trustformers-mobile 0.1.1

//! Mobile Inference Visualizer for Real-time Debugging
//!
//! This module provides comprehensive real-time visualization capabilities for mobile ML inference,
//! enabling developers to inspect tensor flows, intermediate outputs, attention patterns, and model
//! behavior during inference with minimal performance impact.

use crate::{
    mobile_performance_profiler::MobileMetricsSnapshot,
    model_debugger::{AnomalySeverity, AnomalyType},
};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;
use trustformers_core::TrustformersError;

/// Configuration for inference visualizer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InferenceVisualizerConfig {
    /// Enable visualization
    pub enabled: bool,
    /// Visualization mode
    pub mode: VisualizationMode,
    /// Real-time rendering configuration
    pub real_time_config: RealTimeVisualizationConfig,
    /// Tensor visualization settings
    pub tensor_visualization: TensorVisualizationConfig,
    /// Attention visualization settings
    pub attention_visualization: AttentionVisualizationConfig,
    /// Flow visualization settings
    pub flow_visualization: FlowVisualizationConfig,
    /// Performance visualization settings
    pub performance_visualization: PerformanceVisualizationConfig,
    /// Export settings
    pub export_config: VisualizationExportConfig,
}

/// Visualization modes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum VisualizationMode {
    /// Full visualization (high detail, higher overhead)
    Full,
    /// Lightweight visualization (optimized for mobile)
    Lightweight,
    /// Debug mode (maximum detail for debugging)
    Debug,
    /// Performance mode (minimal overhead)
    Performance,
    /// Custom configuration
    Custom,
}

/// Real-time visualization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealTimeVisualizationConfig {
    /// Enable real-time rendering
    pub enabled: bool,
    /// Update frequency (FPS)
    pub update_fps: u32,
    /// Maximum history frames
    pub max_history_frames: usize,
    /// Enable animation
    pub enable_animation: bool,
    /// Animation duration (ms)
    pub animation_duration_ms: u64,
    /// Buffer size for smooth rendering
    pub render_buffer_size: usize,
}

/// Tensor visualization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorVisualizationConfig {
    /// Enable tensor visualization
    pub enabled: bool,
    /// Visualization type
    pub visualization_type: TensorVisualizationType,
    /// Color mapping scheme
    pub color_scheme: ColorScheme,
    /// Normalize values
    pub normalize_values: bool,
    /// Show tensor statistics overlay
    pub show_statistics: bool,
    /// Highlight anomalies
    pub highlight_anomalies: bool,
    /// Maximum tensor size to visualize
    pub max_tensor_size: usize,
}

/// Tensor visualization types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TensorVisualizationType {
    /// Heatmap representation
    Heatmap,
    /// 3D surface plot
    Surface3D,
    /// Histogram distribution
    Histogram,
    /// Line plot for 1D tensors
    LinePlot,
    /// Bar chart
    BarChart,
    /// Scatter plot
    ScatterPlot,
}

/// Color schemes for visualization
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ColorScheme {
    /// Viridis (perceptually uniform)
    Viridis,
    /// Plasma (high contrast)
    Plasma,
    /// Inferno (heat-like)
    Inferno,
    /// Magma (volcanic)
    Magma,
    /// Jet (rainbow)
    Jet,
    /// Grayscale
    Grayscale,
    /// Custom colors
    Custom,
}

/// Attention visualization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionVisualizationConfig {
    /// Enable attention visualization
    pub enabled: bool,
    /// Show attention matrices
    pub show_attention_matrices: bool,
    /// Show attention flow
    pub show_attention_flow: bool,
    /// Show head specialization
    pub show_head_specialization: bool,
    /// Maximum sequence length to visualize
    pub max_sequence_length: usize,
    /// Head sampling for large models
    pub head_sampling_rate: f32,
}

/// Flow visualization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowVisualizationConfig {
    /// Enable flow visualization
    pub enabled: bool,
    /// Show data flow graph
    pub show_data_flow: bool,
    /// Show gradient flow
    pub show_gradient_flow: bool,
    /// Show computation graph
    pub show_computation_graph: bool,
    /// Flow animation speed
    pub flow_animation_speed: f32,
    /// Show bottlenecks
    pub highlight_bottlenecks: bool,
}

/// Performance visualization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceVisualizationConfig {
    /// Enable performance visualization
    pub enabled: bool,
    /// Show real-time metrics
    pub show_real_time_metrics: bool,
    /// Show memory usage
    pub show_memory_usage: bool,
    /// Show timing breakdown
    pub show_timing_breakdown: bool,
    /// Show thermal state
    pub show_thermal_state: bool,
    /// Performance history length
    pub history_length: usize,
}

/// Visualization export configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationExportConfig {
    /// Enable automatic export
    pub auto_export: bool,
    /// Export format
    pub format: VisualizationExportFormat,
    /// Export quality
    pub quality: ExportQuality,
    /// Include metadata
    pub include_metadata: bool,
    /// Export directory
    pub export_directory: String,
}

/// Export formats
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum VisualizationExportFormat {
    /// PNG image
    PNG,
    /// SVG vector graphics
    SVG,
    /// Interactive HTML
    HTML,
    /// Video (MP4)
    MP4,
    /// Raw data (JSON)
    JSON,
}

/// Export quality levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ExportQuality {
    Low,
    Medium,
    High,
    Ultra,
}

/// Visualization frame containing all visualization data for a single inference step
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationFrame {
    /// Frame ID
    pub frame_id: String,
    /// Timestamp
    pub timestamp: u64,
    /// Model ID
    pub model_id: String,
    /// Inference step
    pub inference_step: usize,
    /// Tensor visualizations
    pub tensor_visualizations: Vec<TensorVisualization>,
    /// Attention visualizations
    pub attention_visualizations: Vec<AttentionVisualization>,
    /// Flow visualization
    pub flow_visualization: Option<FlowVisualization>,
    /// Performance visualization
    pub performance_visualization: Option<PerformanceVisualization>,
    /// Detected anomalies
    pub anomalies: Vec<VisualizationAnomaly>,
    /// Frame metadata
    pub metadata: FrameMetadata,
}

/// Individual tensor visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorVisualization {
    /// Tensor name
    pub name: String,
    /// Tensor shape
    pub shape: Vec<usize>,
    /// Visualization type
    pub visualization_type: TensorVisualizationType,
    /// Visualization data
    pub data: VisualizationData,
    /// Statistics
    pub statistics: TensorStatistics,
    /// Color mapping
    pub color_mapping: ColorMapping,
    /// Anomaly highlights
    pub anomaly_highlights: Vec<AnomalyHighlight>,
}

/// Attention visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionVisualization {
    /// Layer name
    pub layer_name: String,
    /// Head index
    pub head_index: usize,
    /// Attention matrix
    pub attention_matrix: AttentionMatrix,
    /// Attention flow
    pub attention_flow: Option<AttentionFlow>,
    /// Head specialization info
    pub head_specialization: Option<HeadSpecialization>,
    /// Sequence information
    pub sequence_info: SequenceInfo,
}

/// Flow visualization showing data/gradient flow
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowVisualization {
    /// Data flow graph
    pub data_flow: Option<DataFlowGraph>,
    /// Gradient flow graph
    pub gradient_flow: Option<GradientFlowGraph>,
    /// Computation graph
    pub computation_graph: Option<ComputationGraph>,
    /// Bottleneck locations
    pub bottlenecks: Vec<FlowBottleneck>,
    /// Flow statistics
    pub flow_statistics: FlowStatistics,
}

/// Performance visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceVisualization {
    /// Real-time metrics
    pub real_time_metrics: MetricsVisualization,
    /// Memory usage visualization
    pub memory_visualization: MemoryVisualization,
    /// Timing breakdown
    pub timing_breakdown: TimingVisualization,
    /// Thermal visualization
    pub thermal_visualization: ThermalVisualization,
    /// Performance trends
    pub performance_trends: PerformanceTrends,
}

/// Visualization-specific anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationAnomaly {
    /// Anomaly type
    pub anomaly_type: VisualizationAnomalyType,
    /// Severity
    pub severity: AnomalySeverity,
    /// Location in visualization
    pub location: VisualizationLocation,
    /// Description
    pub description: String,
    /// Visual indicators
    pub visual_indicators: Vec<VisualIndicator>,
    /// Suggested actions
    pub suggested_actions: Vec<String>,
}

/// Visualization anomaly types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum VisualizationAnomalyType {
    /// Visual anomaly in tensor
    TensorAnomaly,
    /// Attention pattern anomaly
    AttentionAnomaly,
    /// Flow disruption
    FlowDisruption,
    /// Performance anomaly
    PerformanceAnomaly,
    /// Rendering anomaly
    RenderingAnomaly,
}

/// Frame metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FrameMetadata {
    /// Inference duration
    pub inference_duration: Duration,
    /// Rendering duration
    pub rendering_duration: Duration,
    /// Memory usage during frame
    pub memory_usage_mb: f32,
    /// CPU usage during frame
    pub cpu_usage_percent: f32,
    /// GPU usage during frame
    pub gpu_usage_percent: f32,
    /// Quality metrics
    pub quality_metrics: QualityMetrics,
}

/// Main inference visualizer
pub struct MobileInferenceVisualizer {
    config: InferenceVisualizerConfig,
    frame_buffer: Arc<Mutex<VecDeque<VisualizationFrame>>>,
    renderer: VisualizationRenderer,
    analyzer: VisualizationAnalyzer,
    export_manager: VisualizationExportManager,
    session_tracker: VisualizationSession,
    real_time_monitor: RealTimeVisualizationMonitor,
}

/// Visualization renderer
struct VisualizationRenderer {
    tensor_renderer: TensorRenderer,
    attention_renderer: AttentionRenderer,
    flow_renderer: FlowRenderer,
    performance_renderer: PerformanceRenderer,
    composite_renderer: CompositeRenderer,
}

/// Visualization analyzer
struct VisualizationAnalyzer {
    pattern_detector: PatternDetector,
    anomaly_detector: VisualizationAnomalyDetector,
    quality_assessor: QualityAssessor,
}

/// Visualization session tracking
struct VisualizationSession {
    session_id: String,
    start_time: Instant,
    frame_count: usize,
    total_render_time: Duration,
    session_statistics: SessionStatistics,
}

/// Real-time visualization monitor
struct RealTimeVisualizationMonitor {
    config: RealTimeVisualizationConfig,
    frame_buffer: VecDeque<VisualizationFrame>,
    render_pipeline: RenderPipeline,
    performance_tracker: VisualizationPerformanceTracker,
}

impl Default for InferenceVisualizerConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            mode: VisualizationMode::Lightweight,
            real_time_config: RealTimeVisualizationConfig::default(),
            tensor_visualization: TensorVisualizationConfig::default(),
            attention_visualization: AttentionVisualizationConfig::default(),
            flow_visualization: FlowVisualizationConfig::default(),
            performance_visualization: PerformanceVisualizationConfig::default(),
            export_config: VisualizationExportConfig::default(),
        }
    }
}

impl Default for RealTimeVisualizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            update_fps: 30,
            max_history_frames: 100,
            enable_animation: true,
            animation_duration_ms: 500,
            render_buffer_size: 10,
        }
    }
}

impl Default for TensorVisualizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            visualization_type: TensorVisualizationType::Heatmap,
            color_scheme: ColorScheme::Viridis,
            normalize_values: true,
            show_statistics: true,
            highlight_anomalies: true,
            max_tensor_size: 1000000, // 1M elements
        }
    }
}

impl Default for AttentionVisualizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            show_attention_matrices: true,
            show_attention_flow: true,
            show_head_specialization: true,
            max_sequence_length: 512,
            head_sampling_rate: 1.0, // Show all heads by default
        }
    }
}

impl Default for FlowVisualizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            show_data_flow: true,
            show_gradient_flow: false, // Expensive
            show_computation_graph: true,
            flow_animation_speed: 1.0,
            highlight_bottlenecks: true,
        }
    }
}

impl Default for PerformanceVisualizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            show_real_time_metrics: true,
            show_memory_usage: true,
            show_timing_breakdown: true,
            show_thermal_state: true,
            history_length: 60, // 60 frames of history
        }
    }
}

impl Default for VisualizationExportConfig {
    fn default() -> Self {
        Self {
            auto_export: false,
            format: VisualizationExportFormat::PNG,
            quality: ExportQuality::Medium,
            include_metadata: true,
            export_directory: "/tmp/trustformers_visualizations".to_string(),
        }
    }
}

impl MobileInferenceVisualizer {
    /// Create new inference visualizer
    pub fn new(config: InferenceVisualizerConfig) -> Result<Self> {
        let frame_buffer = Arc::new(Mutex::new(VecDeque::new()));
        let renderer = VisualizationRenderer::new(&config)?;
        let analyzer = VisualizationAnalyzer::new(&config)?;
        let export_manager = VisualizationExportManager::new(config.export_config.clone())?;
        let session_tracker = VisualizationSession::new()?;
        let real_time_monitor = RealTimeVisualizationMonitor::new(config.real_time_config.clone())?;

        Ok(Self {
            config,
            frame_buffer,
            renderer,
            analyzer,
            export_manager,
            session_tracker,
            real_time_monitor,
        })
    }

    /// Start visualization session
    pub fn start_session(&mut self, model_id: &str) -> Result<String> {
        if !self.config.enabled {
            return Err(TrustformersError::runtime_error("Visualizer is disabled".into()).into());
        }

        self.session_tracker.start(model_id)?;

        if self.config.real_time_config.enabled {
            self.real_time_monitor.start()?;
        }

        tracing::info!(
            "Started visualization session: {}",
            self.session_tracker.session_id
        );
        Ok(self.session_tracker.session_id.clone())
    }

    /// Stop visualization session
    pub fn stop_session(&mut self) -> Result<VisualizationSessionReport> {
        self.session_tracker.end()?;
        self.real_time_monitor.stop()?;

        let report = self.generate_session_report()?;

        if self.config.export_config.auto_export {
            self.export_session(&report)?;
        }

        Ok(report)
    }

    /// Visualize inference step
    pub fn visualize_inference_step(
        &mut self,
        model_id: &str,
        step: usize,
        input_tensors: &[Tensor],
        output_tensors: &[Tensor],
        intermediate_tensors: &[(&str, &Tensor)],
        attention_weights: Option<&[(&str, usize, &Tensor)]>, // (layer_name, head_idx, weights)
        performance_metrics: &MobileMetricsSnapshot,
    ) -> Result<VisualizationFrame> {
        let start_time = Instant::now();

        let frame_id = format!(
            "{}_{}_frame_{}",
            model_id, self.session_tracker.session_id, step
        );
        let timestamp =
            SystemTime::now().duration_since(UNIX_EPOCH).unwrap_or_default().as_millis() as u64;

        // Generate tensor visualizations
        let mut tensor_visualizations = Vec::new();

        if self.config.tensor_visualization.enabled {
            // Input tensors
            for (i, tensor) in input_tensors.iter().enumerate() {
                if tensor.len() <= self.config.tensor_visualization.max_tensor_size {
                    let viz = self.renderer.render_tensor(&format!("input_{}", i), tensor)?;
                    tensor_visualizations.push(viz);
                }
            }

            // Output tensors
            for (i, tensor) in output_tensors.iter().enumerate() {
                if tensor.len() <= self.config.tensor_visualization.max_tensor_size {
                    let viz = self.renderer.render_tensor(&format!("output_{}", i), tensor)?;
                    tensor_visualizations.push(viz);
                }
            }

            // Intermediate tensors
            for (name, tensor) in intermediate_tensors {
                if tensor.len() <= self.config.tensor_visualization.max_tensor_size {
                    let viz = self.renderer.render_tensor(name, tensor)?;
                    tensor_visualizations.push(viz);
                }
            }
        }

        // Generate attention visualizations
        let mut attention_visualizations = Vec::new();
        if self.config.attention_visualization.enabled {
            if let Some(attention_weights) = attention_weights {
                for (layer_name, head_idx, weights) in attention_weights {
                    let seq_len = self.estimate_sequence_length(weights);
                    if seq_len <= self.config.attention_visualization.max_sequence_length {
                        let viz = self.renderer.render_attention(layer_name, *head_idx, weights)?;
                        attention_visualizations.push(viz);
                    }
                }
            }
        }

        // Generate flow visualization
        let flow_visualization = if self.config.flow_visualization.enabled {
            Some(self.renderer.render_flow(input_tensors, output_tensors, intermediate_tensors)?)
        } else {
            None
        };

        // Generate performance visualization
        let performance_visualization = if self.config.performance_visualization.enabled {
            Some(self.renderer.render_performance(performance_metrics)?)
        } else {
            None
        };

        // Detect anomalies
        let anomalies = self.analyzer.detect_anomalies(
            &tensor_visualizations,
            &attention_visualizations,
            &flow_visualization,
            &performance_visualization,
        )?;

        let rendering_duration = start_time.elapsed();

        // Create frame metadata
        let metadata = FrameMetadata {
            inference_duration: Duration::from_millis(50), // Would get actual duration
            rendering_duration,
            memory_usage_mb: performance_metrics.memory.heap_used_mb,
            cpu_usage_percent: performance_metrics.cpu.usage_percent,
            gpu_usage_percent: performance_metrics.gpu.usage_percent,
            quality_metrics: self.analyzer.assess_quality(&tensor_visualizations)?,
        };

        // Create visualization frame
        let frame = VisualizationFrame {
            frame_id: frame_id.clone(),
            timestamp,
            model_id: model_id.to_string(),
            inference_step: step,
            tensor_visualizations,
            attention_visualizations,
            flow_visualization,
            performance_visualization,
            anomalies,
            metadata,
        };

        // Store frame
        self.store_frame(frame.clone())?;

        // Update session tracking
        self.session_tracker.update_frame_stats(&frame);

        // Update real-time monitor
        if self.config.real_time_config.enabled {
            self.real_time_monitor.update_frame(&frame)?;
        }

        tracing::debug!(
            "Generated visualization frame: {} ({}ms)",
            frame_id,
            rendering_duration.as_millis()
        );

        Ok(frame)
    }

    /// Get visualization frame by ID
    pub fn get_frame(&self, frame_id: &str) -> Result<Option<VisualizationFrame>> {
        let frames = self
            .frame_buffer
            .lock()
            .map_err(|_| TrustformersError::runtime_error("Failed to acquire lock".into()))?;

        Ok(frames.iter().find(|f| f.frame_id == frame_id).cloned())
    }

    /// Get recent frames
    pub fn get_recent_frames(&self, limit: Option<usize>) -> Result<Vec<VisualizationFrame>> {
        let frames = self
            .frame_buffer
            .lock()
            .map_err(|_| TrustformersError::runtime_error("Failed to acquire lock".into()))?;

        let result = if let Some(limit) = limit {
            frames.iter().rev().take(limit).cloned().collect()
        } else {
            frames.iter().cloned().collect()
        };

        Ok(result)
    }

    /// Export visualization frame
    pub fn export_frame(&self, frame: &VisualizationFrame) -> Result<String> {
        self.export_manager.export_frame(frame)
    }

    /// Export entire session
    pub fn export_session(&self, report: &VisualizationSessionReport) -> Result<String> {
        self.export_manager.export_session(report)
    }

    /// Get real-time visualization state
    pub fn get_real_time_state(&self) -> Result<RealTimeVisualizationState> {
        self.real_time_monitor.get_current_state()
    }

    /// Generate interactive visualization
    pub fn generate_interactive_visualization(
        &self,
        frames: &[VisualizationFrame],
    ) -> Result<InteractiveVisualization> {
        self.renderer.generate_interactive(frames)
    }

    // Private helper methods

    fn store_frame(&self, frame: VisualizationFrame) -> Result<()> {
        let mut frames = self
            .frame_buffer
            .lock()
            .map_err(|_| TrustformersError::runtime_error("Failed to acquire lock".into()))?;

        frames.push_back(frame);

        // Keep only recent frames
        let max_frames = self.config.real_time_config.max_history_frames;
        while frames.len() > max_frames {
            frames.pop_front();
        }

        Ok(())
    }

    fn estimate_sequence_length(&self, attention_weights: &Tensor) -> usize {
        // Estimate sequence length from attention tensor shape
        let shape = attention_weights.shape();
        if shape.len() >= 2 {
            shape[shape.len() - 1]
        } else {
            0
        }
    }

    fn generate_session_report(&self) -> Result<VisualizationSessionReport> {
        let frames = self.get_recent_frames(None)?;
        let anomaly_summary = self.generate_anomaly_summary(&frames);

        Ok(VisualizationSessionReport {
            session_id: self.session_tracker.session_id.clone(),
            total_frames: frames.len(),
            session_duration: self.session_tracker.get_duration(),
            total_render_time: self.session_tracker.total_render_time,
            average_frame_time: if frames.is_empty() {
                Duration::from_millis(0)
            } else {
                self.session_tracker.total_render_time / frames.len() as u32
            },
            frames,
            session_statistics: self.session_tracker.session_statistics.clone(),
            anomaly_summary,
        })
    }

    fn generate_anomaly_summary(&self, frames: &[VisualizationFrame]) -> AnomalySummary {
        let mut summary = AnomalySummary {
            total_anomalies: 0,
            anomaly_types: HashMap::new(),
            severity_distribution: HashMap::new(),
            frames_with_anomalies: 0,
        };

        for frame in frames {
            if !frame.anomalies.is_empty() {
                summary.frames_with_anomalies += 1;

                for anomaly in &frame.anomalies {
                    summary.total_anomalies += 1;

                    *summary.anomaly_types.entry(anomaly.anomaly_type).or_insert(0) += 1;
                    *summary.severity_distribution.entry(anomaly.severity).or_insert(0) += 1;
                }
            }
        }

        summary
    }
}

// Implementation of helper structs and data types

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationData {
    /// Raw data points
    pub data_points: Vec<f32>,
    /// Data dimensions
    pub dimensions: Vec<usize>,
    /// Data range
    pub value_range: (f32, f32),
    /// Normalized data
    pub normalized_data: Option<Vec<f32>>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorStatistics {
    pub mean: f32,
    pub std_dev: f32,
    pub min_value: f32,
    pub max_value: f32,
    pub zero_count: usize,
    pub nan_count: usize,
    pub inf_count: usize,
    pub sparsity: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ColorMapping {
    pub scheme: ColorScheme,
    pub min_color: [u8; 3],
    pub max_color: [u8; 3],
    pub special_colors: HashMap<String, [u8; 3]>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyHighlight {
    pub location: Vec<usize>,
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub highlight_color: [u8; 3],
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionMatrix {
    pub matrix_data: Vec<Vec<f32>>,
    pub sequence_length: usize,
    pub head_dim: usize,
    pub attention_type: AttentionType,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AttentionType {
    SelfAttention,
    CrossAttention,
    MultiQueryAttention,
    GroupedQueryAttention,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionFlow {
    pub flow_vectors: Vec<FlowVector>,
    pub flow_strength: Vec<f32>,
    pub dominant_patterns: Vec<AttentionPattern>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowVector {
    pub from_position: usize,
    pub to_position: usize,
    pub strength: f32,
    pub direction: FlowDirection,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FlowDirection {
    Forward,
    Backward,
    Bidirectional,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionPattern {
    pub pattern_type: AttentionPatternType,
    pub confidence: f32,
    pub affected_positions: Vec<usize>,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AttentionPatternType {
    Diagonal,
    Vertical,
    Horizontal,
    Block,
    Sparse,
    Dense,
    Custom,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HeadSpecialization {
    pub specialization_type: HeadSpecializationType,
    pub specialization_score: f32,
    pub representative_patterns: Vec<String>,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum HeadSpecializationType {
    Positional,
    Syntactic,
    Semantic,
    Local,
    Global,
    Redundant,
    Unknown,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SequenceInfo {
    pub sequence_length: usize,
    pub token_types: Vec<String>,
    pub special_tokens: Vec<SpecialToken>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpecialToken {
    pub position: usize,
    pub token_type: String,
    pub token_value: String,
}

// Additional visualization data structures
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataFlowGraph {
    pub nodes: Vec<FlowNode>,
    pub edges: Vec<FlowEdge>,
    pub flow_metrics: FlowMetrics,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowNode {
    pub id: String,
    pub name: String,
    pub node_type: FlowNodeType,
    pub position: (f32, f32),
    pub data_size: usize,
    pub processing_time: Duration,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FlowNodeType {
    Input,
    Layer,
    Operation,
    Output,
    Branch,
    Merge,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowEdge {
    pub from_node: String,
    pub to_node: String,
    pub data_flow_rate: f32,
    pub edge_type: FlowEdgeType,
    pub bottleneck_score: f32,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FlowEdgeType {
    Data,
    Control,
    Gradient,
    Attention,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowMetrics {
    pub total_flow_rate: f32,
    pub average_latency: Duration,
    pub bottleneck_count: usize,
    pub flow_efficiency: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientFlowGraph {
    pub gradient_magnitudes: Vec<f32>,
    pub flow_directions: Vec<FlowDirection>,
    pub vanishing_regions: Vec<GradientRegion>,
    pub exploding_regions: Vec<GradientRegion>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientRegion {
    pub start_layer: usize,
    pub end_layer: usize,
    pub severity: f32,
    pub gradient_norm: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationGraph {
    pub operations: Vec<Operation>,
    pub dependencies: Vec<Dependency>,
    pub critical_path: Vec<String>,
    pub parallelization_opportunities: Vec<ParallelizationOpportunity>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Operation {
    pub id: String,
    pub operation_type: String,
    pub input_shapes: Vec<Vec<usize>>,
    pub output_shapes: Vec<Vec<usize>>,
    pub flops: u64,
    pub memory_usage: usize,
    pub execution_time: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Dependency {
    pub from_operation: String,
    pub to_operation: String,
    pub dependency_type: DependencyType,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DependencyType {
    Data,
    Control,
    Memory,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelizationOpportunity {
    pub operations: Vec<String>,
    pub potential_speedup: f32,
    pub memory_requirement: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowBottleneck {
    pub location: String,
    pub bottleneck_type: BottleneckType,
    pub severity: f32,
    pub impact_score: f32,
    pub suggested_optimizations: Vec<String>,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum BottleneckType {
    Computation,
    Memory,
    Communication,
    Synchronization,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FlowStatistics {
    pub total_operations: usize,
    pub critical_path_length: Duration,
    pub parallelization_factor: f32,
    pub memory_efficiency: f32,
    pub compute_efficiency: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricsVisualization {
    pub cpu_timeline: Vec<(u64, f32)>,
    pub memory_timeline: Vec<(u64, f32)>,
    pub gpu_timeline: Vec<(u64, f32)>,
    pub inference_timeline: Vec<(u64, f32)>,
    pub current_values: HashMap<String, f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryVisualization {
    pub memory_breakdown: HashMap<String, f32>,
    pub memory_timeline: Vec<(u64, MemorySnapshot)>,
    pub allocation_patterns: Vec<AllocationPattern>,
    pub leak_indicators: Vec<LeakIndicator>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemorySnapshot {
    pub heap_used: f32,
    pub heap_free: f32,
    pub native_used: f32,
    pub graphics_used: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationPattern {
    pub pattern_type: AllocationPatternType,
    pub frequency: f32,
    pub size_distribution: Vec<(usize, usize)>, // (size, count)
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AllocationPatternType {
    Constant,
    Periodic,
    Burst,
    Gradual,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LeakIndicator {
    pub location: String,
    pub growth_rate: f32,
    pub confidence: f32,
    pub suggested_action: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimingVisualization {
    pub operation_timings: Vec<OperationTiming>,
    pub timing_breakdown: HashMap<String, Duration>,
    pub critical_path: Vec<String>,
    pub optimization_opportunities: Vec<OptimizationOpportunity>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationTiming {
    pub operation_name: String,
    pub start_time: Duration,
    pub end_time: Duration,
    pub duration: Duration,
    pub percentage_of_total: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationOpportunity {
    pub operation: String,
    pub opportunity_type: OptimizationType,
    pub potential_speedup: f32,
    pub implementation_difficulty: f32,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationType {
    Parallelization,
    Caching,
    AlgorithmOptimization,
    HardwareOptimization,
    MemoryOptimization,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThermalVisualization {
    pub temperature_timeline: Vec<(u64, f32)>,
    pub thermal_zones: Vec<ThermalZone>,
    pub throttling_events: Vec<ThrottlingEvent>,
    pub thermal_prediction: ThermalPrediction,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThermalZone {
    pub zone_name: String,
    pub current_temperature: f32,
    pub critical_temperature: f32,
    pub thermal_state: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThrottlingEvent {
    pub timestamp: u64,
    pub severity: f32,
    pub affected_components: Vec<String>,
    pub duration: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThermalPrediction {
    pub predicted_peak: f32,
    pub time_to_peak: Duration,
    pub confidence: f32,
    pub recommended_actions: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTrends {
    pub latency_trend: TrendData,
    pub throughput_trend: TrendData,
    pub memory_trend: TrendData,
    pub efficiency_trend: TrendData,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendData {
    pub current_value: f32,
    pub trend_direction: TrendDirection,
    pub trend_strength: f32,
    pub prediction: Option<TrendPrediction>,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TrendDirection {
    Improving,
    Stable,
    Degrading,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendPrediction {
    pub predicted_value: f32,
    pub confidence: f32,
    pub time_horizon: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationLocation {
    pub component: String,
    pub layer: Option<String>,
    pub tensor_name: Option<String>,
    pub coordinates: Option<Vec<usize>>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualIndicator {
    pub indicator_type: VisualIndicatorType,
    pub color: [u8; 3],
    pub intensity: f32,
    pub blinking: bool,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum VisualIndicatorType {
    Highlight,
    Border,
    Overlay,
    Arrow,
    Text,
    Icon,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityMetrics {
    pub render_quality: f32,
    pub data_accuracy: f32,
    pub visual_clarity: f32,
    pub information_density: f32,
    pub performance_impact: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationSessionReport {
    pub session_id: String,
    pub total_frames: usize,
    pub session_duration: Duration,
    pub total_render_time: Duration,
    pub average_frame_time: Duration,
    pub frames: Vec<VisualizationFrame>,
    pub session_statistics: SessionStatistics,
    pub anomaly_summary: AnomalySummary,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SessionStatistics {
    pub frames_generated: usize,
    pub anomalies_detected: usize,
    pub average_render_time: Duration,
    pub peak_memory_usage: f32,
    pub total_data_processed: usize,
    pub quality_score: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalySummary {
    pub total_anomalies: usize,
    pub anomaly_types: HashMap<VisualizationAnomalyType, usize>,
    pub severity_distribution: HashMap<AnomalySeverity, usize>,
    pub frames_with_anomalies: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealTimeVisualizationState {
    pub current_frame: Option<VisualizationFrame>,
    pub frame_rate: f32,
    pub render_performance: RenderPerformance,
    pub buffer_status: BufferStatus,
    pub active_visualizations: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RenderPerformance {
    pub frames_per_second: f32,
    pub average_render_time: Duration,
    pub dropped_frames: usize,
    pub render_quality: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BufferStatus {
    pub current_size: usize,
    pub maximum_size: usize,
    pub utilization: f32,
    pub overflow_count: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InteractiveVisualization {
    pub visualization_id: String,
    pub visualization_type: InteractiveVisualizationType,
    pub html_content: String,
    pub javascript_code: String,
    pub css_styles: String,
    pub data_json: String,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum InteractiveVisualizationType {
    Dashboard,
    Timeline,
    FlowGraph,
    TensorExplorer,
    AttentionViewer,
    PerformanceMonitor,
}

// Implementation stubs for helper components

impl VisualizationRenderer {
    fn new(_config: &InferenceVisualizerConfig) -> Result<Self> {
        Ok(Self {
            tensor_renderer: TensorRenderer,
            attention_renderer: AttentionRenderer,
            flow_renderer: FlowRenderer,
            performance_renderer: PerformanceRenderer,
            composite_renderer: CompositeRenderer,
        })
    }

    fn render_tensor(&self, name: &str, tensor: &Tensor) -> Result<TensorVisualization> {
        let data = tensor.data()?;
        let shape = tensor.shape().to_vec();

        let statistics = TensorStatistics {
            mean: data.iter().sum::<f32>() / data.len() as f32,
            std_dev: 0.0, // Would calculate actual std dev
            min_value: data.iter().fold(f32::INFINITY, |acc, &val| acc.min(val)),
            max_value: data.iter().fold(f32::NEG_INFINITY, |acc, &val| acc.max(val)),
            zero_count: data.iter().filter(|&&val| val == 0.0).count(),
            nan_count: data.iter().filter(|&&val| val.is_nan()).count(),
            inf_count: data.iter().filter(|&&val| val.is_infinite()).count(),
            sparsity: data.iter().filter(|&&val| val == 0.0).count() as f32 / data.len() as f32,
        };

        Ok(TensorVisualization {
            name: name.to_string(),
            shape,
            visualization_type: TensorVisualizationType::Heatmap,
            data: VisualizationData {
                data_points: data,
                dimensions: tensor.shape().to_vec(),
                value_range: (statistics.min_value, statistics.max_value),
                normalized_data: None,
            },
            statistics,
            color_mapping: ColorMapping {
                scheme: ColorScheme::Viridis,
                min_color: [0, 0, 0],
                max_color: [255, 255, 255],
                special_colors: HashMap::new(),
            },
            anomaly_highlights: Vec::new(),
        })
    }

    fn render_attention(
        &self,
        layer_name: &str,
        head_idx: usize,
        weights: &Tensor,
    ) -> Result<AttentionVisualization> {
        let shape = weights.shape();
        let seq_len = if shape.len() >= 2 { shape[shape.len() - 1] } else { 0 };

        Ok(AttentionVisualization {
            layer_name: layer_name.to_string(),
            head_index: head_idx,
            attention_matrix: AttentionMatrix {
                matrix_data: vec![vec![0.5; seq_len]; seq_len], // Simplified
                sequence_length: seq_len,
                head_dim: shape.get(shape.len().saturating_sub(2)).copied().unwrap_or(64),
                attention_type: AttentionType::SelfAttention,
            },
            attention_flow: None,
            head_specialization: None,
            sequence_info: SequenceInfo {
                sequence_length: seq_len,
                token_types: vec!["token".to_string(); seq_len],
                special_tokens: Vec::new(),
            },
        })
    }

    fn render_flow(
        &self,
        _input_tensors: &[Tensor],
        _output_tensors: &[Tensor],
        _intermediate_tensors: &[(&str, &Tensor)],
    ) -> Result<FlowVisualization> {
        Ok(FlowVisualization {
            data_flow: Some(DataFlowGraph {
                nodes: Vec::new(),
                edges: Vec::new(),
                flow_metrics: FlowMetrics {
                    total_flow_rate: 1.0,
                    average_latency: Duration::from_millis(10),
                    bottleneck_count: 0,
                    flow_efficiency: 0.95,
                },
            }),
            gradient_flow: None,
            computation_graph: None,
            bottlenecks: Vec::new(),
            flow_statistics: FlowStatistics {
                total_operations: 10,
                critical_path_length: Duration::from_millis(50),
                parallelization_factor: 2.0,
                memory_efficiency: 0.8,
                compute_efficiency: 0.9,
            },
        })
    }

    fn render_performance(
        &self,
        metrics: &MobileMetricsSnapshot,
    ) -> Result<PerformanceVisualization> {
        Ok(PerformanceVisualization {
            real_time_metrics: MetricsVisualization {
                cpu_timeline: vec![(metrics.timestamp, metrics.cpu.usage_percent)],
                memory_timeline: vec![(metrics.timestamp, metrics.memory.heap_used_mb)],
                gpu_timeline: vec![(metrics.timestamp, metrics.gpu.usage_percent)],
                inference_timeline: vec![(
                    metrics.timestamp,
                    metrics.inference.avg_latency_ms as f32,
                )],
                current_values: {
                    let mut values = HashMap::new();
                    values.insert("cpu_usage".to_string(), metrics.cpu.usage_percent);
                    values.insert("memory_usage".to_string(), metrics.memory.heap_used_mb);
                    values.insert("gpu_usage".to_string(), metrics.gpu.usage_percent);
                    values
                },
            },
            memory_visualization: MemoryVisualization {
                memory_breakdown: {
                    let mut breakdown = HashMap::new();
                    breakdown.insert("heap".to_string(), metrics.memory.heap_used_mb);
                    breakdown.insert("native".to_string(), metrics.memory.native_used_mb);
                    breakdown.insert("graphics".to_string(), metrics.memory.graphics_used_mb);
                    breakdown
                },
                memory_timeline: vec![(
                    metrics.timestamp,
                    MemorySnapshot {
                        heap_used: metrics.memory.heap_used_mb,
                        heap_free: metrics.memory.heap_free_mb,
                        native_used: metrics.memory.native_used_mb,
                        graphics_used: metrics.memory.graphics_used_mb,
                    },
                )],
                allocation_patterns: Vec::new(),
                leak_indicators: Vec::new(),
            },
            timing_breakdown: TimingVisualization {
                operation_timings: Vec::new(),
                timing_breakdown: HashMap::new(),
                critical_path: Vec::new(),
                optimization_opportunities: Vec::new(),
            },
            thermal_visualization: ThermalVisualization {
                temperature_timeline: vec![(metrics.timestamp, metrics.thermal.temperature_c)],
                thermal_zones: Vec::new(),
                throttling_events: Vec::new(),
                thermal_prediction: ThermalPrediction {
                    predicted_peak: metrics.thermal.temperature_c + 5.0,
                    time_to_peak: Duration::from_secs(60),
                    confidence: 0.7,
                    recommended_actions: Vec::new(),
                },
            },
            performance_trends: PerformanceTrends {
                latency_trend: TrendData {
                    current_value: metrics.inference.avg_latency_ms as f32,
                    trend_direction: TrendDirection::Stable,
                    trend_strength: 0.1,
                    prediction: None,
                },
                throughput_trend: TrendData {
                    current_value: metrics.inference.throughput_per_sec as f32,
                    trend_direction: TrendDirection::Stable,
                    trend_strength: 0.1,
                    prediction: None,
                },
                memory_trend: TrendData {
                    current_value: metrics.memory.heap_used_mb,
                    trend_direction: TrendDirection::Stable,
                    trend_strength: 0.1,
                    prediction: None,
                },
                efficiency_trend: TrendData {
                    current_value: 0.85,
                    trend_direction: TrendDirection::Stable,
                    trend_strength: 0.1,
                    prediction: None,
                },
            },
        })
    }

    fn generate_interactive(
        &self,
        _frames: &[VisualizationFrame],
    ) -> Result<InteractiveVisualization> {
        Ok(InteractiveVisualization {
            visualization_id: "interactive_viz".to_string(),
            visualization_type: InteractiveVisualizationType::Dashboard,
            html_content: "<div>Interactive Visualization</div>".to_string(),
            javascript_code: "// JavaScript code".to_string(),
            css_styles: "/* CSS styles */".to_string(),
            data_json: "{}".to_string(),
        })
    }
}

impl VisualizationAnalyzer {
    fn new(_config: &InferenceVisualizerConfig) -> Result<Self> {
        Ok(Self {
            pattern_detector: PatternDetector,
            anomaly_detector: VisualizationAnomalyDetector,
            quality_assessor: QualityAssessor,
        })
    }

    fn detect_anomalies(
        &self,
        _tensor_visualizations: &[TensorVisualization],
        _attention_visualizations: &[AttentionVisualization],
        _flow_visualization: &Option<FlowVisualization>,
        _performance_visualization: &Option<PerformanceVisualization>,
    ) -> Result<Vec<VisualizationAnomaly>> {
        Ok(Vec::new()) // Would implement actual anomaly detection
    }

    fn assess_quality(
        &self,
        _tensor_visualizations: &[TensorVisualization],
    ) -> Result<QualityMetrics> {
        Ok(QualityMetrics {
            render_quality: 0.9,
            data_accuracy: 0.95,
            visual_clarity: 0.85,
            information_density: 0.8,
            performance_impact: 0.1,
        })
    }
}

impl VisualizationExportManager {
    fn new(_config: VisualizationExportConfig) -> Result<Self> {
        Ok(Self { config: _config })
    }

    fn export_frame(&self, _frame: &VisualizationFrame) -> Result<String> {
        Ok("/tmp/visualization_frame.png".to_string())
    }

    fn export_session(&self, _report: &VisualizationSessionReport) -> Result<String> {
        Ok("/tmp/visualization_session.html".to_string())
    }
}

struct VisualizationExportManager {
    config: VisualizationExportConfig,
}

impl VisualizationSession {
    fn new() -> Result<Self> {
        Ok(Self {
            session_id: format!(
                "vis_session_{}",
                SystemTime::now().duration_since(UNIX_EPOCH).unwrap_or_default().as_millis()
            ),
            start_time: Instant::now(),
            frame_count: 0,
            total_render_time: Duration::from_millis(0),
            session_statistics: SessionStatistics {
                frames_generated: 0,
                anomalies_detected: 0,
                average_render_time: Duration::from_millis(0),
                peak_memory_usage: 0.0,
                total_data_processed: 0,
                quality_score: 0.0,
            },
        })
    }

    fn start(&mut self, _model_id: &str) -> Result<()> {
        self.start_time = Instant::now();
        Ok(())
    }

    fn end(&mut self) -> Result<()> {
        if self.frame_count > 0 {
            self.session_statistics.average_render_time =
                self.total_render_time / self.frame_count as u32;
        }
        Ok(())
    }

    fn update_frame_stats(&mut self, frame: &VisualizationFrame) {
        self.frame_count += 1;
        self.total_render_time += frame.metadata.rendering_duration;
        self.session_statistics.frames_generated += 1;
        self.session_statistics.anomalies_detected += frame.anomalies.len();

        if frame.metadata.memory_usage_mb > self.session_statistics.peak_memory_usage {
            self.session_statistics.peak_memory_usage = frame.metadata.memory_usage_mb;
        }
    }

    fn get_duration(&self) -> Duration {
        self.start_time.elapsed()
    }
}

impl RealTimeVisualizationMonitor {
    fn new(config: RealTimeVisualizationConfig) -> Result<Self> {
        Ok(Self {
            config,
            frame_buffer: VecDeque::new(),
            render_pipeline: RenderPipeline,
            performance_tracker: VisualizationPerformanceTracker,
        })
    }

    fn start(&mut self) -> Result<()> {
        tracing::info!("Started real-time visualization monitor");
        Ok(())
    }

    fn stop(&mut self) -> Result<()> {
        tracing::info!("Stopped real-time visualization monitor");
        Ok(())
    }

    fn update_frame(&mut self, frame: &VisualizationFrame) -> Result<()> {
        self.frame_buffer.push_back(frame.clone());

        if self.frame_buffer.len() > self.config.max_history_frames {
            self.frame_buffer.pop_front();
        }

        Ok(())
    }

    fn get_current_state(&self) -> Result<RealTimeVisualizationState> {
        Ok(RealTimeVisualizationState {
            current_frame: self.frame_buffer.back().cloned(),
            frame_rate: 30.0,
            render_performance: RenderPerformance {
                frames_per_second: 30.0,
                average_render_time: Duration::from_millis(16),
                dropped_frames: 0,
                render_quality: 0.9,
            },
            buffer_status: BufferStatus {
                current_size: self.frame_buffer.len(),
                maximum_size: self.config.max_history_frames,
                utilization: self.frame_buffer.len() as f32 / self.config.max_history_frames as f32,
                overflow_count: 0,
            },
            active_visualizations: vec!["tensor".to_string(), "performance".to_string()],
        })
    }
}

// Placeholder structs
struct TensorRenderer;
struct AttentionRenderer;
struct FlowRenderer;
struct PerformanceRenderer;
struct CompositeRenderer;
struct PatternDetector;
struct VisualizationAnomalyDetector;
struct QualityAssessor;
struct RenderPipeline;
struct VisualizationPerformanceTracker;

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

    #[test]
    fn test_visualizer_creation() {
        let config = InferenceVisualizerConfig::default();
        let visualizer = MobileInferenceVisualizer::new(config);
        assert!(visualizer.is_ok());
    }

    #[test]
    fn test_visualization_config() {
        let config = InferenceVisualizerConfig::default();
        assert!(config.enabled);
        assert_eq!(config.mode, VisualizationMode::Lightweight);
        assert!(config.tensor_visualization.enabled);
        assert!(config.real_time_config.enabled);
    }

    #[test]
    fn test_tensor_visualization_types() {
        let viz_type = TensorVisualizationType::Heatmap;
        assert_eq!(viz_type, TensorVisualizationType::Heatmap);

        let color_scheme = ColorScheme::Viridis;
        assert_eq!(color_scheme, ColorScheme::Viridis);
    }

    #[test]
    fn test_attention_patterns() {
        let pattern = AttentionPatternType::Diagonal;
        assert_eq!(pattern, AttentionPatternType::Diagonal);

        let attention_type = AttentionType::SelfAttention;
        assert_eq!(attention_type, AttentionType::SelfAttention);
    }

    #[test]
    fn test_visualization_frame() {
        let frame = VisualizationFrame {
            frame_id: "test_frame".to_string(),
            timestamp: 0,
            model_id: "test_model".to_string(),
            inference_step: 0,
            tensor_visualizations: Vec::new(),
            attention_visualizations: Vec::new(),
            flow_visualization: None,
            performance_visualization: None,
            anomalies: Vec::new(),
            metadata: FrameMetadata {
                inference_duration: Duration::from_millis(50),
                rendering_duration: Duration::from_millis(10),
                memory_usage_mb: 128.0,
                cpu_usage_percent: 25.0,
                gpu_usage_percent: 40.0,
                quality_metrics: QualityMetrics {
                    render_quality: 0.9,
                    data_accuracy: 0.95,
                    visual_clarity: 0.85,
                    information_density: 0.8,
                    performance_impact: 0.1,
                },
            },
        };

        assert_eq!(frame.frame_id, "test_frame");
        assert_eq!(frame.inference_step, 0);
    }
}