(dataset_path)

        # 所有图片均为黑底白字
        # 6万张训练图片：60000x28x28。标签只有第一维。
        train_images: numpy.ndarray = dataset['x_train']
        train_labels: numpy.ndarray = dataset['y_train']
        # 1万张测试图片：10000x28x28。标签只有第一维。
        test_images: numpy.ndarray = dataset['x_test']
        test_labels: numpy.ndarray = dataset['y_test']

        # 定义数据转换器
        trans = tvtrans.Compose([
            # 从uint8转换为float32并自动归一化到0-1区间
            # tvtrans.ToTensor(),
            tvtrans.ToImage(),
            tvtrans.ToDtype(torch.float32, scale=True),

            # 为了符合后面图像的输入颜色通道条件，要在最后挤出一个新的维度
            #tvtrans.Lambda(lambda x: x.unsqueeze(-1))

            # 这个特定的标准化参数 (0.1307, 0.3081) 是 MNIST 数据集的标准化参数，这些数值是MNIST训练集的全局均值和标准差。
            # 这种标准化有助于模型训练时的数值稳定性和收敛速度。
            #tvtrans.Normalize((0.1307,), (0.3081,)),
        ])

        # 创建数据集
        train_dataset = MnistDataset(train_images, train_labels, transform=trans)
        test_dataset = MnistDataset(test_images, test_labels, transform=trans)


        # 赋值到自身
        self.train_loader = DataLoader(dataset=train_dataset,
                                     batch_size=batch_size,
                                     shuffle=False)
        self.test_loader = DataLoader(dataset=test_dataset,
                                    batch_size=batch_size,
                                    shuffle=False)