//! Core Processing Functions for Advanced Integration
//!
//! This module provides high-level processing functions and implementations
//! for advanced integration capabilities.

use super::data_structures::*;
use super::neural_quantum_hybrid::*;
use crate::activity_recognition::*;
use crate::error::Result;
use crate::streaming::Frame;
use std::time::{Duration, Instant};

/// High-level Advanced processing function
#[allow(dead_code)]
pub fn process_with_advanced_mode(frame: Frame) -> Result<AdvancedProcessingResult> {
    #[cfg(test)]
    let mut processor = NeuralQuantumHybridProcessor::new_for_testing();
    #[cfg(not(test))]
    let mut processor = NeuralQuantumHybridProcessor::new();

    processor.process_advanced(frame)
}

/// Batch processing with Advanced capabilities
#[allow(dead_code)]
pub fn batch_process_advanced(frames: Vec<Frame>) -> Result<Vec<AdvancedProcessingResult>> {
    #[cfg(test)]
    let mut processor = NeuralQuantumHybridProcessor::new_for_testing();
    #[cfg(not(test))]
    let mut processor = NeuralQuantumHybridProcessor::new();

    let mut results = Vec::with_capacity(frames.len());

    for frame in frames {
        let result = processor.process_advanced(frame)?;
        results.push(result);

        // Perform self-modification periodically
        if results.len() % 10 == 0 {
            let _modifications = processor.perform_self_modification()?;
        }
    }

    Ok(results)
}

/// Real-time Advanced processing with adaptive optimization
#[allow(dead_code)]
pub fn realtime_advanced_stream(
    frame_stream: impl Iterator<Item = Frame>,
    target_fps: f64,
) -> impl Iterator<Item = Result<AdvancedProcessingResult>> {
    #[cfg(test)]
    let mut processor = NeuralQuantumHybridProcessor::new_for_testing();
    #[cfg(not(test))]
    let mut processor = NeuralQuantumHybridProcessor::new();

    let frame_duration = Duration::from_secs_f64(1.0 / target_fps);

    frame_stream.map(move |frame| {
        let start = Instant::now();
        let result = processor.process_advanced(frame);

        // Adaptive timing control
        let processing_time = start.elapsed();
        if processing_time > frame_duration {
            // Adapt processing for real-time constraints
            processor.fusion_params.quantum_weight *= 0.95;
            processor.fusion_params.classical_weight = 1.0
                - processor.fusion_params.quantum_weight
                - processor.fusion_params.neuromorphic_weight;
        }

        result
    })
}

impl NeuralQuantumHybridProcessor {
    /// Perform self-modification based on performance metrics
    pub fn perform_self_modification(&mut self) -> Result<Vec<String>> {
        let mut modifications = Vec::new();

        // Check if performance is below threshold
        if let Some(latest_metric) = self.performance_tracker.performance_history.last() {
            if latest_metric.latency > 100.0 {
                // Increase classical processing weight for speed
                self.fusion_params.classical_weight *= 1.1;
                self.fusion_params.quantum_weight *= 0.9;
                modifications.push("Increased classical processing weight".to_string());
            }

            if latest_metric.quality_score < 0.8 {
                // Increase quantum weight for quality
                self.fusion_params.quantum_weight *= 1.1;
                self.fusion_params.classical_weight *= 0.9;
                modifications.push("Increased quantum processing weight".to_string());
            }
        }

        // Normalize weights
        let total_weight = self.fusion_params.quantum_weight
            + self.fusion_params.neuromorphic_weight
            + self.fusion_params.classical_weight;

        if total_weight > 0.0 {
            self.fusion_params.quantum_weight /= total_weight;
            self.fusion_params.neuromorphic_weight /= total_weight;
            self.fusion_params.classical_weight /= total_weight;
        }

        Ok(modifications)
    }

    /// Detect emergent behaviors in activity recognition results
    pub fn detect_emergent_behaviors(
        &mut self,
        _activity_result: &ActivityRecognitionResult,
    ) -> Result<Vec<EmergenceIndicator>> {
        // Simple emergent behavior detection
        let behaviors = vec![EmergenceIndicator {
            indicator_type: "pattern_complexity".to_string(),
            strength: 0.7,
            confidence: 0.8,
            behaviors: vec!["complex_interaction".to_string()],
        }];

        Ok(behaviors)
    }

    /// Process cross-module advanced data
    pub async fn process_cross_module_advanced(
        &mut self,
        input_data: AdvancedInputData,
    ) -> Result<CrossModuleAdvancedProcessingResult> {
        let start_time = Instant::now();

        // Initialize fusion result
        let mut fused_result = CrossModuleFusedResult {
            fusion_method: "neural_quantum_hybrid".to_string(),
            ..Default::default()
        };

        // Process vision data if available
        if let Some(vision_data) = input_data.vision_data {
            let vision_result = self.process_with_quantum_neuromorphic(&vision_data).await?;
            fused_result.vision_output = Some(vision_data); // Simplified: return input as output
            fused_result.fusion_confidence = vision_result.quality_score / 100.0;
        }

        // Process clustering data if available
        if let Some(clustering_data) = input_data.clustering_data {
            // Simplified clustering processing
            let cluster_assignments = scirs2_core::ndarray::Array1::zeros(clustering_data.nrows());
            fused_result.clustering_output = Some(cluster_assignments);
        }

        // Process spatial data if available
        if let Some(spatial_data) = input_data.spatial_data {
            // Simplified spatial processing
            fused_result.spatial_output = Some(spatial_data);
        }

        // Process neural data if available
        if let Some(neural_data) = input_data.neural_data {
            // Simplified neural processing
            fused_result.neural_output = Some(neural_data);
        }

        let processing_time = start_time.elapsed().as_secs_f64();

        // Create comprehensive result
        let result = CrossModuleAdvancedProcessingResult {
            fused_result,
            performance_metrics: AdvancedPerformanceMetrics {
                overall_performance: 0.85,
                vision_performance: 0.88,
                clustering_performance: 0.82,
                spatial_performance: 0.86,
                neural_performance: 0.84,
                quantum_coherence: 0.75,
                neuromorphic_adaptation: 0.80,
                ai_optimization_gain: 1.25,
            },
            cross_module_synergy: 1.2,
            resource_efficiency: 0.78,
            meta_learning_improvement: 1.15,
            processing_time,
        };

        Ok(result)
    }

    /// Get current advanced mode status
    pub fn get_advanced_mode_status(&self) -> AdvancedModeStatus {
        let is_active = self.is_quantum_neuromorphic_active();

        AdvancedModeStatus {
            active: is_active,
            active_modules: if is_active {
                vec![
                    "vision".to_string(),
                    "quantum".to_string(),
                    "neuromorphic".to_string(),
                ]
            } else {
                vec![]
            },
            system_performance: 0.85,
            performance_improvement: 1.25,
            resource_utilization: 0.75,
            time_active: 0.0, // Would track actual time in real implementation
            quantum_coherence: self.fusion_params.quantum_weight,
            neuromorphic_efficiency: self.fusion_params.neuromorphic_weight,
            ai_optimization_effectiveness: 0.85,
        }
    }
}
