torsh-text 0.1.2

//! Advanced Prosodic Analysis Module
//!
//! This module provides sophisticated high-level prosodic analysis capabilities
//! that combine and synthesize results from multiple specialized analyzers
//! to produce comprehensive prosodic fluency assessments.

use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, HashSet};
use std::hash::{Hash, Hasher};
use thiserror::Error;

use super::config::AdvancedProsodicConfig;
use super::results::{
    AdvancedProsodicMetrics, IntonationMetrics, PhonologicalMetrics, ProsodicCoherenceMetrics,
    ProsodicComplexityAnalysis, ProsodicFluencyProfile, ProsodicIntegrationMetrics,
    ProsodicPatternSynthesis, RhythmMetrics, StressMetrics, TimingMetrics,
};

/// Advanced prosodic analysis engine for comprehensive fluency assessment
#[derive(Debug, Clone)]
pub struct AdvancedProsodicAnalyzer {
    config: AdvancedProsodicConfig,
    integration_engine: ProsodicIntegrationEngine,
    coherence_analyzer: ProsodicCoherenceAnalyzer,
    complexity_analyzer: ProsodicComplexityAnalyzer,
    pattern_synthesizer: ProsodicPatternSynthesizer,
    fluency_profiler: ProsodicFluencyProfiler,
    cross_analyzer: CrossComponentAnalyzer,
    analysis_cache: HashMap<u64, AdvancedProsodicMetrics>,
}

/// Integration engine for combining multiple prosodic analysis components
#[derive(Debug, Clone)]
pub struct ProsodicIntegrationEngine {
    integration_weights: HashMap<String, f64>,
    component_priorities: HashMap<String, f64>,
    interaction_matrix: HashMap<(String, String), f64>,
    synthesis_algorithms: Vec<SynthesisAlgorithm>,
}

/// Coherence analyzer for prosodic consistency assessment
#[derive(Debug, Clone)]
pub struct ProsodicCoherenceAnalyzer {
    coherence_metrics: HashMap<String, f64>,
    consistency_thresholds: HashMap<String, f64>,
    pattern_alignment_weights: HashMap<String, f64>,
    temporal_coherence_analyzer: TemporalCoherenceAnalyzer,
}

/// Complexity analyzer for overall prosodic complexity assessment
#[derive(Debug, Clone)]
pub struct ProsodicComplexityAnalyzer {
    complexity_models: Vec<ComplexityModel>,
    weighting_functions: HashMap<String, Box<dyn Fn(&[f64]) -> f64>>,
    hierarchical_weights: HashMap<String, f64>,
    emergence_detector: EmergenceDetector,
}

/// Pattern synthesizer for high-level prosodic pattern identification
#[derive(Debug, Clone)]
pub struct ProsodicPatternSynthesizer {
    synthesis_templates: Vec<PatternSynthesisTemplate>,
    pattern_hierarchy: PatternHierarchy,
    cross_component_patterns: HashMap<String, CrossComponentPattern>,
    pattern_significance_analyzer: PatternSignificanceAnalyzer,
}

/// Fluency profiler for comprehensive prosodic fluency assessment
#[derive(Debug, Clone)]
pub struct ProsodicFluencyProfiler {
    profiling_dimensions: Vec<FluencyDimension>,
    fluency_models: HashMap<String, FluencyModel>,
    benchmark_comparisons: HashMap<String, BenchmarkProfile>,
    fluency_trajectory_analyzer: FluencyTrajectoryAnalyzer,
}

/// Cross-component analyzer for inter-module relationships
#[derive(Debug, Clone)]
pub struct CrossComponentAnalyzer {
    component_correlations: HashMap<(String, String), f64>,
    interaction_patterns: Vec<InteractionPattern>,
    dependency_graph: DependencyGraph,
    emergent_property_detector: EmergentPropertyDetector,
}

/// Temporal coherence analyzer for time-based consistency
#[derive(Debug, Clone)]
pub struct TemporalCoherenceAnalyzer {
    temporal_windows: Vec<f64>,
    coherence_functions: HashMap<String, Box<dyn Fn(&[f64]) -> f64>>,
    stability_metrics: HashMap<String, f64>,
}

/// Emergence detector for identifying emergent prosodic properties
#[derive(Debug, Clone)]
pub struct EmergenceDetector {
    emergence_patterns: Vec<EmergencePattern>,
    threshold_functions: HashMap<String, f64>,
    interaction_weights: HashMap<String, f64>,
}

/// Pattern hierarchy for organizing prosodic patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternHierarchy {
    pub levels: Vec<HierarchyLevel>,
    pub cross_level_connections: HashMap<String, Vec<String>>,
    pub level_weights: HashMap<String, f64>,
}

/// Synthesis algorithm for combining prosodic components
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SynthesisAlgorithm {
    pub name: String,
    pub algorithm_type: String,
    pub input_components: Vec<String>,
    pub output_metrics: Vec<String>,
    pub weighting_scheme: String,
    pub normalization_method: String,
}

/// Complexity model for prosodic complexity assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityModel {
    pub name: String,
    pub model_type: String,
    pub input_dimensions: Vec<String>,
    pub complexity_function: String,
    pub scaling_factors: HashMap<String, f64>,
    pub interaction_terms: Vec<String>,
}

/// Pattern synthesis template for high-level pattern identification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternSynthesisTemplate {
    pub name: String,
    pub pattern_type: String,
    pub required_components: Vec<String>,
    pub synthesis_rules: Vec<String>,
    pub significance_threshold: f64,
    pub contextual_modifiers: HashMap<String, f64>,
}

/// Cross-component pattern for multi-module patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossComponentPattern {
    pub name: String,
    pub component_dependencies: Vec<String>,
    pub interaction_type: String,
    pub pattern_signature: String,
    pub detection_algorithm: String,
    pub significance_weight: f64,
}

/// Fluency dimension for prosodic fluency assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FluencyDimension {
    pub name: String,
    pub dimension_type: String,
    pub measurement_components: Vec<String>,
    pub scoring_algorithm: String,
    pub weight_in_profile: f64,
    pub benchmark_ranges: HashMap<String, (f64, f64)>,
}

/// Fluency model for comprehensive assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FluencyModel {
    pub name: String,
    pub model_type: String,
    pub input_dimensions: Vec<String>,
    pub output_score_range: (f64, f64),
    pub calibration_parameters: HashMap<String, f64>,
    pub validation_metrics: HashMap<String, f64>,
}

/// Benchmark profile for comparative assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkProfile {
    pub profile_name: String,
    pub population_type: String,
    pub metric_ranges: HashMap<String, (f64, f64)>,
    pub percentile_distributions: HashMap<String, Vec<f64>>,
    pub reference_standards: HashMap<String, f64>,
}

/// Fluency trajectory analyzer for temporal patterns
#[derive(Debug, Clone)]
pub struct FluencyTrajectoryAnalyzer {
    trajectory_models: Vec<TrajectoryModel>,
    temporal_patterns: HashMap<String, Vec<f64>>,
    trend_detection_algorithms: HashMap<String, Box<dyn Fn(&[f64]) -> f64>>,
}

/// Interaction pattern for component relationships
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InteractionPattern {
    pub name: String,
    pub interaction_type: String,
    pub participating_components: Vec<String>,
    pub interaction_strength: f64,
    pub temporal_dynamics: String,
    pub context_sensitivity: f64,
}

/// Dependency graph for component relationships
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DependencyGraph {
    pub nodes: HashMap<String, DependencyNode>,
    pub edges: Vec<DependencyEdge>,
    pub graph_metrics: HashMap<String, f64>,
}

/// Emergent property detector for identifying higher-order patterns
#[derive(Debug, Clone)]
pub struct EmergentPropertyDetector {
    emergence_indicators: HashMap<String, f64>,
    detection_algorithms: HashMap<String, Box<dyn Fn(&HashMap<String, f64>) -> f64>>,
    significance_thresholds: HashMap<String, f64>,
}

/// Hierarchy level in pattern organization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HierarchyLevel {
    pub level_name: String,
    pub level_index: usize,
    pub patterns: Vec<String>,
    pub abstraction_degree: f64,
    pub parent_level: Option<String>,
    pub child_levels: Vec<String>,
}

/// Trajectory model for fluency development
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrajectoryModel {
    pub model_name: String,
    pub trajectory_type: String,
    pub time_scale: String,
    pub model_parameters: HashMap<String, f64>,
    pub prediction_accuracy: f64,
}

/// Emergence pattern for detecting emergent properties
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmergencePattern {
    pub pattern_name: String,
    pub emergence_type: String,
    pub prerequisite_conditions: Vec<String>,
    pub emergence_indicators: Vec<String>,
    pub stability_requirements: HashMap<String, f64>,
}

/// Dependency node in the component graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DependencyNode {
    pub node_id: String,
    pub component_type: String,
    pub influence_weight: f64,
    pub connectivity_degree: usize,
}

/// Dependency edge in the component graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DependencyEdge {
    pub source_node: String,
    pub target_node: String,
    pub edge_type: String,
    pub dependency_strength: f64,
    pub temporal_lag: f64,
}

/// Pattern significance analyzer for evaluating pattern importance
#[derive(Debug, Clone)]
pub struct PatternSignificanceAnalyzer {
    significance_metrics: HashMap<String, f64>,
    statistical_tests: HashMap<String, Box<dyn Fn(&[f64]) -> f64>>,
    effect_size_calculators: HashMap<String, Box<dyn Fn(&[f64], &[f64]) -> f64>>,
}

/// Errors that can occur during advanced prosodic analysis
#[derive(Debug, Error)]
pub enum AdvancedProsodicAnalysisError {
    #[error("Integration failed between components: {components:?}")]
    IntegrationError { components: Vec<String> },

    #[error("Coherence analysis failed for dimension: {dimension}")]
    CoherenceAnalysisError { dimension: String },

    #[error("Complexity calculation failed: {calculation}")]
    ComplexityCalculationError { calculation: String },

    #[error("Pattern synthesis failed for template: {template}")]
    PatternSynthesisError { template: String },

    #[error("Fluency profiling failed: {profile}")]
    FluencyProfilingError { profile: String },

    #[error("Cross-component analysis failed: {analysis}")]
    CrossComponentAnalysisError { analysis: String },

    #[error("Cache operation failed: {operation}")]
    CacheError { operation: String },
}

impl AdvancedProsodicAnalyzer {
    /// Creates a new advanced prosodic analyzer with the specified configuration
    pub fn new(config: AdvancedProsodicConfig) -> Self {
        let integration_engine = ProsodicIntegrationEngine::new(&config);
        let coherence_analyzer = ProsodicCoherenceAnalyzer::new(&config);
        let complexity_analyzer = ProsodicComplexityAnalyzer::new(&config);
        let pattern_synthesizer = ProsodicPatternSynthesizer::new(&config);
        let fluency_profiler = ProsodicFluencyProfiler::new(&config);
        let cross_analyzer = CrossComponentAnalyzer::new(&config);

        Self {
            config,
            integration_engine,
            coherence_analyzer,
            complexity_analyzer,
            pattern_synthesizer,
            fluency_profiler,
            cross_analyzer,
            analysis_cache: HashMap::new(),
        }
    }

    /// Performs comprehensive advanced prosodic analysis
    pub fn analyze(
        &mut self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<AdvancedProsodicMetrics, AdvancedProsodicAnalysisError> {
        // Generate cache key
        let cache_key = self.generate_cache_key(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        );

        // Check cache first
        if let Some(cached_result) = self.analysis_cache.get(&cache_key) {
            return Ok(cached_result.clone());
        }

        // Perform advanced integration analysis
        let integration_metrics = self.analyze_component_integration(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )?;

        // Analyze prosodic coherence
        let coherence_metrics = self.analyze_prosodic_coherence(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )?;

        // Perform complexity analysis
        let complexity_analysis = self.analyze_prosodic_complexity(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )?;

        // Synthesize high-level patterns
        let pattern_synthesis = self.synthesize_prosodic_patterns(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )?;

        // Generate comprehensive fluency profile
        let fluency_profile = self.generate_fluency_profile(
            &integration_metrics,
            &coherence_metrics,
            &complexity_analysis,
            &pattern_synthesis,
        )?;

        // Perform cross-component analysis
        let cross_component_insights = self.analyze_cross_components(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )?;

        // Calculate overall advanced metrics
        let overall_sophistication = self.calculate_overall_sophistication(
            &integration_metrics,
            &complexity_analysis,
            &pattern_synthesis,
        );

        let fluency_impact_assessment = self.assess_fluency_impact(
            &fluency_profile,
            &coherence_metrics,
            &cross_component_insights,
        );

        let metrics = AdvancedProsodicMetrics {
            integration_metrics,
            coherence_metrics,
            complexity_analysis,
            pattern_synthesis,
            fluency_profile,
            cross_component_insights,
            overall_sophistication,
            fluency_impact_assessment,
            analysis_confidence: self.calculate_analysis_confidence(),
        };

        // Cache the result
        if self.config.enable_caching {
            self.analysis_cache.insert(cache_key, metrics.clone());
        }

        Ok(metrics)
    }

    /// Analyzes integration between prosodic components
    fn analyze_component_integration(
        &mut self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<ProsodicIntegrationMetrics, AdvancedProsodicAnalysisError> {
        self.integration_engine.integrate_components(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )
    }

    /// Analyzes prosodic coherence across components
    fn analyze_prosodic_coherence(
        &mut self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<ProsodicCoherenceMetrics, AdvancedProsodicAnalysisError> {
        self.coherence_analyzer.analyze_coherence(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )
    }

    /// Analyzes overall prosodic complexity
    fn analyze_prosodic_complexity(
        &mut self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<ProsodicComplexityAnalysis, AdvancedProsodicAnalysisError> {
        self.complexity_analyzer.analyze_complexity(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )
    }

    /// Synthesizes high-level prosodic patterns
    fn synthesize_prosodic_patterns(
        &mut self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<ProsodicPatternSynthesis, AdvancedProsodicAnalysisError> {
        self.pattern_synthesizer.synthesize_patterns(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )
    }

    /// Generates comprehensive fluency profile
    fn generate_fluency_profile(
        &mut self,
        integration_metrics: &ProsodicIntegrationMetrics,
        coherence_metrics: &ProsodicCoherenceMetrics,
        complexity_analysis: &ProsodicComplexityAnalysis,
        pattern_synthesis: &ProsodicPatternSynthesis,
    ) -> Result<ProsodicFluencyProfile, AdvancedProsodicAnalysisError> {
        self.fluency_profiler.generate_profile(
            integration_metrics,
            coherence_metrics,
            complexity_analysis,
            pattern_synthesis,
        )
    }

    /// Analyzes cross-component relationships and emergent properties
    fn analyze_cross_components(
        &mut self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<HashMap<String, f64>, AdvancedProsodicAnalysisError> {
        self.cross_analyzer.analyze_cross_components(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        )
    }

    /// Calculates overall sophistication score
    fn calculate_overall_sophistication(
        &self,
        integration_metrics: &ProsodicIntegrationMetrics,
        complexity_analysis: &ProsodicComplexityAnalysis,
        pattern_synthesis: &ProsodicPatternSynthesis,
    ) -> f64 {
        let weights = &self.config.sophistication_weights;

        weights.integration_weight * integration_metrics.overall_integration_score
            + weights.complexity_weight * complexity_analysis.overall_complexity_score
            + weights.pattern_weight * pattern_synthesis.pattern_sophistication_score
    }

    /// Assesses overall impact on fluency
    fn assess_fluency_impact(
        &self,
        fluency_profile: &ProsodicFluencyProfile,
        coherence_metrics: &ProsodicCoherenceMetrics,
        cross_component_insights: &HashMap<String, f64>,
    ) -> f64 {
        let base_fluency = fluency_profile.overall_fluency_score;
        let coherence_modifier = coherence_metrics.overall_coherence_score - 0.5;
        let emergent_modifier = cross_component_insights
            .get("emergent_fluency_boost")
            .unwrap_or(&0.0);

        (base_fluency + coherence_modifier * 0.2 + emergent_modifier * 0.3)
            .max(0.0)
            .min(1.0)
    }

    /// Calculates confidence in the advanced analysis results
    fn calculate_analysis_confidence(&self) -> f64 {
        let base_confidence = 0.88;
        let sophistication_bonus = if self.config.high_sophistication_analysis {
            0.08
        } else {
            0.0
        };
        let cache_penalty = if self.analysis_cache.len() > 2000 {
            -0.03
        } else {
            0.0
        };

        (base_confidence + sophistication_bonus + cache_penalty)
            .max(0.0)
            .min(1.0)
    }

    /// Generates a cache key for the advanced analysis
    fn generate_cache_key(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> u64 {
        let mut hasher = std::collections::hash_map::DefaultHasher::new();

        rhythm_metrics
            .beat_strength_variability
            .to_bits()
            .hash(&mut hasher);
        stress_metrics
            .stress_prominence_score
            .to_bits()
            .hash(&mut hasher);
        intonation_metrics.pitch_range.to_bits().hash(&mut hasher);
        timing_metrics.speech_rate.to_bits().hash(&mut hasher);
        phonological_metrics
            .complexity_score
            .to_bits()
            .hash(&mut hasher);
        self.config.integration_algorithm.hash(&mut hasher);

        hasher.finish()
    }

    /// Updates the analysis configuration
    pub fn update_config(&mut self, new_config: AdvancedProsodicConfig) {
        self.config = new_config;

        // Update sub-analyzers
        self.integration_engine.update_config(&self.config);
        self.coherence_analyzer.update_config(&self.config);
        self.complexity_analyzer.update_config(&self.config);
        self.pattern_synthesizer.update_config(&self.config);
        self.fluency_profiler.update_config(&self.config);
        self.cross_analyzer.update_config(&self.config);

        // Clear cache if configuration changed significantly
        if self.config.clear_cache_on_config_change {
            self.analysis_cache.clear();
        }
    }

    /// Clears the analysis cache
    pub fn clear_cache(&mut self) {
        self.analysis_cache.clear();
    }

    /// Gets current cache statistics
    pub fn get_cache_stats(&self) -> (usize, usize) {
        (self.analysis_cache.len(), self.analysis_cache.capacity())
    }
}

impl ProsodicIntegrationEngine {
    fn new(config: &AdvancedProsodicConfig) -> Self {
        Self {
            integration_weights: Self::create_integration_weights(),
            component_priorities: Self::create_component_priorities(),
            interaction_matrix: Self::create_interaction_matrix(),
            synthesis_algorithms: Self::create_synthesis_algorithms(),
        }
    }

    fn integrate_components(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<ProsodicIntegrationMetrics, AdvancedProsodicAnalysisError> {
        // Calculate component integration scores
        let rhythm_integration =
            self.calculate_component_integration_score("rhythm", rhythm_metrics.beat_consistency);
        let stress_integration =
            self.calculate_component_integration_score("stress", stress_metrics.stress_consistency);
        let intonation_integration = self.calculate_component_integration_score(
            "intonation",
            intonation_metrics.contour_smoothness,
        );
        let timing_integration =
            self.calculate_component_integration_score("timing", timing_metrics.tempo_consistency);
        let phonological_integration = self.calculate_component_integration_score(
            "phonological",
            1.0 - phonological_metrics
                .phonotactic_constraints
                .violation_density,
        );

        // Calculate cross-component interactions
        let component_interactions = self.calculate_component_interactions(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        );

        // Calculate overall integration score
        let overall_integration_score = self.calculate_overall_integration_score(
            rhythm_integration,
            stress_integration,
            intonation_integration,
            timing_integration,
            phonological_integration,
            &component_interactions,
        );

        // Detect integration patterns
        let integration_patterns = self.detect_integration_patterns(&component_interactions);

        // Calculate integration stability
        let integration_stability = self.calculate_integration_stability(&component_interactions);

        Ok(ProsodicIntegrationMetrics {
            rhythm_integration_score: rhythm_integration,
            stress_integration_score: stress_integration,
            intonation_integration_score: intonation_integration,
            timing_integration_score: timing_integration,
            phonological_integration_score: phonological_integration,
            component_interactions,
            overall_integration_score,
            integration_patterns,
            integration_stability,
            cross_component_synergy: self.calculate_synergy_score(&component_interactions),
        })
    }

    fn calculate_component_integration_score(&self, component: &str, base_score: f64) -> f64 {
        let weight = self.integration_weights.get(component).unwrap_or(&1.0);
        let priority = self.component_priorities.get(component).unwrap_or(&1.0);

        base_score * weight * priority
    }

    fn calculate_component_interactions(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> HashMap<String, f64> {
        let mut interactions = HashMap::new();

        // Rhythm-Stress interaction
        let rhythm_stress = self.calculate_pairwise_interaction(
            rhythm_metrics.beat_consistency,
            stress_metrics.stress_consistency,
            "rhythm-stress",
        );
        interactions.insert("rhythm-stress".to_string(), rhythm_stress);

        // Rhythm-Intonation interaction
        let rhythm_intonation = self.calculate_pairwise_interaction(
            rhythm_metrics.beat_consistency,
            intonation_metrics.contour_smoothness,
            "rhythm-intonation",
        );
        interactions.insert("rhythm-intonation".to_string(), rhythm_intonation);

        // Stress-Intonation interaction
        let stress_intonation = self.calculate_pairwise_interaction(
            stress_metrics.stress_prominence_score,
            intonation_metrics.pitch_range,
            "stress-intonation",
        );
        interactions.insert("stress-intonation".to_string(), stress_intonation);

        // Timing-Phonological interaction
        let timing_phonological = self.calculate_pairwise_interaction(
            timing_metrics.speech_rate,
            phonological_metrics.complexity_score,
            "timing-phonological",
        );
        interactions.insert("timing-phonological".to_string(), timing_phonological);

        // Add more complex interactions
        let global_coherence = self.calculate_global_coherence(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        );
        interactions.insert("global-coherence".to_string(), global_coherence);

        interactions
    }

    fn calculate_pairwise_interaction(
        &self,
        score1: f64,
        score2: f64,
        interaction_type: &str,
    ) -> f64 {
        let interaction_weight = self
            .interaction_matrix
            .get(&(interaction_type.to_string(), interaction_type.to_string()))
            .unwrap_or(&1.0);

        // Calculate interaction strength based on correlation and mutual information
        let correlation = (score1 * score2).sqrt();
        let mutual_information = self.calculate_mutual_information(score1, score2);

        (correlation + mutual_information) * 0.5 * interaction_weight
    }

    fn calculate_mutual_information(&self, x: f64, y: f64) -> f64 {
        // Simplified mutual information calculation
        let joint_entropy = -(x * y * (x * y).ln() + (1.0 - x * y) * (1.0 - x * y).ln());
        let marginal_entropy_x = -(x * x.ln() + (1.0 - x) * (1.0 - x).ln());
        let marginal_entropy_y = -(y * y.ln() + (1.0 - y) * (1.0 - y).ln());

        marginal_entropy_x + marginal_entropy_y - joint_entropy
    }

    fn calculate_global_coherence(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> f64 {
        let scores = vec![
            rhythm_metrics.beat_consistency,
            stress_metrics.stress_consistency,
            intonation_metrics.contour_smoothness,
            timing_metrics.tempo_consistency,
            1.0 - phonological_metrics
                .phonotactic_constraints
                .violation_density,
        ];

        // Calculate coefficient of variation as a measure of coherence
        let mean = scores.iter().sum::<f64>() / scores.len() as f64;
        let variance =
            scores.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / scores.len() as f64;
        let std_dev = variance.sqrt();

        1.0 - (std_dev / mean.max(0.001)) // Higher coherence = lower variation
    }

    fn calculate_overall_integration_score(
        &self,
        rhythm: f64,
        stress: f64,
        intonation: f64,
        timing: f64,
        phonological: f64,
        interactions: &HashMap<String, f64>,
    ) -> f64 {
        let component_average = (rhythm + stress + intonation + timing + phonological) / 5.0;
        let interaction_bonus = interactions.values().sum::<f64>() / interactions.len() as f64;

        (component_average * 0.7 + interaction_bonus * 0.3)
            .max(0.0)
            .min(1.0)
    }

    fn detect_integration_patterns(&self, interactions: &HashMap<String, f64>) -> Vec<String> {
        let mut patterns = Vec::new();

        // High integration pattern
        if interactions.values().all(|&v| v > 0.7) {
            patterns.push("high-integration".to_string());
        }

        // Selective integration pattern
        if interactions.values().filter(|&&v| v > 0.8).count() >= 2
            && interactions.values().filter(|&&v| v < 0.4).count() >= 1
        {
            patterns.push("selective-integration".to_string());
        }

        // Global coherence pattern
        if let Some(&global_coherence) = interactions.get("global-coherence") {
            if global_coherence > 0.85 {
                patterns.push("global-coherence".to_string());
            }
        }

        patterns
    }

    fn calculate_integration_stability(&self, interactions: &HashMap<String, f64>) -> f64 {
        let values: Vec<f64> = interactions.values().cloned().collect();
        let mean = values.iter().sum::<f64>() / values.len() as f64;
        let variance =
            values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;

        1.0 - variance.sqrt() // Higher stability = lower variance
    }

    fn calculate_synergy_score(&self, interactions: &HashMap<String, f64>) -> f64 {
        // Synergy occurs when the whole is greater than the sum of its parts
        let interaction_sum = interactions.values().sum::<f64>();
        let expected_sum = interactions.len() as f64 * 0.5; // Expected average interaction

        (interaction_sum / expected_sum).min(2.0).max(0.0)
    }

    fn create_integration_weights() -> HashMap<String, f64> {
        let mut weights = HashMap::new();
        weights.insert("rhythm".to_string(), 1.0);
        weights.insert("stress".to_string(), 1.1);
        weights.insert("intonation".to_string(), 1.2);
        weights.insert("timing".to_string(), 0.9);
        weights.insert("phonological".to_string(), 0.8);
        weights
    }

    fn create_component_priorities() -> HashMap<String, f64> {
        let mut priorities = HashMap::new();
        priorities.insert("rhythm".to_string(), 1.0);
        priorities.insert("stress".to_string(), 1.1);
        priorities.insert("intonation".to_string(), 1.2);
        priorities.insert("timing".to_string(), 0.95);
        priorities.insert("phonological".to_string(), 0.85);
        priorities
    }

    fn create_interaction_matrix() -> HashMap<(String, String), f64> {
        let mut matrix = HashMap::new();
        matrix.insert(
            ("rhythm-stress".to_string(), "rhythm-stress".to_string()),
            1.2,
        );
        matrix.insert(
            (
                "rhythm-intonation".to_string(),
                "rhythm-intonation".to_string(),
            ),
            1.1,
        );
        matrix.insert(
            (
                "stress-intonation".to_string(),
                "stress-intonation".to_string(),
            ),
            1.3,
        );
        matrix.insert(
            (
                "timing-phonological".to_string(),
                "timing-phonological".to_string(),
            ),
            1.0,
        );
        matrix
    }

    fn create_synthesis_algorithms() -> Vec<SynthesisAlgorithm> {
        vec![
            SynthesisAlgorithm {
                name: "Weighted Integration".to_string(),
                algorithm_type: "linear_combination".to_string(),
                input_components: vec![
                    "rhythm".to_string(),
                    "stress".to_string(),
                    "intonation".to_string(),
                ],
                output_metrics: vec!["integration_score".to_string()],
                weighting_scheme: "priority_based".to_string(),
                normalization_method: "min_max".to_string(),
            },
            SynthesisAlgorithm {
                name: "Nonlinear Synthesis".to_string(),
                algorithm_type: "nonlinear_combination".to_string(),
                input_components: vec!["timing".to_string(), "phonological".to_string()],
                output_metrics: vec!["complexity_integration".to_string()],
                weighting_scheme: "adaptive".to_string(),
                normalization_method: "z_score".to_string(),
            },
        ]
    }

    fn update_config(&mut self, config: &AdvancedProsodicConfig) {
        if config.adaptive_integration_weights {
            // Update integration weights based on performance
            for (component, weight) in &mut self.integration_weights {
                *weight *= 1.0 + config.adaptation_rate;
            }
        }
    }
}

// Note: The other sub-analyzers (CoherenceAnalyzer, ComplexityAnalyzer, etc.) would follow
// similar patterns but are abbreviated here due to space constraints. In a real implementation,
// each would have their own comprehensive implementation with specialized analysis methods.

impl ProsodicCoherenceAnalyzer {
    fn new(config: &AdvancedProsodicConfig) -> Self {
        Self {
            coherence_metrics: Self::create_coherence_metrics(),
            consistency_thresholds: Self::create_consistency_thresholds(),
            pattern_alignment_weights: Self::create_pattern_alignment_weights(),
            temporal_coherence_analyzer: TemporalCoherenceAnalyzer::new(config),
        }
    }

    fn analyze_coherence(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> Result<ProsodicCoherenceMetrics, AdvancedProsodicAnalysisError> {
        // Calculate coherence across different dimensions
        let temporal_coherence = self.temporal_coherence_analyzer.analyze_temporal_coherence(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            timing_metrics,
        );

        let structural_coherence = self.calculate_structural_coherence(
            rhythm_metrics,
            stress_metrics,
            intonation_metrics,
            phonological_metrics,
        );

        let perceptual_coherence = self.calculate_perceptual_coherence(
            intonation_metrics,
            timing_metrics,
            phonological_metrics,
        );

        let overall_coherence_score =
            (temporal_coherence + structural_coherence + perceptual_coherence) / 3.0;

        Ok(ProsodicCoherenceMetrics {
            temporal_coherence,
            structural_coherence,
            perceptual_coherence,
            overall_coherence_score,
            coherence_stability: self.calculate_coherence_stability(&[
                temporal_coherence,
                structural_coherence,
                perceptual_coherence,
            ]),
            pattern_alignment_score: self
                .calculate_pattern_alignment(rhythm_metrics, stress_metrics),
            consistency_violations: self.detect_consistency_violations(&[
                temporal_coherence,
                structural_coherence,
                perceptual_coherence,
            ]),
        })
    }

    fn calculate_structural_coherence(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> f64 {
        // Simplified structural coherence calculation
        let rhythm_structure = rhythm_metrics.beat_consistency;
        let stress_structure = stress_metrics.stress_consistency;
        let intonation_structure = intonation_metrics.contour_smoothness;
        let phonological_structure = 1.0
            - phonological_metrics
                .phonotactic_constraints
                .violation_density;

        (rhythm_structure + stress_structure + intonation_structure + phonological_structure) / 4.0
    }

    fn calculate_perceptual_coherence(
        &self,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
        phonological_metrics: &PhonologicalMetrics,
    ) -> f64 {
        // Focus on perceptually salient features
        let pitch_coherence = intonation_metrics.contour_smoothness;
        let timing_coherence = timing_metrics.tempo_consistency;
        let phonological_coherence = 1.0 - phonological_metrics.complexity_score / 10.0; // Normalize

        (pitch_coherence * 0.4 + timing_coherence * 0.4 + phonological_coherence * 0.2)
            .max(0.0)
            .min(1.0)
    }

    fn calculate_coherence_stability(&self, coherence_scores: &[f64]) -> f64 {
        let mean = coherence_scores.iter().sum::<f64>() / coherence_scores.len() as f64;
        let variance = coherence_scores
            .iter()
            .map(|&x| (x - mean).powi(2))
            .sum::<f64>()
            / coherence_scores.len() as f64;

        1.0 - variance.sqrt() // Higher stability = lower variance
    }

    fn calculate_pattern_alignment(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
    ) -> f64 {
        // Calculate how well rhythm and stress patterns align
        let rhythm_regularity = rhythm_metrics.beat_consistency;
        let stress_regularity = stress_metrics.stress_consistency;

        (rhythm_regularity * stress_regularity).sqrt()
    }

    fn detect_consistency_violations(&self, coherence_scores: &[f64]) -> Vec<String> {
        let mut violations = Vec::new();

        for (i, &score) in coherence_scores.iter().enumerate() {
            let threshold = match i {
                0 => self.consistency_thresholds.get("temporal").unwrap_or(&0.6),
                1 => self
                    .consistency_thresholds
                    .get("structural")
                    .unwrap_or(&0.65),
                2 => self
                    .consistency_thresholds
                    .get("perceptual")
                    .unwrap_or(&0.7),
                _ => &0.6,
            };

            if score < *threshold {
                violations.push(format!("coherence_violation_{}", i));
            }
        }

        violations
    }

    fn create_coherence_metrics() -> HashMap<String, f64> {
        let mut metrics = HashMap::new();
        metrics.insert("temporal_weight".to_string(), 1.0);
        metrics.insert("structural_weight".to_string(), 1.1);
        metrics.insert("perceptual_weight".to_string(), 1.2);
        metrics
    }

    fn create_consistency_thresholds() -> HashMap<String, f64> {
        let mut thresholds = HashMap::new();
        thresholds.insert("temporal".to_string(), 0.6);
        thresholds.insert("structural".to_string(), 0.65);
        thresholds.insert("perceptual".to_string(), 0.7);
        thresholds
    }

    fn create_pattern_alignment_weights() -> HashMap<String, f64> {
        let mut weights = HashMap::new();
        weights.insert("rhythm_stress_alignment".to_string(), 1.2);
        weights.insert("intonation_timing_alignment".to_string(), 1.1);
        weights.insert("phonological_prosodic_alignment".to_string(), 1.0);
        weights
    }

    fn update_config(&mut self, config: &AdvancedProsodicConfig) {
        if config.adaptive_coherence_thresholds {
            for (_, threshold) in &mut self.consistency_thresholds {
                *threshold *= 1.0 + config.adaptation_rate * 0.5;
            }
        }
    }
}

impl TemporalCoherenceAnalyzer {
    fn new(config: &AdvancedProsodicConfig) -> Self {
        Self {
            temporal_windows: vec![1.0, 2.0, 5.0, 10.0], // Time windows in seconds
            coherence_functions: HashMap::new(), // Would be populated with closure functions
            stability_metrics: Self::create_stability_metrics(),
        }
    }

    fn analyze_temporal_coherence(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
        timing_metrics: &TimingMetrics,
    ) -> f64 {
        // Analyze coherence across different time scales
        let short_term_coherence =
            self.calculate_short_term_coherence(rhythm_metrics, timing_metrics);
        let medium_term_coherence =
            self.calculate_medium_term_coherence(stress_metrics, intonation_metrics);
        let long_term_coherence =
            self.calculate_long_term_coherence(rhythm_metrics, stress_metrics, intonation_metrics);

        (short_term_coherence + medium_term_coherence + long_term_coherence) / 3.0
    }

    fn calculate_short_term_coherence(
        &self,
        rhythm_metrics: &RhythmMetrics,
        timing_metrics: &TimingMetrics,
    ) -> f64 {
        // Focus on beat-to-beat consistency
        rhythm_metrics.beat_consistency * timing_metrics.tempo_consistency
    }

    fn calculate_medium_term_coherence(
        &self,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
    ) -> f64 {
        // Focus on phrase-level consistency
        let stress_phrase_consistency = stress_metrics.stress_consistency;
        let intonation_phrase_consistency = intonation_metrics.contour_smoothness;

        (stress_phrase_consistency + intonation_phrase_consistency) / 2.0
    }

    fn calculate_long_term_coherence(
        &self,
        rhythm_metrics: &RhythmMetrics,
        stress_metrics: &StressMetrics,
        intonation_metrics: &IntonationMetrics,
    ) -> f64 {
        // Focus on discourse-level consistency
        let rhythm_discourse = rhythm_metrics.beat_consistency;
        let stress_discourse = stress_metrics.stress_consistency;
        let intonation_discourse = intonation_metrics.contour_smoothness;

        (rhythm_discourse + stress_discourse + intonation_discourse) / 3.0
    }

    fn create_stability_metrics() -> HashMap<String, f64> {
        let mut metrics = HashMap::new();
        metrics.insert("short_term_stability".to_string(), 0.8);
        metrics.insert("medium_term_stability".to_string(), 0.75);
        metrics.insert("long_term_stability".to_string(), 0.7);
        metrics
    }
}

// Additional abbreviated implementations for other analyzers...
// (ComplexityAnalyzer, PatternSynthesizer, FluencyProfiler, CrossComponentAnalyzer)
// These would follow similar comprehensive patterns in a full implementation.

#[cfg(test)]
mod tests {
    use super::*;
    use crate::metrics::fluency::prosodic::results::*;

    #[test]
    fn test_advanced_prosodic_analyzer_creation() {
        let config = AdvancedProsodicConfig::default();
        let analyzer = AdvancedProsodicAnalyzer::new(config);
        assert_eq!(analyzer.analysis_cache.len(), 0);
    }

    #[test]
    fn test_integration_engine_creation() {
        let config = AdvancedProsodicConfig::default();
        let engine = ProsodicIntegrationEngine::new(&config);
        assert!(!engine.integration_weights.is_empty());
        assert!(!engine.component_priorities.is_empty());
    }

    #[test]
    fn test_coherence_analyzer_creation() {
        let config = AdvancedProsodicConfig::default();
        let analyzer = ProsodicCoherenceAnalyzer::new(&config);
        assert!(!analyzer.coherence_metrics.is_empty());
        assert!(!analyzer.consistency_thresholds.is_empty());
    }

    #[test]
    fn test_temporal_coherence_analyzer() {
        let config = AdvancedProsodicConfig::default();
        let analyzer = TemporalCoherenceAnalyzer::new(&config);
        assert!(!analyzer.temporal_windows.is_empty());
        assert_eq!(analyzer.temporal_windows.len(), 4);
    }
}