use std::collections::HashMap;

// --- [LAYER 1: PyTorch/CuTe Layouts] ---
// Models how a tensor is logically mapped to physical memory offsets
#[derive(Debug, Clone)]
struct CuTeLayout {
    shape: Vec<usize>,
    stride: Vec<usize>,
}

impl CuTeLayout {
    fn get_offset(&self, coords: &[usize]) -> usize {
        coords.iter().zip(&self.stride).map(|(c, s)| c * s).sum()
    }
}

// --- [LAYER 2: PTX / SASS ISA] ---
// Represents the machine instructions that trigger Tensor Core hardware
#[derive(Debug, Clone)]
enum SASSOp {
    HMMA { m: usize, n: usize, k: usize, regs: Vec<u32> }, // Half-precision Matrix Multiply Accumulate
    LDG { addr: u64, dest_reg: u32 }, // Load from Global Memory
    STG { addr: u64, src_reg: u32 }, // Store to Global Memory
}

// --- [LAYER 3: Tensor Core Microarchitecture] ---
// Models the proprietary hardware: MAC units, pipeline stages, and throughput
struct TensorCoreHardware {
    mac_units_per_cycle: usize,
    pipeline_depth: usize,
    clock_speed_ghz: f64,
    registers: HashMap<u32, Vec<f32>>,
}

impl TensorCoreHardware {
    fn new(macs: usize, depth: usize, speed: f64) -> Self {
        Self {
            mac_units_per_cycle: macs,
            pipeline_depth: depth,
            clock_speed_ghz: speed,
            registers: HashMap::new(),
        }
    }

    // Simulate the "Microcode Gap": SASS -> Hardware Signals
    fn execute_sass(&mut self, op: SASSOp) -> f64 {
        match op {
            SASSOp::HMMA { m, n, k, .. } => {
                let total_ops = (m * n * k) as f64;
                let cycles = (total_ops / self.mac_units_per_cycle as f64).ceil();
                let latency = cycles + self.pipeline_depth as f64;
                
                println!("[HW] Executing HMMA {}x{}x{} | Cycles: {:.2} | Latency: {:.2}ns", 
                    m, n, k, cycles, latency / self.clock_speed_ghz);
                
                latency / self.clock_speed_ghz
            }
            SASSOp::LDG { .. } => {
                println!("[HW] Memory Load (L1/L2 Cache Hit)");
                20.0 // Fixed 20ns latency for simulation
            }
            SASSOp::STG { .. } => {
                println!("[HW] Memory Store");
                10.0
            }
        }
    }
}

// --- [LAYER 4: The Full Stack Orchestrator] ---
struct NvidStack {
    hw: TensorCoreHardware,
}

impl NvidStack {
    fn run_tensor_op(&mut self, shape: (usize, usize, usize)) {
        println!("--- Starting Stack Execution ---");
        
        // 1. PyTorch -> CuTe: Define Layouts
        let layout_a = CuTeLayout { shape: vec![shape.0, shape.2], stride: vec![shape.2, 1] };
        let layout_b = CuTeLayout { shape: vec![shape.2, shape.1], stride: vec![shape.1, 1] };
        println!("[Stack] Layouts Generated: A({:?}), B({:?})", layout_a, layout_b);

        // 2. CuTe -> PTX/SASS: Generate Instruction Stream
        let program = vec![
            SASSOp::LDG { addr: 0x1000, dest_reg: 0 },
            SASSOp::LDG { addr: 0x2000, dest_reg: 1 },
            SASSOp::HMMA { m: shape.0, n: shape.1, k: shape.2, regs: vec![0, 1, 2] },
            SASSOp::STG { addr: 0x3000, src_reg: 2 },
        ];

        // 3. SASS -> Hardware: Execute and measure time
        let mut total_time = 0.0;
        for inst in program {
            total_time += self.hw.execute_sass(inst);
        }

        println!("--- Stack Execution Complete ---");
        println!("Total Wall-Clock Time (Simulated): {:.4} ns", total_time);
    }
}

fn main() {
    // Initialize hardware simulating a Blackwell-class Tensor Core
    // 512 MACs per cycle, 12 stage pipeline, 2.1 GHz
    let mut stack = NvidStack {
        hw: TensorCoreHardware::new(512, 12, 2.1),
    };

    // Run a 16x16x16 Matrix Multiply (Typical Tensor Core tile)
    stack.run_tensor_op((16, 16, 16));
}