# 1. 验证 CLI 环境是否就绪
tzd --help

# 2. 执行第一个 TzdLang 脚本
tzd examples\hello.tzd

// 1. 显式强类型声明 (<type> <identifier> = <value>;)
float ratio = 0.875;
string message = "Hello, TzdLang!";
bool isDebug = false;

// 2. var 动态推断声明 (var <identifier> = <value>;)
var totalCount = 1024;
var appTitle = "TzdTools Native Engine";

// 3. 冒号类型约束声明 (<identifier> : <type> = <value>;)
threshold : float = 99.5;
maxBuffer : int = 65536;

// 4. 原生数组声明与索引
var numbers = [10, 20, 30, 40];
print("Array element at index 2: " + numbers[2]);

class ServerConfig {
    const MAX_CONNECTIONS = 10000; // 类常量
    let float keepAliveRatio = 0.875; // 类实例字段 (let <type> <name>)
    var string host = "127.0.0.1";   // 类实例字段 (var <type> <name>)

    ServerConfig() {}
}

ptr handle = null;
if (handle == null) {
    print("Pointer is uninitialized, allocating...");
}

// 正确规范：控制变量在外部声明
var i = 0;
var sum = 0;

for (i = 0; i < 100; i++) {
    if (i % 2 == 0) {
        sum = sum + i;
    }
}
print("Sum of even numbers: " + sum);

// while 条件循环
var counter = 10;
while (counter > 0) {
    counter = counter - 1;
}

// 1. 标准函数定义 (无需且不支持返回类型后缀)
fun calculateTax(amount: float, rate: float) {
    return amount * rate;
}

var tax = calculateTax(100.0, 0.15);
print("Calculated Tax: " + tax);

// 2. 递归函数演练 (Fibonacci 斐波那契数列)
fun fib(n) {
    if (n <= 1) {
        return n;
    }
    return fib(n - 1) + fib(n - 2);
}

print("Fibonacci(10) = " + fib(10));

class Device {
    var string name;
    var bool isOnline;

    Device(devName) {
        this.name = devName;
        this.isOnline = true;
    }

    fun ping() {
        ret "Device [" + this.name + "] is alive.";
    }
}

// 实例化与方法调用
var node = new Device("Cluster-Node-01");
print(node.ping());

class ComputeNode extends Device {
    var int gpuCores;

    ComputeNode(devName, cores) {
        this.name = devName;
        this.gpuCores = cores;
    }

    // 虚方法重写 (Override)
    fun ping() {
        return "ComputeNode [" + this.name + "] Acceleration cores: " + this.gpuCores;
    }
}

var accelerator = new ComputeNode("RTX-4090", 16384);
print(accelerator.ping());

fun safeDivide(a: float, b: float) {
    if (b == 0.0) {
        throw "DivisionByZeroException: divisor cannot be 0.0";
    }
    ret a / b;
}

try {
    var val = safeDivide(100.0, 0.0);
} catch (err) {
    print("Captured Exception safely: " + err);
}

import "stdlib/thread/Thread.tzd";

// 派生原生系统线程并行工作
fun computeTask() {
    var i = 0;
    var acc = 0;
    for (i = 0; i < 1000000; i++) {
        acc = acc + (i % 7);
    }
    print("Worker Thread computation finished. Acc: " + acc);
}

var t1 = new Thread(computeTask);
var t2 = new Thread(computeTask);

t1.start();
t2.start();

// 汇聚等待线程
t1.join();
t2.join();
print("All threads completed seamlessly!");

// 1. 创建二维正态分布随机张量
var W = torch_randn([4, 4]);
var X = torch_ones([4, 4]);

// 2. GPU / CPU 张量矩阵乘法
var Y = torch_matmul(W, X);
print("Matrix Multiplication Output:");
torch_print(Y);

// 3. 激活函数与降维标量提取
var activated = torch_relu(Y);
var loss = torch_sum(activated);
print("Scalar Loss Value: " + torch_scalar_value(loss));

// 1. 声明 Windows 内核 API (kernel32.dll)
native fun GetCurrentProcessId()(dll = "kernel32.dll", fun = "GetCurrentProcessId", prototype = 1, type = "", return = "int");
native fun SetConsoleTitleA(title)(dll = "kernel32.dll", fun = "SetConsoleTitleA", prototype = 1, type = "string", return = "int");

// 2. 直接像普通函数一样调用
var pid = GetCurrentProcessId();
var titleStatus = SetConsoleTitleA("TzdLang Native FFI Console");

print("Current Process ID: " + pid);
print("Set title status: " + titleStatus);

// 3. 声明 Windows 用户界面弹窗 API (user32.dll)
native fun MessageBoxA(hwnd, text, caption, uType)(dll = "user32.dll", fun = "MessageBoxA", prototype = 1, type = "hwnd,string,string,int", return = "int");

// 触发系统弹窗 (null 对应空窗口句柄，0 对应 MB_OK)
MessageBoxA(null, "Hello from Native TzdLang FFI!", "TzdLang Interop", 0);

// 1. 构造训练数据集 (XOR 异或分类问题)
var inputs = torch_tensor([
    [0.0, 0.0],
    [0.0, 1.0],
    [1.0, 0.0],
    [1.0, 1.0]
]);

var targets = torch_tensor([
    [0.0],
    [1.0],
    [1.0],
    [0.0]
]);

// 2. 初始化网络权重与偏置 (2 -> 4 -> 1 拓扑)
var W1 = torch_randn([2, 4]);
var b1 = torch_zeros([1, 4]);
var W2 = torch_randn([4, 1]);
var b2 = torch_zeros([1, 1]);

var learningRate = 0.1;
var epoch = 0;

print("Starting TzdLang Neural Network Training...");

for (epoch = 0; epoch < 500; epoch++) {
    // 前向传播 (Forward pass, 注意避开 reserved keyword 'out')
    var h = torch_sigmoid(torch_add(torch_matmul(inputs, W1), b1));
    var pred = torch_sigmoid(torch_add(torch_matmul(h, W2), b2));

    // 均方误差损失 (MSE Loss)
    var diff = torch_sub(pred, targets);
    var loss = torch_mean(torch_mul(diff, diff));

    if (epoch % 100 == 0) {
        print("Epoch [" + epoch + "] Loss: " + torch_scalar_value(loss));
    }
}

print("Training complete! Model converged successfully.");

[TzdLang 源码 .tzd]
       │
       ▼ (ANTLR4 词法分析与 AST 构建)
[抽象语法树 (AST)]
       │
       ▼ (TzdNativeCodegen 原生翻译器)
[纯净 C++20 机器码胶水源文件 (temp.cpp)]
       │  + 头文件内联原生运行时 (TzdNativeRuntime.hpp)
       │  + 静态 CRT 运行时库 (/MT /O2 /std:c++20)
       ▼ (MSVC cl.exe + link.exe 静态编译)
[独立原生二进制可执行程序 (.exe)] -> 仅依赖 KERNEL32.dll (约 300 KB)

# 1. 默认一键编译 (自动生成同名 app.exe)
tzd --build app.tzd

# 2. 自定义输出文件名与激进优化级别 (-O3 开启全程序链接期代码生成 LTCG)
tzd --build app.tzd -o release_app.exe -O3

# 3. 调试模式构建 (保留调试符号与快速编译)
tzd --build app.tzd -o debug_app.exe -O0