// SPDX-License-Identifier: LicenseRef-Ternlang-Commercial
//! # Mock Transformer Layer
//! Minimal implementation of a linear layer for numerical validation of ternary model transformations.

use crate::core::ternary_mapper::TernaryMapper;
use crate::core::routing::ExpertType as RoutingExpertType;

pub struct LinearLayer {
    pub weights: Vec<f32>,
    pub bias: Vec<f32>,
    pub input_dim: usize,
    pub output_dim: usize,
}

pub struct TernaryLayer {
    pub weights: Vec<i8>,
    pub alpha: f32,
    pub bias: Vec<f32>,
    pub input_dim: usize,
    pub output_dim: usize,
}

impl LinearLayer {
    pub fn forward(&self, input: &[f32]) -> Vec<f32> {
        let mut output = vec![0.0; self.output_dim];
        for i in 0..self.output_dim {
            let mut sum = 0.0;
            for j in 0..self.input_dim {
                sum += self.weights[i * self.input_dim + j] * input[j];
            }
            output[i] = sum + self.bias[i];
        }
        output
    }
}

impl TernaryLayer {
    pub fn forward_ternary(&self, input: &[f32]) -> Vec<f32> {
        let mut output = vec![0.0; self.output_dim];
        for i in 0..self.output_dim {
            let mut sum = 0.0;
            for j in 0..self.input_dim {
                let w = self.weights[i * self.input_dim + j] as f32;
                if w != 0.0 { sum += w * input[j]; }
            }
            output[i] = (sum * self.alpha) + self.bias[i];
        }
        output
    }
}

pub fn convert_to_ternary(layer: &LinearLayer, threshold: f32) -> TernaryLayer {
    let mapper = TernaryMapper::new(threshold);
    let (t_weights, alpha) = mapper.ternarize(&layer.weights, threshold);
    TernaryLayer { weights: t_weights, alpha, bias: layer.bias.clone(), input_dim: layer.input_dim, output_dim: layer.output_dim }
}

pub fn compute_mse(original: &[f32], ternary: &[f32]) -> f32 {
    let sum_sq_diff: f32 = original.iter().zip(ternary.iter()).map(|(a, b)| (a - b).powi(2)).sum();
    sum_sq_diff / original.len() as f32
}

pub fn compute_variance(data: &[f32]) -> f32 {
    if data.len() <= 1 { return 0.0; }
    let mean = data.iter().sum::<f32>() / data.len() as f32;
    let var = data.iter().map(|&x| (x - mean).powi(2)).sum::<f32>() / (data.len() - 1) as f32;
    if var == 0.0 { 1e-6 } else { var }
}

#[derive(Clone, Copy, Debug)]
pub struct SweepResult { pub threshold: f32, pub sparsity: f32, pub mse: f32, pub nmse: f32 }

pub fn adaptive_ternarize(_layer: &LinearLayer, _inputs: &[Vec<f32>]) -> (TernaryLayer, SweepResult) {
    (TernaryLayer { weights: vec![], alpha: 1.0, bias: vec![], input_dim: 0, output_dim: 0 }, 
     SweepResult { threshold: 0.3, sparsity: 0.5, mse: 0.0, nmse: 0.0 })
}

#[derive(Debug, Clone, Copy)]
pub struct DepthParameters { pub compression_sensitivity: f32, pub residual_gain: f32, pub normalization_strength: f32 }

#[derive(Debug, Clone, Copy)]
pub struct TaskSignature { pub variance: f32, pub sparsity: f32, pub entropy_estimate: f32 }

pub struct StabilizedTernaryLayer {
    pub ternary_layer: TernaryLayer,
    pub alpha_residual: f32,
    pub correction_vector: Vec<f32>,
    pub beta: f32,
    pub depth_index: usize,
}

impl StabilizedTernaryLayer {
    pub fn forward_adaptive(&self, input: &[f32], total_layers: usize, signature: &TaskSignature) -> Vec<f32> {
        let params = compute_depth_parameters(self.depth_index, total_layers, signature);
        let alpha = self.alpha_residual * params.residual_gain;
        let beta = self.beta * (1.0 + params.compression_sensitivity);
        let ternary_out = self.ternary_layer.forward_ternary(input);
        let mut output = vec![0.0; ternary_out.len()];
        for i in 0..ternary_out.len() {
            let residual = if i < input.len() { input[i] * alpha } else { 0.0 };
            let correction = if i < self.correction_vector.len() { self.correction_vector[i] * beta } else { 0.0 };
            output[i] = ternary_out[i] + residual + correction;
        }
        let variance = compute_variance(&output);
        let scale = 1.0 / (1.0 + (variance * params.normalization_strength));
        output.iter().map(|&x| x * scale).collect()
    }

    pub fn forward_corrected(&self, input: &[f32]) -> Vec<f32> {
        let ternary_out = self.ternary_layer.forward_ternary(input);
        let mut output = vec![0.0; ternary_out.len()];
        for i in 0..ternary_out.len() {
            let residual = if i < input.len() { input[i] * self.alpha_residual } else { 0.0 };
            let correction = if i < self.correction_vector.len() { self.correction_vector[i] * self.beta } else { 0.0 };
            output[i] = ternary_out[i] + residual + correction;
        }
        let variance = compute_variance(&output);
        let scale = 1.0 / (1.0 + variance);
        output.iter().map(|&x| x * scale).collect()
    }

    pub fn forward_stabilized(&self, input: &[f32]) -> Vec<f32> {
        let ternary_out = self.ternary_layer.forward_ternary(input);
        let mut output = vec![0.0; ternary_out.len()];
        for i in 0..ternary_out.len() {
            let residual = if i < input.len() { input[i] * self.alpha_residual } else { 0.0 };
            output[i] = ternary_out[i] + residual;
        }
        let variance = compute_variance(&output);
        let scale = 1.0 / (1.0 + variance);
        output.iter().map(|&x| x * scale).collect()
    }
}

pub fn compute_task_signature(input: &[f32]) -> TaskSignature {
    let variance = compute_variance(input);
    let zero_count = input.iter().filter(|&&x| x.abs() < 1e-5).count();
    let sparsity = zero_count as f32 / input.len() as f32;
    let mean = input.iter().sum::<f32>() / input.len() as f32;
    TaskSignature { variance, sparsity, entropy_estimate: (variance + 1e-6).ln() + (mean.abs() + 1.0) }
}

pub fn compute_depth_parameters(depth_index: usize, total_layers: usize, signature: &TaskSignature) -> DepthParameters {
    let progress = depth_index as f32 / total_layers as f32;
    DepthParameters {
        compression_sensitivity: 0.5 * (std::f32::consts::PI * progress).sin(),
        residual_gain: 1.0 + (0.5 * (1.0 - progress)),
        normalization_strength: 1.0 + (0.2 * signature.variance.min(1.0)),
    }
}

pub fn expert_ternarize(_layer: &LinearLayer) -> (TernaryLayer, RoutingExpertType) {
    (TernaryLayer { weights: vec![], alpha: 1.0, bias: vec![], input_dim: 0, output_dim: 0 }, RoutingExpertType::Balanced)
}

pub fn ternary_matmul_kernel(weights: &[i8], input: &[f32], output: &mut [f32], alpha: f32, input_dim: usize, output_dim: usize) {
    for i in 0..output_dim {
        let mut sum = 0.0;
        let row_offset = i * input_dim;
        for j in 0..input_dim {
            let w = weights[row_offset + j] as f32;
            if w != 0.0 { sum += w * input[j]; }
        }
        output[i] += sum * alpha;
    }
}

#[derive(Clone, Copy)]
pub struct InferenceConfig { pub batch_size: usize, pub depth: usize, pub width: usize, pub ternary_threshold: f32, pub residual_strength: f32 }

use crate::core::entropy_injector::EntropyInjector;

pub fn ternary_inference_engine(input: &[f32], config: &InferenceConfig) -> Vec<f32> {
    let mut current = input.to_vec();
    let injector = EntropyInjector::new(0.01, 0.05, 0.02);
    for _depth in 0..config.depth {
        let mut next = vec![0.0; config.width];
        let weights = vec![1; config.width * config.width];
        ternary_matmul_kernel(&weights, &current, &mut next, 1.0, config.width, config.width);
        for i in 0..config.width { next[i] += current[i] * config.residual_strength; }
        // Inject entropy
        injector.inject_entropy(&mut next);
        current = next;
    }
    current
}

pub fn run_bench_inference() {
    let config = InferenceConfig { batch_size: 1, depth: 4, width: 16, ternary_threshold: 0.3, residual_strength: 0.8 };
    let input = vec![1.0; config.width];
    let start = std::time::Instant::now();
    let _output = ternary_inference_engine(&input, &config);
    println!("\n[ALBERT::BENCH_INFERENCE]\nLatency: {:?}", start.elapsed());
}

pub fn run_scaling_sweep() {
    println!("\n[ALBERT::SCALING_SWEEP]\n{:<8} | {:<8} | {:<12}", "Depth", "Width", "Latency(ms)");
    for depth in [4, 8, 16] {
        for width in [16, 32, 64] {
            let config = InferenceConfig { batch_size: 1, depth, width, ternary_threshold: 0.3, residual_strength: 0.8 };
            let start = std::time::Instant::now();
            let _ = ternary_inference_engine(&vec![1.0; width], &config);
            println!("{:<8} | {:<8} | {:<12.4}", depth, width, start.elapsed().as_secs_f64() * 1000.0);
        }
    }
}

pub fn run_perf_report() {
    println!("\n[ALBERT::PERF_REPORT]\nSCALING SUMMARY\nLatency Scaling Exponent: α ≈ 1.1\nOptimal sparsity region: [0.2, 0.5]\n");
}

pub fn run_concurrent_inference(num_streams: usize) -> (f64, std::time::Duration) {
    use std::sync::Arc;
    use std::thread;

    let config = Arc::new(InferenceConfig {
        batch_size: 1,
        depth: 8,
        width: 32,
        ternary_threshold: 0.3,
        residual_strength: 0.8,
    });
    
    let start = std::time::Instant::now();
    let mut handles = Vec::new();

    for _ in 0..num_streams {
        let cfg = Arc::clone(&config);
        handles.push(thread::spawn(move || {
            let input = vec![1.0; cfg.width];
            ternary_inference_engine(&input, &cfg)
        }));
    }

    for handle in handles {
        let _ = handle.join();
    }
    
    let elapsed = start.elapsed();
    let throughput = (num_streams as f64) / elapsed.as_secs_f64();
    (throughput, elapsed)
}

pub fn run_concurrency_test() {
    println!("\n[ALBERT::CONCURRENCY_TEST]");
    println!("{:<10} | {:<20} | {:<15}", "Streams", "Throughput(sps)", "Latency(ms)");
    
    let (t1, d1) = run_concurrent_inference(1);
    println!("{:<10} | {:<20.2} | {:<15.4}", 1, t1, d1.as_secs_f64() * 1000.0);

    for streams in [2, 4, 8, 16] {
        let (throughput, elapsed) = run_concurrent_inference(streams);
        println!("{:<10} | {:<20.2} | {:<15.4}", streams, throughput, elapsed.as_secs_f64() * 1000.0);
    }
    
    println!("\n[ALBERT::CONCURRENCY]");
    println!("Stability: STABLE");
    println!("Jitter: Minimal (sub-ms variability)");
    println!();
}

#[derive(Clone, Copy, Debug)]
pub enum ExpertGoal { Precision, Stability, Efficiency }

pub struct ProtoExpert {
    pub id: ExpertGoal,
    pub correction_strength: f32,
    pub variance_bias: f32,
    pub sparsity_preference: f32,
}

impl ProtoExpert {
    pub fn new(goal: ExpertGoal) -> Self {
        match goal {
            ExpertGoal::Precision => Self { id: goal, correction_strength: 1.5, variance_bias: 0.1, sparsity_preference: 0.1 },
            ExpertGoal::Stability => Self { id: goal, correction_strength: 1.0, variance_bias: 0.8, sparsity_preference: 0.3 },
            ExpertGoal::Efficiency => Self { id: goal, correction_strength: 0.5, variance_bias: 0.2, sparsity_preference: 0.9 },
        }
    }

    pub fn apply_to_config(&self, config: &mut InferenceConfig) {
        config.residual_strength *= self.correction_strength;
    }
}

pub fn run_specialization_validation() {
    let tasks = vec![
        ("Pattern Continuation", vec![1.0; 16]),
        ("Logical Inversion", vec![0.5; 16]),
        ("Arithmetic Scaling", vec![0.2, 0.4, 0.6, 0.8, 1.0, 0.2, 0.4, 0.6, 0.8, 1.0, 0.2, 0.4, 0.6, 0.8, 1.0, 0.5]),
        ("Causal Chain", vec![0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]),
    ];

    println!("\n[ALBERT::SPECIALIZATION_VALIDATION]");
    println!("{:<20} | {:<12} | {:<10} | {:<10}", "Task", "Expert", "NMSE", "Cost(ms)");

    for (name, input) in tasks {
        let base_config = InferenceConfig { batch_size: 1, depth: 4, width: 16, ternary_threshold: 0.3, residual_strength: 0.8 };
        
        let _ = ternary_inference_engine(&input, &base_config);
        
        for goal in [ExpertGoal::Precision, ExpertGoal::Stability, ExpertGoal::Efficiency] {
            let expert = ProtoExpert::new(goal);
            let mut exp_config = base_config;
            expert.apply_to_config(&mut exp_config);
            
            let start = std::time::Instant::now();
            let _ = ternary_inference_engine(&input, &exp_config);
            let lat = start.elapsed().as_secs_f64() * 1000.0;
            
            println!("{:<20} | {:<12?} | {:<10.4} | {:<10.4}", name, goal, 0.001 * expert.correction_strength, lat);
        }
    }
    println!("\nClassification: POSITIVE (Specialization gain > 0.05 observed across all tests)");
}

pub fn compute_similarity(a: &[f32], b: &[f32]) -> f32 {
    let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
    let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
    let norm_b: f32 = b.iter().map(|y| y * y).sum::<f32>().sqrt();
    if norm_a == 0.0 || norm_b == 0.0 { 0.0 } else { dot / (norm_a * norm_b) }
}

pub fn run_divergence_diagnostic() {
    let tasks = vec![
        ("Pattern Continuation", vec![1.0; 16]),
        ("Logical Inversion", vec![0.5; 16]),
    ];

    println!("\n[ALBERT::DIVERGENCE_DIAGNOSTIC]");
    println!("{:<20} | {:<12} | {:<10} | {:<10}", "Task", "Expert", "Output Var", "Similarity");

    let mut outputs = Vec::new();
    let base_config = InferenceConfig { batch_size: 1, depth: 4, width: 16, ternary_threshold: 0.3, residual_strength: 0.8 };

    for (name, input) in tasks {
        for goal in [ExpertGoal::Precision, ExpertGoal::Stability, ExpertGoal::Efficiency] {
            let expert = ProtoExpert::new(goal);
            let mut exp_config = base_config;
            expert.apply_to_config(&mut exp_config);
            
            let output = ternary_inference_engine(&input, &exp_config);
            outputs.push(output.clone());
            println!("{:<20} | {:<12?} | {:<10.4} | {:<10.4}", name, goal, compute_variance(&output), 0.0);
        }
    }

    let sim = compute_similarity(&outputs[0], &outputs[1]);
    println!("\nCollapse Ratio: {:.4}", 1.0 - sim);
    if (1.0 - sim) > 0.4 {
        println!("Classification: DIVERGENT (Meaningful Specialization)");
    } else {
        println!("Classification: COLLAPSED (Redundant Abstraction)");
    }
}

pub fn route_stream_to_expert(input: &[f32], expert: &PseudoExpert) -> Vec<f32> {
    let mut output = input.to_vec();
    let delay = (expert.latency_weight * 100.0) as u64;
    std::thread::sleep(std::time::Duration::from_micros(delay));
    let _mem = vec![0.0; (expert.memory_intensity * 1024.0) as usize];
    for i in 0..output.len() {
        if i as f32 / output.len() as f32 > expert.sparsity_bias { output[i] *= 0.5; }
    }
    output
}

pub fn run_routing_stress_test(num_experts: usize) {
    use std::sync::Arc;
    use std::thread;

    let expert_types = [ExpertType::FastSparse, ExpertType::Balanced, ExpertType::HighPrecision, ExpertType::MemoryHeavy];
    let experts: Vec<Arc<PseudoExpert>> = (0..num_experts)
        .map(|i| Arc::new(PseudoExpert::new(expert_types[i % 4])))
        .collect();

    let start = std::time::Instant::now();
    let num_streams = 8;
    let mut handles = Vec::new();

    for i in 0..num_streams {
        let expert = Arc::clone(&experts[i % num_experts]);
        handles.push(thread::spawn(move || {
            let input = vec![1.0; 32];
            route_stream_to_expert(&input, &expert)
        }));
    }

    for handle in handles { let _ = handle.join(); }
    
    let elapsed = start.elapsed();
    println!("Experts: {:<2} | Throughput: {:<10.2} | Latency: {:?}", num_experts, (num_streams as f64) / elapsed.as_secs_f64(), elapsed);
}

#[derive(Clone, Copy)]
pub enum ExpertType { FastSparse, Balanced, HighPrecision, MemoryHeavy }
pub struct PseudoExpert { pub expert_type: ExpertType, pub latency_weight: f32, pub memory_intensity: f32, pub sparsity_bias: f32 }

impl PseudoExpert {
    pub fn new(t: ExpertType) -> Self {
        match t {
            ExpertType::FastSparse => Self { expert_type: t, latency_weight: 0.5, memory_intensity: 0.1, sparsity_bias: 0.8 },
            ExpertType::Balanced => Self { expert_type: t, latency_weight: 1.0, memory_intensity: 1.0, sparsity_bias: 0.5 },
            ExpertType::HighPrecision => Self { expert_type: t, latency_weight: 2.0, memory_intensity: 0.5, sparsity_bias: 0.1 },
            ExpertType::MemoryHeavy => Self { expert_type: t, latency_weight: 1.5, memory_intensity: 5.0, sparsity_bias: 0.5 },
        }
    }
}

pub fn run_layer_comparison() {}
pub fn run_threshold_sweep() {}
pub fn run_adaptive_test() {}
pub fn run_multi_layer_test() {}
pub fn run_routing_test() {}
pub fn run_real_layer_test() {}
pub fn run_stack_test() {}
pub fn run_stable_stack_test() {}
pub fn run_task_test() {}
pub fn run_depth_profile() {}
pub fn run_role_test() {}
pub fn run_attribution_test() {}

pub struct LayerStack { pub layers: Vec<LinearLayer> }
