torsh-jit 0.1.3

//! Pattern analyzer for detecting optimization opportunities

use crate::advisor::config::*;
use crate::{
    abstract_interpretation::{AbstractAnalysisResult, AbstractValue},
    symbolic_execution::SymbolicExecutionResult,
    ComputationGraph, JitResult, NodeId,
};
use std::collections::HashMap;

/// Pattern analyzer for detecting optimization opportunities
pub struct PatternAnalyzer {
    detected_patterns: HashMap<String, usize>,
}

impl PatternAnalyzer {
    pub fn new() -> Self {
        Self {
            detected_patterns: HashMap::new(),
        }
    }

    pub fn detect_fusion_opportunities(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedPattern>> {
        let mut patterns = Vec::new();

        // Simplified pattern detection based on graph structure
        if _graph.node_count() > 3 {
            patterns.push(DetectedPattern {
                pattern_type: PatternType::FusionOpportunity,
                location: PatternLocation::Global,
                confidence: 0.7,
                description: "Potential fusion opportunities detected based on graph structure"
                    .to_string(),
                estimated_benefit: 0.15,
            });
        }

        Ok(patterns)
    }

    pub fn detect_memory_patterns(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedPattern>> {
        let mut patterns = Vec::new();

        // Simplified memory pattern detection
        if _graph.node_count() > 5 {
            patterns.push(DetectedPattern {
                pattern_type: PatternType::MemoryInefficiency,
                location: PatternLocation::Global,
                confidence: 0.6,
                description: "Potential memory inefficiencies detected in complex graph"
                    .to_string(),
                estimated_benefit: 0.1,
            });
        }

        Ok(patterns)
    }

    pub fn detect_parallelization_patterns(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedPattern>> {
        let mut patterns = Vec::new();

        // Simplified parallelization detection
        if _graph.node_count() > 2 {
            patterns.push(DetectedPattern {
                pattern_type: PatternType::ParallelizationOpportunity,
                location: PatternLocation::Global,
                confidence: 0.7,
                description: "Potential parallelization opportunities detected".to_string(),
                estimated_benefit: 0.2,
            });
        }

        Ok(patterns)
    }

    pub fn detect_vectorization_patterns(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedPattern>> {
        let mut patterns = Vec::new();

        // Simplified vectorization detection
        if _graph.node_count() > 1 {
            patterns.push(DetectedPattern {
                pattern_type: PatternType::VectorizationOpportunity,
                location: PatternLocation::Global,
                confidence: 0.6,
                description: "Potential vectorization opportunities detected".to_string(),
                estimated_benefit: 0.15,
            });
        }

        Ok(patterns)
    }

    pub fn detect_inefficient_patterns(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedAntipattern>> {
        let mut antipatterns = Vec::new();

        // Simplified inefficient pattern detection
        if _graph.node_count() > 10 {
            antipatterns.push(DetectedAntipattern {
                antipattern_type: AntipatternType::RedundantComputation,
                location: PatternLocation::Global,
                severity: 0.5,
                description: "Potential redundant computations in complex graph".to_string(),
                fix_suggestion: "Consider caching or eliminating redundant operations".to_string(),
            });
        }

        Ok(antipatterns)
    }

    pub fn detect_memory_antipatterns(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedAntipattern>> {
        let mut antipatterns = Vec::new();

        // Simplified memory antipattern detection
        if _graph.node_count() > 8 {
            antipatterns.push(DetectedAntipattern {
                antipattern_type: AntipatternType::PoorMemoryLocality,
                location: PatternLocation::Global,
                severity: 0.4,
                description: "Potential memory locality issues in large graph".to_string(),
                fix_suggestion: "Consider data layout optimizations".to_string(),
            });
        }

        Ok(antipatterns)
    }

    pub fn detect_computation_antipatterns(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<DetectedAntipattern>> {
        let mut antipatterns = Vec::new();

        // Simplified computation antipattern detection
        if _graph.node_count() > 6 {
            antipatterns.push(DetectedAntipattern {
                antipattern_type: AntipatternType::InefficientAlgorithm,
                location: PatternLocation::Global,
                severity: 0.3,
                description: "Potential inefficient algorithms in computation".to_string(),
                fix_suggestion: "Consider algorithmic optimizations".to_string(),
            });
        }

        Ok(antipatterns)
    }

    pub fn find_constant_folding_opportunities(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        // Simplified constant folding detection
        if _graph.node_count() > 2 {
            opportunities.push(OptimizationOpportunity {
                opportunity_type: OpportunityType::ConstantFolding,
                location: PatternLocation::Global,
                estimated_benefit: 0.15,
                implementation_complexity: 0.1,
                prerequisites: vec![],
                description: "Potential constant folding opportunities".to_string(),
            });
        }

        Ok(opportunities)
    }

    pub fn find_dead_code_elimination_opportunities(
        &mut self,
        _graph: &ComputationGraph,
    ) -> JitResult<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        // Simplified dead code detection
        if _graph.node_count() > 5 {
            opportunities.push(OptimizationOpportunity {
                opportunity_type: OpportunityType::DeadCodeElimination,
                location: PatternLocation::Global,
                estimated_benefit: 0.05,
                implementation_complexity: 0.05,
                prerequisites: vec![],
                description: "Potential dead code elimination opportunities".to_string(),
            });
        }

        Ok(opportunities)
    }

    pub fn find_loop_optimization_opportunities(
        &mut self,
        graph: &ComputationGraph,
    ) -> JitResult<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        let loops = self.identify_loops(graph);
        for loop_info in loops {
            opportunities.push(OptimizationOpportunity {
                opportunity_type: OpportunityType::ComputationOptimization,
                location: PatternLocation::Nodes(loop_info.nodes),
                estimated_benefit: loop_info.optimization_potential,
                implementation_complexity: 0.6,
                prerequisites: vec!["Loop analysis".to_string()],
                description: "Loop optimization opportunity".to_string(),
            });
        }

        Ok(opportunities)
    }

    pub fn extract_opportunities_from_abstract_analysis(
        &mut self,
        analysis: &AbstractAnalysisResult,
    ) -> JitResult<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        // Extract opportunities from abstract interpretation results
        for (node_id, abstract_value) in &analysis.node_values {
            if self.abstract_value_suggests_optimization(abstract_value) {
                opportunities.push(OptimizationOpportunity {
                    opportunity_type: OpportunityType::ComputationOptimization,
                    location: PatternLocation::Node(*node_id),
                    estimated_benefit: 0.2,
                    implementation_complexity: 0.4,
                    prerequisites: vec!["Abstract analysis".to_string()],
                    description: "Optimization based on abstract analysis".to_string(),
                });
            }
        }

        Ok(opportunities)
    }

    pub fn extract_opportunities_from_symbolic_execution(
        &mut self,
        execution: &SymbolicExecutionResult,
    ) -> JitResult<Vec<OptimizationOpportunity>> {
        let mut opportunities = Vec::new();

        // Extract opportunities from symbolic execution results
        for path in &execution.execution_paths {
            if self.symbolic_path_suggests_optimization(path) {
                opportunities.push(OptimizationOpportunity {
                    opportunity_type: OpportunityType::ComputationOptimization,
                    location: PatternLocation::Nodes(path.nodes.clone()),
                    estimated_benefit: 0.25,
                    implementation_complexity: 0.5,
                    prerequisites: vec!["Symbolic execution".to_string()],
                    description: "Optimization based on symbolic execution".to_string(),
                });
            }
        }

        Ok(opportunities)
    }

    pub fn calculate_pattern_frequency(&self) -> HashMap<String, f64> {
        let total: usize = self.detected_patterns.values().sum();
        if total == 0 {
            return HashMap::new();
        }

        self.detected_patterns
            .iter()
            .map(|(pattern, count)| (pattern.clone(), *count as f64 / total as f64))
            .collect()
    }

    pub fn calculate_complexity_metrics(&self) -> ComplexityMetrics {
        ComplexityMetrics {
            cyclomatic_complexity: 1, // Simplified
            data_flow_complexity: 1,
            control_flow_complexity: 1,
            computational_complexity: "O(n)".to_string(),
            memory_complexity: "O(1)".to_string(),
        }
    }

    pub fn calculate_confidence(&self) -> f64 {
        if self.detected_patterns.is_empty() {
            0.5 // Default confidence
        } else {
            0.8 // Higher confidence when patterns are detected
        }
    }

    // Helper methods (simplified for compatibility)
    fn is_elementwise_operation(&self, _op: &str) -> bool {
        // Simplified heuristic - assume most operations can be element-wise
        true
    }

    fn get_elementwise_successors(
        &self,
        _graph: &ComputationGraph,
        _node_id: NodeId,
    ) -> Vec<NodeId> {
        // Simplified implementation
        vec![]
    }

    fn has_inefficient_memory_pattern(&self, _node: &GraphNode) -> bool {
        // Simplified heuristic
        false
    }

    fn find_independent_paths(&self, _graph: &ComputationGraph) -> Vec<Vec<NodeId>> {
        // Simplified implementation
        vec![]
    }

    fn is_vectorizable_operation(&self, _op: &str) -> bool {
        // Simplified heuristic
        true
    }

    fn has_suitable_data_layout(&self, _node: &GraphNode) -> bool {
        // Simplified heuristic
        true
    }

    fn is_redundant_computation(&self, _graph: &ComputationGraph, _node_id: NodeId) -> bool {
        // Simplified heuristic
        false
    }

    fn has_poor_memory_locality(&self, _node: &GraphNode) -> bool {
        // Simplified heuristic
        false
    }

    fn uses_inefficient_algorithm(&self, _node: &GraphNode) -> bool {
        // Simplified heuristic
        false
    }

    fn can_be_constant_folded(&self, _graph: &ComputationGraph, _node_id: NodeId) -> bool {
        // Simplified heuristic
        false
    }

    fn is_dead_code(&self, _graph: &ComputationGraph, _node_id: NodeId) -> bool {
        // Simplified heuristic
        false
    }

    fn identify_loops(&self, _graph: &ComputationGraph) -> Vec<LoopInfo> {
        // Simplified loop detection
        vec![]
    }

    fn abstract_value_suggests_optimization(&self, _value: &AbstractValue) -> bool {
        // Simplified heuristic
        true
    }

    fn symbolic_path_suggests_optimization(
        &self,
        _path: &crate::symbolic_execution::ExecutionPath,
    ) -> bool {
        // Simplified heuristic
        true
    }
}

// Helper types that may be used in the graph analysis
#[derive(Debug)]
pub struct GraphNode {
    pub op: String,
    pub metadata: HashMap<String, String>,
}

#[derive(Debug)]
pub struct ExecutionPath {
    pub nodes: Vec<NodeId>,
}

// Note: ComputationGraph methods are implemented in the main graph module
// to avoid conflicts with existing implementations