import os
import torch
import torch.nn as nn
from torchvision import transforms, utils
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
import torch.multiprocessing
from PIL import Image  # 自定义数据集需要 import PIL
import matplotlib.pyplot as plt


# 判别器
class Detector(nn.Module):
    def __init__(self):
        super(Detector, self).__init__()
        self.model = nn.Sequential(
            # 3 x 64 x 64 -> 64 x 32 x 32
            nn.Conv2d(3, 64, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),

            # 64 x 32 x 32 -> 128 x 16 x 16
            nn.Conv2d(64, 128, 4, 2, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),

            # 128 x 16 x 16 -> 256 x 8 x 8
            nn.Conv2d(128, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),

            # 256 x 8 x 8 -> 512 x 4 x 4
            nn.Conv2d(256, 512, 4, 2, 1, bias=False),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            # 512 x 4 x 4 -> 1024 x 2 x 2
            nn.Conv2d(512, 1024, 4, 2, 1, bias=False),
            nn.BatchNorm2d(1024),
            nn.LeakyReLU(0.2, inplace=True),

            # 1024 x 2 x 2 -> 1 x 1 x 1
            nn.Conv2d(1024, 1, 2, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.model(x)


# 生成器
class Generator(nn.Module):
    def __init__(self, z_dim=100):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            # z -> 1024 x 4 x 4
            nn.ConvTranspose2d(z_dim, 1024, 4, 1, 0, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(True),

            # 1024 x 4 x 4 -> 512 x 8 x 8
            nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(True),

            # 512 x 8 x 8 -> 256 x 16 x 16
            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True),

            # 256 x 16 x 16 -> 128 x 32 x 32
            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(True),

            # 128 x 32 x 32 -> 3 x 64 x 64
            nn.ConvTranspose2d(128, 3, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, z):
        return self.model(z)


# 数据集加载（使用自定义路径 ./data/images）
class FlatImageDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        if not os.path.exists(root_dir):
            raise FileNotFoundError(f"Dataset directory '{root_dir}' not found. Please create it and add images.")
        self.image_files = [f for f in os.listdir(root_dir) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp'))]
        if len(self.image_files) == 0:
            raise ValueError(f"No valid image files found in '{root_dir}'. Supported: .png, .jpg, .jpeg, .bmp")
        self.image_paths = [os.path.join(root_dir, f) for f in self.image_files]

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        img_path = self.image_paths[idx]
        image = Image.open(img_path).convert('RGB')  # 确保转为 RGB（3 通道）
        if self.transform:
            image = self.transform(image)
        return image, 0  # 返回图像和虚拟标签（GAN 不使用）


# 使用自定义数据集
transform = transforms.Compose([
    transforms.Resize(64),
    transforms.CenterCrop(64),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载自定义数据集
dataset = FlatImageDataset(root_dir='./data/images', transform=transform)
print(f"Loaded {len(dataset)} images from ./data/images")  # 调试：打印数据集大小
dataloader = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=2)  # 减小 num_workers 避免 Windows 问题


# 参数
z_dim = 100
num_epochs = 100  # 6.2w
lr_d = 0.001
lr_g = 0.002
n_g_steps = 2  # 标准 DCGAN是1步G
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# 模型与优化器
d = Detector().to(device)
g = Generator(z_dim=z_dim).to(device)
criterion = nn.BCELoss()
optimizer_d = torch.optim.Adam(d.parameters(), lr=lr_d, betas=(0.5, 0.999))
optimizer_g = torch.optim.Adam(g.parameters(), lr=lr_g, betas=(0.5, 0.999))

# 固定噪声用于观察训练过程
z_fixed = torch.randn(64, z_dim, 1, 1, device=device)

# 创建保存目录
os.makedirs("results", exist_ok=True)


# 根据保存的 G dict 生成图片的函数
def generate_from_g_dict(model_path, z_dim=100, num_images=64, output_path='generated.png'):
    """
    从保存的生成器 state_dict 文件加载模型，并生成图片保存。
    """
    if not os.path.exists(model_path):
        print(f"Model path '{model_path}' not found. Skipping generation.")
        return
    # 加载生成器并恢复权重
    g_loaded = Generator(z_dim=z_dim).to(device)
    g_loaded.load_state_dict(torch.load(model_path, map_location=device))
    g_loaded.eval()

    # 生成假图像
    with torch.no_grad():
        z = torch.randn(num_images, z_dim, 1, 1, device=device)
        fake_images = g_loaded(z).detach().cpu()

    # 保存图片
    utils.save_image(fake_images, output_path, normalize=True, nrow=8)
    print(f"Generated images saved to {output_path}")


# 训练循环
def train():
    for epoch in range(1, num_epochs + 1):
        loss_d_total, loss_g_total = 0, 0
        for real_images, _ in tqdm(dataloader, desc=f"Epoch {epoch}/{num_epochs}", leave=False):
            real_images = real_images.to(device)
            B = real_images.size(0)

            # 标签平滑（真实 = 0.9, 假 = 0.0）
            real_labels = torch.full((B, 1, 1, 1), 0.9, device=device)
            fake_labels = torch.full((B, 1, 1, 1), 0.0, device=device)

            # 生成假图像
            z = torch.randn(B, z_dim, 1, 1, device=device)
            fake_images = g(z)

            # 判别器训练
            output_real = d(real_images)
            output_fake = d(fake_images.detach())

            loss_real = criterion(output_real, real_labels)
            loss_fake = criterion(output_fake, fake_labels)
            loss_d = loss_real + loss_fake

            optimizer_d.zero_grad()
            loss_d.backward()
            optimizer_d.step()

            # 生成器训练（标准 BCE）
            for _ in range(n_g_steps):
                #每次生成器更新前重新生成假图像
                z = torch.randn(B, z_dim, 1, 1, device=device)
                fake_images = g(z)

                output = d(fake_images)
                loss_g = criterion(output, real_labels)  # 欺骗 D：希望 D 输出真

                optimizer_g.zero_grad()
                loss_g.backward()
                optimizer_g.step()
                loss_g_total += loss_g.item()

            loss_d_total += loss_d.item()

        # 平均损失（G 损失已累加 n_g_steps 次）
        avg_loss_d = loss_d_total / len(dataloader)
        avg_loss_g = loss_g_total / (len(dataloader) * n_g_steps)  # 修复：除以总 G 步数
        print(f"Epoch [{epoch}/{num_epochs}]  Loss_D: {avg_loss_d:.4f}  Loss_G: {avg_loss_g:.4f}")

        loss_history = {"D": [], "G": []}

        # 每轮结束时：
        loss_history["D"].append(avg_loss_d)
        loss_history["G"].append(avg_loss_g)

        # 最后画图：
        plt.plot(loss_history["D"], label="Loss_D")
        plt.plot(loss_history["G"], label="Loss_G")
        plt.legend()
        plt.savefig("results/loss_curve.png")

        # 每2轮保存一次生成图像和 G 的 state_dict
        if epoch % 2 == 0:
            with torch.no_grad():
                fake = g(z_fixed).detach().cpu()
                utils.save_image(fake, f"results/epoch_{epoch}.png", normalize=True, nrow=8)

            # 保存 G 的 state_dict（dict 形式）
            g_state_dict_path = f"results/g_epoch_{epoch}.pth"
            torch.save(g.state_dict(), g_state_dict_path)
            print(f"Generator state_dict saved to {g_state_dict_path}")


if __name__ == "__main__":
    torch.multiprocessing.freeze_support()
    #train()
    for i in range(100):
        text = 'results/generated_after_train' + str(i) + '.png'
        generate_from_g_dict('results/g_epoch_85.pth', output_path=text)