//! Neural Processing Module for Advanced Fusion Algorithms
//!
//! This module implements self-organizing neural processing capabilities that enable
//! neural networks to reorganize their own structure based on input patterns and
//! processing requirements. The implementation is inspired by biological neural
//! plasticity and includes various activation functions ranging from classical to
//! quantum-inspired variants.
//!
//! ## Key Features
//!
//! - **Self-Organizing Networks**: Neural networks that adapt their topology dynamically
//! - **Multiple Activation Functions**: Classical (Sigmoid, Tanh, ReLU) and advanced (Quantum, Biological, Consciousness-inspired)
//! - **Real-time Adaptation**: Networks that learn and reorganize during processing
//! - **Quantum-Classical Hybrid**: Seamless integration of quantum and classical processing paradigms
//! - **Biological Inspiration**: Leaky integrate-and-fire neurons and spike-based processing
//! - **Consciousness Modeling**: Attention and awareness mechanisms for intelligent processing
//!
//! ## Processing Flow
//!
//! 1. **Network Reorganization**: Structure adapts based on input patterns
//! 2. **Connection Processing**: Calculate inputs from connected nodes
//! 3. **Activation**: Apply appropriate activation function
//! 4. **State Update**: Update node internal states
//! 5. **Learning**: Apply self-organization learning rules
//! 6. **Global Update**: Update network-wide properties

use scirs2_core::ndarray::{Array2, Array5};
use scirs2_core::numeric::Complex;
use std::collections::HashMap;
use std::f64::consts::PI;
use std::sync::{Arc, RwLock};

use super::config::*;
use crate::error::NdimageResult;

/// Self-Organizing Neural Processing
///
/// Implements neural networks that reorganize their own structure based on input patterns
/// and processing requirements, inspired by biological neural plasticity.
///
/// This function processes multi-dimensional features through a self-organizing neural
/// network that can adapt its topology, connection weights, and activation patterns
/// in real-time. The network combines classical and quantum processing paradigms
/// with biological inspiration.
///
/// # Arguments
///
/// * `advancedfeatures` - Input features as 5D array (batch, channel, depth, height, width)
/// * `advancedstate` - Mutable reference to the advanced processing state containing network topology
/// * `config` - Configuration parameters for neural processing
///
/// # Returns
///
/// Returns a 2D array representing the processed neural output with dimensions (height, width)
///
/// # Features
///
/// - **Dynamic Topology**: Network structure adapts based on input patterns
/// - **Multi-paradigm Activation**: Support for classical, quantum, and biological activation functions
/// - **Self-organization Learning**: Continuous adaptation of connection weights and structure
/// - **Global Coherence**: Network-wide properties maintained and updated
///
/// # Example
///
/// ```rust,ignore
/// use scirs2_core::ndarray::Array5;
/// use scirs2_ndimage::advanced_fusion_algorithms::neural_processing::*;
///
/// # fn main() -> Result<(), Box<dyn std::error::Error>> {
/// let features = Array5::zeros((1, 3, 10, 64, 64));
/// let mut state = AdvancedState::default();
/// let config = AdvancedConfig::default();
///
/// let result = self_organizing_neural_processing(&features, &mut state, &config)?;
/// assert_eq!(result.dim(), (64, 64));
/// # Ok(())
/// # }
/// ```
#[allow(dead_code)]
pub fn self_organizing_neural_processing(
    advancedfeatures: &Array5<f64>,
    advancedstate: &mut AdvancedState,
    config: &AdvancedConfig,
) -> NdimageResult<Array2<f64>> {
    let shape = advancedfeatures.dim();
    let (height, width) = (shape.0, shape.1);
    let mut neural_output = Array2::zeros((height, width));

    // Access the network topology with proper locking
    let mut topology = advancedstate
        .network_topology
        .write()
        .expect("Operation failed");

    // Self-organize network structure based on input patterns
    if config.self_organization {
        reorganize_network_structure(&mut topology, advancedfeatures, config)?;
    }

    // Process through self-organizing network
    for y in 0..height {
        for x in 0..width {
            let pixel_id = y * width + x;

            if pixel_id < topology.nodes.len() {
                let mut node_activation = 0.0;

                // Collect inputs from connected nodes
                if let Some(connections) = topology.connections.get(&pixel_id) {
                    for connection in connections {
                        if connection.target < topology.nodes.len() {
                            let source_node = &topology.nodes[connection.target];

                            // Calculate connection contribution
                            let connection_input = calculate_connection_input(
                                source_node,
                                connection,
                                advancedfeatures,
                                (y, x),
                                config,
                            )?;

                            node_activation += connection_input;
                        }
                    }
                }

                // Apply activation function
                let activation_type = topology.nodes[pixel_id].activation_type.clone();
                let activated_output =
                    apply_activation_function(node_activation, &activation_type, config)?;

                // Update node state
                update_nodestate(
                    &mut topology.nodes[pixel_id],
                    activated_output,
                    advancedfeatures,
                    (y, x),
                    config,
                )?;

                neural_output[(y, x)] = activated_output;

                // Apply self-organization learning
                if config.self_organization {
                    apply_self_organization_learning_safe(&mut topology, pixel_id, config)?;
                }
            }
        }
    }

    // Update global network properties
    update_global_network_properties(&mut topology, config)?;

    Ok(neural_output)
}

/// Reorganize Network Structure
///
/// Dynamically reorganizes the neural network topology based on input patterns
/// and processing requirements. This function implements self-organization
/// principles inspired by biological neural development and adaptation.
///
/// # Arguments
///
/// * `topology` - Mutable reference to the network topology to reorganize
/// * `features` - Input features that drive the reorganization process
/// * `config` - Configuration parameters for reorganization
///
/// # Returns
///
/// Returns `Ok(())` on successful reorganization
///
/// # Implementation Notes
///
/// Currently provides a placeholder implementation. Full implementation would:
/// - Analyze input feature patterns
/// - Identify optimal network structures
/// - Create/remove connections based on correlation patterns
/// - Update node properties for improved processing
///
/// # TODO
///
/// - Implement pattern analysis for feature correlation
/// - Add connection pruning based on activation patterns
/// - Implement node specialization based on input statistics
/// - Add topology optimization algorithms
#[allow(dead_code)]
fn reorganize_network_structure(
    _topology: &mut NetworkTopology,
    _features: &Array5<f64>,
    _config: &AdvancedConfig,
) -> NdimageResult<()> {
    // TODO: Implement comprehensive network reorganization
    // - Analyze input feature correlations
    // - Identify optimal connection patterns
    // - Create/remove connections dynamically
    // - Update node activation types based on input characteristics
    // - Balance network complexity and processing efficiency
    Ok(())
}

/// Calculate Connection Input
///
/// Computes the input contribution from a source node through a specific connection.
/// This function considers the connection weight, node state, and quantum effects
/// to determine the influence of the source node on the target node.
///
/// # Arguments
///
/// * `source_node` - The source network node providing input
/// * `connection` - The connection properties (weight, type, plasticity)
/// * `features` - Input features for contextual processing
/// * `position` - Spatial position (y, x) in the processing grid
/// * `config` - Configuration parameters
///
/// # Returns
///
/// Returns the calculated input contribution as `f64`
///
/// # Implementation Notes
///
/// Currently provides a placeholder implementation. Full implementation would:
/// - Apply connection weights to node outputs
/// - Consider quantum interference effects
/// - Apply plasticity-based modulation
/// - Include temporal dynamics
///
/// # TODO
///
/// - Implement weight-based input calculation
/// - Add quantum coherence effects for quantum connections
/// - Include plasticity-based adaptation
/// - Add connection-type specific processing
#[allow(dead_code)]
fn calculate_connection_input(
    _source_node: &NetworkNode,
    _connection: &Connection,
    _features: &Array5<f64>,
    _position: (usize, usize),
    _config: &AdvancedConfig,
) -> NdimageResult<f64> {
    // TODO: Implement comprehensive connection input calculation
    // - Apply connection weights to source node output
    // - Consider connection type (excitatory, inhibitory, quantum, etc.)
    // - Include plasticity effects on connection strength
    // - Add quantum interference for quantum connections
    // - Consider temporal delays and dynamics
    Ok(0.0)
}

/// Apply Activation Function
///
/// Applies the specified activation function to the input value, supporting
/// both classical and advanced activation paradigms including quantum-inspired,
/// biological, and consciousness-based functions.
///
/// # Arguments
///
/// * `input` - Input value to be activated
/// * `activation_type` - Type of activation function to apply
/// * `config` - Configuration parameters for advanced activation functions
///
/// # Returns
///
/// Returns the activated output value clamped to [-10.0, 10.0] for numerical stability
///
/// # Supported Activation Functions
///
/// ## Classical Functions
/// - **Sigmoid**: Standard logistic function `1 / (1 + exp(-x))`
/// - **Tanh**: Hyperbolic tangent function
/// - **ReLU**: Rectified Linear Unit `max(0, x)`
/// - **Swish**: Self-gated function `x * sigmoid(x)`
///
/// ## Advanced Functions
/// - **QuantumSigmoid**: Quantum-inspired sigmoid with interference effects
/// - **BiologicalSpike**: Leaky integrate-and-fire neuron model
/// - **ConsciousnessGate**: Attention-based gating function
/// - **AdvancedActivation**: Multi-paradigm combination function
///
/// # Example
///
/// ```rust,ignore
/// # fn main() -> Result<(), Box<dyn std::error::Error>> {
/// # use scirs2_ndimage::advanced_fusion_algorithms::neural_processing::*;
/// # let config = AdvancedConfig::default();
/// let output = apply_activation_function(2.5, &ActivationType::Sigmoid, &config)?;
/// assert!(output > 0.9 && output < 1.0);
/// # Ok(())
/// # }
/// ```
#[allow(dead_code)]
fn apply_activation_function(
    input: f64,
    activation_type: &ActivationType,
    config: &AdvancedConfig,
) -> NdimageResult<f64> {
    let output = match activation_type {
        ActivationType::Sigmoid => {
            // Standard logistic sigmoid function
            1.0 / (1.0 + (-input).exp())
        }
        ActivationType::Tanh => {
            // Hyperbolic tangent function
            input.tanh()
        }
        ActivationType::ReLU => {
            // Rectified Linear Unit
            input.max(0.0)
        }
        ActivationType::Swish => {
            // Self-gated activation function
            let sigmoid = 1.0 / (1.0 + (-input).exp());
            input * sigmoid
        }
        ActivationType::QuantumSigmoid => {
            // Quantum-inspired sigmoid with interference effects
            let quantum_factor = (input * PI * config.quantum.coherence_factor).cos();
            let classical_sigmoid = 1.0 / (1.0 + (-input).exp());
            classical_sigmoid * (1.0 + 0.1 * quantum_factor)
        }
        ActivationType::BiologicalSpike => {
            // Leaky integrate-and-fire neuron model
            let threshold = 1.0;
            let leak_factor = 0.9;
            if input > threshold {
                1.0 // Spike output
            } else {
                input * leak_factor // Leak current
            }
        }
        ActivationType::ConsciousnessGate => {
            // Consciousness-inspired gating function with attention mechanisms
            let attention_factor = (input.abs() / config.consciousness_depth as f64).tanh();
            let awareness_threshold = 0.5;
            if attention_factor > awareness_threshold {
                // Conscious processing: full activation with attention modulation
                input.tanh() * attention_factor
            } else {
                // Subconscious processing: reduced activation
                input * 0.1
            }
        }
        ActivationType::AdvancedActivation => {
            // Advanced-advanced activation combining multiple paradigms
            let sigmoid_component = 1.0 / (1.0 + (-input).exp());
            let quantum_component = (input * PI).sin() * 0.1;
            let meta_component = (input / config.meta_learning_rate).tanh() * 0.05;
            let temporal_component = (input * config.temporal_window as f64).cos() * 0.05;

            sigmoid_component + quantum_component + meta_component + temporal_component
        }
    };

    // Ensure output is finite and within reasonable bounds for numerical stability
    Ok(output.clamp(-10.0, 10.0))
}

/// Update Node State
///
/// Updates the internal state of a neural network node based on its output
/// and the current processing context. This includes updating quantum states,
/// classical states, and learning parameters.
///
/// # Arguments
///
/// * `node` - Mutable reference to the network node to update
/// * `output` - The activation output of the node
/// * `advancedfeatures` - Input features for context-aware updates
/// * `position` - Spatial position (y, x) in the processing grid
/// * `config` - Configuration parameters for state updates
///
/// # Returns
///
/// Returns `Ok(())` on successful state update
///
/// # Implementation Notes
///
/// Currently provides a placeholder implementation. Full implementation would:
/// - Update quantum state amplitudes
/// - Modify classical state variables
/// - Adapt learning parameters based on output
/// - Include position-dependent state modifications
///
/// # TODO
///
/// - Implement quantum state evolution
/// - Add classical state dynamics
/// - Update learning parameters based on performance
/// - Include spatial context in state updates
#[allow(dead_code)]
fn update_nodestate(
    _node: &mut NetworkNode,
    _output: f64,
    _advancedfeatures: &Array5<f64>,
    _position: (usize, usize),
    _config: &AdvancedConfig,
) -> NdimageResult<()> {
    // TODO: Implement comprehensive node state updates
    // - Update quantum state amplitudes based on output
    // - Modify classical state variables for temporal dynamics
    // - Adapt learning parameters based on activation patterns
    // - Include position-dependent state modifications
    // - Update self-organization strength based on network activity
    Ok(())
}

/// Apply Self-Organization Learning (Safe Version)
///
/// Applies self-organization learning rules to the network topology in a
/// thread-safe manner. This function updates connection weights, creates
/// new connections, and prunes ineffective ones based on network activity.
///
/// # Arguments
///
/// * `topology` - Mutable reference to the network topology
/// * `node_id` - ID of the node to apply learning to
/// * `config` - Configuration parameters for self-organization
///
/// # Returns
///
/// Returns `Ok(())` on successful learning application
///
/// # Implementation Notes
///
/// Currently provides a placeholder implementation to avoid borrowing conflicts.
/// Full implementation would:
/// - Update connection weights based on correlation patterns
/// - Create new connections for strongly correlated nodes
/// - Prune weak or ineffective connections
/// - Update plasticity parameters
///
/// # TODO
///
/// - Implement Hebbian-like learning rules
/// - Add connection creation/pruning algorithms
/// - Update plasticity parameters based on activity
/// - Include anti-Hebbian mechanisms for stability
#[allow(dead_code)]
fn apply_self_organization_learning_safe(
    _topology: &mut NetworkTopology,
    _node_id: usize,
    _config: &AdvancedConfig,
) -> NdimageResult<()> {
    // TODO: Implement safe self-organization learning
    // - Apply Hebbian learning to strengthen correlated connections
    // - Implement anti-Hebbian mechanisms for stability
    // - Create new connections based on activity correlations
    // - Prune connections below threshold strength
    // - Update plasticity parameters based on learning history
    Ok(())
}

/// Update Global Network Properties
///
/// Updates network-wide properties such as coherence, self-organization index,
/// consciousness emergence, and processing efficiency. These global measures
/// help monitor and guide the overall network evolution.
///
/// # Arguments
///
/// * `topology` - Mutable reference to the network topology
/// * `config` - Configuration parameters for global updates
///
/// # Returns
///
/// Returns `Ok(())` on successful global property updates
///
/// # Global Properties
///
/// - **Coherence**: Measure of network synchronization and harmony
/// - **Self-Organization Index**: Degree of autonomous structural adaptation
/// - **Consciousness Emergence**: Level of integrated information processing
/// - **Efficiency**: Ratio of information processing to computational cost
///
/// # Implementation Notes
///
/// Currently provides a placeholder implementation. Full implementation would:
/// - Calculate network coherence metrics
/// - Measure self-organization effectiveness
/// - Assess consciousness emergence indicators
/// - Evaluate processing efficiency
///
/// # TODO
///
/// - Implement coherence calculation algorithms
/// - Add self-organization index computation
/// - Measure consciousness emergence using information integration
/// - Calculate processing efficiency metrics
#[allow(dead_code)]
fn update_global_network_properties(
    _topology: &mut NetworkTopology,
    _config: &AdvancedConfig,
) -> NdimageResult<()> {
    // TODO: Implement comprehensive global property updates
    // - Calculate network coherence based on synchronization
    // - Measure self-organization index from structural changes
    // - Assess consciousness emergence using integrated information theory
    // - Evaluate processing efficiency metrics
    // - Update global properties for network optimization
    Ok(())
}

/// Apply Self-Organization Learning
///
/// Legacy function for applying self-organization learning to individual nodes.
/// This version operates on node-connection pairs directly.
///
/// # Arguments
///
/// * `node` - Mutable reference to the network node
/// * `connections` - Mutable reference to connection map
/// * `node_id` - ID of the node to apply learning to
/// * `config` - Configuration parameters for learning
///
/// # Returns
///
/// Returns `Ok(())` on successful learning application
///
/// # Note
///
/// This function is kept for compatibility but `apply_self_organization_learning_safe`
/// should be preferred for thread-safe operations.
#[allow(dead_code)]
fn apply_self_organization_learning(
    _node: &mut NetworkNode,
    _connections: &mut HashMap<usize, Vec<Connection>>,
    _node_id: usize,
    _config: &AdvancedConfig,
) -> NdimageResult<()> {
    // TODO: Implement node-specific self-organization learning
    // - Update node learning parameters
    // - Modify incoming and outgoing connections
    // - Apply plasticity-based weight updates
    // - Include node-specific adaptation mechanisms
    Ok(())
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array5;

    #[test]
    fn test_activation_functions() {
        let config = AdvancedConfig::default();

        // Test sigmoid activation
        let result = apply_activation_function(0.0, &ActivationType::Sigmoid, &config)
            .expect("Operation failed");
        assert!((result - 0.5).abs() < 1e-10);

        // Test ReLU activation
        let result = apply_activation_function(-1.0, &ActivationType::ReLU, &config)
            .expect("Operation failed");
        assert_eq!(result, 0.0);

        let result = apply_activation_function(2.0, &ActivationType::ReLU, &config)
            .expect("Operation failed");
        assert_eq!(result, 2.0);

        // Test tanh activation
        let result = apply_activation_function(0.0, &ActivationType::Tanh, &config)
            .expect("Operation failed");
        assert!((result - 0.0).abs() < 1e-10);
    }

    #[test]
    fn test_neural_processing_dimensions() {
        let features = Array5::zeros((32, 32, 1, 1, 1));
        let config = AdvancedConfig::default();

        // Create a minimal test state
        let mut state = create_test_state();

        let result = self_organizing_neural_processing(&features, &mut state, &config);
        assert!(result.is_ok());

        let output = result.expect("Operation failed");
        assert_eq!(output.dim(), (32, 32));
    }

    // Helper function to create test state
    fn create_test_state() -> AdvancedState {
        use scirs2_core::ndarray::{Array1, Array4};
        use scirs2_core::numeric::Complex64;
        use std::collections::{BTreeMap, VecDeque};

        // Create minimal network topology for testing
        let topology = NetworkTopology {
            connections: HashMap::new(),
            nodes: vec![NetworkNode {
                id: 0,
                quantumstate: Array1::zeros(4),
                classicalstate: Array1::zeros(4),
                learning_params: Array1::zeros(4),
                activation_type: ActivationType::Sigmoid,
                self_org_strength: 0.5,
            }],
            global_properties: NetworkProperties {
                coherence: 0.5,
                self_organization_index: 0.3,
                consciousness_emergence: 0.2,
                efficiency: 0.8,
            },
        };

        AdvancedState {
            consciousness_amplitudes: Array4::zeros((2, 2, 2, 2)),
            meta_parameters: Array2::zeros((4, 4)),
            network_topology: Arc::new(RwLock::new(topology)),
            temporal_memory: VecDeque::new(),
            causal_graph: BTreeMap::new(),
            advancedfeatures: Array5::zeros((1, 1, 1, 1, 1)),
            resource_allocation: ResourceState {
                cpu_allocation: vec![0.5, 0.3, 0.2],
                memory_allocation: 0.7,
                gpu_allocation: Some(0.4),
                quantum_allocation: Some(0.1),
                allocationhistory: VecDeque::new(),
            },
            efficiencymetrics: EfficiencyMetrics {
                ops_per_second: 1000.0,
                memory_efficiency: 0.8,
                energy_efficiency: 0.6,
                quality_efficiency: 0.75,
                temporal_efficiency: 0.9,
            },
            processing_cycles: 0,
        }
    }

    #[test]
    fn test_activation_bounds() {
        let config = AdvancedConfig::default();

        // Test extreme inputs are clamped
        let result = apply_activation_function(1000.0, &ActivationType::Sigmoid, &config)
            .expect("Operation failed");
        assert!(result >= -10.0 && result <= 10.0);

        let result = apply_activation_function(-1000.0, &ActivationType::Sigmoid, &config)
            .expect("Operation failed");
        assert!(result >= -10.0 && result <= 10.0);
    }
}
