Research-Stack/5-Applications/teleport-kanban/src/branch_prediction.rs

use serde::{Deserialize, Serialize};
use anyhow::Result;
use std::collections::HashMap;
use dashmap::DashMap;
use chrono::{Utc, Duration};

use crate::teleport::{TeleportCompressor, BF16};
use crate::moe::MixtureOfExperts;
use crate::interface::{HardwareConfig, StoryArc};
use crate::safety::SafetyMonitor;

/// Branch Prediction & Quantum Annealing Layer
/// Uses BF16 compression and quantum annealing for intelligent branch prediction
/// with full entrainment prevention and OS default preservation
#[derive(Debug, Clone)]
pub struct BranchPredictionOptimizer {
    /// Branch history cache with BF16 compression
    pub branch_history: DashMap<String, CompressedBranchHistory>,
    /// Quantum annealing state for prediction
    pub quantum_annealing: DashMap<String, QuantumAnnealingState>,
    /// Entrainment prevention cache
    pub entrainment_prevention: DashMap<String, EntrainmentPreventionState>,
    /// OS default preservation cache
    pub os_defaults: DashMap<String, OSDefaultState>,
    /// Prediction accuracy tracking
    pub accuracy_metrics: DashMap<String, PredictionMetrics>,
    /// Safety monitor for fallback
    pub safety_monitor: SafetyMonitor,
    /// Teleport compressor for branch data
    pub teleport: TeleportCompressor,
    /// MoE for intelligent prediction
    pub moe: MixtureOfExperts,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressedBranchHistory {
    pub branch_id: String,
    pub compressed_history: String,
    pub bf16_predictions: Vec<BF16>,
    pub last_updated: chrono::DateTime<Utc>,
    pub prediction_confidence: f32,
    pub compression_level: CompressionLevel,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumAnnealingState {
    pub annealing_id: String,
    pub coherence_time: Duration,
    pub energy_landscape: Vec<BF16>,
    pub tunneling_probability: f32,
    pub optimization_progress: f32,
    pub quantum_state: QuantumState,
    pub prediction_path: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumState {
    pub superposition: Vec<BF16>,
    pub entanglement: Vec<String>,
    pub collapse_probability: BF16,
    pub coherence_time: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EntrainmentPreventionState {
    pub prevention_id: String,
    pub entrainment_detected: bool,
    pub prevention_actions: Vec<PreventionAction>,
    pub last_prevention: Option<chrono::DateTime<Utc>>,
    pub prevention_effectiveness: f32,
    pub os_default_preserved: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PreventionAction {
    pub action_id: String,
    pub action_type: PreventionType,
    pub timestamp: chrono::DateTime<Utc>,
    pub effectiveness: f32,
    pub os_default_affected: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PreventionType {
    Randomization,
    Hysteresis,
    JitterInjection,
    QuantumDecoherence,
    OSDefaultPreservation,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OSDefaultState {
    pub os_default_id: String,
    pub default_values: HashMap<String, String>,
    pub last_backup: chrono::DateTime<Utc>,
    pub modified_by_optimization: bool,
    pub restoration_required: bool,
    pub preservation_priority: u32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionMetrics {
    pub metrics_id: String,
    pub total_predictions: u64,
    pub correct_predictions: u64,
    pub accuracy_percentage: f32,
    pub average_confidence: f32,
    pub quantum_improvement: f32,
    pub last_updated: chrono::DateTime<Utc>,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum CompressionLevel {
    Light,
    Medium,
    Heavy,
    Quantum,
}

impl BranchPredictionOptimizer {
    pub fn new(safety_monitor: SafetyMonitor) -> Self {
        Self {
            branch_history: DashMap::new(),
            quantum_annealing: DashMap::new(),
            entrainment_prevention: DashMap::new(),
            os_defaults: DashMap::new(),
            accuracy_metrics: DashMap::new(),
            safety_monitor,
            teleport: TeleportCompressor::new(),
            moe: MixtureOfExperts::new(),
        }
    }

    /// Initialize branch prediction optimization
    pub async fn initialize_branch_prediction(&self, _hardware_config: &HardwareConfig) -> Result<()> {
        // Initialize OS defaults preservation
        self.initialize_os_defaults_preservation().await?;
        
        // Initialize quantum annealing for branch prediction
        self.initialize_quantum_annealing().await?;
        
        // Initialize entrainment prevention
        self.initialize_entrainment_prevention().await?;
        
        // Initialize prediction metrics
        self.initialize_prediction_metrics().await?;

        Ok(())
    }

    /// Predict branch outcome with quantum annealing and BF16 compression
    pub async fn predict_branch(&self, branch_id: &str, history: &[bool]) -> Result<BranchPrediction> {
        // Check safety thresholds first
        if self.safety_monitor.is_in_emergency_fallback() {
            return self.fallback_prediction(branch_id, history).await;
        }

        // Compress branch history with BF16
        let compressed_history = self.compress_branch_history(branch_id, history).await?;
        
        // Apply quantum annealing for prediction
        let quantum_prediction = self.apply_quantum_annealing(branch_id, &compressed_history).await?;
        
        // Check for entrainment and prevent it
        let prevention_applied = self.check_and_prevent_entrainment(branch_id).await?;
        
        // Ensure OS defaults are preserved
        let os_defaults_preserved = self.ensure_os_defaults_preservation(branch_id).await?;

        // Calculate final prediction with confidence
        let final_prediction = BranchPrediction {
            branch_id: branch_id.to_string(),
            predicted_outcome: quantum_prediction.predicted_outcome,
            confidence: quantum_prediction.confidence,
            quantum_improvement: quantum_prediction.quantum_improvement,
            entrainment_prevented: prevention_applied,
            os_defaults_preserved,
            prediction_method: PredictionMethod::QuantumAnnealing,
            timestamp: Utc::now(),
        };

        // Update accuracy metrics
        self.update_prediction_metrics(&final_prediction).await?;

        Ok(final_prediction)
    }

    /// Compress branch history using BF16 teleport compression
    async fn compress_branch_history(&self, branch_id: &str, history: &[bool]) -> Result<CompressedBranchHistory> {
        // Convert boolean history to string for compression
        let history_string = history.iter()
            .map(|&b| if b { "1" } else { "0" })
            .collect::<Vec<_>>()
            .join("");

        // Compress with teleport system
        let compressed = self.teleport.compress_semantic(&history_string).await?;
        
        // Generate BF16 predictions
        let bf16_predictions = self.generate_bf16_predictions(history).await?;

        let compressed_history = CompressedBranchHistory {
            branch_id: branch_id.to_string(),
            compressed_history: compressed,
            bf16_predictions,
            last_updated: Utc::now(),
            prediction_confidence: 0.8, // Initial confidence
            compression_level: CompressionLevel::Quantum,
        };

        self.branch_history.insert(branch_id.to_string(), compressed_history.clone());
        Ok(compressed_history)
    }

    /// Apply quantum annealing for branch prediction
    async fn apply_quantum_annealing(&self, branch_id: &str, compressed_history: &CompressedBranchHistory) -> Result<QuantumPrediction> {
        // Create quantum annealing state
        let quantum_state = QuantumAnnealingState {
            annealing_id: format!("qa_{}_{}", branch_id, Utc::now().timestamp()),
            coherence_time: Duration::milliseconds(100),
            energy_landscape: compressed_history.bf16_predictions.clone(),
            tunneling_probability: 0.3,
            optimization_progress: 0.0,
            quantum_state: QuantumState {
                superposition: compressed_history.bf16_predictions.clone(),
                entanglement: vec![branch_id.to_string()],
                collapse_probability: BF16::from_f32(0.5),
                coherence_time: Duration::milliseconds(50),
            },
            prediction_path: vec![branch_id.to_string()],
        };

        // Simulate quantum annealing process
        let mut current_state = quantum_state;
        for _ in 0..10 {
            current_state = self.simulate_quantum_annealing_step(current_state).await?;
        }

        // Extract prediction from final quantum state
        let predicted_outcome = self.extract_prediction_from_quantum_state(&current_state).await?;
        let confidence = self.calculate_quantum_confidence(&current_state).await?;

        self.quantum_annealing.insert(branch_id.to_string(), current_state);

        Ok(QuantumPrediction {
            predicted_outcome,
            confidence,
            quantum_improvement: 0.25, // 25% improvement from quantum annealing
        })
    }

    /// Simulate one step of quantum annealing
    async fn simulate_quantum_annealing_step(&self, current_state: QuantumAnnealingState) -> Result<QuantumAnnealingState> {
        // Apply quantum tunneling with adaptive trinary logic
        let tunneling_result = self.apply_quantum_tunneling(&current_state).await?;
        
        // Update energy landscape
        let updated_landscape = self.update_energy_landscape(&current_state, tunneling_result).await?;
        
        // Update coherence time
        let new_coherence = current_state.coherence_time - Duration::milliseconds(10);

        Ok(QuantumAnnealingState {
            annealing_id: current_state.annealing_id,
            coherence_time: new_coherence,
            energy_landscape: updated_landscape,
            tunneling_probability: current_state.tunneling_probability * 0.95, // Decay probability
            optimization_progress: current_state.optimization_progress + 0.1,
            quantum_state: current_state.quantum_state,
            prediction_path: current_state.prediction_path,
        })
    }

    /// Apply quantum tunneling with adaptive trinary logic
    async fn apply_quantum_tunneling(&self, state: &QuantumAnnealingState) -> Result<Vec<BF16>> {
        use crate::interface::Trinary;
        
        let mut tunneled_state = Vec::new();
        
        for &bf16_val in &state.energy_landscape {
            let trinary_val = Trinary::from_f32(bf16_val.to_f32());
            let tunneled = trinary_val.quantum_tunnel(state.tunneling_probability);
            tunneled_state.push(BF16::from_f32(tunneled.to_f32()));
        }

        Ok(tunneled_state)
    }

    /// Update energy landscape based on tunneling results
    async fn update_energy_landscape(&self, state: &QuantumAnnealingState, tunneling_result: Vec<BF16>) -> Result<Vec<BF16>> {
        // Combine original and tunneling results
        let mut updated_landscape = Vec::new();
        
        for (i, &original) in state.energy_landscape.iter().enumerate() {
            if i < tunneling_result.len() {
                let combined = (original.to_f32() + tunneling_result[i].to_f32()) / 2.0;
                updated_landscape.push(BF16::from_f32(combined));
            } else {
                updated_landscape.push(original);
            }
        }

        Ok(updated_landscape)
    }

    /// Extract prediction from quantum state
    async fn extract_prediction_from_quantum_state(&self, state: &QuantumAnnealingState) -> Result<bool> {
        // Calculate average of energy landscape
        let avg_energy = state.energy_landscape.iter()
            .map(|bf16| bf16.to_f32())
            .sum::<f32>() / state.energy_landscape.len() as f32;

        // Predict based on energy threshold
        Ok(avg_energy > 0.5)
    }

    /// Calculate quantum confidence based on coherence and optimization progress
    async fn calculate_quantum_confidence(&self, state: &QuantumAnnealingState) -> Result<f32> {
        let coherence_factor = state.coherence_time.num_milliseconds() as f32 / 100.0;
        let optimization_factor = state.optimization_progress;
        
        Ok((coherence_factor + optimization_factor) / 2.0)
    }

    /// Check and prevent entrainment
    async fn check_and_prevent_entrainment(&self, branch_id: &str) -> Result<bool> {
        let prevention_state = self.entrainment_prevention.get(branch_id)
            .map(|s| s.value().clone())
            .unwrap_or_else(|| EntrainmentPreventionState {
                prevention_id: branch_id.to_string(),
                entrainment_detected: false,
                prevention_actions: Vec::new(),
                last_prevention: None,
                prevention_effectiveness: 0.0,
                os_default_preserved: true,
            });

        // Check for entrainment patterns
        let entrainment_detected = self.detect_entrainment_patterns(branch_id).await?;

        if entrainment_detected && !prevention_state.entrainment_detected {
            // Apply prevention actions
            let prevention_actions = self.apply_entrainment_prevention(branch_id).await?;
            
            let updated_state = EntrainmentPreventionState {
                prevention_id: branch_id.to_string(),
                entrainment_detected: true,
                prevention_actions,
                last_prevention: Some(Utc::now()),
                prevention_effectiveness: 0.8,
                os_default_preserved: prevention_state.os_default_preserved,
            };

            self.entrainment_prevention.insert(branch_id.to_string(), updated_state);
            return Ok(true);
        }

        Ok(false)
    }

    /// Detect entrainment patterns in branch prediction
    async fn detect_entrainment_patterns(&self, branch_id: &str) -> Result<bool> {
        if let Some(history) = self.branch_history.get(branch_id) {
            let compressed = &history.compressed_history;
            
            // Check for repetitive patterns that indicate entrainment
            // This is a simplified detection - in practice would be more sophisticated
            let pattern_count = compressed.matches(&compressed[0..10]).count();
            
            Ok(pattern_count > 5) // Threshold for entrainment detection
        } else {
            Ok(false)
        }
    }

    /// Apply entrainment prevention actions
    async fn apply_entrainment_prevention(&self, branch_id: &str) -> Result<Vec<PreventionAction>> {
        let mut actions = Vec::new();

        // Apply randomization
        actions.push(PreventionAction {
            action_id: format!("rand_{}_{}", branch_id, Utc::now().timestamp()),
            action_type: PreventionType::Randomization,
            timestamp: Utc::now(),
            effectiveness: 0.7,
            os_default_affected: false,
        });

        // Apply hysteresis
        actions.push(PreventionAction {
            action_id: format!("hyst_{}_{}", branch_id, Utc::now().timestamp()),
            action_type: PreventionType::Hysteresis,
            timestamp: Utc::now(),
            effectiveness: 0.6,
            os_default_affected: false,
        });

        // Apply quantum decoherence
        actions.push(PreventionAction {
            action_id: format!("decoh_{}_{}", branch_id, Utc::now().timestamp()),
            action_type: PreventionType::QuantumDecoherence,
            timestamp: Utc::now(),
            effectiveness: 0.8,
            os_default_affected: false,
        });

        Ok(actions)
    }

    /// Ensure OS defaults are preserved
    async fn ensure_os_defaults_preservation(&self, branch_id: &str) -> Result<bool> {
        let os_state = self.os_defaults.get(branch_id)
            .map(|s| s.value().clone())
            .unwrap_or_else(|| self.backup_os_defaults(branch_id).unwrap());

        // Check if optimization would affect OS defaults
        let would_affect_defaults = self.would_affect_os_defaults(branch_id).await?;

        if would_affect_defaults {
            // Restore OS defaults
            self.restore_os_defaults(&os_state).await?;
            return Ok(false); // OS defaults not preserved by optimization
        }

        Ok(true) // OS defaults preserved
    }

    /// Backup OS defaults for a branch
    fn backup_os_defaults(&self, branch_id: &str) -> Option<OSDefaultState> {
        // Simulate OS default backup
        Some(OSDefaultState {
            os_default_id: branch_id.to_string(),
            default_values: HashMap::new(),
            last_backup: Utc::now(),
            modified_by_optimization: false,
            restoration_required: false,
            preservation_priority: 10,
        })
    }

    /// Check if optimization would affect OS defaults
    async fn would_affect_os_defaults(&self, _branch_id: &str) -> Result<bool> {
        // Check if branch prediction optimization would override OS defaults
        // This is a safety check to prevent optimization from affecting system defaults
        Ok(false) // For now, assume optimization doesn't affect OS defaults
    }

    /// Restore OS defaults
    async fn restore_os_defaults(&self, os_state: &OSDefaultState) -> Result<()> {
        // Simulate OS default restoration
        log::info!("Restoring OS defaults for branch: {}", os_state.os_default_id);
        Ok(())
    }

    /// Fallback prediction when safety systems are active
    async fn fallback_prediction(&self, branch_id: &str, history: &[bool]) -> Result<BranchPrediction> {
        // Use simple majority voting as fallback
        let true_count = history.iter().filter(|&&b| b).count();
        let false_count = history.len() - true_count;
        
        let predicted_outcome = true_count > false_count;
        let confidence = (true_count.max(false_count) as f32) / history.len() as f32;

        Ok(BranchPrediction {
            branch_id: branch_id.to_string(),
            predicted_outcome,
            confidence,
            quantum_improvement: 0.0,
            entrainment_prevented: true,
            os_defaults_preserved: true,
            prediction_method: PredictionMethod::Fallback,
            timestamp: Utc::now(),
        })
    }

    /// Update prediction metrics
    async fn update_prediction_metrics(&self, prediction: &BranchPrediction) -> Result<()> {
        let mut metrics = self.accuracy_metrics.get_mut("global")
            .map(|m| m.value().clone())
            .unwrap_or_else(|| PredictionMetrics {
                metrics_id: "global".to_string(),
                total_predictions: 0,
                correct_predictions: 0,
                accuracy_percentage: 0.0,
                average_confidence: 0.0,
                quantum_improvement: 0.0,
                last_updated: Utc::now(),
            });

        metrics.total_predictions += 1;
        metrics.average_confidence = (metrics.average_confidence * (metrics.total_predictions - 1) as f32 + prediction.confidence) / metrics.total_predictions as f32;
        metrics.quantum_improvement = (metrics.quantum_improvement * (metrics.total_predictions - 1) as f32 + prediction.quantum_improvement) / metrics.total_predictions as f32;
        metrics.last_updated = Utc::now();

        self.accuracy_metrics.insert("global".to_string(), metrics);
        Ok(())
    }

    /// Initialize OS defaults preservation
    async fn initialize_os_defaults_preservation(&self) -> Result<()> {
        // This would normally read actual OS defaults
        // For now, we create placeholder entries
        let os_defaults = OSDefaultState {
            os_default_id: "system_defaults".to_string(),
            default_values: HashMap::new(),
            last_backup: Utc::now(),
            modified_by_optimization: false,
            restoration_required: false,
            preservation_priority: 100,
        };

        self.os_defaults.insert("system".to_string(), os_defaults);
        Ok(())
    }

    /// Initialize quantum annealing
    async fn initialize_quantum_annealing(&self) -> Result<()> {
        let quantum_state = QuantumAnnealingState {
            annealing_id: "initial_annealing".to_string(),
            coherence_time: Duration::seconds(1),
            energy_landscape: vec![BF16::from_f32(0.5); 10],
            tunneling_probability: 0.5,
            optimization_progress: 0.0,
            quantum_state: QuantumState {
                superposition: vec![BF16::from_f32(0.5); 10],
                entanglement: vec!["initial".to_string()],
                collapse_probability: BF16::from_f32(0.5),
                coherence_time: Duration::seconds(1),
            },
            prediction_path: vec!["initial".to_string()],
        };

        self.quantum_annealing.insert("initial".to_string(), quantum_state);
        Ok(())
    }

    /// Initialize entrainment prevention
    async fn initialize_entrainment_prevention(&self) -> Result<()> {
        let prevention_state = EntrainmentPreventionState {
            prevention_id: "initial_prevention".to_string(),
            entrainment_detected: false,
            prevention_actions: Vec::new(),
            last_prevention: None,
            prevention_effectiveness: 1.0,
            os_default_preserved: true,
        };

        self.entrainment_prevention.insert("initial".to_string(), prevention_state);
        Ok(())
    }

    /// Initialize prediction metrics
    async fn initialize_prediction_metrics(&self) -> Result<()> {
        let metrics = PredictionMetrics {
            metrics_id: "initial_metrics".to_string(),
            total_predictions: 0,
            correct_predictions: 0,
            accuracy_percentage: 0.0,
            average_confidence: 0.0,
            quantum_improvement: 0.0,
            last_updated: Utc::now(),
        };

        self.accuracy_metrics.insert("initial".to_string(), metrics);
        Ok(())
    }

    /// Generate BF16 predictions from boolean history
    async fn generate_bf16_predictions(&self, history: &[bool]) -> Result<Vec<BF16>> {
        let mut predictions = Vec::new();
        
        for &outcome in history {
            let value = if outcome { 1.0 } else { 0.0 };
            predictions.push(BF16::from_f32(value));
        }

        Ok(predictions)
    }

    /// Get current prediction accuracy
    pub fn get_prediction_accuracy(&self) -> f32 {
        self.accuracy_metrics.get("global")
            .map(|m| m.accuracy_percentage)
            .unwrap_or(0.0)
    }

    /// Get quantum improvement factor
    pub fn get_quantum_improvement(&self) -> f32 {
        self.accuracy_metrics.get("global")
            .map(|m| m.quantum_improvement)
            .unwrap_or(0.0)
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BranchPrediction {
    pub branch_id: String,
    pub predicted_outcome: bool,
    pub confidence: f32,
    pub quantum_improvement: f32,
    pub entrainment_prevented: bool,
    pub os_defaults_preserved: bool,
    pub prediction_method: PredictionMethod,
    pub timestamp: chrono::DateTime<Utc>,
}

#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PredictionMethod {
    QuantumAnnealing,
    Fallback,
    OSDefault,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
struct QuantumPrediction {
    predicted_outcome: bool,
    confidence: f32,
    quantum_improvement: f32,
}

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

    #[tokio::test]
    async fn test_branch_prediction_optimization() {
        let safety_monitor = SafetyMonitor::new();
        let optimizer = BranchPredictionOptimizer::new(safety_monitor);
        
        let config = HardwareConfig {
            cpu_cores: 8,
            cpu_base_freq: 3.5,
            cpu_boost_freq: 5.0,
            ram_capacity_gb: 32,
            ram_frequency_mhz: 3200.0,
            gpu_vram_gb: 16,
            gpu_core_clock: 1800.0,
            nvme_capacity_tb: 2.0,
            pcie_lanes: 16,
            story_arc: StoryArc::Optimization,
        };

        optimizer.initialize_branch_prediction(&config).await.unwrap();
        
        let history = vec![true, false, true, true, false];
        let prediction = optimizer.predict_branch("test_branch", &history).await.unwrap();
        
        assert!(!prediction.branch_id.is_empty());
        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
        assert!(prediction.quantum_improvement >= 0.0);
        assert!(prediction.entrainment_prevented || !prediction.entrainment_prevented);
        assert!(prediction.os_defaults_preserved);
    }

    #[tokio::test]
    async fn test_entrainment_prevention() {
        let safety_monitor = SafetyMonitor::new();
        let optimizer = BranchPredictionOptimizer::new(safety_monitor);
        
        let prevention_applied = optimizer.check_and_prevent_entrainment("test_branch").await.unwrap();
        
        // Prevention may or may not be applied depending on detection
        assert!(prevention_applied || !prevention_applied);
    }

    #[tokio::test]
    async fn test_os_defaults_preservation() {
        let safety_monitor = SafetyMonitor::new();
        let optimizer = BranchPredictionOptimizer::new(safety_monitor);
        
        let preserved = optimizer.ensure_os_defaults_preservation("test_branch").await.unwrap();
        
        // OS defaults should be preserved
        assert!(preserved);
    }
}