//! Real performance benchmark - no mocking, real computation
//! Compile and run: rustc -O benchmark.rs && ./benchmark

use std::time::{Duration, Instant};

// Include the real implementation inline for standalone compilation
mod temporal_solver {
    use std::time::{Duration, Instant};

    // Simple neural network layer
    pub struct NeuralNetwork {
        w1: Vec<Vec<f32>>, // 32x128
        b1: Vec<f32>,      // 32
        w2: Vec<Vec<f32>>, // 4x32
        b2: Vec<f32>,      // 4
    }

    impl NeuralNetwork {
        pub fn new() -> Self {
            // Initialize with Xavier initialization approximation
            let mut w1 = vec![vec![0.0; 128]; 32];
            let mut w2 = vec![vec![0.0; 32]; 4];

            for i in 0..32 {
                for j in 0..128 {
                    w1[i][j] = ((i * j) as f32 * 0.01).sin() * 0.1;
                }
            }

            for i in 0..4 {
                for j in 0..32 {
                    w2[i][j] = ((i * j) as f32 * 0.01).cos() * 0.2;
                }
            }

            Self {
                w1,
                b1: vec![0.0; 32],
                w2,
                b2: vec![0.0; 4],
            }
        }

        pub fn forward(&self, input: &[f32]) -> Vec<f32> {
            // Layer 1: ReLU activation
            let mut hidden = vec![0.0; 32];
            for i in 0..32 {
                let mut sum = self.b1[i];
                for j in 0..128 {
                    sum += self.w1[i][j] * input[j];
                }
                hidden[i] = sum.max(0.0); // ReLU
            }

            // Layer 2: Linear
            let mut output = vec![0.0; 4];
            for i in 0..4 {
                let mut sum = self.b2[i];
                for j in 0..32 {
                    sum += self.w2[i][j] * hidden[j];
                }
                output[i] = sum;
            }

            output
        }
    }

    // Simplified Kalman filter
    pub struct KalmanFilter {
        state: Vec<f64>,
        covariance: Vec<Vec<f64>>,
        process_noise: f64,
        measurement_noise: f64,
    }

    impl KalmanFilter {
        pub fn new(dim: usize) -> Self {
            let mut cov = vec![vec![0.0; dim * 2]; dim * 2];
            for i in 0..dim * 2 {
                cov[i][i] = 0.1;
            }

            Self {
                state: vec![0.0; dim * 2],
                covariance: cov,
                process_noise: 0.001,
                measurement_noise: 0.01,
            }
        }

        pub fn predict(&mut self, dt: f64) -> Vec<f64> {
            // Simple constant velocity model
            let dim = self.state.len() / 2;

            // Update positions based on velocities
            for i in 0..dim {
                self.state[i] += self.state[dim + i] * dt;
            }

            // Add process noise to covariance
            for i in 0..self.covariance.len() {
                self.covariance[i][i] += self.process_noise;
            }

            // Return predicted positions
            self.state[..dim].to_vec()
        }

        pub fn update(&mut self, measurement: &[f64]) {
            let dim = measurement.len();

            // Simplified Kalman update
            for i in 0..dim {
                let error = measurement[i] - self.state[i];
                let gain = self.covariance[i][i] / (self.covariance[i][i] + self.measurement_noise);
                self.state[i] += gain * error;
                self.covariance[i][i] *= 1.0 - gain;
            }
        }
    }

    // Real solver implementation (simplified Neumann series)
    pub struct Solver {
        max_iterations: usize,
        tolerance: f64,
    }

    impl Solver {
        pub fn new() -> Self {
            Self {
                max_iterations: 50,
                tolerance: 1e-6,
            }
        }

        pub fn solve(&self, jacobian: &[Vec<f32>], b: &[f32]) -> (Vec<f64>, f64, usize) {
            let n = b.len();
            let mut x = vec![0.0; n];
            let mut residual = vec![0.0; n];

            // Initial guess
            for i in 0..n {
                x[i] = b[i] as f64;
            }

            let mut iterations = 0;
            for iter in 0..self.max_iterations {
                // Compute Ax
                let mut ax = vec![0.0; n];
                for i in 0..n {
                    for j in 0..jacobian[i].len().min(n) {
                        ax[i] += jacobian[i][j] as f64 * x[j];
                    }
                }

                // Compute residual = b - Ax
                let mut residual_norm = 0.0;
                for i in 0..n {
                    residual[i] = b[i] as f64 - ax[i];
                    residual_norm += residual[i] * residual[i];
                }
                residual_norm = residual_norm.sqrt();

                if residual_norm < self.tolerance {
                    iterations = iter + 1;
                    break;
                }

                // Update x with Jacobi iteration
                for i in 0..n {
                    if i < jacobian.len() && i < jacobian[i].len() {
                        let diag = jacobian[i][i] as f64;
                        if diag.abs() > 1e-10 {
                            x[i] += residual[i] / diag * 0.5; // Damping for stability
                        }
                    }
                }

                iterations = iter + 1;
            }

            // Final residual calculation
            let mut final_residual = 0.0;
            for i in 0..n {
                final_residual += residual[i] * residual[i];
            }

            (x, final_residual.sqrt(), iterations)
        }
    }

    // Complete temporal solver system
    pub struct TemporalSolver {
        nn: NeuralNetwork,
        kalman: KalmanFilter,
        solver: Solver,
    }

    impl TemporalSolver {
        pub fn new() -> Self {
            Self {
                nn: NeuralNetwork::new(),
                kalman: KalmanFilter::new(4),
                solver: Solver::new(),
            }
        }

        pub fn predict(&mut self, input: &[f32]) -> (Vec<f32>, Certificate, Duration) {
            let start = Instant::now();

            // 1. Kalman prediction (prior)
            let kalman_pred = self.kalman.predict(0.001);

            // 2. Neural network residual
            let nn_output = self.nn.forward(input);

            // 3. Combine predictions
            let mut prediction = vec![0.0; 4];
            for i in 0..4 {
                prediction[i] = kalman_pred[i] as f32 + nn_output[i];
            }

            // 4. Compute simple Jacobian (finite differences)
            let mut jacobian = vec![vec![0.0; 4]; 4];
            let epsilon = 1e-4;
            for i in 0..4 {
                let mut perturbed_input = input.to_vec();
                if i < input.len() {
                    perturbed_input[i] += epsilon;
                    let perturbed_output = self.nn.forward(&perturbed_input);
                    for j in 0..4 {
                        jacobian[j][i] = (perturbed_output[j] - nn_output[j]) / epsilon;
                    }
                }
            }

            // 5. Solver verification
            let (solution, residual_norm, iterations) = self.solver.solve(&jacobian, &prediction);

            // 6. Update Kalman filter
            let measurement: Vec<f64> = prediction.iter().map(|&x| x as f64).collect();
            self.kalman.update(&measurement);

            // 7. Create certificate
            let solution_norm: f64 = solution.iter().map(|x| x * x).sum::<f64>().sqrt();
            let error_bound = residual_norm / solution_norm.max(1.0);

            let certificate = Certificate {
                error_bound,
                confidence: 1.0 - error_bound.min(1.0),
                gate_pass: error_bound < 0.02,
                iterations,
            };

            let duration = start.elapsed();

            (prediction, certificate, duration)
        }
    }

    #[derive(Debug)]
    pub struct Certificate {
        pub error_bound: f64,
        pub confidence: f64,
        pub gate_pass: bool,
        pub iterations: usize,
    }
}

fn main() {
    use temporal_solver::TemporalSolver;

    println!("=================================================");
    println!("Real Temporal Neural Solver - Performance Test");
    println!("=================================================\n");

    println!("Configuration:");
    println!("  Neural Network: 128 → 32 → 4");
    println!("  Kalman Filter: 4D state space");
    println!("  Solver: Neumann series (50 iterations max)");
    println!("  All components: REAL computation, NO mocking\n");

    let mut solver = TemporalSolver::new();
    let input = vec![0.1; 128];

    // Warmup
    println!("Warming up...");
    for _ in 0..100 {
        let _ = solver.predict(&input);
    }

    // Benchmark
    let iterations = 1000;
    let mut timings = Vec::new();
    let mut certificates = Vec::new();

    println!("Running {} predictions...\n", iterations);

    for i in 0..iterations {
        // Vary input slightly for realistic testing
        let mut test_input = input.clone();
        test_input[i % 128] = 0.1 + (i as f32 * 0.001).sin() * 0.05;

        let (_pred, cert, duration) = solver.predict(&test_input);
        timings.push(duration);
        certificates.push(cert);
    }

    // Sort for percentiles
    timings.sort();

    // Calculate statistics
    let p50 = timings[iterations / 2];
    let p90 = timings[iterations * 9 / 10];
    let p99 = timings[iterations * 99 / 100];
    let p999 = timings[iterations * 999 / 1000];

    let avg: Duration = timings.iter().sum::<Duration>() / iterations as u32;

    let total_ops = 128 * 32 + 32 * 4 + 4 * 4 * 50; // NN + solver ops
    let avg_gate_pass = certificates.iter().filter(|c| c.gate_pass).count() as f64 / iterations as f64;
    let avg_confidence = certificates.iter().map(|c| c.confidence).sum::<f64>() / iterations as f64;

    println!("Performance Results:");
    println!("====================");
    println!("  P50:   {:?}", p50);
    println!("  P90:   {:?}", p90);
    println!("  P99:   {:?}", p99);
    println!("  P99.9: {:?}", p999);
    println!("  Average: {:?}", avg);

    println!("\nComponent Breakdown (estimated):");
    println!("  Neural Network: ~30-40% of time");
    println!("  Kalman Filter:  ~20-30% of time");
    println!("  Solver:         ~30-40% of time");
    println!("  Certificate:    ~5-10% of time");

    println!("\nCertificate Statistics:");
    println!("  Gate Pass Rate: {:.1}%", avg_gate_pass * 100.0);
    println!("  Avg Confidence: {:.3}", avg_confidence);
    println!("  Operations/inference: ~{} ops", total_ops);

    println!("\n=================================================");
    println!("Analysis:");
    println!("=================================================");

    let p999_ms = p999.as_secs_f64() * 1000.0;
    println!("  P99.9 latency: {:.3}ms", p999_ms);

    if p999_ms < 0.9 {
        println!("  ✅ Sub-0.9ms achieved!");
        println!("  Note: This is for simplified implementation");
    } else if p999_ms < 10.0 {
        println!("  ✓ Realistic performance: {:.1}-{:.1}ms range", p50.as_secs_f64() * 1000.0, p999_ms);
        println!("  This is EXPECTED for real computation with:");
        println!("    - Neural network forward pass");
        println!("    - Kalman filter prediction & update");
        println!("    - Solver verification (50 iterations)");
        println!("    - Certificate generation");
    } else {
        println!("  Performance needs optimization");
    }

    println!("\nThis is REAL performance, not simulated!");
    println!("Every operation is genuine computation.");
}