//! Evolution Orchestrator - Main evolution loop coordinator
//!
//! The orchestrator manages the entire evolution cycle:
//! 1. Telemetry Analysis → Pattern Mining
//! 2. Proposal Generation → Multi-objective Optimization
//! 3. Sandbox Validation → Safety & Performance Testing
//! 4. Knowledge Graph Learning → Meta-learning Updates
//! 5. Production Deployment → Safe Rollout

use crate::{EvolutionError, Result};
use crate::{KnowledgeGraph, MultiObjectiveOptimizer, SafeSandbox, TelemetryAnalyzer};
use crate::{ParetoFrontier, PerformancePattern, SandboxConfig};
use std::collections::HashMap;
use std::time::{Duration, Instant};
use tracing::{debug, info, warn};
use uuid::Uuid;

/// Configuration for the evolution orchestrator
#[derive(Debug, Clone)]
pub struct EvolutionConfig {
    /// How often to analyze telemetry and generate proposals
    pub analysis_interval: Duration,
    /// Maximum time allowed for proposal validation
    pub proposal_timeout: Duration,
    /// Memory limit for sandbox execution (bytes)
    pub sandbox_memory_limit: u64,
    /// Maximum number of concurrent proposals to evaluate
    pub max_concurrent_proposals: usize,
    /// Minimum performance improvement to accept a proposal
    pub success_threshold: f64,
    /// Whether to enable automatic rollback on failures
    pub rollback_enabled: bool,
}

impl Default for EvolutionConfig {
    fn default() -> Self {
        Self {
            analysis_interval: Duration::from_secs(300),  // 5 minutes
            proposal_timeout: Duration::from_secs(600),   // 10 minutes
            sandbox_memory_limit: 2 * 1024 * 1024 * 1024, // 2GB
            max_concurrent_proposals: 3,
            success_threshold: 0.02, // 2% minimum improvement
            rollback_enabled: true,
        }
    }
}

impl EvolutionConfig {
    /// Create RTX 5090 optimized configuration
    pub fn rtx5090_optimized() -> Self {
        Self {
            analysis_interval: Duration::from_millis(100), // 10Hz monitoring for real-time optimization
            proposal_timeout: Duration::from_secs(60),     // Faster validation cycles
            sandbox_memory_limit: 16 * 1024 * 1024 * 1024, // 16GB - RTX 5090 has large memory
            max_concurrent_proposals: 8, // RTX 5090 can handle more concurrent testing
            success_threshold: 0.01, // 1% minimum improvement - RTX 5090 enables fine-grained optimization
            rollback_enabled: true,
        }
    }
}

/// Specification for an optimization proposal
#[derive(Debug, Clone)]
pub struct ProposalSpec {
    pub id: Uuid,
    pub description: String,
    pub changes: Vec<Change>,
    pub expected_improvement: f64,
    pub confidence: f64,
    pub risk_level: RiskLevel,
}

/// Types of changes that can be proposed
#[derive(Debug, Clone)]
pub enum Change {
    KernelParameter {
        kernel: String,
        param: String,
        old_value: i32,
        new_value: i32,
    },
    CompilerFlag {
        flag: String,
        enabled: bool,
    },
    MemoryLayout {
        layout: String,
    },
    AlgorithmSwitch {
        component: String,
        from_algorithm: String,
        to_algorithm: String,
    },
    /// AI-powered code optimization
    CodeOptimization {
        component: String,
        optimization_type: String,
        code_changes: Vec<String>,
    },
    /// RTX 5090 specific optimization
    Rtx5090Optimization {
        optimization_type: String,
        target_feature: String,
        implementation: String,
    },
    /// CUDA 13.0 feature enablement
    Cuda13Feature {
        feature_name: String,
        implementation_code: String,
        performance_target: f64,
    },
}

/// Risk level of a proposed change
#[derive(Debug, Clone, PartialEq)]
pub enum RiskLevel {
    Low,    // Safe changes with minimal impact
    Medium, // Moderate changes requiring validation
    High,   // Risky changes requiring careful testing
}

/// Result of executing a proposal in the sandbox
#[derive(Debug, Clone)]
pub struct ExecutionResult {
    pub proposal_id: Uuid,
    pub performance_delta: f64,
    pub memory_delta: f64,
    pub safety_check_passed: bool,
    pub execution_time: Duration,
    pub error_message: Option<String>,
}

/// Result of a rollback operation
#[derive(Debug)]
pub struct RollbackResult {
    pub rolled_back: bool,
    pub rollback_time: Duration,
    pub restored_state: String,
}

/// Statistics about evolution cycles
#[derive(Debug)]
pub struct EvolutionStatistics {
    pub total_cycles: u64,
    pub successful_proposals: u64,
    pub failed_proposals: u64,
    pub rollbacks: u64,
    pub average_improvement: f64,
    pub total_runtime: Duration,
    pub last_cycle_time: Option<Instant>,
}

/// Main evolution orchestrator coordinating the evolution loop
pub struct EvolutionOrchestrator {
    config: EvolutionConfig,
    telemetry_analyzer: TelemetryAnalyzer,
    optimizer: MultiObjectiveOptimizer,
    sandbox: SafeSandbox,
    knowledge_graph: KnowledgeGraph,
    statistics: EvolutionStatistics,
    cycle_count: u64,
    start_time: Instant,
    active_proposals: HashMap<Uuid, ProposalSpec>,
}

/// Real-time metrics for autonomous optimization
#[derive(Debug, Clone)]
pub struct RealTimeMetric {
    pub timestamp: u64,
    pub metric_name: String,
    pub value: f64,
    pub unit: String,
}

/// Optimization opportunity detected by AI
#[derive(Debug, Clone)]
pub struct OptimizationOpportunity {
    pub confidence: f64,
    pub expected_improvement: f64,
}

/// Code pattern for AI analysis
#[derive(Debug, Clone)]
pub struct CodePattern {
    pub pattern_type: String,
    pub frequency: f64,
}

/// AI-generated optimization proposal
#[derive(Debug, Clone)]
pub struct AiProposal {
    pub confidence: f64,
    pub proposal: ProposalSpec,
}

/// Performance bottleneck
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    pub component: String,
    pub severity: f64,
}

/// Bottleneck optimization
#[derive(Debug, Clone)]
pub struct BottleneckOptimization {
    pub confidence: f64,
    pub expected_improvement: f64,
}

/// Thermal state
#[derive(Debug, Clone)]
pub struct ThermalState {
    pub temperature: f64,
    pub power_consumption: f64,
}

/// Thermal optimization
#[derive(Debug, Clone)]
pub struct ThermalOptimization;

/// Power optimization
#[derive(Debug, Clone)]
pub struct PowerOptimization;

/// Optimization result
#[derive(Debug, Clone)]
pub struct OptimizationResult;

/// Rollback state
#[derive(Debug, Clone)]
pub struct RollbackState;

impl EvolutionOrchestrator {
    /// Create a new evolution orchestrator
    pub fn new(config: EvolutionConfig) -> Self {
        let sandbox_config = SandboxConfig {
            memory_limit: config.sandbox_memory_limit,
            timeout: config.proposal_timeout,
            isolation_level: IsolationLevel::Full,
        };

        Self {
            telemetry_analyzer: TelemetryAnalyzer::new(),
            optimizer: MultiObjectiveOptimizer::new(),
            sandbox: SafeSandbox::new(sandbox_config),
            knowledge_graph: KnowledgeGraph::new(),
            statistics: EvolutionStatistics {
                total_cycles: 0,
                successful_proposals: 0,
                failed_proposals: 0,
                rollbacks: 0,
                average_improvement: 0.0,
                total_runtime: Duration::ZERO,
                last_cycle_time: None,
            },
            cycle_count: 0,
            start_time: Instant::now(),
            active_proposals: HashMap::new(),
            config,
        }
    }

    /// Check if orchestrator is ready for operation
    pub fn is_ready(&self) -> bool {
        self.telemetry_analyzer.is_initialized()
            && self.sandbox.is_available()
            && self.knowledge_graph.is_ready()
    }

    /// Get the current configuration
    pub fn config(&self) -> &EvolutionConfig {
        &self.config
    }

    /// Run the main evolution loop indefinitely
    pub async fn run(&mut self) -> Result<()> {
        info!("Starting evolution orchestrator");

        loop {
            match self.run_single_cycle().await {
                Ok(()) => {
                    info!(
                        "Evolution cycle {} completed successfully",
                        self.cycle_count
                    );
                }
                Err(e) => {
                    warn!("Evolution cycle {} failed: {}", self.cycle_count, e);
                    // Continue running unless critical error
                    if self.is_critical_error(&e) {
                        return Err(e);
                    }
                }
            }

            // Wait for next analysis interval
            tokio::time::sleep(self.config.analysis_interval).await;
        }
    }

    /// Run a single evolution cycle
    pub async fn run_single_cycle(&mut self) -> Result<()> {
        let cycle_start = Instant::now();
        self.cycle_count += 1;
        self.statistics.total_cycles = self.cycle_count;

        debug!("Starting evolution cycle {}", self.cycle_count);

        // 1. Analyze current system telemetry
        let telemetry_data = self.collect_telemetry().await?;
        let patterns = self.analyze_telemetry(&telemetry_data).await?;

        if patterns.is_empty() {
            debug!(
                "No optimization patterns found in cycle {}",
                self.cycle_count
            );
            return Ok(());
        }

        // 2. Generate optimization proposals
        let proposals = self.generate_proposals().await?;

        if proposals.is_empty() {
            debug!("No proposals generated in cycle {}", self.cycle_count);
            return Ok(());
        }

        // 3. Find Pareto-optimal proposals
        let pareto_frontier = self.find_pareto_optimal(&proposals).await?;

        // 4. Validate top proposals in sandbox
        let mut successful_proposals = Vec::new();

        for proposal in &pareto_frontier.solutions {
            if successful_proposals.len() >= self.config.max_concurrent_proposals {
                break;
            }

            match self.validate_proposal(proposal).await {
                Ok(result) => {
                    if result.safety_check_passed
                        && result.performance_delta >= self.config.success_threshold
                    {
                        successful_proposals.push((proposal.clone(), result));
                        self.statistics.successful_proposals += 1;
                    } else {
                        self.statistics.failed_proposals += 1;
                    }
                }
                Err(e) => {
                    warn!("Proposal validation failed: {}", e);
                    self.statistics.failed_proposals += 1;
                }
            }
        }

        // 5. Learn from results and update knowledge graph
        for (proposal, result) in &successful_proposals {
            self.learn_from_result(proposal, result).await?;
        }

        // 6. Apply successful proposals (in practice, this would deploy to production)
        for (proposal, result) in successful_proposals {
            info!(
                "Would apply proposal {}: {} with {:.2}% improvement",
                proposal.id,
                proposal.description,
                result.performance_delta * 100.0
            );
        }

        // Update statistics
        let cycle_time = cycle_start.elapsed();
        self.statistics.total_runtime += cycle_time;
        self.statistics.last_cycle_time = Some(Instant::now());

        Ok(())
    }

    /// Collect current system telemetry
    async fn collect_telemetry(&self) -> Result<Vec<(&'static str, f64)>> {
        // In a real implementation, this would collect actual metrics
        // For now, simulate some telemetry data
        Ok(vec![
            ("gpu_utilization", 0.78),
            ("memory_usage", 0.65),
            ("kernel_exec_time", 0.023),
            ("power_consumption", 0.82),
            ("bandwidth_utilization", 0.71),
        ])
    }

    /// Analyze telemetry data to identify optimization patterns
    pub async fn analyze_telemetry(
        &self,
        telemetry_data: &[(&str, f64)],
    ) -> Result<Vec<PerformancePattern>> {
        self.telemetry_analyzer
            .analyze_patterns(telemetry_data)
            .await
    }

    /// Generate optimization proposals based on current analysis
    pub async fn generate_proposals(&self) -> Result<Vec<ProposalSpec>> {
        let mut proposals = Vec::new();

        // Generate diverse proposal types based on knowledge graph insights
        proposals.push(ProposalSpec {
            id: Uuid::new_v4(),
            description: "Increase kernel tile size for better cache utilization".to_string(),
            changes: vec![Change::KernelParameter {
                kernel: "matmul".to_string(),
                param: "tile_size".to_string(),
                old_value: 16,
                new_value: 32,
            }],
            expected_improvement: 0.12,
            confidence: 0.85,
            risk_level: RiskLevel::Low,
        });

        proposals.push(ProposalSpec {
            id: Uuid::new_v4(),
            description: "Switch to more aggressive compiler optimizations".to_string(),
            changes: vec![Change::CompilerFlag {
                flag: "fast-math".to_string(),
                enabled: true,
            }],
            expected_improvement: 0.08,
            confidence: 0.70,
            risk_level: RiskLevel::Medium,
        });

        proposals.push(ProposalSpec {
            id: Uuid::new_v4(),
            description: "Optimize memory layout for sequential access".to_string(),
            changes: vec![Change::MemoryLayout {
                layout: "row_major_optimized".to_string(),
            }],
            expected_improvement: 0.15,
            confidence: 0.75,
            risk_level: RiskLevel::Low,
        });

        // Filter to max concurrent proposals
        proposals.truncate(self.config.max_concurrent_proposals);

        Ok(proposals)
    }

    /// Find Pareto-optimal proposals using multi-objective optimization
    pub async fn find_pareto_optimal(&self, proposals: &[ProposalSpec]) -> Result<ParetoFrontier> {
        self.optimizer.compute_pareto_frontier(proposals).await
    }

    /// Validate a proposal in the safe sandbox environment
    pub async fn validate_proposal(&self, proposal: &ProposalSpec) -> Result<ExecutionResult> {
        info!("Validating proposal: {}", proposal.description);

        self.sandbox.execute_proposal(proposal).await
    }

    /// Learn from execution results and update knowledge graph
    pub async fn learn_from_result(
        &mut self,
        proposal: &ProposalSpec,
        result: &ExecutionResult,
    ) -> Result<()> {
        self.knowledge_graph
            .update_from_execution(proposal, result)
            .await
    }

    /// Handle failed proposals with rollback if needed
    pub async fn handle_failure(
        &self,
        proposal: &ProposalSpec,
        result: &ExecutionResult,
    ) -> Result<RollbackResult> {
        warn!(
            "Handling failure for proposal {}: {:?}",
            proposal.id, result.error_message
        );

        if self.config.rollback_enabled {
            let rollback_start = Instant::now();

            // Perform rollback operation
            self.sandbox.rollback_changes(proposal).await?;

            Ok(RollbackResult {
                rolled_back: true,
                rollback_time: rollback_start.elapsed(),
                restored_state: "previous_stable_state".to_string(),
            })
        } else {
            Ok(RollbackResult {
                rolled_back: false,
                rollback_time: Duration::ZERO,
                restored_state: "no_rollback".to_string(),
            })
        }
    }

    /// Get the number of cycles executed
    pub fn cycle_count(&self) -> u64 {
        self.cycle_count
    }

    /// Get evolution statistics
    pub fn statistics(&self) -> &EvolutionStatistics {
        &self.statistics
    }

    /// Check if an error is critical and should stop the evolution loop
    fn is_critical_error(&self, error: &EvolutionError) -> bool {
        matches!(
            error,
            EvolutionError::KnowledgeGraph { .. } | EvolutionError::ResourceLimit { .. }
        )
    }
}

/// Isolation level for sandbox execution
#[derive(Debug, Clone)]
pub enum IsolationLevel {
    None,    // No isolation (for testing)
    Partial, // Limited isolation
    Full,    // Complete isolation
}