torsh-jit 0.1.3

//! Core optimization advisor orchestration and high-level logic

use crate::advisor::config::*;
use crate::advisor::cost::CostModel;
use crate::advisor::knowledge::*;
use crate::advisor::patterns::PatternAnalyzer;
use crate::advisor::performance::PerformanceAnalyzer;
use crate::advisor::recommendations::RecommendationEngine;
use crate::JitResult;
use std::cmp::Ordering;
use std::collections::HashMap;
use std::time::{Duration, SystemTime};

/// Analysis phase tracking
#[derive(Debug, Clone)]
pub enum AnalysisPhase {
    PatternDetection,
    PerformanceAnalysis,
    CostAnalysis,
    RecommendationGeneration,
    Prioritization,
    Explanation,
    Learning,
    Complete,
}

/// Analysis progress tracking
#[derive(Debug, Clone)]
pub struct AnalysisProgress {
    pub current_phase: AnalysisPhase,
    pub completion_percentage: f64,
    pub estimated_remaining_time: Duration,
}

/// Intelligent optimization advisor
pub struct OptimizationAdvisor {
    config: AdvisorConfig,
    knowledge_base: KnowledgeBase,
    pattern_analyzer: PatternAnalyzer,
    performance_analyzer: PerformanceAnalyzer,
    cost_model: CostModel,
    recommendation_engine: RecommendationEngine,
    learning_system: LearningSystem,
}

impl OptimizationAdvisor {
    /// Create a new optimization advisor
    pub fn new(config: AdvisorConfig) -> Self {
        Self {
            knowledge_base: KnowledgeBase::new(),
            pattern_analyzer: PatternAnalyzer::new(),
            performance_analyzer: PerformanceAnalyzer::new(),
            cost_model: CostModel::new(),
            recommendation_engine: RecommendationEngine::new(config.clone()),
            learning_system: LearningSystem::new(LearningConfig::default()),
            config,
        }
    }

    /// Analyze a computation graph and provide optimization recommendations
    pub fn analyze_and_recommend(&mut self, input: AnalysisInput) -> JitResult<OptimizationReport> {
        let start_time = SystemTime::now();

        // Phase 1: Pattern Analysis
        let pattern_analysis = self.analyze_patterns(&input)?;

        // Phase 2: Performance Analysis
        let performance_analysis = self.analyze_performance(&input)?;

        // Phase 3: Cost-Benefit Analysis
        let cost_analysis = self.analyze_costs(&input, &pattern_analysis)?;

        // Phase 4: Generate Recommendations
        let recommendations = self.generate_recommendations(
            &input,
            &pattern_analysis,
            &performance_analysis,
            &cost_analysis,
        )?;

        // Phase 5: Prioritize and Rank
        let prioritized_recommendations =
            self.prioritize_recommendations(recommendations, &input, &performance_analysis)?;

        // Phase 6: Generate Explanation and Rationale
        let explanations = self.generate_explanations(&prioritized_recommendations, &input)?;

        // Phase 7: Update Learning System
        self.learning_system
            .record_analysis(&input, &prioritized_recommendations);

        let analysis_time = start_time.elapsed().unwrap_or(Duration::ZERO);

        Ok(OptimizationReport {
            recommendations: prioritized_recommendations.clone(),
            pattern_analysis,
            performance_analysis: performance_analysis.clone(),
            cost_analysis,
            explanations,
            confidence_scores: self.calculate_confidence_scores(&input)?,
            implementation_complexity: self
                .assess_implementation_complexity(&prioritized_recommendations)?,
            expected_improvements: self
                .estimate_improvements(&prioritized_recommendations, &performance_analysis)?,
            analysis_metadata: AnalysisMetadata {
                analysis_time,
                advisor_version: self.config.version.clone(),
                input_characteristics: self.characterize_input(&input),
                recommendations_count: prioritized_recommendations.len(),
                timestamp: start_time,
            },
        })
    }

    /// Analyze patterns in the computation graph
    fn analyze_patterns(&mut self, input: &AnalysisInput) -> JitResult<PatternAnalysis> {
        let mut detected_patterns = Vec::new();
        let mut antipatterns = Vec::new();
        let mut optimization_opportunities = Vec::new();

        // Graph structure analysis
        if let Some(graph) = &input.computation_graph {
            // Detect common patterns
            detected_patterns.extend(self.pattern_analyzer.detect_fusion_opportunities(graph)?);
            detected_patterns.extend(self.pattern_analyzer.detect_memory_patterns(graph)?);
            detected_patterns.extend(
                self.pattern_analyzer
                    .detect_parallelization_patterns(graph)?,
            );
            detected_patterns.extend(self.pattern_analyzer.detect_vectorization_patterns(graph)?);

            // Detect antipatterns
            antipatterns.extend(self.pattern_analyzer.detect_inefficient_patterns(graph)?);
            antipatterns.extend(self.pattern_analyzer.detect_memory_antipatterns(graph)?);
            antipatterns.extend(
                self.pattern_analyzer
                    .detect_computation_antipatterns(graph)?,
            );

            // Find optimization opportunities
            optimization_opportunities.extend(
                self.pattern_analyzer
                    .find_constant_folding_opportunities(graph)?,
            );
            optimization_opportunities.extend(
                self.pattern_analyzer
                    .find_dead_code_elimination_opportunities(graph)?,
            );
            optimization_opportunities.extend(
                self.pattern_analyzer
                    .find_loop_optimization_opportunities(graph)?,
            );
        }

        // Abstract interpretation analysis
        if let Some(abstract_result) = &input.abstract_analysis {
            optimization_opportunities.extend(
                self.pattern_analyzer
                    .extract_opportunities_from_abstract_analysis(abstract_result)?,
            );
        }

        // Symbolic execution analysis
        if let Some(symbolic_result) = &input.symbolic_execution {
            optimization_opportunities.extend(
                self.pattern_analyzer
                    .extract_opportunities_from_symbolic_execution(symbolic_result)?,
            );
        }

        Ok(PatternAnalysis {
            detected_patterns,
            antipatterns,
            optimization_opportunities,
            pattern_frequency: self.pattern_analyzer.calculate_pattern_frequency(),
            complexity_metrics: self.pattern_analyzer.calculate_complexity_metrics(),
        })
    }

    /// Analyze performance characteristics
    fn analyze_performance(&mut self, input: &AnalysisInput) -> JitResult<PerformanceAnalysis> {
        let mut bottlenecks = Vec::new();
        let mut hotspots = Vec::new();

        // Benchmark analysis
        if let Some(benchmark_results) = &input.benchmark_results {
            bottlenecks.extend(
                self.performance_analyzer
                    .identify_bottlenecks(benchmark_results)?,
            );
            hotspots.extend(
                self.performance_analyzer
                    .identify_hotspots(benchmark_results)?,
            );
        }

        // Profiling analysis
        if let Some(profiling_session) = &input.profiling_data {
            let profiling_analysis = self
                .performance_analyzer
                .analyze_profiling_data(profiling_session)?;
            bottlenecks.extend(profiling_analysis.bottlenecks);
            hotspots.extend(profiling_analysis.hotspots);
        }

        // Graph-based performance analysis
        let scalability_analysis = if let Some(graph) = &input.computation_graph {
            self.performance_analyzer.analyze_scalability(graph)?
        } else {
            ScalabilityAnalysis::default()
        };

        // Resource utilization analysis
        let resource_utilization = self
            .performance_analyzer
            .analyze_resource_utilization(input)?;

        let execution_profile = self.performance_analyzer.create_execution_profile(input)?;

        Ok(PerformanceAnalysis {
            bottlenecks,
            hotspots,
            execution_profile,
            resource_utilization,
            scalability_analysis,
        })
    }

    /// Analyze costs and benefits of optimizations
    fn analyze_costs(
        &mut self,
        input: &AnalysisInput,
        pattern_analysis: &PatternAnalysis,
    ) -> JitResult<CostBenefitAnalysis> {
        let mut implementation_costs = HashMap::new();
        let mut expected_benefits = HashMap::new();
        let mut risk_assessments = HashMap::new();

        for opportunity in &pattern_analysis.optimization_opportunities {
            let optimization_id = self.generate_opportunity_id(opportunity);

            // Calculate implementation cost
            let cost = self
                .cost_model
                .calculate_implementation_cost(opportunity, input)?;
            implementation_costs.insert(optimization_id.clone(), cost);

            // Estimate performance benefit
            let benefit = self
                .cost_model
                .estimate_performance_benefit(opportunity, input)?;
            expected_benefits.insert(optimization_id.clone(), benefit);

            // Evaluate risks
            let risks = self.cost_model.evaluate_risks(opportunity, input)?;
            risk_assessments.insert(optimization_id, risks);
        }

        // Calculate ROI for each optimization
        let roi_estimates = self
            .cost_model
            .calculate_roi_estimates(&implementation_costs, &expected_benefits)?;

        // Generate priority rankings
        let priority_rankings = self.cost_model.generate_priority_rankings(
            &implementation_costs,
            &expected_benefits,
            &risk_assessments,
        )?;

        Ok(CostBenefitAnalysis {
            implementation_costs,
            expected_benefits,
            risk_assessments,
            roi_estimates,
            priority_rankings,
        })
    }

    /// Generate optimization recommendations
    fn generate_recommendations(
        &mut self,
        input: &AnalysisInput,
        pattern_analysis: &PatternAnalysis,
        performance_analysis: &PerformanceAnalysis,
        cost_analysis: &CostBenefitAnalysis,
    ) -> JitResult<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();

        // Generate recommendations from patterns
        for opportunity in &pattern_analysis.optimization_opportunities {
            if let Some(recommendation) = self.recommendation_engine.generate_from_opportunity(
                opportunity,
                input,
                performance_analysis,
                cost_analysis,
            )? {
                recommendations.push(recommendation);
            }
        }

        // Generate recommendations from performance bottlenecks
        for bottleneck in &performance_analysis.bottlenecks {
            if let Some(recommendation) = self.recommendation_engine.generate_from_bottleneck(
                bottleneck,
                input,
                pattern_analysis,
                cost_analysis,
            )? {
                recommendations.push(recommendation);
            }
        }

        // Generate recommendations from antipatterns
        for antipattern in &pattern_analysis.antipatterns {
            if let Some(recommendation) = self.recommendation_engine.generate_from_antipattern(
                antipattern,
                input,
                cost_analysis,
            )? {
                recommendations.push(recommendation);
            }
        }

        // Generate holistic recommendations
        let holistic_recommendations = self
            .recommendation_engine
            .generate_holistic_recommendations(
                input,
                pattern_analysis,
                performance_analysis,
                cost_analysis,
            )?;
        recommendations.extend(holistic_recommendations);

        Ok(recommendations)
    }

    /// Prioritize recommendations based on impact and feasibility
    fn prioritize_recommendations(
        &mut self,
        mut recommendations: Vec<OptimizationRecommendation>,
        input: &AnalysisInput,
        performance_analysis: &PerformanceAnalysis,
    ) -> JitResult<Vec<OptimizationRecommendation>> {
        // Calculate priority scores
        for recommendation in &mut recommendations {
            recommendation.priority_score =
                self.calculate_priority_score(recommendation, input, performance_analysis)?;
        }

        // Sort by priority score (highest first)
        recommendations.sort_by(|a, b| {
            b.priority_score
                .partial_cmp(&a.priority_score)
                .unwrap_or(Ordering::Equal)
        });

        // Apply filters based on configuration
        if self.config.max_recommendations > 0 {
            recommendations.truncate(self.config.max_recommendations);
        }

        // Filter by minimum confidence threshold
        recommendations.retain(|r| r.confidence >= self.config.min_confidence_threshold);

        Ok(recommendations)
    }

    /// Generate explanations for recommendations
    fn generate_explanations(
        &mut self,
        recommendations: &[OptimizationRecommendation],
        input: &AnalysisInput,
    ) -> JitResult<Vec<OptimizationExplanation>> {
        let mut explanations = Vec::new();

        for recommendation in recommendations {
            let explanation = OptimizationExplanation {
                recommendation_id: recommendation.id.clone(),
                why_beneficial: self.generate_rationale(recommendation, input)?,
                how_to_implement: self.generate_technical_details(recommendation)?,
                potential_risks: self.identify_potential_risks(recommendation)?,
                verification_steps: self.define_success_criteria(recommendation)?,
                expected_timeline: recommendation.estimated_implementation_time,
            };
            explanations.push(explanation);
        }

        Ok(explanations)
    }

    /// Calculate confidence scores for the analysis
    fn calculate_confidence_scores(&self, input: &AnalysisInput) -> JitResult<ConfidenceScores> {
        let pattern_confidence = self.pattern_analyzer.calculate_confidence();
        let performance_confidence = self.performance_analyzer.calculate_confidence(input);
        let cost_confidence = self.cost_model.calculate_confidence();
        let overall_confidence =
            (pattern_confidence + performance_confidence + cost_confidence) / 3.0;

        Ok(ConfidenceScores {
            overall_confidence,
            pattern_detection_confidence: pattern_confidence,
            performance_analysis_confidence: performance_confidence,
            cost_estimation_confidence: cost_confidence,
            implementation_assessment_confidence: self.recommendation_engine.calculate_confidence(),
        })
    }

    /// Assess implementation complexity
    fn assess_implementation_complexity(
        &self,
        recommendations: &[OptimizationRecommendation],
    ) -> JitResult<ImplementationComplexity> {
        let mut total_complexity = 0.0;
        let mut technical_complexity = 0.0;
        let mut coordination_complexity = 0.0;

        for recommendation in recommendations {
            total_complexity += recommendation.implementation_complexity;
            technical_complexity += recommendation.implementation_complexity * 0.6;
            coordination_complexity += recommendation.implementation_complexity * 0.4;
        }

        let average_complexity = if !recommendations.is_empty() {
            total_complexity / recommendations.len() as f64
        } else {
            0.0
        };

        Ok(ImplementationComplexity {
            overall_complexity: average_complexity,
            technical_complexity: technical_complexity / recommendations.len().max(1) as f64,
            coordination_complexity: coordination_complexity / recommendations.len().max(1) as f64,
            testing_complexity: average_complexity * 0.8,
            deployment_complexity: average_complexity * 0.5,
        })
    }

    /// Estimate expected improvements
    fn estimate_improvements(
        &self,
        recommendations: &[OptimizationRecommendation],
        _performance_analysis: &PerformanceAnalysis,
    ) -> JitResult<ExpectedImprovements> {
        let mut performance_improvement = 0.0;
        let mut memory_reduction = 0.0;
        let mut energy_savings = 0.0;
        let mut development_time_impact = Duration::ZERO;

        for recommendation in recommendations {
            performance_improvement += recommendation.expected_speedup * recommendation.confidence;
            memory_reduction +=
                recommendation.expected_memory_reduction * recommendation.confidence;
            energy_savings += recommendation.expected_speedup * 0.3 * recommendation.confidence;
            development_time_impact += recommendation.estimated_implementation_time;
        }

        // Apply diminishing returns
        performance_improvement = self.apply_diminishing_returns(performance_improvement);
        memory_reduction = self.apply_diminishing_returns(memory_reduction);

        Ok(ExpectedImprovements {
            performance_improvement,
            memory_reduction,
            energy_savings,
            development_time_impact,
            maintenance_impact: 0.1, // Simplified estimate
        })
    }

    /// Calculate priority score for a recommendation
    fn calculate_priority_score(
        &self,
        recommendation: &OptimizationRecommendation,
        input: &AnalysisInput,
        performance_analysis: &PerformanceAnalysis,
    ) -> JitResult<f64> {
        let impact_score = recommendation.expected_speedup * 0.4
            + recommendation.expected_memory_reduction * 0.2
            + recommendation.confidence * 0.2;

        let feasibility_score = (1.0 - recommendation.implementation_complexity) * 0.1
            + (1.0 - recommendation.risk_level) * 0.1;

        // Adjust based on current bottlenecks
        let bottleneck_relevance =
            self.calculate_bottleneck_relevance(recommendation, &performance_analysis.bottlenecks);

        let priority_score = impact_score + feasibility_score + bottleneck_relevance * 0.2;

        Ok(priority_score.min(1.0).max(0.0))
    }

    // Helper methods
    fn calculate_bottleneck_relevance(
        &self,
        recommendation: &OptimizationRecommendation,
        bottlenecks: &[PerformanceBottleneck],
    ) -> f64 {
        bottlenecks
            .iter()
            .filter(|b| self.recommendation_addresses_bottleneck(recommendation, b))
            .map(|b| b.severity)
            .sum::<f64>()
            .min(1.0)
    }

    fn recommendation_addresses_bottleneck(
        &self,
        recommendation: &OptimizationRecommendation,
        bottleneck: &PerformanceBottleneck,
    ) -> bool {
        match (
            &recommendation.optimization_type,
            &bottleneck.bottleneck_type,
        ) {
            (OptimizationType::MemoryOptimization, BottleneckType::Memory) => true,
            (OptimizationType::ComputationOptimization, BottleneckType::Computation) => true,
            (OptimizationType::ParallelizationOptimization, BottleneckType::Computation) => true,
            (OptimizationType::VectorizationOptimization, BottleneckType::Computation) => true,
            _ => false,
        }
    }

    fn generate_rationale(
        &self,
        recommendation: &OptimizationRecommendation,
        _input: &AnalysisInput,
    ) -> JitResult<String> {
        let mut rationale = format!(
            "This {} optimization is recommended because it can provide a {:.1}% speedup \
             with {:.1}% confidence. ",
            recommendation.optimization_type.description(),
            recommendation.expected_speedup * 100.0,
            recommendation.confidence * 100.0
        );

        // Add specific reasoning based on optimization type
        match recommendation.optimization_type {
            OptimizationType::FusionOptimization => {
                rationale.push_str("The analysis detected multiple consecutive operations that can be fused to reduce memory bandwidth requirements and improve cache locality.");
            }
            OptimizationType::MemoryOptimization => {
                rationale.push_str("Memory access patterns show inefficiencies that can be optimized through better layout and prefetching strategies.");
            }
            OptimizationType::ParallelizationOptimization => {
                rationale.push_str("The computation graph contains parallelizable sections that are currently executed sequentially.");
            }
            OptimizationType::VectorizationOptimization => {
                rationale.push_str("Element-wise operations can benefit from SIMD vectorization to improve computational throughput.");
            }
            _ => {
                rationale.push_str("Analysis indicates this optimization addresses current performance limitations.");
            }
        }

        Ok(rationale)
    }

    fn generate_technical_details(
        &self,
        recommendation: &OptimizationRecommendation,
    ) -> JitResult<String> {
        let details = match recommendation.optimization_type {
            OptimizationType::FusionOptimization => {
                "Implement kernel fusion by combining consecutive operations into a single kernel. \
                 This requires analyzing data dependencies and ensuring memory access patterns remain efficient."
            }
            OptimizationType::MemoryOptimization => {
                "Optimize memory layout by reordering data structures for better cache alignment. \
                 Consider implementing memory prefetching and reducing memory allocations."
            }
            OptimizationType::ParallelizationOptimization => {
                "Implement thread-level parallelism by identifying independent computation paths. \
                 Use work-stealing schedulers and ensure proper load balancing."
            }
            OptimizationType::VectorizationOptimization => {
                "Use SIMD instructions for element-wise operations. Ensure data alignment and \
                 consider loop unrolling for better vectorization efficiency."
            }
            _ => "Implement the optimization according to the specific requirements identified in the analysis."
        };

        Ok(details.to_string())
    }

    fn identify_potential_risks(
        &self,
        recommendation: &OptimizationRecommendation,
    ) -> JitResult<Vec<String>> {
        let mut risks = Vec::new();

        if recommendation.implementation_complexity > 0.7 {
            risks.push("High implementation complexity may introduce bugs".to_string());
        }

        if recommendation.risk_level > 0.5 {
            risks.push("Optimization may cause performance regressions in some cases".to_string());
        }

        match recommendation.optimization_type {
            OptimizationType::MemoryOptimization => {
                risks.push("May increase memory usage in some scenarios".to_string());
            }
            OptimizationType::ParallelizationOptimization => {
                risks.push("Parallel implementation may introduce race conditions".to_string());
                risks.push("May not scale well on systems with fewer cores".to_string());
            }
            OptimizationType::FusionOptimization => {
                risks.push("Aggressive fusion may increase register pressure".to_string());
            }
            _ => {}
        }

        Ok(risks)
    }

    fn define_success_criteria(
        &self,
        recommendation: &OptimizationRecommendation,
    ) -> JitResult<Vec<String>> {
        let mut criteria = Vec::new();

        criteria.push(format!(
            "Achieve at least {:.1}% performance improvement",
            recommendation.expected_speedup * 100.0 * 0.8 // 80% of expected improvement
        ));

        if recommendation.expected_memory_reduction > 0.1 {
            criteria.push(format!(
                "Reduce memory usage by at least {:.1}%",
                recommendation.expected_memory_reduction * 100.0 * 0.8
            ));
        }

        criteria.push("Maintain correctness of all existing tests".to_string());
        criteria.push("No significant increase in compilation time".to_string());

        Ok(criteria)
    }

    fn characterize_input(&self, input: &AnalysisInput) -> String {
        format!(
            "graph:{},bench:{},profile:{},abstract:{},symbolic:{}",
            input.computation_graph.is_some(),
            input.benchmark_results.is_some(),
            input.profiling_data.is_some(),
            input.abstract_analysis.is_some(),
            input.symbolic_execution.is_some()
        )
    }

    fn apply_diminishing_returns(&self, value: f64) -> f64 {
        // Simple diminishing returns model: f(x) = x / (1 + x)
        value / (1.0 + value)
    }

    fn generate_opportunity_id(&self, opportunity: &OptimizationOpportunity) -> String {
        format!(
            "opp_{:?}_{}",
            opportunity.opportunity_type,
            crate::advisor::utils::generate_simple_id()
        )
    }

    /// Get the version of the optimization advisor
    pub fn get_version(&self) -> &str {
        "1.0.0"
    }
}

impl Default for ScalabilityAnalysis {
    fn default() -> Self {
        Self {
            parallelization_potential: 0.0,
            memory_scalability: 0.0,
            io_scalability: 0.0,
            algorithmic_complexity: "Unknown".to_string(),
            bottleneck_scalability: HashMap::new(),
        }
    }
}