/// Calculates attention weights using Cartan matrix semantics.
/// 
/// This function implements the core semantic attention mechanism
/// that enables neural networks to understand mathematical structures.
/// 
/// # Arguments
/// * `input` - Input tensor with shape [batch_size, seq_len, dim]
/// * `cartan_matrix` - Cartan matrix defining the root system
/// 
/// # Returns
/// Attention weights tensor with shape [batch_size, heads, seq_len, seq_len]
/// 
/// # Example
/// ```
/// use ruv_fann::attention::semantic_attention;
/// let weights = semantic_attention(&input, &cartan_matrix)?;
/// ```
pub fn semantic_attention(
    input: &Tensor,
    cartan_matrix: &CartanMatrix,
) -> Result<Tensor, AttentionError> {
    // Implementation...
}

// Use proper error types
#[derive(Debug, thiserror::Error)]
pub enum NeuralError {
    #[error("Invalid tensor dimensions: expected {expected}, got {actual}")]
    InvalidDimensions { expected: usize, actual: usize },
    
    #[error("WASM module failed to load: {0}")]
    WasmLoadError(#[from] wasmtime::Error),
    
    #[error("GPU memory allocation failed")]
    GpuMemoryError,
}

// Use Result types consistently
fn train_network(data: &TrainingData) -> Result<Network, NeuralError> {
    let weights = initialize_weights(data.input_size())?;
    let network = Network::new(weights)?;
    Ok(network)
}

// Use SIMD when available
#[cfg(target_feature = "simd128")]
fn vectorized_operation(data: &[f32]) -> Vec<f32> {
    use std::simd::*;
    // SIMD implementation
}

#[cfg(not(target_feature = "simd128"))]
fn vectorized_operation(data: &[f32]) -> Vec<f32> {
    // Fallback implementation
}

// Prefer rayon for parallelization
use rayon::prelude::*;

fn parallel_training(batches: &[TrainingBatch]) -> Vec<ModelUpdate> {
    batches
        .par_iter()
        .map(|batch| train_batch(batch))
        .collect()
}

// Use appropriate data structures
use std::collections::HashMap;
use indexmap::IndexMap; // For ordered maps
use smallvec::SmallVec; // For small vectors

// Manage GPU memory carefully
struct GpuBuffer {
    buffer: wgpu::Buffer,
    size: u64,
}

impl Drop for GpuBuffer {
    fn drop(&mut self) {
        // Ensure proper cleanup
        self.buffer.destroy();
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use approx::assert_relative_eq;
    use proptest::prelude::*;

    #[test]
    fn test_semantic_attention_basic() {
        let input = Tensor::zeros(&[2, 10, 64]);
        let cartan = CartanMatrix::a_series(3);
        
        let result = semantic_attention(&input, &cartan).unwrap();
        assert_eq!(result.shape(), &[2, 8, 10, 10]);
    }

    #[test]
    fn test_attention_weights_sum_to_one() {
        let input = Tensor::random(&[1, 5, 32]);
        let cartan = CartanMatrix::b_series(2);
        
        let weights = semantic_attention(&input, &cartan).unwrap();
        let sums = weights.sum_dim(-1);
        
        for &sum in sums.iter() {
            assert_relative_eq!(sum, 1.0, epsilon = 1e-6);
        }
    }

    proptest! {
        #[test]
        fn test_attention_properties(
            batch_size in 1..10usize,
            seq_len in 1..50usize,
            dim in 16..128usize,
        ) {
            let input = Tensor::random(&[batch_size, seq_len, dim]);
            let cartan = CartanMatrix::a_series(3);
            
            let result = semantic_attention(&input, &cartan);
            prop_assert!(result.is_ok());
            
            let weights = result.unwrap();
            prop_assert_eq!(weights.shape()[0], batch_size);
            prop_assert_eq!(weights.shape()[2], seq_len);
        }
    }
}

// tests/integration/neural_pipeline.rs
use ruv_fann::prelude::*;

#[tokio::test]
async fn test_full_training_pipeline() {
    let config = NetworkConfig::default()
        .with_layers(&[784, 128, 64, 10])
        .with_activation(ActivationFunction::ReLU)
        .with_cartan_attention(true);
    
    let mut network = Network::new(config).unwrap();
    let training_data = load_test_dataset().unwrap();
    
    // Train the network
    let trainer = Trainer::new()
        .with_learning_rate(0.001)
        .with_batch_size(32);
    
    let metrics = trainer.train(&mut network, &training_data).await.unwrap();
    
    assert!(metrics.final_accuracy > 0.8);
    assert!(metrics.convergence_epochs < 100);
}

// benches/attention_benchmarks.rs
use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId};
use ruv_fann::attention::*;

fn benchmark_semantic_attention(c: &mut Criterion) {
    let mut group = c.benchmark_group("semantic_attention");
    
    for seq_len in [32, 64, 128, 256].iter() {
        group.bench_with_input(
            BenchmarkId::new("cartan_attention", seq_len),
            seq_len,
            |b, &seq_len| {
                let input = Tensor::random(&[1, seq_len, 64]);
                let cartan = CartanMatrix::a_series(4);
                
                b.iter(|| {
                    semantic_attention(black_box(&input), black_box(&cartan))
                });
            },
        );
    }
    group.finish();
}

criterion_group!(benches, benchmark_semantic_attention);
criterion_main!(benches);

// Always include benchmarks for performance-critical code
use criterion::{criterion_group, criterion_main, Criterion};

fn benchmark_critical_function(c: &mut Criterion) {
    let data = setup_benchmark_data();
    
    c.bench_function("critical_function", |b| {
        b.iter(|| {
            critical_function(black_box(&data))
        });
    });
}

/// Computes semantic attention using Cartan matrix structure.
/// 
/// This function implements attention mechanisms that understand
/// the underlying mathematical structure of the data through
/// Cartan matrices from Lie algebra theory.
/// 
/// # Parameters
/// 
/// * `query` - Query tensor with shape [batch, seq_len, d_model]
/// * `key` - Key tensor with shape [batch, seq_len, d_model]  
/// * `value` - Value tensor with shape [batch, seq_len, d_model]
/// * `cartan_matrix` - Cartan matrix defining the root system
/// 
/// # Returns
/// 
/// Returns the attention output tensor with shape [batch, seq_len, d_model]
/// 
/// # Errors
/// 
/// Returns `AttentionError` if:
/// - Input tensors have incompatible shapes
/// - Cartan matrix has invalid dimensions
/// - Memory allocation fails
/// 
/// # Examples
/// 
/// ```rust
/// use ruv_fann::attention::{semantic_attention, CartanMatrix};
/// use ruv_fann::tensor::Tensor;
/// 
/// let query = Tensor::random(&[2, 10, 64]);
/// let key = Tensor::random(&[2, 10, 64]);
/// let value = Tensor::random(&[2, 10, 64]);
/// let cartan = CartanMatrix::a_series(3);
/// 
/// let output = semantic_attention(&query, &key, &value, &cartan)?;
/// assert_eq!(output.shape(), &[2, 10, 64]);
/// ```
/// 
/// # Performance
/// 
/// This function is optimized for:
/// - SIMD vectorization on x86_64
/// - Parallel computation across attention heads
/// - Memory-efficient implementation with minimal allocations
/// 
/// Typical performance: ~50 μs for sequence length 128 on modern CPUs.