scirs2_text/

text_coordinator.rs

//! Advanced Text Processing Coordinator
//!
//! This module provides the ultimate text processing coordination system that
//! integrates all advanced features for maximum performance and intelligence.
//! It combines neural architectures, transformers, SIMD operations, and
//! real-time adaptation into a unified advanced-performance system.
//!
//! Key features:
//! - Optimized text processing with GPU/SIMD acceleration
//! - Advanced neural text understanding with transformer ensembles
//! - Real-time performance optimization and adaptation
//! - Advanced-memory efficient text operations
//! - AI-driven text analysis with predictive capabilities
//! - Multi-modal text processing coordination

use crate::error::{Result, TextError};
use crate::multilingual::{Language, LanguageDetectionResult};
use crate::named_entity_recognition::{extract_entities, NerPatternConfig};
use crate::sentiment::{LexiconSentimentAnalyzer, Sentiment, SentimentResult, SentimentWordCounts};
use crate::transformer::*;
use crate::vectorize::{TfidfVectorizer, Vectorizer};
use scirs2_core::ndarray::{Array1, Array2};
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

/// Optimization strategy for performance tuning
#[derive(Debug)]
pub enum OptimizationStrategy {
    /// Balanced optimization between performance and memory
    Balanced,
    /// Optimize for maximum performance
    Performance,
    /// Optimize for memory efficiency
    Memory,
    /// Conservative optimization approach
    Conservative,
}

/// Ensemble voting strategy for neural model coordination
#[derive(Debug)]
pub enum EnsembleVotingStrategy {
    /// Use weighted average of model outputs
    WeightedAverage,
    /// Use majority vote among models
    Majority,
    /// Use stacking ensemble approach
    Stacking,
}

/// Adaptation strategy for real-time optimization
#[derive(Debug)]
pub enum AdaptationStrategy {
    /// Conservative adaptation with minimal changes
    Conservative,
    /// Aggressive adaptation for maximum optimization
    Aggressive,
    /// Balanced adaptation approach
    Balanced,
}

/// Neural architecture trait for implementing custom architectures
#[allow(dead_code)]
pub trait NeuralArchitecture: std::fmt::Debug {
    // Trait methods would be defined here
}

// Define missing types for Advanced mode
/// Text complexity analysis results
#[derive(Debug, Clone, Default)]
pub struct TextComplexityAnalysis {
    /// Readability score (0.0-1.0)
    pub readability_score: f64,
    /// Complexity level description
    pub complexity_level: String,
    /// Sentence complexity score
    pub sentence_complexity: f64,
    /// Vocabulary complexity score
    pub vocabulary_complexity: f64,
}

/// Text style analysis results
#[derive(Debug, Clone, Default)]
pub struct TextStyleAnalysis {
    /// Formality score (0.0-1.0)
    pub formality_score: f64,
    /// Detected tone
    pub tone: String,
    /// Writing style description
    pub writing_style: String,
    /// Sentiment polarity (-1.0 to 1.0)
    pub sentiment_polarity: f64,
}

/// Predictive text insights
#[derive(Debug, Clone, Default)]
pub struct PredictiveTextInsights {
    /// Next word predictions
    pub next_word_predictions: Vec<String>,
    /// Topic predictions
    pub topic_predictions: Vec<String>,
    /// Sentiment prediction score
    pub sentiment_prediction: f64,
    /// Quality prediction score
    pub quality_prediction: f64,
}

/// Text anomaly detection result
#[derive(Debug, Clone)]
pub struct TextAnomaly {
    /// Type of anomaly detected
    pub anomaly_type: String,
    /// Severity score (0.0-1.0)
    pub severity: f64,
    /// Description of the anomaly
    pub description: String,
    /// Location of anomaly in text
    pub location: Option<usize>,
}

/// Named entity recognition result
#[derive(Debug, Clone)]
pub struct NamedEntity {
    /// Entity text
    pub text: String,
    /// Entity type (Person, Organization, etc.)
    pub entity_type: String,
    /// Start position in text
    pub start_pos: usize,
    /// End position in text
    pub end_pos: usize,
    /// Confidence score (0.0-1.0)
    pub confidence: f64,
}

/// Text quality metrics
#[derive(Debug, Clone, Default)]
pub struct TextQualityMetrics {
    /// Coherence score (0.0-1.0)
    pub coherence_score: f64,
    /// Clarity score (0.0-1.0)
    pub clarity_score: f64,
    /// Grammatical correctness score (0.0-1.0)
    pub grammatical_score: f64,
    /// Completeness score (0.0-1.0)
    pub completeness_score: f64,
}

/// Neural processing outputs
#[derive(Debug, Clone)]
pub struct NeuralProcessingOutputs {
    /// Text embeddings
    pub embeddings: Array2<f64>,
    /// Attention weights
    pub attentionweights: Array2<f64>,
    /// Layer outputs
    pub layer_outputs: Vec<Array2<f64>>,
}

/// Topic modeling result
#[derive(Debug, Clone)]
pub struct TopicModelingResult {
    /// Identified topics
    pub topics: Vec<String>,
    /// Topic probabilities
    pub topic_probabilities: Vec<f64>,
    /// Dominant topic
    pub dominant_topic: String,
    /// Topic coherence score
    pub topic_coherence: f64,
}

/// Text processing performance metrics
#[derive(Debug, Clone)]
pub struct TextPerformanceMetrics {
    /// Throughput (items per second)
    pub throughput: f64,
    /// Processing latency
    pub latency: Duration,
    /// Memory usage in bytes
    pub memory_usage: usize,
    /// CPU utilization percentage
    pub cpu_utilization: f64,
    /// Total processing time
    pub processing_time: Duration,
    /// Memory efficiency score
    pub memory_efficiency: f64,
    /// Accuracy estimate
    pub accuracy_estimate: f64,
}

/// Processing timing breakdown
#[derive(Debug, Clone)]
pub struct ProcessingTimingBreakdown {
    /// Preprocessing time
    pub preprocessing_time: Duration,
    /// Processing time
    pub processing_time: Duration,
    /// Postprocessing time
    pub postprocessing_time: Duration,
    /// Neural processing time
    pub neural_processing_time: Duration,
    /// Analytics time
    pub analytics_time: Duration,
    /// Optimization time
    pub optimization_time: Duration,
    /// Total time
    pub total_time: Duration,
}

// Placeholder types for complex systems
// OptimizationStrategy is defined as enum below

/// Performance metrics snapshot
#[derive(Debug)]
pub struct PerformanceMetricsSnapshot;

/// Adaptive optimization parameters
#[derive(Debug)]
pub struct AdaptiveOptimizationParams;

/// Hardware capability detector
#[derive(Debug)]
pub struct HardwareCapabilityDetector;
impl HardwareCapabilityDetector {
    fn new() -> Self {
        HardwareCapabilityDetector
    }
}

// EnsembleVotingStrategy is defined as enum below

/// Model performance metrics
#[derive(Debug)]
pub struct ModelPerformanceMetrics;

/// Dynamic model selector
#[derive(Debug)]
pub struct DynamicModelSelector;
impl DynamicModelSelector {
    fn new() -> Self {
        DynamicModelSelector
    }
}

/// Text memory pool
#[derive(Debug)]
pub struct TextMemoryPool;
impl TextMemoryPool {
    fn new() -> Self {
        TextMemoryPool
    }
}

/// Text cache manager
#[derive(Debug)]
pub struct TextCacheManager;
impl TextCacheManager {
    fn new() -> Self {
        TextCacheManager
    }
}

/// Memory usage predictor
#[derive(Debug)]
pub struct MemoryUsagePredictor;
impl MemoryUsagePredictor {
    fn new() -> Self {
        MemoryUsagePredictor
    }
}

/// Garbage collection optimizer
#[derive(Debug)]
pub struct GarbageCollectionOptimizer;
impl GarbageCollectionOptimizer {
    fn new() -> Self {
        GarbageCollectionOptimizer
    }
}

// AdaptationStrategy is defined as enum below

/// Performance monitor
#[derive(Debug)]
pub struct PerformanceMonitor;

/// Adaptation triggers
#[derive(Debug)]
pub struct AdaptationTriggers;

/// Adaptive learning system
#[derive(Debug)]
pub struct AdaptiveLearningSystem;
impl AdaptiveLearningSystem {
    fn new() -> Self {
        AdaptiveLearningSystem
    }
}

/// Analytics pipeline
#[derive(Debug)]
pub struct AnalyticsPipeline;

/// Insight generator
#[derive(Debug)]
pub struct InsightGenerator;
impl InsightGenerator {
    fn new() -> Self {
        InsightGenerator
    }
}

/// Text anomaly detector
#[derive(Debug)]
pub struct TextAnomalyDetector;
impl TextAnomalyDetector {
    fn new() -> Self {
        TextAnomalyDetector
    }
}

/// Predictive text modeler
#[derive(Debug)]
pub struct PredictiveTextModeler;
impl PredictiveTextModeler {
    fn new() -> Self {
        PredictiveTextModeler
    }
}

/// Text image processor
#[derive(Debug)]
pub struct TextImageProcessor;
impl TextImageProcessor {
    fn new() -> Self {
        TextImageProcessor
    }
}

/// Text audio processor
#[derive(Debug)]
pub struct TextAudioProcessor;
impl TextAudioProcessor {
    fn new() -> Self {
        TextAudioProcessor
    }
}

/// Cross modal attention
#[derive(Debug)]
pub struct CrossModalAttention;
impl CrossModalAttention {
    fn new() -> Self {
        CrossModalAttention
    }
}

/// Multi modal fusion strategies
#[derive(Debug)]
pub struct MultiModalFusionStrategies;
impl MultiModalFusionStrategies {
    fn new() -> Self {
        MultiModalFusionStrategies
    }
}

/// Text performance tracker
#[derive(Debug)]
pub struct TextPerformanceTracker;

/// Advanced classification result
#[derive(Debug, Clone)]
pub struct AdvancedClassificationResult {
    /// Classification class
    pub class: String,
    /// Confidence score
    pub confidence: f64,
    /// Class probabilities
    pub probabilities: HashMap<String, f64>,
}

/// Performance bottleneck
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    /// Component name
    pub component: String,
    /// Impact score
    pub impact: f64,
    /// Description of bottleneck
    pub description: String,
    /// Suggested fix
    pub suggested_fix: String,
}

/// Advanced multiple text result
#[derive(Debug)]
pub struct AdvancedMultipleTextResult {
    /// Individual results
    pub results: Vec<AdvancedTextResult>,
    /// Aggregated analytics
    pub aggregated_analytics: AdvancedTextAnalytics,
    /// Multi-text insights
    pub multitext_insights: HashMap<String, f64>,
    /// Overall performance metrics
    pub overall_performance: TextPerformanceMetrics,
    /// Optimization recommendations
    pub optimization_recommendations: Vec<String>,
}

/// Advanced Text Processing Coordinator
///
/// The central intelligence system that coordinates all Advanced mode operations
/// for text processing, providing adaptive optimization, intelligent resource
/// management, and performance enhancement.
pub struct AdvancedTextCoordinator {
    /// Configuration settings
    config: AdvancedTextConfig,

    /// Performance optimization engine
    performance_optimizer: Arc<Mutex<PerformanceOptimizer>>,

    /// Neural processing ensemble
    neural_ensemble: Arc<RwLock<NeuralProcessingEnsemble>>,

    /// Memory optimization system
    memory_optimizer: Arc<Mutex<TextMemoryOptimizer>>,

    /// Real-time adaptation engine
    adaptive_engine: Arc<Mutex<AdaptiveTextEngine>>,

    /// Advanced analytics and insights
    analytics_engine: Arc<RwLock<TextAnalyticsEngine>>,

    /// Multi-modal processing coordinator
    #[allow(dead_code)]
    multimodal_coordinator: MultiModalTextCoordinator,

    /// Performance metrics tracker
    performance_tracker: Arc<RwLock<TextPerformanceTracker>>,
}

/// Configuration for Advanced text processing
#[derive(Debug, Clone)]
pub struct AdvancedTextConfig {
    /// Enable GPU acceleration for text processing
    pub enable_gpu_acceleration: bool,

    /// Enable SIMD optimizations
    pub enable_simd_optimizations: bool,

    /// Enable neural ensemble processing
    pub enable_neural_ensemble: bool,

    /// Enable real-time adaptation
    pub enable_real_time_adaptation: bool,

    /// Enable advanced analytics
    pub enable_advanced_analytics: bool,

    /// Enable multi-modal processing
    pub enable_multimodal: bool,

    /// Maximum memory usage (MB)
    pub max_memory_usage_mb: usize,

    /// Performance optimization level (0-3)
    pub optimization_level: u8,

    /// Target processing throughput (documents/second)
    pub target_throughput: f64,

    /// Enable predictive text processing
    pub enable_predictive_processing: bool,
}

impl Default for AdvancedTextConfig {
    fn default() -> Self {
        Self {
            enable_gpu_acceleration: true,
            enable_simd_optimizations: true,
            enable_neural_ensemble: true,
            enable_real_time_adaptation: true,
            enable_advanced_analytics: true,
            enable_multimodal: true,
            max_memory_usage_mb: 8192, // 8GB default
            optimization_level: 2,
            target_throughput: 1000.0, // 1000 docs/sec
            enable_predictive_processing: true,
        }
    }
}

/// Advanced-performance text processing result
#[derive(Debug)]
pub struct AdvancedTextResult {
    /// Primary processing result
    pub primary_result: TextProcessingResult,

    /// Advanced analytics insights
    pub analytics: AdvancedTextAnalytics,

    /// Performance metrics
    pub performance_metrics: TextPerformanceMetrics,

    /// Applied optimizations
    pub optimizations_applied: Vec<String>,

    /// Confidence scores for different aspects
    pub confidence_scores: HashMap<String, f64>,

    /// Processing time breakdown
    pub timing_breakdown: ProcessingTimingBreakdown,
}

/// Comprehensive text processing result
#[derive(Debug)]
pub struct TextProcessingResult {
    /// Vectorized representation
    pub vectors: Array2<f64>,

    /// Sentiment analysis results
    pub sentiment: SentimentResult,

    /// Topic modeling results
    pub topics: TopicModelingResult,

    /// Named entity recognition results
    pub entities: Vec<NamedEntity>,

    /// Text quality metrics
    pub quality_metrics: TextQualityMetrics,

    /// Neural processing outputs
    pub neural_outputs: NeuralProcessingOutputs,
}

/// Advanced text analytics results
#[derive(Debug)]
pub struct AdvancedTextAnalytics {
    /// Semantic similarity scores
    pub semantic_similarities: HashMap<String, f64>,

    /// Text complexity analysis
    pub complexity_analysis: TextComplexityAnalysis,

    /// Language detection results
    pub language_detection: LanguageDetectionResult,

    /// Style analysis
    pub style_analysis: TextStyleAnalysis,

    /// Anomaly detection results
    pub anomalies: Vec<TextAnomaly>,

    /// Predictive insights
    pub predictions: PredictiveTextInsights,
}

impl AdvancedTextAnalytics {
    fn empty() -> Self {
        AdvancedTextAnalytics {
            semantic_similarities: HashMap::new(),
            complexity_analysis: TextComplexityAnalysis::default(),
            language_detection: LanguageDetectionResult {
                language: Language::Unknown,
                confidence: 0.0,
                alternatives: Vec::new(),
            },
            style_analysis: TextStyleAnalysis::default(),
            anomalies: Vec::new(),
            predictions: PredictiveTextInsights::default(),
        }
    }
}

/// Computes a genuine (non-fabricated) [`TextProcessingResult`] for a batch
/// of texts, shared by [`AdvancedTextCoordinator::processtexts_standard`]
/// and [`NeuralProcessingEnsemble::processtexts_ensemble`] (both of which
/// previously returned all-zero embeddings, a constant `Neutral`/0.5
/// sentiment, a hardcoded single "general" topic, and empty entities
/// regardless of the input text).
///
/// - `vectors`: real TF-IDF vectorization of the batch (replaces
///   `Array2::zeros`).
/// - `sentiment`: real lexicon-based sentiment
///   ([`LexiconSentimentAnalyzer`]), aggregated (mean score/confidence,
///   summed word counts) across the batch (replaces a constant `Neutral`).
/// - `topics`: the batch's top TF-IDF-weighted terms, used as topic labels
///   (replaces a hardcoded `["general"]` with probability `1.0`).
/// - `entities`: rule-based named-entity extraction
///   ([`extract_entities`]) run over every text (replaces an always-empty
///   `Vec`).
/// - `neural_outputs`: honestly *derived* from the real TF-IDF vectors --
///   `embeddings` is a fixed-width (down-)projection of each text's real
///   vector, `attentionweights` is the real pairwise cosine-similarity
///   matrix between texts, and `layer_outputs` reuses the same embeddings
///   as a single layer. These are not a genuine transformer forward pass
///   (no pretrained model is available to run here), but they are real,
///   text-dependent computations rather than fabricated zeros -- documented
///   as such rather than presented as authentic transformer internals.
fn compute_real_text_processing(texts: &[String]) -> Result<TextProcessingResult> {
    const NEURAL_EMBEDDING_DIM: usize = 50;

    if texts.is_empty() {
        return Ok(TextProcessingResult {
            vectors: Array2::zeros((0, 0)),
            sentiment: SentimentResult {
                sentiment: Sentiment::Neutral,
                confidence: 0.0,
                score: 0.0,
                word_counts: SentimentWordCounts::default(),
            },
            topics: TopicModelingResult {
                topics: Vec::new(),
                topic_probabilities: Vec::new(),
                dominant_topic: String::new(),
                topic_coherence: 0.0,
            },
            entities: Vec::new(),
            quality_metrics: TextQualityMetrics::default(),
            neural_outputs: NeuralProcessingOutputs {
                embeddings: Array2::zeros((0, NEURAL_EMBEDDING_DIM)),
                attentionweights: Array2::zeros((0, 0)),
                layer_outputs: vec![Array2::zeros((0, NEURAL_EMBEDDING_DIM))],
            },
        });
    }

    let text_refs: Vec<&str> = texts.iter().map(String::as_str).collect();

    // Real TF-IDF vectorization.
    let mut vectorizer = TfidfVectorizer::default();
    let vectors = vectorizer.fit_transform(&text_refs)?;

    // Real lexicon-based sentiment, aggregated over the batch.
    let sentiment_analyzer = LexiconSentimentAnalyzer::with_basiclexicon();
    let per_text_sentiment = sentiment_analyzer.analyze_batch(&text_refs)?;
    let n = per_text_sentiment.len().max(1) as f64;
    let avg_score = per_text_sentiment.iter().map(|s| s.score).sum::<f64>() / n;
    let avg_confidence = per_text_sentiment.iter().map(|s| s.confidence).sum::<f64>() / n;
    let mut word_counts = SentimentWordCounts::default();
    for s in &per_text_sentiment {
        word_counts.positive_words += s.word_counts.positive_words;
        word_counts.negative_words += s.word_counts.negative_words;
        word_counts.neutral_words += s.word_counts.neutral_words;
        word_counts.total_words += s.word_counts.total_words;
    }
    let sentiment = SentimentResult {
        sentiment: Sentiment::from_score(avg_score),
        score: avg_score,
        confidence: avg_confidence,
        word_counts,
    };

    // Real (TF-IDF-weight-based) topic terms: sum each term's weight across
    // the batch and take the highest-weighted terms as topic labels.
    let vocab_map = vectorizer.vocabulary_map(); // word -> column index
    let mut inv_vocab = vec![String::new(); vocab_map.len()];
    for (word, idx) in &vocab_map {
        if *idx < inv_vocab.len() {
            inv_vocab[*idx] = word.clone();
        }
    }
    let n_top_topics = 3.min(inv_vocab.len());
    let mut term_weights: Vec<(usize, f64)> = (0..vectors.ncols())
        .map(|j| (j, vectors.column(j).sum()))
        .collect();
    term_weights.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
    let total_weight: f64 = term_weights.iter().map(|(_, w)| w).sum::<f64>().max(1e-12);

    let (topic_labels, topic_probabilities): (Vec<String>, Vec<f64>) = if n_top_topics == 0 {
        (vec!["general".to_string()], vec![1.0])
    } else {
        term_weights
            .iter()
            .take(n_top_topics)
            .map(|&(idx, w)| (inv_vocab[idx].clone(), w / total_weight))
            .unzip()
    };
    let dominant_topic = topic_labels
        .first()
        .cloned()
        .unwrap_or_else(|| "general".to_string());
    // A simple, real coherence proxy: how much of the batch's total term
    // weight the reported topic terms actually account for.
    let topic_coherence = topic_probabilities.iter().sum::<f64>().clamp(0.0, 1.0);

    let topics = TopicModelingResult {
        topics: topic_labels,
        topic_probabilities,
        dominant_topic,
        topic_coherence,
    };

    // Real rule-based named-entity extraction over every text.
    let ner_config = NerPatternConfig::default();
    let mut entities = Vec::new();
    for text in texts {
        for e in extract_entities(text, &ner_config)? {
            entities.push(NamedEntity {
                text: e.text,
                entity_type: format!("{:?}", e.entity_type),
                start_pos: e.start,
                end_pos: e.end,
                confidence: e.confidence,
            });
        }
    }

    // Neural-output fields honestly derived from the real TF-IDF vectors
    // (see this function's doc comment).
    let n_texts = texts.len();
    let n_cols = vectors.ncols().max(1);
    let mut embeddings = Array2::zeros((n_texts, NEURAL_EMBEDDING_DIM));
    for i in 0..n_texts {
        for j in 0..n_cols {
            let bucket = j % NEURAL_EMBEDDING_DIM;
            embeddings[[i, bucket]] += vectors[[i, j]];
        }
    }
    let mut attentionweights = Array2::zeros((n_texts, n_texts));
    for i in 0..n_texts {
        for j in 0..n_texts {
            let row_i = vectors.row(i);
            let row_j = vectors.row(j);
            let dot = row_i.dot(&row_j);
            let norm_i = row_i.dot(&row_i).sqrt();
            let norm_j = row_j.dot(&row_j).sqrt();
            attentionweights[[i, j]] = if norm_i > 0.0 && norm_j > 0.0 {
                dot / (norm_i * norm_j)
            } else {
                0.0
            };
        }
    }
    let layer_outputs = vec![embeddings.clone()];

    Ok(TextProcessingResult {
        vectors,
        sentiment,
        topics,
        entities,
        quality_metrics: TextQualityMetrics::default(),
        neural_outputs: NeuralProcessingOutputs {
            embeddings,
            attentionweights,
            layer_outputs,
        },
    })
}

/// Performance optimization engine for text processing
pub struct PerformanceOptimizer {
    /// Current optimization strategy
    #[allow(dead_code)]
    strategy: OptimizationStrategy,

    /// Performance history
    #[allow(dead_code)]
    performance_history: Vec<PerformanceMetricsSnapshot>,

    /// Adaptive optimization parameters
    #[allow(dead_code)]
    adaptive_params: AdaptiveOptimizationParams,

    /// Hardware capability detector
    #[allow(dead_code)]
    hardware_detector: HardwareCapabilityDetector,
}

/// Neural processing ensemble for advanced text understanding
pub struct NeuralProcessingEnsemble {
    /// Transformer models for different tasks
    #[allow(dead_code)]
    transformers: HashMap<String, TransformerModel>,

    /// Specialized neural architectures
    #[allow(dead_code)]
    neural_architectures: HashMap<String, Box<dyn NeuralArchitecture>>,

    /// Ensemble voting strategy
    #[allow(dead_code)]
    voting_strategy: EnsembleVotingStrategy,

    /// Model performance tracking
    #[allow(dead_code)]
    model_performance: HashMap<String, ModelPerformanceMetrics>,

    /// Dynamic model selection
    #[allow(dead_code)]
    model_selector: DynamicModelSelector,
}

/// Memory optimization system for text processing
pub struct TextMemoryOptimizer {
    /// Memory pool for text data
    #[allow(dead_code)]
    text_memory_pool: TextMemoryPool,

    /// Cache management system
    #[allow(dead_code)]
    cache_manager: TextCacheManager,

    /// Memory usage predictor
    #[allow(dead_code)]
    usage_predictor: MemoryUsagePredictor,

    /// Garbage collection optimizer
    #[allow(dead_code)]
    gc_optimizer: GarbageCollectionOptimizer,
}

/// Real-time adaptation engine
pub struct AdaptiveTextEngine {
    /// Adaptation strategy
    #[allow(dead_code)]
    strategy: AdaptationStrategy,

    /// Performance monitors
    #[allow(dead_code)]
    monitors: Vec<PerformanceMonitor>,

    /// Adaptation triggers
    #[allow(dead_code)]
    triggers: AdaptationTriggers,

    /// Learning system for optimization
    #[allow(dead_code)]
    learning_system: AdaptiveLearningSystem,
}

/// Advanced text analytics engine
pub struct TextAnalyticsEngine {
    /// Analytics pipelines
    #[allow(dead_code)]
    pipelines: HashMap<String, AnalyticsPipeline>,

    /// Insight generation system
    #[allow(dead_code)]
    insight_generator: InsightGenerator,

    /// Anomaly detection system
    #[allow(dead_code)]
    anomaly_detector: TextAnomalyDetector,

    /// Predictive modeling system
    #[allow(dead_code)]
    predictive_modeler: PredictiveTextModeler,
}

/// Multi-modal text processing coordinator
pub struct MultiModalTextCoordinator {
    /// Text-image processing
    #[allow(dead_code)]
    text_image_processor: TextImageProcessor,

    /// Text-audio processing
    #[allow(dead_code)]
    text_audio_processor: TextAudioProcessor,

    /// Cross-modal attention mechanisms
    #[allow(dead_code)]
    cross_modal_attention: CrossModalAttention,

    /// Multi-modal fusion strategies
    #[allow(dead_code)]
    fusion_strategies: MultiModalFusionStrategies,
}

impl AdvancedTextCoordinator {
    /// Create a new Advanced text coordinator
    pub fn new(config: AdvancedTextConfig) -> Result<Self> {
        let performance_optimizer = Arc::new(Mutex::new(PerformanceOptimizer::new(&config)?));
        #[allow(clippy::arc_with_non_send_sync)]
        let neural_ensemble = Arc::new(RwLock::new(NeuralProcessingEnsemble::new(&config)?));
        let memory_optimizer = Arc::new(Mutex::new(TextMemoryOptimizer::new(&config)?));
        let adaptive_engine = Arc::new(Mutex::new(AdaptiveTextEngine::new(&config)?));
        let analytics_engine = Arc::new(RwLock::new(TextAnalyticsEngine::new(&config)?));
        let multimodal_coordinator = MultiModalTextCoordinator::new(&config)?;
        let performance_tracker = Arc::new(RwLock::new(TextPerformanceTracker::new()));

        Ok(AdvancedTextCoordinator {
            config,
            performance_optimizer,
            neural_ensemble,
            memory_optimizer,
            adaptive_engine,
            analytics_engine,
            multimodal_coordinator,
            performance_tracker,
        })
    }

    /// Advanced-optimized text processing with full feature coordination
    pub fn advanced_processtext(&self, texts: &[String]) -> Result<AdvancedTextResult> {
        let start_time = Instant::now();
        let mut optimizations_applied = Vec::new();

        // Step 1: Memory optimization and pre-allocation
        if self.config.enable_simd_optimizations {
            let memory_optimizer = self.memory_optimizer.lock().expect("Operation failed");
            memory_optimizer.optimize_for_batch(texts.len())?;
            optimizations_applied.push("Memory pre-allocation optimization".to_string());
        }

        // Step 2: Apply performance optimizations
        let performance_optimizer = self.performance_optimizer.lock().expect("Operation failed");
        let optimal_strategy = performance_optimizer.determine_optimal_strategy(texts)?;
        optimizations_applied.push(format!("Performance strategy: {optimal_strategy:?}"));
        drop(performance_optimizer);

        // Step 3: Neural ensemble processing
        let primary_result = if self.config.enable_neural_ensemble {
            let neural_ensemble = self.neural_ensemble.read().expect("Operation failed");
            let result = neural_ensemble.processtexts_ensemble(texts)?;
            optimizations_applied.push("Neural ensemble processing".to_string());
            result
        } else {
            self.processtexts_standard(texts)?
        };

        // Step 4: Advanced analytics
        let analytics = if self.config.enable_advanced_analytics {
            let analytics_engine = self.analytics_engine.read().expect("Operation failed");
            let result = analytics_engine.analyze_comprehensive(texts, &primary_result)?;
            optimizations_applied.push("Advanced analytics processing".to_string());
            result
        } else {
            AdvancedTextAnalytics::empty()
        };

        // Step 5: Real-time adaptation
        if self.config.enable_real_time_adaptation {
            let adaptive_engine = self.adaptive_engine.lock().expect("Operation failed");
            AdaptiveTextEngine::adapt_based_on_performance(&start_time.elapsed())?;
            optimizations_applied.push("Real-time performance adaptation".to_string());
        }

        let total_time = start_time.elapsed();

        // Step 6: Performance tracking and metrics
        let performance_metrics = self.calculate_performance_metrics(texts.len(), total_time)?;
        let confidence_scores =
            AdvancedTextCoordinator::calculate_confidence_scores(&primary_result, &analytics)?;
        let timing_breakdown = self.calculate_timing_breakdown(total_time)?;

        Ok(AdvancedTextResult {
            primary_result,
            analytics,
            performance_metrics,
            optimizations_applied,
            confidence_scores,
            timing_breakdown,
        })
    }

    /// Optimized semantic similarity with advanced optimizations
    pub fn advanced_semantic_similarity(
        &self,
        text1: &str,
        text2: &str,
    ) -> Result<AdvancedSemanticSimilarityResult> {
        let start_time = Instant::now();

        // Use neural ensemble for deep semantic understanding
        let neural_ensemble = self.neural_ensemble.read().expect("Operation failed");
        let embeddings1 = neural_ensemble.get_advanced_embeddings(text1)?;
        let embeddings2 = neural_ensemble.get_advanced_embeddings(text2)?;
        drop(neural_ensemble);

        // Apply multiple similarity metrics with SIMD optimization
        let cosine_similarity = if self.config.enable_simd_optimizations {
            self.simd_cosine_similarity(&embeddings1, &embeddings2)?
        } else {
            self.standard_cosine_similarity(&embeddings1, &embeddings2)?
        };

        let semantic_similarity = self.calculate_semantic_similarity(&embeddings1, &embeddings2)?;
        let contextual_similarity = self.calculate_contextual_similarity(text1, text2)?;

        // Advanced analytics
        let analytics = if self.config.enable_advanced_analytics {
            let analytics_engine = self.analytics_engine.read().expect("Operation failed");
            analytics_engine.analyze_similarity_context(text1, text2, cosine_similarity)?
        } else {
            SimilarityAnalytics::empty()
        };

        Ok(AdvancedSemanticSimilarityResult {
            cosine_similarity,
            semantic_similarity,
            contextual_similarity,
            analytics,
            processing_time: start_time.elapsed(),
            confidence_score: self.calculate_similarity_confidence(cosine_similarity)?,
        })
    }

    /// Advanced-optimized batch text classification
    pub fn advanced_classify_batch(
        &self,
        texts: &[String],
        categories: &[String],
    ) -> Result<AdvancedBatchClassificationResult> {
        let start_time = Instant::now();

        // Memory optimization for batch processing
        let memory_optimizer = self.memory_optimizer.lock().expect("Operation failed");
        memory_optimizer.optimize_for_classification_batch(texts.len(), categories.len())?;
        drop(memory_optimizer);

        // Neural ensemble classification
        let neural_ensemble = self.neural_ensemble.read().expect("Operation failed");
        let classifications = neural_ensemble.classify_batch_ensemble(texts, categories)?;
        drop(neural_ensemble);

        // Advanced confidence estimation
        let confidence_estimates =
            AdvancedTextCoordinator::calculate_classification_confidence(&classifications)?;

        // Performance analytics
        let performance_metrics = TextPerformanceMetrics {
            processing_time: start_time.elapsed(),
            throughput: texts.len() as f64 / start_time.elapsed().as_secs_f64(),
            memory_efficiency: 0.95, // Would be measured
            accuracy_estimate: confidence_estimates.iter().sum::<f64>()
                / confidence_estimates.len() as f64,
            latency: start_time.elapsed(),
            memory_usage: 1024 * 1024, // 1MB placeholder
            cpu_utilization: 75.0,
        };

        Ok(AdvancedBatchClassificationResult {
            classifications,
            confidence_estimates,
            performance_metrics,
            processing_time: start_time.elapsed(),
        })
    }

    /// Advanced-advanced topic modeling with dynamic optimization
    pub fn advanced_topic_modeling(
        &self,
        documents: &[String],
        num_topics: usize,
    ) -> Result<AdvancedTopicModelingResult> {
        let start_time = Instant::now();

        // Adaptive parameter optimization
        let adaptive_engine = self.adaptive_engine.lock().expect("Operation failed");
        let optimal_params =
            AdaptiveTextEngine::optimize_topic_modeling_params(documents, num_topics)?;
        drop(adaptive_engine);

        // Neural-enhanced topic modeling
        let neural_ensemble = self.neural_ensemble.read().expect("Operation failed");
        let enhanced_topics =
            neural_ensemble.enhanced_topic_modeling(documents, &optimal_params)?;
        drop(neural_ensemble);

        // Advanced topic analytics
        let analytics_engine = self.analytics_engine.read().expect("Operation failed");
        let topic_analytics =
            TextAnalyticsEngine::analyze_topic_quality(&enhanced_topics, documents)?;
        drop(analytics_engine);

        let quality_metrics =
            AdvancedTextCoordinator::calculate_topic_quality_metrics(&enhanced_topics)?;

        Ok(AdvancedTopicModelingResult {
            topics: enhanced_topics,
            topic_analytics,
            optimal_params,
            processing_time: start_time.elapsed(),
            quality_metrics,
        })
    }

    /// Get comprehensive performance report
    pub fn get_performance_report(&self) -> Result<AdvancedTextPerformanceReport> {
        let performance_tracker = self.performance_tracker.read().expect("Operation failed");
        let current_metrics = performance_tracker.get_current_metrics();
        let historical_analysis = performance_tracker.analyze_historical_performance();
        let optimization_recommendations = self.generate_optimization_recommendations()?;
        drop(performance_tracker);

        Ok(AdvancedTextPerformanceReport {
            current_metrics,
            historical_analysis,
            optimization_recommendations,
            system_utilization: self.analyze_system_utilization()?,
            bottleneck_analysis: self.identify_performance_bottlenecks()?,
        })
    }

    // Private helper methods

    fn processtexts_standard(&self, texts: &[String]) -> Result<TextProcessingResult> {
        compute_real_text_processing(texts)
    }

    fn simd_cosine_similarity(&self, a: &Array1<f64>, b: &Array1<f64>) -> Result<f64> {
        // SIMD-optimized cosine similarity
        if a.len() != b.len() {
            return Err(TextError::InvalidInput(
                "Vector dimensions must match".into(),
            ));
        }

        let dot_product = a.dot(b);
        let norm_a = a.dot(a).sqrt();
        let norm_b = b.dot(b).sqrt();

        if norm_a == 0.0 || norm_b == 0.0 {
            Ok(0.0)
        } else {
            Ok(dot_product / (norm_a * norm_b))
        }
    }

    fn standard_cosine_similarity(&self, a: &Array1<f64>, b: &Array1<f64>) -> Result<f64> {
        // Standard cosine similarity implementation
        self.simd_cosine_similarity(a, b) // Same implementation for now
    }

    fn calculate_semantic_similarity(&self, a: &Array1<f64>, b: &Array1<f64>) -> Result<f64> {
        // Enhanced semantic similarity using multiple metrics
        if a.len() != b.len() {
            return Err(TextError::InvalidInput(
                "Vector dimensions must match".into(),
            ));
        }

        // Cosine similarity
        let cosine_sim = {
            let dot_product = a.dot(b);
            let norm_a = a.dot(a).sqrt();
            let norm_b = b.dot(b).sqrt();

            if norm_a == 0.0 || norm_b == 0.0 {
                0.0
            } else {
                dot_product / (norm_a * norm_b)
            }
        };

        // Euclidean distance-based similarity
        let euclidean_dist = a
            .iter()
            .zip(b.iter())
            .map(|(&x, &y)| (x - y).powi(2))
            .sum::<f64>()
            .sqrt();
        let euclidean_sim = 1.0 / (1.0 + euclidean_dist);

        // Manhattan distance-based similarity
        let manhattan_dist = a
            .iter()
            .zip(b.iter())
            .map(|(&x, &y)| (x - y).abs())
            .sum::<f64>();
        let manhattan_sim = 1.0 / (1.0 + manhattan_dist);

        // Weighted combination of similarities
        let semantic_similarity = cosine_sim * 0.5 + euclidean_sim * 0.3 + manhattan_sim * 0.2;

        Ok(semantic_similarity.clamp(0.0, 1.0))
    }

    fn calculate_contextual_similarity(&self, text1: &str, text2: &str) -> Result<f64> {
        // Enhanced contextual similarity based on text features

        // Word overlap analysis
        let words1: std::collections::HashSet<String> = text1
            .split_whitespace()
            .map(|w| {
                w.to_lowercase()
                    .chars()
                    .filter(|c| c.is_alphabetic())
                    .collect()
            })
            .filter(|w: &String| w.len() > 2)
            .collect();

        let words2: std::collections::HashSet<String> = text2
            .split_whitespace()
            .map(|w| {
                w.to_lowercase()
                    .chars()
                    .filter(|c| c.is_alphabetic())
                    .collect()
            })
            .filter(|w: &String| w.len() > 2)
            .collect();

        let intersection = words1.intersection(&words2).count();
        let union = words1.union(&words2).count();
        let jaccard_similarity = if union > 0 {
            intersection as f64 / union as f64
        } else {
            0.0
        };

        // Length-based similarity
        let len1 = text1.len() as f64;
        let len2 = text2.len() as f64;
        let length_similarity = 1.0 - (len1 - len2).abs() / (len1 + len2).max(1.0);

        // Sentence structure similarity (simplified)
        let sent_count1 = text1.matches('.').count() + 1;
        let sent_count2 = text2.matches('.').count() + 1;
        let structure_similarity = 1.0
            - ((sent_count1 as i32 - sent_count2 as i32).abs() as f64)
                / (sent_count1 + sent_count2) as f64;

        // Combined contextual similarity
        let contextual_similarity =
            jaccard_similarity * 0.6 + length_similarity * 0.2 + structure_similarity * 0.2;

        Ok(contextual_similarity.clamp(0.0, 1.0))
    }

    fn calculate_performance_metrics(
        &self,
        batch_size: usize,
        processing_time: Duration,
    ) -> Result<TextPerformanceMetrics> {
        Ok(TextPerformanceMetrics {
            processing_time,
            throughput: batch_size as f64 / processing_time.as_secs_f64(),
            memory_efficiency: 0.92, // Would be measured
            accuracy_estimate: 0.95, // Would be calculated from results
            latency: processing_time,
            memory_usage: 1024 * 1024, // 1MB placeholder
            cpu_utilization: 70.0,
        })
    }

    fn calculate_confidence_scores(
        self_result: &TextProcessingResult,
        _analytics: &AdvancedTextAnalytics,
    ) -> Result<HashMap<String, f64>> {
        let mut scores = HashMap::new();
        scores.insert("overall_confidence".to_string(), 0.93);
        scores.insert("sentiment_confidence".to_string(), 0.87);
        scores.insert("topic_confidence".to_string(), 0.91);
        scores.insert("entity_confidence".to_string(), 0.89);
        Ok(scores)
    }

    fn calculate_timing_breakdown(
        &self,
        total_time: Duration,
    ) -> Result<ProcessingTimingBreakdown> {
        Ok(ProcessingTimingBreakdown {
            preprocessing_time: Duration::from_millis(total_time.as_millis() as u64 / 10),
            processing_time: Duration::from_millis(total_time.as_millis() as u64 * 4 / 10),
            postprocessing_time: Duration::from_millis(total_time.as_millis() as u64 / 10),
            neural_processing_time: Duration::from_millis(total_time.as_millis() as u64 * 6 / 10),
            analytics_time: Duration::from_millis(total_time.as_millis() as u64 * 2 / 10),
            optimization_time: Duration::from_millis(total_time.as_millis() as u64 / 10),
            total_time,
        })
    }

    fn calculate_similarity_confidence(&self, similarity: f64) -> Result<f64> {
        // Confidence based on similarity score and other factors
        Ok((similarity * 0.8 + 0.2).min(1.0))
    }

    fn calculate_classification_confidence(
        classifications: &[ClassificationResult],
    ) -> Result<Vec<f64>> {
        // Confidence derived from each real classification's score margin:
        // how far the top category's similarity score is above the
        // runner-up. A clear top choice yields high confidence; a close
        // call between the top two categories yields low confidence.
        // Cosine similarities lie in [-1, 1], so the margin (in [0, 2]) is
        // scaled into [0, 1]. Replaces a constant 3-element
        // `[0.92, 0.87, 0.91]` returned regardless of how many
        // classifications (or categories) were actually supplied.
        Ok(classifications
            .iter()
            .map(|c| {
                if c.category_scores.is_empty() {
                    return 0.0;
                }
                let mut sorted = c.category_scores.clone();
                sorted.sort_by(|a, b| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));
                let top = sorted[0];
                let runner_up = sorted.get(1).copied().unwrap_or(-1.0);
                ((top - runner_up) / 2.0).clamp(0.0, 1.0)
            })
            .collect())
    }

    fn calculate_topic_quality_metrics(
        self_topics: &EnhancedTopicModelingResult,
    ) -> Result<TopicQualityMetrics> {
        Ok(TopicQualityMetrics {
            coherence_score: 0.78,
            diversity_score: 0.85,
            stability_score: 0.82,
            interpretability_score: 0.89,
        })
    }

    fn generate_optimization_recommendations(&self) -> Result<Vec<OptimizationRecommendation>> {
        Ok(vec![
            OptimizationRecommendation {
                category: "Memory".to_string(),
                recommendation: "Increase memory pool size for better caching".to_string(),
                impact_estimate: 0.15,
            },
            OptimizationRecommendation {
                category: "Neural Processing".to_string(),
                recommendation: "Enable more transformer models in ensemble".to_string(),
                impact_estimate: 0.08,
            },
        ])
    }

    fn analyze_system_utilization(&self) -> Result<SystemUtilization> {
        Ok(SystemUtilization {
            cpu_utilization: 75.0,
            memory_utilization: 68.0,
            gpu_utilization: 82.0,
            cache_hit_rate: 0.94,
        })
    }

    fn identify_performance_bottlenecks(&self) -> Result<Vec<PerformanceBottleneck>> {
        Ok(vec![PerformanceBottleneck {
            component: "Neural Ensemble".to_string(),
            impact: 0.25,
            description: "Neural processing taking 60% of total time".to_string(),
            suggested_fix: "Optimize transformer inference".to_string(),
        }])
    }
}

// Supporting data structures and trait implementations...

/// Advanced semantic similarity result
#[derive(Debug)]
pub struct AdvancedSemanticSimilarityResult {
    /// Cosine similarity score between text embeddings
    pub cosine_similarity: f64,
    /// Deep semantic similarity using neural models
    pub semantic_similarity: f64,
    /// Contextual similarity considering meaning and context
    pub contextual_similarity: f64,
    /// Advanced analytics for the similarity comparison
    pub analytics: SimilarityAnalytics,
    /// Time taken to process the similarity calculation
    pub processing_time: Duration,
    /// Confidence score in the similarity results
    pub confidence_score: f64,
}

/// Advanced batch classification result
#[derive(Debug)]
pub struct AdvancedBatchClassificationResult {
    /// Classification results for each input text
    pub classifications: Vec<ClassificationResult>,
    /// Confidence estimates for each classification
    pub confidence_estimates: Vec<f64>,
    /// Performance metrics for the batch processing
    pub performance_metrics: TextPerformanceMetrics,
    /// Total time taken for batch classification
    pub processing_time: Duration,
}

/// Advanced topic modeling result
#[derive(Debug)]
pub struct AdvancedTopicModelingResult {
    /// Enhanced topic modeling results with neural enhancements
    pub topics: EnhancedTopicModelingResult,
    /// Advanced analytics for topic quality and coherence
    pub topic_analytics: TopicAnalytics,
    /// Optimal parameters used for topic modeling
    pub optimal_params: TopicModelingParams,
    /// Time taken for topic modeling processing
    pub processing_time: Duration,
    /// Quality metrics for the generated topics
    pub quality_metrics: TopicQualityMetrics,
}

// Placeholder implementations for referenced types...
// (In a real implementation, these would be fully implemented)

// Removed duplicate struct definitions - using the original definitions above
/// Similarity analytics placeholder
#[derive(Debug)]
pub struct SimilarityAnalytics;
impl SimilarityAnalytics {
    fn empty() -> Self {
        SimilarityAnalytics
    }
}

/// Result of classifying a single text against a set of candidate
/// categories (zero-shot-style: each category is represented by the
/// embedding of its own name, and the text is scored against every
/// category by cosine similarity -- see the private
/// `NeuralProcessingEnsemble::classify_batch_ensemble` method).
#[derive(Debug, Clone)]
pub struct ClassificationResult {
    /// The candidate category with the highest similarity score.
    pub predicted_category: String,
    /// Cosine-similarity score for every candidate category, in the same
    /// order as the `categories` slice passed to
    /// [`AdvancedTextCoordinator::advanced_classify_batch`].
    pub category_scores: Vec<f64>,
}
/// Enhanced topic modeling result placeholder
#[derive(Debug, Clone)]
pub struct EnhancedTopicModelingResult;
// Removed duplicate definition - using the original definition above
/// Topic analytics placeholder
#[derive(Debug)]
pub struct TopicAnalytics;
/// Topic modeling parameters placeholder
#[derive(Debug)]
pub struct TopicModelingParams;
/// Topic quality metrics for evaluating topic modeling results
#[derive(Debug)]
pub struct TopicQualityMetrics {
    /// Topic coherence score (higher is better)
    pub coherence_score: f64,
    /// Topic diversity score (higher is better)
    pub diversity_score: f64,
    /// Topic stability score across runs
    pub stability_score: f64,
    /// Topic interpretability score for human understanding
    pub interpretability_score: f64,
}

/// Comprehensive performance report for Advanced text processing
#[derive(Debug)]
pub struct AdvancedTextPerformanceReport {
    /// Current performance metrics
    pub current_metrics: TextPerformanceMetrics,
    /// Historical performance analysis
    pub historical_analysis: HistoricalAnalysis,
    /// Optimization recommendations for improving performance
    pub optimization_recommendations: Vec<OptimizationRecommendation>,
    /// System resource utilization statistics
    pub system_utilization: SystemUtilization,
    /// Analysis of performance bottlenecks
    pub bottleneck_analysis: Vec<PerformanceBottleneck>,
}

/// Historical performance analysis placeholder
#[derive(Debug)]
pub struct HistoricalAnalysis;
/// Optimization recommendation for improving performance
#[derive(Debug)]
pub struct OptimizationRecommendation {
    /// Category of the optimization (e.g., "Memory", "CPU", "GPU")
    pub category: String,
    /// Detailed recommendation description
    pub recommendation: String,
    /// Estimated performance impact (0.0 to 1.0)
    pub impact_estimate: f64,
}
/// System resource utilization metrics
#[derive(Debug)]
pub struct SystemUtilization {
    /// CPU utilization percentage (0.0 to 100.0)
    pub cpu_utilization: f64,
    /// Memory utilization percentage (0.0 to 100.0)
    pub memory_utilization: f64,
    /// GPU utilization percentage (0.0 to 100.0)
    pub gpu_utilization: f64,
    /// Cache hit rate (0.0 to 1.0)
    pub cache_hit_rate: f64,
}
/// Performance bottleneck analysis
// Implementation stubs for the various components...
impl PerformanceOptimizer {
    fn new(config: &AdvancedTextConfig) -> Result<Self> {
        Ok(PerformanceOptimizer {
            strategy: OptimizationStrategy::Balanced,
            performance_history: Vec::new(),
            adaptive_params: AdaptiveOptimizationParams,
            hardware_detector: HardwareCapabilityDetector::new(),
        })
    }

    fn determine_optimal_strategy(&self, texts: &[String]) -> Result<OptimizationStrategy> {
        Ok(OptimizationStrategy::Performance)
    }
}

impl NeuralProcessingEnsemble {
    fn new(config: &AdvancedTextConfig) -> Result<Self> {
        Ok(NeuralProcessingEnsemble {
            transformers: HashMap::new(),
            neural_architectures: HashMap::new(),
            voting_strategy: EnsembleVotingStrategy::WeightedAverage,
            model_performance: HashMap::new(),
            model_selector: DynamicModelSelector::new(),
        })
    }

    fn processtexts_ensemble(&self, texts: &[String]) -> Result<TextProcessingResult> {
        compute_real_text_processing(texts)
    }

    fn get_advanced_embeddings(&self, text: &str) -> Result<Array1<f64>> {
        // Generate meaningful embeddings based on text features
        let embedding_dim = 768;
        let mut embedding = Array1::zeros(embedding_dim);

        // Text features
        let text_len = text.len() as f64;
        let word_count = text.split_whitespace().count() as f64;
        let char_diversity = text.chars().collect::<std::collections::HashSet<_>>().len() as f64;
        let avg_word_len = if word_count > 0.0 {
            text_len / word_count
        } else {
            0.0
        };

        // N-gram analysis for more sophisticated features
        let bigrams: std::collections::HashSet<String> = text
            .chars()
            .collect::<Vec<_>>()
            .windows(2)
            .map(|w| {
                let w0 = &w[0];
                let w1 = &w[1];
                format!("{w0}{w1}")
            })
            .collect();
        let bigram_diversity = bigrams.len() as f64;

        // Generate embedding based on multiple text features
        for i in 0..embedding_dim {
            let feature_index = i as f64;
            let base_features = [
                text_len * 0.001,
                word_count * 0.01,
                char_diversity * 0.02,
                avg_word_len * 0.05,
                bigram_diversity * 0.001,
            ];

            let feature_weight = (feature_index * 0.1).sin().abs();
            let weighted_sum: f64 = base_features
                .iter()
                .enumerate()
                .map(|(j, &val)| val * (1.0 + j as f64 * 0.1))
                .sum();

            embedding[i] = weighted_sum * feature_weight * 0.1;
        }

        // Normalize the embedding
        let norm = embedding.dot(&embedding).sqrt();
        if norm > 0.0 {
            embedding.mapv_inplace(|x| x / norm);
        }

        Ok(embedding)
    }

    fn classify_batch_ensemble(
        &self,
        texts: &[String],
        categories: &[String],
    ) -> Result<Vec<ClassificationResult>> {
        if categories.is_empty() {
            return Err(TextError::InvalidInput(
                "advanced_classify_batch requires at least one category".to_string(),
            ));
        }

        // Zero-shot-style classification: represent each category by the
        // embedding of its own name/keyword, then score every text against
        // every category by cosine similarity. This is not a trained
        // classifier, but it is a real, text-and-category-dependent
        // computation -- unlike the previous version, which computed a text
        // embedding and several text features and then discarded all of
        // them, unconditionally pushing an empty `ClassificationResult` for
        // every text regardless of `texts` or `categories`.
        let category_embeddings: Vec<Array1<f64>> = categories
            .iter()
            .map(|c| self.get_advanced_embeddings(c))
            .collect::<Result<Vec<_>>>()?;

        let mut results = Vec::with_capacity(texts.len());
        for text in texts {
            let text_embedding = self.get_advanced_embeddings(text)?;

            let category_scores: Vec<f64> = category_embeddings
                .iter()
                .map(|cat_emb| {
                    let dot = text_embedding.dot(cat_emb);
                    let norm_text = text_embedding.dot(&text_embedding).sqrt();
                    let norm_cat = cat_emb.dot(cat_emb).sqrt();
                    if norm_text > 0.0 && norm_cat > 0.0 {
                        dot / (norm_text * norm_cat)
                    } else {
                        0.0
                    }
                })
                .collect();

            let best_idx = category_scores
                .iter()
                .enumerate()
                .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                .map(|(idx, _)| idx)
                .unwrap_or(0);

            results.push(ClassificationResult {
                predicted_category: categories[best_idx].clone(),
                category_scores,
            });
        }

        Ok(results)
    }

    fn enhanced_topic_modeling(
        &self,
        documents: &[String],
        _params: &TopicModelingParams,
    ) -> Result<EnhancedTopicModelingResult> {
        // Enhanced topic modeling using text analysis
        // This is a simplified implementation for demonstration

        // Analyze documents for common patterns
        let mut word_frequencies: std::collections::HashMap<String, usize> =
            std::collections::HashMap::new();
        let mut _total_words = 0;

        for doc in documents {
            for word in doc.split_whitespace() {
                let clean_word = word
                    .to_lowercase()
                    .chars()
                    .filter(|c| c.is_alphabetic())
                    .collect::<String>();

                if clean_word.len() > 2 {
                    // Filter out very short words
                    *word_frequencies.entry(clean_word).or_insert(0) += 1;
                    _total_words += 1;
                }
            }
        }

        // Simple topic extraction based on word frequency patterns
        let _top_words: Vec<_> = word_frequencies
            .iter()
            .filter(|(_, &count)| count > 1) // Only words that appear multiple times
            .collect();

        Ok(EnhancedTopicModelingResult)
    }
}

impl TextMemoryOptimizer {
    fn new(config: &AdvancedTextConfig) -> Result<Self> {
        Ok(TextMemoryOptimizer {
            text_memory_pool: TextMemoryPool::new(),
            cache_manager: TextCacheManager::new(),
            usage_predictor: MemoryUsagePredictor::new(),
            gc_optimizer: GarbageCollectionOptimizer::new(),
        })
    }

    fn optimize_for_batch(&self, batch_size: usize) -> Result<()> {
        Ok(()) // Placeholder
    }

    fn optimize_for_classification_batch(
        &self,
        num_texts: usize,
        _num_categories: usize,
    ) -> Result<()> {
        Ok(()) // Placeholder
    }
}

impl AdaptiveTextEngine {
    fn new(config: &AdvancedTextConfig) -> Result<Self> {
        Ok(AdaptiveTextEngine {
            strategy: AdaptationStrategy::Conservative,
            monitors: Vec::new(),
            triggers: AdaptationTriggers,
            learning_system: AdaptiveLearningSystem::new(),
        })
    }

    fn adapt_based_on_performance(selfelapsed: &Duration) -> Result<()> {
        Ok(()) // Placeholder
    }

    fn optimize_topic_modeling_params(
        self_documents: &[String],
        _num_topics: usize,
    ) -> Result<TopicModelingParams> {
        Ok(TopicModelingParams) // Placeholder
    }
}

impl TextAnalyticsEngine {
    fn new(config: &AdvancedTextConfig) -> Result<Self> {
        Ok(TextAnalyticsEngine {
            pipelines: HashMap::new(),
            insight_generator: InsightGenerator::new(),
            anomaly_detector: TextAnomalyDetector::new(),
            predictive_modeler: PredictiveTextModeler::new(),
        })
    }

    fn analyze_comprehensive(
        &self,
        _texts: &[String],
        _result: &TextProcessingResult,
    ) -> Result<AdvancedTextAnalytics> {
        Ok(AdvancedTextAnalytics::empty()) // Placeholder
    }

    fn analyze_similarity_context(
        &self,
        text1: &str,
        text2: &str,
        _similarity: f64,
    ) -> Result<SimilarityAnalytics> {
        Ok(SimilarityAnalytics) // Placeholder
    }

    fn analyze_topic_quality(
        self_topics: &EnhancedTopicModelingResult,
        _documents: &[String],
    ) -> Result<TopicAnalytics> {
        Ok(TopicAnalytics) // Placeholder
    }
}

impl MultiModalTextCoordinator {
    fn new(config: &AdvancedTextConfig) -> Result<Self> {
        Ok(MultiModalTextCoordinator {
            text_image_processor: TextImageProcessor::new(),
            text_audio_processor: TextAudioProcessor::new(),
            cross_modal_attention: CrossModalAttention::new(),
            fusion_strategies: MultiModalFusionStrategies::new(),
        })
    }
}

impl TextPerformanceTracker {
    fn new() -> Self {
        TextPerformanceTracker {
            // Implementation fields would go here
        }
    }

    fn get_current_metrics(&self) -> TextPerformanceMetrics {
        TextPerformanceMetrics {
            processing_time: Duration::from_millis(100),
            throughput: 500.0,
            memory_efficiency: 0.92,
            accuracy_estimate: 0.94,
            latency: Duration::from_millis(100),
            memory_usage: 1024 * 1024, // 1MB
            cpu_utilization: 75.0,
        }
    }

    fn analyze_historical_performance(&self) -> HistoricalAnalysis {
        HistoricalAnalysis // Placeholder
    }
}

// Duplicate implementations removed - using the earlier implementations above

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

    #[test]
    fn test_advanced_coordinator_creation() {
        let config = AdvancedTextConfig::default();
        let coordinator = AdvancedTextCoordinator::new(config);
        assert!(coordinator.is_ok());
    }

    #[test]
    fn test_advanced_processtext() {
        let config = AdvancedTextConfig::default();
        let coordinator = AdvancedTextCoordinator::new(config).expect("Operation failed");

        let texts = vec![
            "This is a test document for Advanced processing.".to_string(),
            "Another document with different content.".to_string(),
        ];

        let result = coordinator.advanced_processtext(&texts);
        assert!(result.is_ok());

        let advanced_result = result.expect("Operation failed");
        assert!(!advanced_result.optimizations_applied.is_empty());
        assert!(advanced_result.performance_metrics.throughput > 0.0);
    }

    #[test]
    fn test_advanced_semantic_similarity() {
        let config = AdvancedTextConfig::default();
        let coordinator = AdvancedTextCoordinator::new(config).expect("Operation failed");

        let result = coordinator
            .advanced_semantic_similarity("The cat sat on the mat", "A feline rested on the rug");

        assert!(result.is_ok());
        let similarity_result = result.expect("Operation failed");
        assert!(similarity_result.cosine_similarity >= 0.0);
        assert!(similarity_result.cosine_similarity <= 1.0);
        assert!(similarity_result.confidence_score > 0.0);
    }

    /// Regression test for `compute_real_text_processing` (shared by
    /// `processtexts_standard` and `processtexts_ensemble`), which used to
    /// be two independent stubs: all-zero `Array2::zeros((n, 768))`
    /// embeddings and an unconditional `Neutral`/0.5/0.5 sentiment
    /// regardless of text content.
    #[test]
    fn test_compute_real_text_processing_reflects_actual_content() {
        let texts = vec![
            "I absolutely love this wonderful, fantastic, amazing product!".to_string(),
            "This is a terrible, horrible, awful experience and I hate it.".to_string(),
            "Please contact us at info@example.com for more information.".to_string(),
        ];

        let result = compute_real_text_processing(&texts).expect("Operation failed");

        // Real (TF-IDF) vectors: non-zero and different per-row, since each
        // text uses different words. Previously this was an all-zero
        // (texts.len(), 768) matrix regardless of content.
        assert_eq!(result.vectors.nrows(), texts.len());
        assert!(result.vectors.iter().any(|&v| v != 0.0));
        assert_ne!(result.vectors.row(0), result.vectors.row(1));

        // Real lexicon sentiment: a batch this polarized cannot land
        // exactly on the old hardcoded Neutral/0.5/0.5.
        assert!(
            result.sentiment.score != 0.5 || result.sentiment.confidence != 0.5,
            "sentiment should reflect actual (highly polarized) text content, got {:?}",
            result.sentiment
        );
        assert!(result.sentiment.word_counts.total_words > 0);

        // Real named-entity extraction should find the email address.
        assert!(
            result
                .entities
                .iter()
                .any(|e| e.text.contains("info@example.com")),
            "expected the email address to be extracted as an entity, got {:?}",
            result.entities
        );

        // Real topic terms: not the hardcoded single "general" topic.
        assert!(!result.topics.topics.is_empty());
        assert_ne!(result.topics.dominant_topic, "");

        // Neural outputs derived from the real vectors must be non-zero and
        // non-uniform (previously always `Array2::zeros`).
        assert_eq!(result.neural_outputs.embeddings.nrows(), texts.len());
        assert!(result.neural_outputs.embeddings.iter().any(|&v| v != 0.0));
        assert_eq!(
            result.neural_outputs.attentionweights.dim(),
            (texts.len(), texts.len())
        );
        // Self-similarity must be (near) 1.0 for a non-degenerate vector.
        assert!((result.neural_outputs.attentionweights[[0, 0]] - 1.0).abs() < 1e-6);
    }

    #[test]
    fn test_compute_real_text_processing_empty_batch() {
        let result = compute_real_text_processing(&[]).expect("Operation failed");
        assert_eq!(result.vectors.nrows(), 0);
        assert!(result.entities.is_empty());
    }

    /// Regression test for `classify_batch_ensemble` +
    /// `calculate_classification_confidence`, which used to push an empty
    /// `ClassificationResult` unit value for every text (regardless of
    /// `texts`/`categories`) and then report a constant confidence vector
    /// `[0.92, 0.87, 0.91]` regardless of how many classifications were
    /// actually produced.
    #[test]
    fn test_advanced_classify_batch_scores_depend_on_content() {
        let config = AdvancedTextConfig::default();
        let coordinator = AdvancedTextCoordinator::new(config).expect("Operation failed");

        let texts = vec![
            "The quarterback threw a touchdown pass in the football game.".to_string(),
            "The chef prepared a delicious pasta dish with fresh tomatoes.".to_string(),
        ];
        let categories = vec!["sports".to_string(), "cooking".to_string()];

        let result = coordinator
            .advanced_classify_batch(&texts, &categories)
            .expect("Operation failed");

        assert_eq!(result.classifications.len(), texts.len());
        // Confidence estimates must actually track the number of
        // classifications produced, not a hardcoded 3-element vector.
        assert_eq!(result.confidence_estimates.len(), texts.len());

        for classification in &result.classifications {
            assert_eq!(classification.category_scores.len(), categories.len());
            assert!(categories.contains(&classification.predicted_category));
        }

        // Confidence values must be real, non-constant numbers in [0, 1],
        // not the old hardcoded [0.92, 0.87, 0.91].
        for &c in &result.confidence_estimates {
            assert!((0.0..=1.0).contains(&c));
        }
        assert_ne!(result.confidence_estimates, vec![0.92, 0.87, 0.91]);
    }

    #[test]
    fn test_advanced_classify_batch_rejects_empty_categories() {
        let config = AdvancedTextConfig::default();
        let coordinator = AdvancedTextCoordinator::new(config).expect("Operation failed");
        let texts = vec!["some text".to_string()];

        let result = coordinator.advanced_classify_batch(&texts, &[]);
        assert!(result.is_err());
    }
}