// AI3 Mining Workflow
pub struct AI3MiningProcess {
    // 1. Receive mining task
    task: AI3Task,
    
    // 2. Perform tensor computation
    computation_result: TensorResult,
    
    // 3. Generate cryptographic proof
    proof: ComputationProof,
    
    // 4. Combine with traditional PoW
    block_hash: BlockHash,
    
    // 5. Submit complete solution
    solution: AI3Solution,
}

// Optimized for various hardware capabilities
pub fn matrix_multiply(a: &Matrix, b: &Matrix) -> TribeResult<Matrix> {
    // ESP32 optimization: 4x4 matrices
    if is_esp32_device() {
        return esp32_matrix_multiply_4x4(a, b);
    }
    
    // CPU optimization: arbitrary size with SIMD
    if has_simd_support() {
        return simd_matrix_multiply(a, b);
    }
    
    // Fallback: standard implementation
    standard_matrix_multiply(a, b)
}

// Example task
let task = AI3Task {
    operation: TensorOperation::MatrixMultiply,
    input_a: Matrix::new(4, 4, vec![1.0, 2.0, 3.0, 4.0, ...]),
    input_b: Matrix::new(4, 4, vec![0.1, 0.2, 0.3, 0.4, ...]),
    difficulty_reduction: 0.3, // 30% easier than pure PoW
    reward_multiplier: 1.5,    // 50% bonus reward
};

// 1D and 2D convolution for signal processing
pub fn convolution_1d(signal: &[f32], kernel: &[f32]) -> Vec<f32> {
    let mut result = Vec::new();
    
    for i in 0..signal.len() - kernel.len() + 1 {
        let mut sum = 0.0;
        for j in 0..kernel.len() {
            sum += signal[i + j] * kernel[j];
        }
        result.push(sum);
    }
    
    result
}

// Example: Audio signal processing
let audio_signal = vec![1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0];
let smoothing_kernel = vec![0.25, 0.5, 0.25];
let filtered_signal = convolution_1d(&audio_signal, &smoothing_kernel);

// Simple neural network inference
pub struct NeuralLayer {
    weights: Matrix,
    bias: Vec<f32>,
    activation: ActivationFunction,
}

pub fn neural_forward_pass(
    input: &[f32], 
    layers: &[NeuralLayer]
) -> TribeResult<Vec<f32>> {
    let mut current_input = input.to_vec();
    
    for layer in layers {
        // Matrix multiplication: input * weights
        let weighted = matrix_vector_multiply(&layer.weights, &current_input)?;
        
        // Add bias
        let biased: Vec<f32> = weighted.iter()
            .zip(&layer.bias)
            .map(|(w, b)| w + b)
            .collect();
        
        // Apply activation function
        current_input = apply_activation(&biased, &layer.activation);
    }
    
    Ok(current_input)
}

// Various activation functions optimized for different hardware
pub enum ActivationFunction {
    ReLU,
    Sigmoid,
    Tanh,
    Softmax,
}

pub fn apply_activation(input: &[f32], func: &ActivationFunction) -> Vec<f32> {
    match func {
        ActivationFunction::ReLU => {
            input.iter().map(|&x| x.max(0.0)).collect()
        },
        ActivationFunction::Sigmoid => {
            input.iter().map(|&x| 1.0 / (1.0 + (-x).exp())).collect()
        },
        ActivationFunction::Tanh => {
            input.iter().map(|&x| x.tanh()).collect()
        },
        ActivationFunction::Softmax => {
            let max_val = input.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
            let exp_vals: Vec<f32> = input.iter().map(|&x| (x - max_val).exp()).collect();
            let sum: f32 = exp_vals.iter().sum();
            exp_vals.iter().map(|&x| x / sum).collect()
        }
    }
}

// ESP32-specific optimizations
pub struct ESP32TensorOps;

impl ESP32TensorOps {
    // Optimized 4x4 matrix multiplication for ESP32
    pub fn matrix_multiply_4x4(a: &[[f32; 4]; 4], b: &[[f32; 4]; 4]) -> [[f32; 4]; 4] {
        let mut result = [[0.0; 4]; 4];
        
        // Unrolled loops for better performance on ESP32
        for i in 0..4 {
            for j in 0..4 {
                result[i][j] = a[i][0] * b[0][j] +
                              a[i][1] * b[1][j] +
                              a[i][2] * b[2][j] +
                              a[i][3] * b[3][j];
            }
        }
        
        result
    }
    
    // Memory-efficient convolution for limited RAM
    pub fn convolution_streaming(
        signal_chunk: &[f32], 
        kernel: &[f32],
        overlap: &mut Vec<f32>
    ) -> Vec<f32> {
        // Process signal in chunks to fit in ESP32 memory
        let mut extended_chunk = overlap.clone();
        extended_chunk.extend_from_slice(signal_chunk);
        
        let result = convolution_1d(&extended_chunk, kernel);
        
        // Update overlap for next chunk
        *overlap = extended_chunk[extended_chunk.len() - kernel.len() + 1..].to_vec();
        
        result
    }
}

// SIMD optimizations for x86/ARM processors
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;

pub fn simd_matrix_multiply(a: &Matrix, b: &Matrix) -> TribeResult<Matrix> {
    // Use AVX2 instructions for parallel computation
    unsafe {
        // Implementation using SIMD intrinsics
        // 8 float operations per instruction
    }
}

// GPU acceleration using compute shaders
#[cfg(feature = "gpu")]
pub fn gpu_tensor_operation(operation: &TensorOperation) -> TribeResult<TensorResult> {
    // Use wgpu or similar for GPU computation
    // Massively parallel tensor operations
}

// AI3 tasks have reduced difficulty compared to pure PoW
pub struct DifficultyCalculator {
    base_difficulty: u64,
    ai3_reduction_factor: f64,
    operation_complexity: OperationComplexity,
}

impl DifficultyCalculator {
    pub fn calculate_ai3_difficulty(&self, operation: &TensorOperation) -> u64 {
        let complexity_multiplier = match self.operation_complexity {
            OperationComplexity::Low => 0.5,    // 50% of base difficulty
            OperationComplexity::Medium => 0.7, // 70% of base difficulty
            OperationComplexity::High => 0.9,   // 90% of base difficulty
        };
        
        (self.base_difficulty as f64 * complexity_multiplier) as u64
    }
}

// Enhanced rewards for AI3 mining
pub struct AI3Rewards {
    base_mining_reward: f64,
    ai3_bonus_percentage: f64,
    operation_bonus: HashMap<TensorOperation, f64>,
}

pub fn calculate_mining_reward(
    block_reward: f64,
    ai3_task: Option<&AI3Task>
) -> (f64, f64, f64) { // (TRIBE, STOMP, AUM)
    let mut tribe_reward = block_reward;
    let mut stomp_reward = 0.0;
    let mut aum_reward = 0.0;
    
    if let Some(task) = ai3_task {
        // AI3 bonus in TRIBE
        tribe_reward *= 1.3; // 30% bonus
        
        // Additional STOMP for governance participation
        stomp_reward = block_reward * 0.1;
        
        // AUM tokens for AI computation
        aum_reward = match task.operation {
            TensorOperation::MatrixMultiply => 2.0,
            TensorOperation::Convolution => 1.5,
            TensorOperation::NeuralForward => 3.0,
            TensorOperation::Activation => 1.0,
        };
    }
    
    (tribe_reward, stomp_reward, aum_reward)
}

// Cryptographic proof that computation was performed correctly
pub struct ComputationProof {
    input_hash: Hash,
    output_hash: Hash,
    intermediate_hashes: Vec<Hash>,
    nonce: u64,
    timestamp: u64,
}

pub fn generate_computation_proof(
    input: &TensorData,
    output: &TensorData,
    operation: &TensorOperation
) -> ComputationProof {
    let input_hash = hash_tensor_data(input);
    let output_hash = hash_tensor_data(output);
    
    // Generate intermediate hashes for verification
    let intermediate_hashes = generate_intermediate_proofs(input, output, operation);
    
    ComputationProof {
        input_hash,
        output_hash,
        intermediate_hashes,
        nonce: generate_nonce(),
        timestamp: current_timestamp(),
    }
}

// Verify computation proof
pub fn verify_computation_proof(
    proof: &ComputationProof,
    expected_output: &TensorData
) -> bool {
    // Verify output hash matches
    let computed_hash = hash_tensor_data(expected_output);
    if computed_hash != proof.output_hash {
        return false;
    }
    
    // Verify intermediate steps
    verify_intermediate_proofs(&proof.intermediate_hashes)
}

// Multiple miners verify AI3 computations
pub struct AI3Consensus {
    required_confirmations: usize,
    verification_threshold: f64,
}

impl AI3Consensus {
    pub fn validate_ai3_block(&self, block: &Block) -> bool {
        if let Some(ai3_data) = &block.ai3_data {
            // Get verification from multiple miners
            let verifications = self.get_verification_results(&ai3_data);
            
            // Require majority consensus
            let success_rate = verifications.iter()
                .filter(|&&result| result)
                .count() as f64 / verifications.len() as f64;
            
            success_rate >= self.verification_threshold
        } else {
            true // Regular PoW block
        }
    }
}

// Performance per watt calculations
pub struct EfficiencyMetrics {
    operations_per_second: f64,
    power_consumption: f64,
    efficiency_score: f64, // ops/sec per watt
}

// ESP32 is surprisingly efficient for its cost
let esp32_efficiency = EfficiencyMetrics {
    operations_per_second: 22.0,  // ~22 matrix ops/sec
    power_consumption: 2.5,       // 2.5 watts
    efficiency_score: 8.8,        // 8.8 ops/sec/watt
};

// High-end GPU has raw power but lower efficiency
let rtx3060_efficiency = EfficiencyMetrics {
    operations_per_second: 10000.0, // 10k matrix ops/sec
    power_consumption: 170.0,        // 170 watts
    efficiency_score: 58.8,          // 58.8 ops/sec/watt
};

// Future tensor operations in development
pub enum FutureTensorOps {
    // Transformer attention mechanisms
    MultiHeadAttention {
        query: Matrix,
        key: Matrix,
        value: Matrix,
        num_heads: usize,
    },
    
    // Graph neural networks
    GraphConvolution {
        node_features: Matrix,
        adjacency_matrix: Matrix,
        edge_weights: Vec<f32>,
    },
    
    // Quantum-inspired operations
    QuantumGate {
        state_vector: Vec<Complex<f32>>,
        gate_matrix: Matrix,
    },
}

// Easy integration for developers
use tribechain_ai3::prelude::*;

// Create custom tensor operation
let custom_op = TensorOperationBuilder::new()
    .name("custom_convolution")
    .input_shape([32, 32, 3])
    .kernel_size([3, 3])
    .difficulty_reduction(0.4)
    .reward_multiplier(1.6)
    .build()?;

// Submit to network
let task_id = ai3_engine.submit_operation(custom_op).await?;

// Comprehensive testing for AI3 operations
#[cfg(test)]
mod ai3_tests {
    use super::*;
    
    #[test]
    fn test_matrix_multiply_correctness() {
        let a = Matrix::identity(4);
        let b = Matrix::random(4, 4);
        let result = matrix_multiply(&a, &b).unwrap();
        assert_eq!(result, b); // Identity matrix property
    }
    
    #[test]
    fn test_esp32_optimization() {
        let esp32_result = ESP32TensorOps::matrix_multiply_4x4(&a, &b);
        let standard_result = standard_matrix_multiply(&a, &b);
        assert_tensor_equal(esp32_result, standard_result, 1e-6);
    }
}