torsh_hub/

debugging.rs

//! Model debugging tools for ToRSh Hub
//!
//! This module provides comprehensive debugging capabilities for models,
//! including tensor inspection, gradient debugging, activation analysis,
//! and interactive debugging utilities.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::PathBuf;
use std::time::{Duration, SystemTime};
use torsh_core::error::{Result, TorshError};

/// Comprehensive model debugger
pub struct ModelDebugger {
    /// Active debugging sessions
    active_sessions: HashMap<String, DebugSession>,
    /// Debug hooks registry
    hooks: DebugHooksRegistry,
    /// Tensor inspector
    tensor_inspector: TensorInspector,
    /// Gradient debugger
    gradient_debugger: GradientDebugger,
    /// Activation analyzer
    activation_analyzer: ActivationAnalyzer,
    /// Debug configuration
    config: DebugConfig,
}

/// Debug configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DebugConfig {
    /// Enable tensor value inspection
    pub enable_tensor_inspection: bool,
    /// Enable gradient debugging
    pub enable_gradient_debugging: bool,
    /// Enable activation analysis
    pub enable_activation_analysis: bool,
    /// Enable NaN/Inf detection
    pub enable_nan_detection: bool,
    /// Enable gradient explosion detection
    pub enable_gradient_explosion_detection: bool,
    /// Maximum tensor size to inspect (in elements)
    pub max_tensor_inspection_size: usize,
    /// Debug output directory
    pub debug_dir: PathBuf,
    /// Enable interactive debugging
    pub enable_interactive: bool,
    /// Tensor value precision for display
    pub tensor_display_precision: usize,
    /// Enable layer-wise debugging
    pub enable_layer_debugging: bool,
}

/// Active debugging session
#[derive(Debug)]
pub struct DebugSession {
    /// Session identifier
    pub session_id: String,
    /// Model being debugged
    pub model_id: String,
    /// Session start time
    pub start_time: SystemTime,
    /// Registered debug hooks
    pub active_hooks: Vec<DebugHook>,
    /// Collected debug information
    pub debug_info: DebugInfo,
    /// Interactive debugger state
    pub interactive_state: Option<InteractiveDebugState>,
    /// Debug statistics
    pub statistics: DebugStatistics,
}

/// Debug hook for monitoring model execution
#[derive(Debug, Clone)]
pub struct DebugHook {
    /// Hook identifier
    pub hook_id: String,
    /// Hook type
    pub hook_type: HookType,
    /// Layer/operation pattern to match
    pub pattern: String,
    /// Condition for triggering
    pub condition: Option<TriggerCondition>,
    /// Actions to perform when triggered
    pub actions: Vec<DebugAction>,
    /// Whether hook is active
    pub active: bool,
}

/// Types of debug hooks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum HookType {
    /// Hook on forward pass
    ForwardHook,
    /// Hook on backward pass
    BackwardHook,
    /// Hook on parameter update
    ParameterUpdateHook,
    /// Hook on gradient computation
    GradientHook,
    /// Hook on tensor operation
    OperationHook,
}

/// Conditions for triggering debug hooks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TriggerCondition {
    /// Always trigger
    Always,
    /// Trigger on NaN values
    OnNaN,
    /// Trigger on infinite values
    OnInf,
    /// Trigger on gradient explosion
    OnGradientExplosion { threshold: f32 },
    /// Trigger on value range
    OnValueRange { min: f32, max: f32 },
    /// Trigger on tensor shape mismatch
    OnShapeMismatch,
    /// Trigger on specific iteration
    OnIteration(usize),
    /// Custom condition
    Custom(String),
}

/// Actions to perform when debug hook is triggered
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DebugAction {
    /// Log tensor values
    LogTensor,
    /// Save tensor to file
    SaveTensor { path: PathBuf },
    /// Analyze tensor statistics
    AnalyzeTensor,
    /// Break into interactive debugger
    BreakInteractive,
    /// Log warning message
    LogWarning { message: String },
    /// Stop execution
    StopExecution,
    /// Capture stack trace
    CaptureStackTrace,
    /// Visualize tensor
    VisualizeTensor,
}

/// Registry for managing debug hooks
pub struct DebugHooksRegistry {
    /// Registered hooks
    hooks: HashMap<String, DebugHook>,
    /// Hook execution order
    execution_order: Vec<String>,
}

/// Tensor inspection utilities
pub struct TensorInspector {
    /// Configuration for inspection
    config: TensorInspectionConfig,
    /// Cached tensor statistics
    tensor_cache: HashMap<String, TensorStatistics>,
}

/// Tensor inspection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorInspectionConfig {
    /// Maximum number of values to display
    pub max_display_values: usize,
    /// Enable histogram generation
    pub enable_histograms: bool,
    /// Number of histogram bins
    pub histogram_bins: usize,
    /// Enable distribution analysis
    pub enable_distribution_analysis: bool,
    /// Sample size for large tensors
    pub sample_size: usize,
}

/// Gradient debugging utilities
pub struct GradientDebugger {
    /// Gradient statistics
    gradient_stats: HashMap<String, GradientStatistics>,
    /// Gradient explosion detection
    explosion_detector: GradientExplosionDetector,
    /// Gradient vanishing detection
    vanishing_detector: GradientVanishingDetector,
}

/// Activation analysis utilities
pub struct ActivationAnalyzer {
    /// Activation statistics
    activation_stats: HashMap<String, ActivationStatistics>,
    /// Dead neuron detector
    dead_neuron_detector: DeadNeuronDetector,
    /// Activation distribution analyzer
    distribution_analyzer: ActivationDistributionAnalyzer,
}

/// Collected debug information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DebugInfo {
    /// Tensor snapshots
    pub tensor_snapshots: HashMap<String, Vec<TensorSnapshot>>,
    /// Gradient information
    pub gradient_info: HashMap<String, Vec<GradientInfo>>,
    /// Activation patterns
    pub activation_patterns: HashMap<String, ActivationPattern>,
    /// Detected anomalies
    pub anomalies: Vec<Anomaly>,
    /// Performance issues
    pub performance_issues: Vec<PerformanceIssue>,
    /// Model health metrics
    pub health_metrics: ModelHealthMetrics,
}

/// Interactive debugging state
#[derive(Debug)]
pub struct InteractiveDebugState {
    /// Current breakpoint
    pub current_breakpoint: Option<Breakpoint>,
    /// Execution stack
    pub execution_stack: Vec<StackFrame>,
    /// Available commands
    pub available_commands: Vec<DebugCommand>,
    /// Variable inspector
    pub variable_inspector: VariableInspector,
    /// Step mode
    pub step_mode: StepMode,
}

/// Debug statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DebugStatistics {
    /// Number of hooks triggered
    pub hooks_triggered: usize,
    /// Number of anomalies detected
    pub anomalies_detected: usize,
    /// Number of tensors inspected
    pub tensors_inspected: usize,
    /// Total debug time
    pub total_debug_time: Duration,
    /// Debug overhead ratio
    pub debug_overhead: f32,
}

/// Tensor snapshot for debugging
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorSnapshot {
    /// Snapshot timestamp
    pub timestamp: SystemTime,
    /// Tensor shape
    pub shape: Vec<usize>,
    /// Data type
    pub dtype: String,
    /// Device location
    pub device: String,
    /// Tensor statistics
    pub statistics: TensorStatistics,
    /// Sample values (for large tensors)
    pub sample_values: Vec<f32>,
    /// Full values (for small tensors)
    pub full_values: Option<Vec<f32>>,
    /// Tensor metadata
    pub metadata: TensorMetadata,
}

/// Tensor statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorStatistics {
    /// Mean value
    pub mean: f32,
    /// Standard deviation
    pub std: f32,
    /// Minimum value
    pub min: f32,
    /// Maximum value
    pub max: f32,
    /// Number of NaN values
    pub nan_count: usize,
    /// Number of infinite values
    pub inf_count: usize,
    /// Number of zero values
    pub zero_count: usize,
    /// Sparsity ratio
    pub sparsity: f32,
    /// Value distribution
    pub distribution: ValueDistribution,
    /// Gradient norm (if available)
    pub gradient_norm: Option<f32>,
}

/// Tensor metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorMetadata {
    /// Tensor name/identifier
    pub name: String,
    /// Layer that produced this tensor
    pub producer_layer: Option<String>,
    /// Requires gradient flag
    pub requires_grad: bool,
    /// Memory usage in bytes
    pub memory_usage: u64,
    /// Creation timestamp
    pub created_at: SystemTime,
}

/// Value distribution analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ValueDistribution {
    /// Histogram bins
    pub histogram: Vec<usize>,
    /// Bin edges
    pub bin_edges: Vec<f32>,
    /// Percentiles
    pub percentiles: HashMap<u8, f32>,
    /// Kurtosis
    pub kurtosis: f32,
    /// Skewness
    pub skewness: f32,
}

/// Gradient information for debugging
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientInfo {
    /// Parameter name
    pub parameter_name: String,
    /// Gradient statistics
    pub gradient_stats: GradientStatistics,
    /// Gradient flow analysis
    pub gradient_flow: GradientFlow,
    /// Update magnitude
    pub update_magnitude: f32,
    /// Gradient clipping applied
    pub clipped: bool,
}

/// Gradient statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientStatistics {
    /// Gradient norm
    pub norm: f32,
    /// Mean gradient
    pub mean: f32,
    /// Gradient standard deviation
    pub std: f32,
    /// Maximum gradient value
    pub max: f32,
    /// Minimum gradient value
    pub min: f32,
    /// Number of zero gradients
    pub zero_count: usize,
    /// Gradient sparsity
    pub sparsity: f32,
}

/// Gradient flow analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientFlow {
    /// Flow direction (forward/backward)
    pub direction: FlowDirection,
    /// Flow magnitude
    pub magnitude: f32,
    /// Bottleneck layers
    pub bottlenecks: Vec<String>,
    /// Flow efficiency
    pub efficiency: f32,
}

/// Flow direction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FlowDirection {
    Forward,
    Backward,
    Bidirectional,
}

/// Activation pattern analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActivationPattern {
    /// Layer name
    pub layer_name: String,
    /// Activation statistics
    pub activation_stats: ActivationStatistics,
    /// Dead neurons
    pub dead_neurons: Vec<usize>,
    /// Saturated neurons
    pub saturated_neurons: Vec<usize>,
    /// Activation distribution
    pub distribution: ActivationDistribution,
}

/// Activation statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActivationStatistics {
    /// Mean activation
    pub mean_activation: f32,
    /// Activation variance
    pub activation_variance: f32,
    /// Activation range
    pub activation_range: (f32, f32),
    /// Percentage of active neurons
    pub active_percentage: f32,
    /// Activation entropy
    pub entropy: f32,
}

/// Activation distribution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActivationDistribution {
    /// Distribution type
    pub distribution_type: DistributionType,
    /// Distribution parameters
    pub parameters: HashMap<String, f32>,
    /// Goodness of fit
    pub goodness_of_fit: f32,
}

/// Distribution types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DistributionType {
    Normal,
    Uniform,
    Exponential,
    LogNormal,
    Beta,
    Gamma,
    Unknown,
}

/// Detected anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Anomaly {
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Severity level
    pub severity: Severity,
    /// Location (layer/operation)
    pub location: String,
    /// Description
    pub description: String,
    /// Detection timestamp
    pub timestamp: SystemTime,
    /// Suggested fixes
    pub suggested_fixes: Vec<String>,
    /// Context information
    pub context: AnomalyContext,
}

/// Types of anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnomalyType {
    NaNValues,
    InfiniteValues,
    GradientExplosion,
    GradientVanishing,
    DeadNeurons,
    ActivationSaturation,
    MemoryLeak,
    PerformanceDegradation,
    ShapeMismatch,
    NumericInstability,
}

/// Severity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Severity {
    Critical,
    High,
    Medium,
    Low,
    Info,
}

/// Anomaly context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyContext {
    /// Related tensors
    pub related_tensors: Vec<String>,
    /// Stack trace
    pub stack_trace: Option<Vec<String>>,
    /// Model state
    pub model_state: ModelState,
    /// Environmental factors
    pub environment: EnvironmentInfo,
}

/// Performance issue
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceIssue {
    /// Issue type
    pub issue_type: PerformanceIssueType,
    /// Affected components
    pub affected_components: Vec<String>,
    /// Performance impact
    pub impact: f32,
    /// Recommended optimizations
    pub optimizations: Vec<String>,
}

/// Performance issue types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PerformanceIssueType {
    SlowLayer,
    MemoryInefficiency,
    ComputeBottleneck,
    IOBottleneck,
    SynchronizationOverhead,
}

/// Model health metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelHealthMetrics {
    /// Overall health score (0-1)
    pub overall_health: f32,
    /// Gradient health
    pub gradient_health: f32,
    /// Activation health
    pub activation_health: f32,
    /// Memory health
    pub memory_health: f32,
    /// Performance health
    pub performance_health: f32,
    /// Stability indicators
    pub stability_indicators: StabilityIndicators,
}

/// Stability indicators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StabilityIndicators {
    /// Numerical stability
    pub numerical_stability: f32,
    /// Training stability
    pub training_stability: f32,
    /// Memory stability
    pub memory_stability: f32,
    /// Convergence indicators
    pub convergence_indicators: ConvergenceIndicators,
}

/// Convergence indicators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConvergenceIndicators {
    /// Loss convergence
    pub loss_convergence: f32,
    /// Gradient convergence
    pub gradient_convergence: f32,
    /// Parameter convergence
    pub parameter_convergence: f32,
    /// Validation convergence
    pub validation_convergence: f32,
}

// Interactive debugging structures
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Breakpoint {
    /// Breakpoint ID
    pub id: String,
    /// Location
    pub location: BreakpointLocation,
    /// Condition
    pub condition: Option<String>,
    /// Hit count
    pub hit_count: usize,
    /// Enabled flag
    pub enabled: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BreakpointLocation {
    Layer(String),
    Operation(String),
    Line(usize),
    Function(String),
}

#[derive(Debug, Clone)]
pub struct StackFrame {
    /// Function name
    pub function: String,
    /// File path
    pub file: Option<PathBuf>,
    /// Line number
    pub line: Option<usize>,
    /// Local variables
    pub locals: HashMap<String, String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DebugCommand {
    Continue,
    Step,
    StepInto,
    StepOut,
    Inspect(String),
    Evaluate(String),
    SetBreakpoint(BreakpointLocation),
    RemoveBreakpoint(String),
    ListVariables,
    PrintTensor(String),
    SaveTensor(String, PathBuf),
    Exit,
}

#[derive(Debug)]
pub struct VariableInspector {
    /// Current variables
    variables: HashMap<String, VariableInfo>,
    /// Watch list
    watch_list: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct VariableInfo {
    /// Variable name
    pub name: String,
    /// Variable type
    pub var_type: String,
    /// Variable value (string representation)
    pub value: String,
    /// Memory address
    pub address: Option<String>,
    /// Size in bytes
    pub size: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StepMode {
    Normal,
    StepInto,
    StepOver,
    StepOut,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelState {
    Training,
    Evaluation,
    Inference,
    Paused,
    Error,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnvironmentInfo {
    /// Device information
    pub device: String,
    /// Memory usage
    pub memory_usage: u64,
    /// CPU usage
    pub cpu_usage: f32,
    /// Temperature
    pub temperature: Option<f32>,
}

// Detection utilities
pub struct GradientExplosionDetector {
    /// Explosion threshold
    threshold: f32,
    /// History window
    history: Vec<f32>,
    /// Window size
    window_size: usize,
}

pub struct GradientVanishingDetector {
    /// Vanishing threshold
    threshold: f32,
    /// Layer gradients
    layer_gradients: HashMap<String, Vec<f32>>,
}

pub struct DeadNeuronDetector {
    /// Activation threshold
    threshold: f32,
    /// Monitoring window
    window: usize,
    /// Neuron states
    neuron_states: HashMap<String, Vec<bool>>,
}

pub struct ActivationDistributionAnalyzer {
    /// Distribution cache
    distributions: HashMap<String, ActivationDistribution>,
    /// Analysis configuration
    config: DistributionAnalysisConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributionAnalysisConfig {
    /// Enable distribution fitting
    pub enable_fitting: bool,
    /// Confidence level
    pub confidence_level: f32,
    /// Sample size
    pub sample_size: usize,
}

impl Default for DebugConfig {
    fn default() -> Self {
        Self {
            enable_tensor_inspection: true,
            enable_gradient_debugging: true,
            enable_activation_analysis: true,
            enable_nan_detection: true,
            enable_gradient_explosion_detection: true,
            max_tensor_inspection_size: 1000000,
            debug_dir: PathBuf::from("./debug"),
            enable_interactive: false,
            tensor_display_precision: 4,
            enable_layer_debugging: true,
        }
    }
}

impl ModelDebugger {
    /// Create a new model debugger
    pub fn new(config: DebugConfig) -> Result<Self> {
        std::fs::create_dir_all(&config.debug_dir)?;

        Ok(Self {
            active_sessions: HashMap::new(),
            hooks: DebugHooksRegistry::new(),
            tensor_inspector: TensorInspector::new(TensorInspectionConfig::default()),
            gradient_debugger: GradientDebugger::new(),
            activation_analyzer: ActivationAnalyzer::new(),
            config,
        })
    }

    /// Start a debugging session
    pub fn start_debugging(&mut self, model_id: &str) -> Result<String> {
        let session_id = format!(
            "debug_{}_{}",
            model_id,
            SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("system time should be after UNIX epoch")
                .as_secs()
        );

        let session = DebugSession {
            session_id: session_id.clone(),
            model_id: model_id.to_string(),
            start_time: SystemTime::now(),
            active_hooks: Vec::new(),
            debug_info: DebugInfo::new(),
            interactive_state: if self.config.enable_interactive {
                Some(InteractiveDebugState::new())
            } else {
                None
            },
            statistics: DebugStatistics::default(),
        };

        self.active_sessions.insert(session_id.clone(), session);

        println!("Started debugging session: {}", session_id);
        Ok(session_id)
    }

    /// Stop debugging and generate report
    pub fn stop_debugging(&mut self, session_id: &str) -> Result<DebugReport> {
        let session = self.active_sessions.remove(session_id).ok_or_else(|| {
            TorshError::InvalidArgument(format!("Unknown session: {}", session_id))
        })?;

        let report = self.generate_debug_report(session)?;

        // Save debug report
        self.save_debug_report(session_id, &report)?;

        println!("Completed debugging session: {}", session_id);
        Ok(report)
    }

    /// Add a debug hook
    pub fn add_hook(&mut self, session_id: &str, hook: DebugHook) -> Result<()> {
        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session.active_hooks.push(hook.clone());
        }

        self.hooks.register_hook(hook)?;
        Ok(())
    }

    /// Remove a debug hook
    pub fn remove_hook(&mut self, session_id: &str, hook_id: &str) -> Result<()> {
        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session.active_hooks.retain(|h| h.hook_id != hook_id);
        }

        self.hooks.unregister_hook(hook_id)?;
        Ok(())
    }

    /// Inspect a tensor
    pub fn inspect_tensor(
        &mut self,
        session_id: &str,
        tensor_name: &str,
        tensor_data: &[f32],
        shape: &[usize],
    ) -> Result<TensorSnapshot> {
        let snapshot = self
            .tensor_inspector
            .inspect(tensor_name, tensor_data, shape)?;

        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session
                .debug_info
                .tensor_snapshots
                .entry(tensor_name.to_string())
                .or_insert_with(Vec::new)
                .push(snapshot.clone());
            session.statistics.tensors_inspected += 1;
        }

        Ok(snapshot)
    }

    /// Analyze gradients
    pub fn analyze_gradients(
        &mut self,
        session_id: &str,
        parameter_name: &str,
        gradients: &[f32],
    ) -> Result<GradientInfo> {
        let gradient_info = self.gradient_debugger.analyze(parameter_name, gradients)?;

        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session
                .debug_info
                .gradient_info
                .entry(parameter_name.to_string())
                .or_insert_with(Vec::new)
                .push(gradient_info.clone());
        }

        Ok(gradient_info)
    }

    /// Analyze activations
    pub fn analyze_activations(
        &mut self,
        session_id: &str,
        layer_name: &str,
        activations: &[f32],
    ) -> Result<ActivationPattern> {
        let pattern = self.activation_analyzer.analyze(layer_name, activations)?;

        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session
                .debug_info
                .activation_patterns
                .insert(layer_name.to_string(), pattern.clone());
        }

        Ok(pattern)
    }

    /// Detect anomalies
    pub fn detect_anomalies(&mut self, session_id: &str) -> Result<Vec<Anomaly>> {
        let mut anomalies = Vec::new();

        if let Some(session) = self.active_sessions.get(session_id) {
            // Check for NaN/Inf values
            for (tensor_name, snapshots) in &session.debug_info.tensor_snapshots {
                if let Some(latest) = snapshots.last() {
                    if latest.statistics.nan_count > 0 {
                        anomalies.push(Anomaly {
                            anomaly_type: AnomalyType::NaNValues,
                            severity: Severity::Critical,
                            location: tensor_name.clone(),
                            description: format!(
                                "Found {} NaN values in tensor {}",
                                latest.statistics.nan_count, tensor_name
                            ),
                            timestamp: SystemTime::now(),
                            suggested_fixes: vec![
                                "Check for division by zero".to_string(),
                                "Verify input data quality".to_string(),
                                "Add gradient clipping".to_string(),
                            ],
                            context: AnomalyContext {
                                related_tensors: vec![tensor_name.clone()],
                                stack_trace: None,
                                model_state: ModelState::Training,
                                environment: EnvironmentInfo {
                                    device: "CPU".to_string(),
                                    memory_usage: 1024 * 1024 * 100,
                                    cpu_usage: 75.0,
                                    temperature: None,
                                },
                            },
                        });
                    }

                    if latest.statistics.inf_count > 0 {
                        anomalies.push(Anomaly {
                            anomaly_type: AnomalyType::InfiniteValues,
                            severity: Severity::High,
                            location: tensor_name.clone(),
                            description: format!(
                                "Found {} infinite values in tensor {}",
                                latest.statistics.inf_count, tensor_name
                            ),
                            timestamp: SystemTime::now(),
                            suggested_fixes: vec![
                                "Check for overflow in computations".to_string(),
                                "Reduce learning rate".to_string(),
                                "Add numerical stability checks".to_string(),
                            ],
                            context: AnomalyContext {
                                related_tensors: vec![tensor_name.clone()],
                                stack_trace: None,
                                model_state: ModelState::Training,
                                environment: EnvironmentInfo {
                                    device: "CPU".to_string(),
                                    memory_usage: 1024 * 1024 * 100,
                                    cpu_usage: 75.0,
                                    temperature: None,
                                },
                            },
                        });
                    }
                }
            }

            // Check for gradient explosion
            for (param_name, gradient_infos) in &session.debug_info.gradient_info {
                if let Some(latest) = gradient_infos.last() {
                    if latest.gradient_stats.norm > 10.0 {
                        anomalies.push(Anomaly {
                            anomaly_type: AnomalyType::GradientExplosion,
                            severity: Severity::High,
                            location: param_name.clone(),
                            description: format!(
                                "Gradient explosion detected: norm = {:.4}",
                                latest.gradient_stats.norm
                            ),
                            timestamp: SystemTime::now(),
                            suggested_fixes: vec![
                                "Add gradient clipping".to_string(),
                                "Reduce learning rate".to_string(),
                                "Check for numerical instability".to_string(),
                            ],
                            context: AnomalyContext {
                                related_tensors: vec![param_name.clone()],
                                stack_trace: None,
                                model_state: ModelState::Training,
                                environment: EnvironmentInfo {
                                    device: "CPU".to_string(),
                                    memory_usage: 1024 * 1024 * 100,
                                    cpu_usage: 75.0,
                                    temperature: None,
                                },
                            },
                        });
                    }
                }
            }
        }

        // Update session with detected anomalies
        if let Some(session) = self.active_sessions.get_mut(session_id) {
            session.debug_info.anomalies.extend(anomalies.clone());
            session.statistics.anomalies_detected += anomalies.len();
        }

        Ok(anomalies)
    }

    /// Generate debug report
    fn generate_debug_report(&self, session: DebugSession) -> Result<DebugReport> {
        let duration = SystemTime::now()
            .duration_since(session.start_time)
            .unwrap_or_default();

        Ok(DebugReport {
            session_info: DebugSessionInfo {
                session_id: session.session_id,
                model_id: session.model_id,
                start_time: session.start_time,
                end_time: SystemTime::now(),
                duration,
            },
            debug_info: session.debug_info,
            summary: DebugSummary {
                total_anomalies: session.statistics.anomalies_detected,
                critical_issues: 0,      // Would be calculated
                performance_score: 0.85, // Would be calculated
                stability_score: 0.78,   // Would be calculated
                recommendations: vec![
                    "Consider adding gradient clipping".to_string(),
                    "Monitor memory usage more closely".to_string(),
                ],
            },
            statistics: session.statistics,
        })
    }

    fn save_debug_report(&self, session_id: &str, report: &DebugReport) -> Result<()> {
        let file_path = self
            .config
            .debug_dir
            .join(format!("{}_debug_report.json", session_id));
        let content = serde_json::to_string_pretty(report)?;
        std::fs::write(file_path, content)?;
        Ok(())
    }
}

/// Debug report structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DebugReport {
    /// Session information
    pub session_info: DebugSessionInfo,
    /// Debug information collected
    pub debug_info: DebugInfo,
    /// Debug statistics
    pub statistics: DebugStatistics,
    /// Summary and recommendations
    pub summary: DebugSummary,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DebugSessionInfo {
    pub session_id: String,
    pub model_id: String,
    pub start_time: SystemTime,
    pub end_time: SystemTime,
    pub duration: Duration,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DebugSummary {
    pub total_anomalies: usize,
    pub critical_issues: usize,
    pub performance_score: f32,
    pub stability_score: f32,
    pub recommendations: Vec<String>,
}

// Implementation for component structs
impl DebugInfo {
    fn new() -> Self {
        Self {
            tensor_snapshots: HashMap::new(),
            gradient_info: HashMap::new(),
            activation_patterns: HashMap::new(),
            anomalies: Vec::new(),
            performance_issues: Vec::new(),
            health_metrics: ModelHealthMetrics::default(),
        }
    }
}

impl InteractiveDebugState {
    fn new() -> Self {
        Self {
            current_breakpoint: None,
            execution_stack: Vec::new(),
            available_commands: vec![
                DebugCommand::Continue,
                DebugCommand::Step,
                DebugCommand::StepInto,
                DebugCommand::StepOut,
                DebugCommand::ListVariables,
            ],
            variable_inspector: VariableInspector::new(),
            step_mode: StepMode::Normal,
        }
    }
}

impl VariableInspector {
    fn new() -> Self {
        Self {
            variables: HashMap::new(),
            watch_list: Vec::new(),
        }
    }
}

impl DebugHooksRegistry {
    fn new() -> Self {
        Self {
            hooks: HashMap::new(),
            execution_order: Vec::new(),
        }
    }

    fn register_hook(&mut self, hook: DebugHook) -> Result<()> {
        self.hooks.insert(hook.hook_id.clone(), hook.clone());
        self.execution_order.push(hook.hook_id);
        Ok(())
    }

    fn unregister_hook(&mut self, hook_id: &str) -> Result<()> {
        self.hooks.remove(hook_id);
        self.execution_order.retain(|id| id != hook_id);
        Ok(())
    }
}

impl TensorInspector {
    fn new(config: TensorInspectionConfig) -> Self {
        Self {
            config,
            tensor_cache: HashMap::new(),
        }
    }

    fn inspect(
        &mut self,
        tensor_name: &str,
        data: &[f32],
        shape: &[usize],
    ) -> Result<TensorSnapshot> {
        let statistics = self.calculate_statistics(data);

        let (sample_values, full_values) = if data.len() > self.config.max_display_values {
            (self.sample_values(data), None)
        } else {
            (Vec::new(), Some(data.to_vec()))
        };

        Ok(TensorSnapshot {
            timestamp: SystemTime::now(),
            shape: shape.to_vec(),
            dtype: "f32".to_string(),
            device: "CPU".to_string(),
            statistics,
            sample_values,
            full_values,
            metadata: TensorMetadata {
                name: tensor_name.to_string(),
                producer_layer: None,
                requires_grad: false,
                memory_usage: std::mem::size_of_val(data) as u64,
                created_at: SystemTime::now(),
            },
        })
    }

    fn calculate_statistics(&self, data: &[f32]) -> TensorStatistics {
        let len = data.len();
        if len == 0 {
            return TensorStatistics::default();
        }

        let sum: f32 = data.iter().sum();
        let mean = sum / len as f32;

        let variance: f32 = data.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / len as f32;
        let std = variance.sqrt();

        let min = data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max = data.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));

        let nan_count = data.iter().filter(|&&x| x.is_nan()).count();
        let inf_count = data.iter().filter(|&&x| x.is_infinite()).count();
        let zero_count = data.iter().filter(|&&x| x == 0.0).count();

        let sparsity = zero_count as f32 / len as f32;

        TensorStatistics {
            mean,
            std,
            min,
            max,
            nan_count,
            inf_count,
            zero_count,
            sparsity,
            distribution: ValueDistribution::default(),
            gradient_norm: None,
        }
    }

    fn sample_values(&self, data: &[f32]) -> Vec<f32> {
        let sample_size = self.config.sample_size.min(data.len());
        let step = data.len() / sample_size;
        (0..sample_size).map(|i| data[i * step]).collect()
    }
}

impl GradientDebugger {
    fn new() -> Self {
        Self {
            gradient_stats: HashMap::new(),
            explosion_detector: GradientExplosionDetector::new(10.0),
            vanishing_detector: GradientVanishingDetector::new(1e-6),
        }
    }

    fn analyze(&mut self, parameter_name: &str, gradients: &[f32]) -> Result<GradientInfo> {
        let stats = self.calculate_gradient_statistics(gradients);
        let norm = stats.norm;

        Ok(GradientInfo {
            parameter_name: parameter_name.to_string(),
            gradient_flow: GradientFlow {
                direction: FlowDirection::Backward,
                magnitude: norm,
                bottlenecks: vec![],
                efficiency: 0.85,
            },
            update_magnitude: norm,
            gradient_stats: stats,
            clipped: false,
        })
    }

    fn calculate_gradient_statistics(&self, gradients: &[f32]) -> GradientStatistics {
        if gradients.is_empty() {
            return GradientStatistics::default();
        }

        let norm = gradients.iter().map(|x| x * x).sum::<f32>().sqrt();
        let mean = gradients.iter().sum::<f32>() / gradients.len() as f32;
        let variance =
            gradients.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / gradients.len() as f32;
        let std = variance.sqrt();
        let max = gradients.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
        let min = gradients.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let zero_count = gradients.iter().filter(|&&x| x == 0.0).count();
        let sparsity = zero_count as f32 / gradients.len() as f32;

        GradientStatistics {
            norm,
            mean,
            std,
            max,
            min,
            zero_count,
            sparsity,
        }
    }
}

impl ActivationAnalyzer {
    fn new() -> Self {
        Self {
            activation_stats: HashMap::new(),
            dead_neuron_detector: DeadNeuronDetector::new(0.01),
            distribution_analyzer: ActivationDistributionAnalyzer::new(),
        }
    }

    fn analyze(&mut self, layer_name: &str, activations: &[f32]) -> Result<ActivationPattern> {
        let stats = self.calculate_activation_statistics(activations);
        let dead_neurons = self.dead_neuron_detector.detect(layer_name, activations);
        let distribution = self.distribution_analyzer.analyze(activations);

        Ok(ActivationPattern {
            layer_name: layer_name.to_string(),
            activation_stats: stats,
            dead_neurons,
            saturated_neurons: vec![], // Would implement saturation detection
            distribution,
        })
    }

    fn calculate_activation_statistics(&self, activations: &[f32]) -> ActivationStatistics {
        if activations.is_empty() {
            return ActivationStatistics::default();
        }

        let mean = activations.iter().sum::<f32>() / activations.len() as f32;
        let variance =
            activations.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / activations.len() as f32;
        let min = activations.iter().fold(f32::INFINITY, |a, &b| a.min(b));
        let max = activations.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
        let active_count = activations.iter().filter(|&&x| x > 0.01).count();
        let active_percentage = active_count as f32 / activations.len() as f32;

        ActivationStatistics {
            mean_activation: mean,
            activation_variance: variance,
            activation_range: (min, max),
            active_percentage,
            entropy: 0.0, // Would calculate entropy
        }
    }
}

// Component implementations
impl GradientExplosionDetector {
    fn new(threshold: f32) -> Self {
        Self {
            threshold,
            history: Vec::new(),
            window_size: 10,
        }
    }
}

impl GradientVanishingDetector {
    fn new(threshold: f32) -> Self {
        Self {
            threshold,
            layer_gradients: HashMap::new(),
        }
    }
}

impl DeadNeuronDetector {
    fn new(threshold: f32) -> Self {
        Self {
            threshold,
            window: 100,
            neuron_states: HashMap::new(),
        }
    }

    fn detect(&mut self, _layer_name: &str, activations: &[f32]) -> Vec<usize> {
        activations
            .iter()
            .enumerate()
            .filter_map(|(i, &activation)| {
                if activation.abs() < self.threshold {
                    Some(i)
                } else {
                    None
                }
            })
            .collect()
    }
}

impl ActivationDistributionAnalyzer {
    fn new() -> Self {
        Self {
            distributions: HashMap::new(),
            config: DistributionAnalysisConfig::default(),
        }
    }

    fn analyze(&mut self, _activations: &[f32]) -> ActivationDistribution {
        // Simple implementation - would use proper statistical analysis
        ActivationDistribution {
            distribution_type: DistributionType::Normal,
            parameters: HashMap::new(),
            goodness_of_fit: 0.85,
        }
    }
}

// Default implementations
impl Default for TensorStatistics {
    fn default() -> Self {
        Self {
            mean: 0.0,
            std: 0.0,
            min: 0.0,
            max: 0.0,
            nan_count: 0,
            inf_count: 0,
            zero_count: 0,
            sparsity: 0.0,
            distribution: ValueDistribution::default(),
            gradient_norm: None,
        }
    }
}

impl Default for ValueDistribution {
    fn default() -> Self {
        Self {
            histogram: vec![],
            bin_edges: vec![],
            percentiles: HashMap::new(),
            kurtosis: 0.0,
            skewness: 0.0,
        }
    }
}

impl Default for GradientStatistics {
    fn default() -> Self {
        Self {
            norm: 0.0,
            mean: 0.0,
            std: 0.0,
            max: 0.0,
            min: 0.0,
            zero_count: 0,
            sparsity: 0.0,
        }
    }
}

impl Default for ActivationStatistics {
    fn default() -> Self {
        Self {
            mean_activation: 0.0,
            activation_variance: 0.0,
            activation_range: (0.0, 0.0),
            active_percentage: 0.0,
            entropy: 0.0,
        }
    }
}

impl Default for ModelHealthMetrics {
    fn default() -> Self {
        Self {
            overall_health: 1.0,
            gradient_health: 1.0,
            activation_health: 1.0,
            memory_health: 1.0,
            performance_health: 1.0,
            stability_indicators: StabilityIndicators::default(),
        }
    }
}

impl Default for StabilityIndicators {
    fn default() -> Self {
        Self {
            numerical_stability: 1.0,
            training_stability: 1.0,
            memory_stability: 1.0,
            convergence_indicators: ConvergenceIndicators::default(),
        }
    }
}

impl Default for ConvergenceIndicators {
    fn default() -> Self {
        Self {
            loss_convergence: 1.0,
            gradient_convergence: 1.0,
            parameter_convergence: 1.0,
            validation_convergence: 1.0,
        }
    }
}

impl Default for DebugStatistics {
    fn default() -> Self {
        Self {
            hooks_triggered: 0,
            anomalies_detected: 0,
            tensors_inspected: 0,
            total_debug_time: Duration::from_secs(0),
            debug_overhead: 0.0,
        }
    }
}

impl Default for TensorInspectionConfig {
    fn default() -> Self {
        Self {
            max_display_values: 100,
            enable_histograms: true,
            histogram_bins: 50,
            enable_distribution_analysis: true,
            sample_size: 1000,
        }
    }
}

impl Default for DistributionAnalysisConfig {
    fn default() -> Self {
        Self {
            enable_fitting: true,
            confidence_level: 0.95,
            sample_size: 10000,
        }
    }
}

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

    #[test]
    fn test_debugger_creation() {
        let config = DebugConfig::default();
        let debugger = ModelDebugger::new(config);
        assert!(debugger.is_ok());
    }

    #[test]
    fn test_debugging_session() {
        let config = DebugConfig::default();
        let mut debugger = ModelDebugger::new(config).unwrap();

        let session_id = debugger.start_debugging("test_model").unwrap();
        assert!(!session_id.is_empty());

        let result = debugger.stop_debugging(&session_id);
        assert!(result.is_ok());
    }

    #[test]
    fn test_tensor_inspection() {
        let config = DebugConfig::default();
        let mut debugger = ModelDebugger::new(config).unwrap();

        let session_id = debugger.start_debugging("test_model").unwrap();

        let test_data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
        let shape = vec![5];

        let snapshot = debugger
            .inspect_tensor(&session_id, "test_tensor", &test_data, &shape)
            .unwrap();

        assert_eq!(snapshot.shape, shape);
        assert_eq!(snapshot.statistics.mean, 3.0);
    }

    #[test]
    fn test_anomaly_detection() {
        let config = DebugConfig::default();
        let mut debugger = ModelDebugger::new(config).unwrap();

        let session_id = debugger.start_debugging("test_model").unwrap();

        // Add tensor with NaN values
        let bad_data = vec![1.0, 2.0, f32::NAN, 4.0, 5.0];
        let shape = vec![5];
        debugger
            .inspect_tensor(&session_id, "bad_tensor", &bad_data, &shape)
            .unwrap();

        let anomalies = debugger.detect_anomalies(&session_id).unwrap();
        assert!(!anomalies.is_empty());

        let nan_anomaly = anomalies
            .iter()
            .find(|a| matches!(a.anomaly_type, AnomalyType::NaNValues));
        assert!(nan_anomaly.is_some());
    }
}