voirs_recognizer/training/

mod.rs

//! Comprehensive Model Training and Fine-tuning Framework
//!
//! This module provides advanced training capabilities for custom ASR models including:
//! - Transfer learning from pre-trained models
//! - Domain-specific adaptation
//! - Few-shot learning capabilities
//! - Continuous learning from user corrections
//! - Federated learning support
//! - Automated hyperparameter optimization

#![allow(clippy::unused_async)] // Functions are async for API consistency and future I/O operations

pub mod transfer_learning;
// Additional modules will be implemented in future versions
// pub mod domain_adaptation;
// pub mod few_shot;
// pub mod continuous_learning;
// pub mod federated;
// pub mod hyperparameter_tuning;
// pub mod data_pipeline;
// pub mod metrics;
// pub mod config;

use crate::RecognitionError;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::time::{Duration, SystemTime};
use tokio::sync::RwLock;
use voirs_sdk::AudioBuffer;

/// Training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingConfig {
    /// Transfer learning configuration
    pub transfer_learning: transfer_learning::TransferLearningConfig,
    /// Maximum epochs for training
    pub max_epochs: u32,
    /// Learning rate
    pub learning_rate: f32,
    /// Batch size
    pub batch_size: usize,
}

impl Default for TrainingConfig {
    fn default() -> Self {
        Self {
            transfer_learning: transfer_learning::TransferLearningConfig::default(),
            max_epochs: 100,
            learning_rate: 0.001,
            batch_size: 32,
        }
    }
}

/// Comprehensive training manager that coordinates all training activities
pub struct TrainingManager {
    /// Transfer learning coordinator
    transfer_learning: transfer_learning::TransferLearningCoordinator,
    /// Current training configuration
    config: TrainingConfig,
    /// Training session state
    session_state: RwLock<TrainingSessionState>,
}

/// State of the current training session
#[derive(Debug, Clone)]
pub struct TrainingSessionState {
    /// Session ID
    pub session_id: String,
    /// Start time
    pub start_time: SystemTime,
    /// Current phase of training
    pub current_phase: TrainingPhase,
    /// Progress percentage (0.0 - 1.0)
    pub progress: f32,
    /// Current epoch/iteration
    pub current_epoch: u32,
    /// Total epochs planned
    pub total_epochs: u32,
    /// Training losses by epoch
    pub training_losses: Vec<f32>,
    /// Validation losses by epoch
    pub validation_losses: Vec<f32>,
    /// Current learning rate
    pub current_learning_rate: f32,
    /// Best validation score achieved
    pub best_validation_score: f32,
    /// Whether training is paused
    pub is_paused: bool,
    /// Training status
    pub status: TrainingStatus,
}

/// Different phases of training
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TrainingPhase {
    /// Initializing training environment
    Initialization,
    /// Loading and preprocessing data
    DataPreparation,
    /// Transfer learning from base model
    TransferLearning,
    /// Domain-specific fine-tuning
    DomainAdaptation,
    /// Few-shot learning optimization
    FewShotOptimization,
    /// Continuous learning from user feedback
    ContinuousLearning,
    /// Model validation and evaluation
    Validation,
    /// Model optimization and quantization
    Optimization,
    /// Final model export and deployment
    Deployment,
    /// Training completed
    Completed,
    /// Training failed
    Failed,
}

/// Training status indicators
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TrainingStatus {
    /// Training is running normally
    Running,
    /// Training is paused
    Paused,
    /// Training completed successfully
    Completed,
    /// Training failed with error
    Failed {
        /// Error message describing the failure
        error: String,
    },
    /// Training was cancelled by user
    Cancelled,
    /// Training is scheduled but not started
    Scheduled,
}

/// Training task specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingTask {
    /// Unique task identifier
    pub task_id: String,
    /// Task name/description
    pub name: String,
    /// Type of training to perform
    pub training_type: TrainingType,
    /// Input data configuration
    pub data_config: DataConfiguration,
    /// Model configuration
    pub model_config: ModelConfiguration,
    /// Training hyperparameters
    pub hyperparameters: Hyperparameters,
    /// Expected completion time
    pub estimated_duration: Duration,
    /// Priority level (1-10, higher = more important)
    pub priority: u8,
    /// Dependencies on other tasks
    pub dependencies: Vec<String>,
    /// Output configuration
    pub output_config: OutputConfiguration,
}

/// Types of training supported
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TrainingType {
    /// Full model training from scratch
    FullTraining,
    /// Transfer learning from pre-trained model
    TransferLearning {
        /// Path to base pre-trained model
        base_model_path: PathBuf,
        /// Layers to freeze during training
        freeze_layers: Vec<String>,
    },
    /// Fine-tuning specific layers
    FineTuning {
        /// Target layers to fine-tune
        target_layers: Vec<String>,
        /// Learning rate scaling factor
        learning_rate_scale: f32,
    },
    /// Domain adaptation
    DomainAdaptation {
        /// Source domain identifier
        source_domain: String,
        /// Target domain identifier
        target_domain: String,
        /// Domain adaptation strategy
        adaptation_strategy: AdaptationStrategy,
    },
    /// Few-shot learning
    FewShotLearning {
        /// Size of support set for few-shot learning
        support_set_size: usize,
        /// Meta-learning strategy to use
        meta_learning_strategy: MetaLearningStrategy,
    },
    /// Continuous learning
    ContinuousLearning {
        /// Frequency of model updates
        update_frequency: Duration,
        /// Strategy for retaining previous knowledge
        retention_strategy: RetentionStrategy,
    },
    /// Federated learning
    FederatedLearning {
        /// Federation configuration
        federation_config: FederationConfig,
        /// Aggregation strategy for federated updates
        aggregation_strategy: AggregationStrategy,
    },
}

/// Domain adaptation strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AdaptationStrategy {
    /// Gradual unfreezing of layers
    GradualUnfreezing,
    /// Domain adversarial training
    DomainAdversarial,
    /// Feature alignment
    FeatureAlignment,
    /// Curriculum learning
    CurriculumLearning,
}

/// Meta-learning strategies for few-shot learning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum MetaLearningStrategy {
    /// Model-Agnostic Meta-Learning (MAML)
    MAML,
    /// Prototypical Networks
    PrototypicalNetworks,
    /// Matching Networks
    MatchingNetworks,
    /// Relation Networks
    RelationNetworks,
}

/// Retention strategies for continuous learning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RetentionStrategy {
    /// Elastic Weight Consolidation
    ElasticWeightConsolidation,
    /// Progressive Neural Networks
    ProgressiveNeuralNetworks,
    /// Memory Replay
    MemoryReplay,
    /// `PackNet`
    PackNet,
}

/// Federation configuration for federated learning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct FederationConfig {
    /// Number of participating clients
    pub num_clients: usize,
    /// Minimum clients required for aggregation
    pub min_clients_for_aggregation: usize,
    /// Communication rounds
    pub communication_rounds: u32,
    /// Client selection strategy
    pub client_selection: ClientSelectionStrategy,
    /// Privacy settings
    pub privacy_config: PrivacyConfiguration,
}

/// Client selection strategies for federated learning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ClientSelectionStrategy {
    /// Random selection
    Random,
    /// Based on data quality
    DataQuality,
    /// Based on computational resources
    ComputationalResources,
    /// Based on communication efficiency
    CommunicationEfficiency,
}

/// Aggregation strategies for federated learning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AggregationStrategy {
    /// Federated Averaging (`FedAvg`)
    FederatedAveraging,
    /// Weighted aggregation by data size
    WeightedByDataSize,
    /// Adaptive aggregation
    Adaptive,
    /// Secure aggregation
    SecureAggregation,
}

/// Privacy configuration for federated learning
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct PrivacyConfiguration {
    /// Enable differential privacy
    pub enable_differential_privacy: bool,
    /// Privacy budget (epsilon)
    pub privacy_budget: f32,
    /// Noise multiplier for differential privacy
    pub noise_multiplier: f32,
    /// Enable secure multiparty computation
    pub enable_secure_mpc: bool,
    /// Homomorphic encryption settings
    pub homomorphic_encryption: Option<HomomorphicEncryptionConfig>,
}

/// Homomorphic encryption configuration
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct HomomorphicEncryptionConfig {
    /// Encryption scheme
    pub scheme: String,
    /// Key size
    pub key_size: usize,
    /// Noise standard deviation
    pub noise_std: f32,
}

/// Data configuration for training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataConfiguration {
    /// Training data paths
    pub training_data_paths: Vec<PathBuf>,
    /// Validation data paths
    pub validation_data_paths: Vec<PathBuf>,
    /// Test data paths
    pub test_data_paths: Vec<PathBuf>,
    /// Data preprocessing settings
    pub preprocessing: PreprocessingConfiguration,
    /// Data augmentation settings
    pub augmentation: AugmentationConfiguration,
    /// Batch size
    pub batch_size: usize,
    /// Number of data loading workers
    pub num_workers: usize,
    /// Data validation settings
    pub validation: DataValidationConfiguration,
}

/// Preprocessing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PreprocessingConfiguration {
    /// Target sample rate
    pub target_sample_rate: u32,
    /// Minimum audio duration in seconds
    pub min_duration_seconds: f32,
    /// Maximum audio duration in seconds
    pub max_duration_seconds: f32,
    /// Normalization settings
    pub normalize_audio: bool,
    /// Noise reduction settings
    pub noise_reduction: bool,
    /// Feature extraction settings
    pub feature_extraction: FeatureExtractionConfig,
}

/// Feature extraction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureExtractionConfig {
    /// Feature type (MFCC, Mel-spectrogram, etc.)
    pub feature_type: FeatureType,
    /// Number of features
    pub num_features: usize,
    /// Window size for STFT
    pub window_size: usize,
    /// Hop length for STFT
    pub hop_length: usize,
    /// Number of FFT points
    pub n_fft: usize,
}

/// Types of audio features
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum FeatureType {
    /// Mel-frequency cepstral coefficients
    MFCC,
    /// Mel-scale spectrogram
    MelSpectrogram,
    /// Log Mel-scale spectrogram
    LogMelSpectrogram,
    /// Raw waveform
    RawWaveform,
    /// Constant-Q transform
    ConstantQ,
    /// Chromagram
    Chromagram,
    /// Spectral centroid
    SpectralCentroid,
}

/// Data augmentation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AugmentationConfiguration {
    /// Enable time stretching
    pub time_stretching: bool,
    /// Enable pitch shifting
    pub pitch_shifting: bool,
    /// Enable noise addition
    pub noise_addition: bool,
    /// Enable reverb addition
    pub reverb_addition: bool,
    /// Enable volume augmentation
    pub volume_augmentation: bool,
    /// Enable speed perturbation
    pub speed_perturbation: bool,
    /// Augmentation probability
    pub augmentation_probability: f32,
}

/// Data validation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataValidationConfiguration {
    /// Validate audio file integrity
    pub validate_audio_integrity: bool,
    /// Check transcription quality
    pub validate_transcriptions: bool,
    /// Minimum transcription length
    pub min_transcription_length: usize,
    /// Maximum transcription length
    pub max_transcription_length: usize,
    /// Audio quality thresholds
    pub audio_quality_thresholds: AudioQualityThresholds,
}

/// Audio quality thresholds for validation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AudioQualityThresholds {
    /// Minimum signal-to-noise ratio
    pub min_snr_db: f32,
    /// Maximum total harmonic distortion
    pub max_thd_percent: f32,
    /// Minimum dynamic range
    pub min_dynamic_range_db: f32,
    /// Maximum clipping percentage
    pub max_clipping_percent: f32,
}

/// Model configuration for training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelConfiguration {
    /// Model architecture type
    pub architecture: ModelArchitecture,
    /// Model size configuration
    pub size_config: ModelSizeConfig,
    /// Layer configurations
    pub layer_configs: Vec<LayerConfiguration>,
    /// Activation functions
    pub activation_functions: HashMap<String, ActivationFunction>,
    /// Regularization settings
    pub regularization: RegularizationConfiguration,
    /// Optimization settings
    pub optimization: OptimizationConfiguration,
}

/// Supported model architectures
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ModelArchitecture {
    /// Transformer-based architecture
    Transformer {
        /// Number of transformer layers
        num_layers: usize,
        /// Number of attention heads
        num_heads: usize,
        /// Model dimension
        d_model: usize,
        /// Feed-forward dimension
        d_ff: usize,
    },
    /// Conformer architecture
    Conformer {
        /// Number of conformer blocks
        num_blocks: usize,
        /// Encoder dimension
        encoder_dim: usize,
        /// Number of attention heads
        attention_heads: usize,
        /// Convolutional kernel size
        conv_kernel_size: usize,
    },
    /// `Wav2Vec2` architecture
    Wav2Vec2 {
        /// Number of feature extractor layers
        feature_extractor_layers: usize,
        /// Number of transformer layers
        transformer_layers: usize,
        /// Embedding dimension
        embedding_dim: usize,
    },
    /// Whisper architecture
    Whisper {
        /// Number of encoder layers
        encoder_layers: usize,
        /// Number of decoder layers
        decoder_layers: usize,
        /// Model dimension
        d_model: usize,
        /// Number of attention heads
        num_heads: usize,
    },
    /// Custom architecture
    Custom {
        /// Path to custom configuration file
        config_path: PathBuf,
    },
}

/// Model size configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelSizeConfig {
    /// Total number of parameters
    pub total_parameters: usize,
    /// Memory footprint in bytes
    pub memory_footprint: usize,
    /// Model depth (number of layers)
    pub depth: usize,
    /// Model width (hidden dimensions)
    pub width: usize,
}

/// Layer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerConfiguration {
    /// Layer name
    pub name: String,
    /// Layer type
    pub layer_type: LayerType,
    /// Input dimensions
    pub input_dims: Vec<usize>,
    /// Output dimensions
    pub output_dims: Vec<usize>,
    /// Layer-specific parameters
    pub parameters: HashMap<String, LayerParameter>,
    /// Whether layer is trainable
    pub trainable: bool,
}

/// Types of neural network layers
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum LayerType {
    /// Linear/Dense layer
    Linear,
    /// Convolutional layer
    Conv1d,
    /// Multi-head attention layer
    MultiHeadAttention,
    /// Feed-forward layer
    FeedForward,
    /// Normalization layer
    LayerNorm,
    /// Dropout layer
    Dropout,
    /// Activation layer
    Activation,
    /// Embedding layer
    Embedding,
    /// LSTM layer
    LSTM,
    /// GRU layer
    GRU,
    /// Custom layer
    Custom {
        /// Custom layer class name
        class_name: String,
    },
}

/// Layer parameter values
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerParameter {
    /// Integer parameter
    Int(i64),
    /// Float parameter
    Float(f64),
    /// String parameter
    String(String),
    /// Boolean parameter
    Bool(bool),
    /// List of parameters
    List(Vec<LayerParameter>),
}

/// Activation function types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ActivationFunction {
    /// `ReLU` activation
    ReLU,
    /// GELU activation
    GELU,
    /// Swish activation
    Swish,
    /// Tanh activation
    Tanh,
    /// Sigmoid activation
    Sigmoid,
    /// Softmax activation
    Softmax,
    /// `LeakyReLU` activation
    LeakyReLU {
        /// Negative slope coefficient
        negative_slope: f32,
    },
    /// ELU activation
    ELU {
        /// Alpha parameter for ELU
        alpha: f32,
    },
}

/// Regularization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegularizationConfiguration {
    /// L1 regularization weight
    pub l1_weight: f32,
    /// L2 regularization weight
    pub l2_weight: f32,
    /// Dropout rate
    pub dropout_rate: f32,
    /// Weight decay
    pub weight_decay: f32,
    /// Gradient clipping threshold
    pub gradient_clip_norm: f32,
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig,
}

/// Early stopping configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EarlyStoppingConfig {
    /// Enable early stopping
    pub enabled: bool,
    /// Metric to monitor for early stopping
    pub monitor_metric: String,
    /// Patience (epochs to wait)
    pub patience: u32,
    /// Minimum improvement threshold
    pub min_delta: f32,
    /// Mode (min or max)
    pub mode: EarlyStoppingMode,
}

/// Early stopping mode
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum EarlyStoppingMode {
    /// Stop when metric stops decreasing
    Min,
    /// Stop when metric stops increasing
    Max,
}

/// Optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationConfiguration {
    /// Optimizer type
    pub optimizer: OptimizerType,
    /// Learning rate scheduler
    pub lr_scheduler: LearningRateScheduler,
    /// Loss function
    pub loss_function: LossFunction,
    /// Gradient accumulation steps
    pub gradient_accumulation_steps: u32,
    /// Mixed precision training
    pub mixed_precision: bool,
    /// Model parallelism settings
    pub model_parallelism: ModelParallelismConfig,
}

/// Optimizer types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizerType {
    /// Adam optimizer
    Adam {
        /// Learning rate
        lr: f32,
        /// Beta1 parameter
        beta1: f32,
        /// Beta2 parameter
        beta2: f32,
        /// Epsilon for numerical stability
        eps: f32,
    },
    /// `AdamW` optimizer
    AdamW {
        /// Learning rate
        lr: f32,
        /// Beta1 parameter
        beta1: f32,
        /// Beta2 parameter
        beta2: f32,
        /// Epsilon for numerical stability
        eps: f32,
        /// Weight decay coefficient
        weight_decay: f32,
    },
    /// SGD optimizer
    SGD {
        /// Learning rate
        lr: f32,
        /// Momentum factor
        momentum: f32,
        /// Dampening for momentum
        dampening: f32,
        /// Weight decay coefficient
        weight_decay: f32,
    },
    /// `RMSprop` optimizer
    RMSprop {
        /// Learning rate
        lr: f32,
        /// Smoothing constant
        alpha: f32,
        /// Epsilon for numerical stability
        eps: f32,
        /// Weight decay coefficient
        weight_decay: f32,
    },
}

/// Learning rate scheduler types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum LearningRateScheduler {
    /// Constant learning rate
    Constant,
    /// Step decay
    StepLR {
        /// Step size for decay
        step_size: u32,
        /// Decay factor
        gamma: f32,
    },
    /// Exponential decay
    ExponentialLR {
        /// Decay factor
        gamma: f32,
    },
    /// Cosine annealing
    CosineAnnealingLR {
        /// Maximum number of iterations
        t_max: u32,
        /// Minimum learning rate
        eta_min: f32,
    },
    /// Reduce on plateau
    ReduceLROnPlateau {
        /// Learning rate reduction factor
        factor: f32,
        /// Number of epochs with no improvement after which learning rate will be reduced
        patience: u32,
        /// Threshold for measuring improvement
        threshold: f32,
    },
    /// Warm-up with cosine decay
    WarmupCosine {
        /// Number of warmup steps
        warmup_steps: u32,
        /// Total number of training steps
        total_steps: u32,
    },
}

/// Loss function types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum LossFunction {
    /// Cross-entropy loss
    CrossEntropy,
    /// CTC (Connectionist Temporal Classification) loss
    CTC,
    /// Attention-based sequence loss
    AttentionSeq2Seq,
    /// Focal loss
    FocalLoss {
        /// Weight factor for class imbalance
        alpha: f32,
        /// Focusing parameter
        gamma: f32,
    },
    /// Label smoothing cross-entropy
    LabelSmoothingCrossEntropy {
        /// Smoothing factor
        smoothing: f32,
    },
    /// Custom loss function
    Custom {
        /// Path to custom loss implementation
        implementation_path: PathBuf,
    },
}

/// Model parallelism configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelParallelismConfig {
    /// Enable data parallelism
    pub enable_data_parallelism: bool,
    /// Enable model parallelism
    pub enable_model_parallelism: bool,
    /// Enable pipeline parallelism
    pub enable_pipeline_parallelism: bool,
    /// Number of pipeline stages
    pub pipeline_stages: usize,
    /// Tensor parallelism degree
    pub tensor_parallel_degree: usize,
}

/// Training hyperparameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Hyperparameters {
    /// Number of training epochs
    pub epochs: u32,
    /// Learning rate
    pub learning_rate: f32,
    /// Batch size
    pub batch_size: usize,
    /// Warmup steps
    pub warmup_steps: u32,
    /// Evaluation frequency (epochs)
    pub eval_frequency: u32,
    /// Save frequency (epochs)
    pub save_frequency: u32,
    /// Logging frequency (steps)
    pub log_frequency: u32,
    /// Random seed for reproducibility
    pub random_seed: u64,
    /// Additional hyperparameters
    pub additional: HashMap<String, HyperparameterValue>,
}

/// Hyperparameter value types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum HyperparameterValue {
    /// Integer value
    Int(i64),
    /// Float value
    Float(f64),
    /// String value
    String(String),
    /// Boolean value
    Bool(bool),
    /// List of values
    List(Vec<HyperparameterValue>),
}

/// Output configuration for training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OutputConfiguration {
    /// Output directory for models and artifacts
    pub output_dir: PathBuf,
    /// Model export formats
    pub export_formats: Vec<ModelExportFormat>,
    /// Whether to save intermediate checkpoints
    pub save_checkpoints: bool,
    /// Checkpoint frequency (epochs)
    pub checkpoint_frequency: u32,
    /// Maximum number of checkpoints to keep
    pub max_checkpoints: usize,
    /// Save training logs
    pub save_logs: bool,
    /// Save training metrics
    pub save_metrics: bool,
    /// Generate training reports
    pub generate_reports: bool,
}

/// Model export formats
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ModelExportFormat {
    /// `PyTorch` format
    PyTorch,
    /// ONNX format
    ONNX,
    /// TensorFlow `SavedModel`
    TensorFlowSavedModel,
    /// TensorFlow Lite
    TensorFlowLite,
    /// `CoreML`
    CoreML,
    /// Quantized ONNX
    QuantizedONNX,
    /// Custom format
    Custom {
        /// Name of the custom export format
        format_name: String,
    },
}

impl TrainingManager {
    /// Create a new training manager with default configuration
    pub async fn new() -> Result<Self, RecognitionError> {
        Self::with_config(TrainingConfig::default()).await
    }

    /// Create a new training manager with custom configuration
    pub async fn with_config(config: TrainingConfig) -> Result<Self, RecognitionError> {
        let transfer_learning =
            transfer_learning::TransferLearningCoordinator::new(&config.transfer_learning).await?;

        let session_state = TrainingSessionState {
            session_id: uuid::Uuid::new_v4().to_string(),
            start_time: SystemTime::now(),
            current_phase: TrainingPhase::Initialization,
            progress: 0.0,
            current_epoch: 0,
            total_epochs: 0,
            training_losses: Vec::new(),
            validation_losses: Vec::new(),
            current_learning_rate: 0.0,
            best_validation_score: f32::NEG_INFINITY,
            is_paused: false,
            status: TrainingStatus::Scheduled,
        };

        Ok(Self {
            transfer_learning,
            config,
            session_state: RwLock::new(session_state),
        })
    }

    /// Start a training task
    pub async fn start_training(&self, task: TrainingTask) -> Result<String, RecognitionError> {
        let mut state = self.session_state.write().await;
        state.session_id = task.task_id.clone();
        state.status = TrainingStatus::Running;
        state.current_phase = TrainingPhase::Initialization;
        state.total_epochs = task.hyperparameters.epochs;
        drop(state);

        // Start training based on task type
        let training_type = task.training_type.clone();
        match training_type {
            TrainingType::TransferLearning { .. } => {
                self.transfer_learning.start_training(task).await
            }
            TrainingType::DomainAdaptation {
                source_domain,
                target_domain,
                adaptation_strategy,
            } => {
                self.start_domain_adaptation(
                    task,
                    source_domain,
                    target_domain,
                    adaptation_strategy,
                )
                .await
            }
            TrainingType::FewShotLearning {
                support_set_size,
                meta_learning_strategy,
            } => {
                self.start_few_shot_learning(task, support_set_size, meta_learning_strategy)
                    .await
            }
            TrainingType::ContinuousLearning {
                update_frequency,
                retention_strategy,
            } => {
                self.start_continuous_learning(task, update_frequency, retention_strategy)
                    .await
            }
            TrainingType::FederatedLearning {
                federation_config,
                aggregation_strategy,
            } => {
                self.start_federated_learning(task, federation_config, aggregation_strategy)
                    .await
            }
            _ => Err(RecognitionError::TrainingError {
                message: "Unsupported training type".to_string(),
                source: None,
            }),
        }
    }

    /// Get current training status
    pub async fn get_status(&self) -> TrainingSessionState {
        self.session_state.read().await.clone()
    }

    /// Pause training
    pub async fn pause_training(&self) -> Result<(), RecognitionError> {
        let mut state = self.session_state.write().await;
        state.is_paused = true;
        state.status = TrainingStatus::Paused;
        Ok(())
    }

    /// Resume training
    pub async fn resume_training(&self) -> Result<(), RecognitionError> {
        let mut state = self.session_state.write().await;
        state.is_paused = false;
        state.status = TrainingStatus::Running;
        Ok(())
    }

    /// Cancel training
    pub async fn cancel_training(&self) -> Result<(), RecognitionError> {
        let mut state = self.session_state.write().await;
        state.status = TrainingStatus::Cancelled;
        Ok(())
    }

    /// Get training metrics (placeholder implementation)
    pub async fn get_metrics(&self) -> Result<HashMap<String, f32>, RecognitionError> {
        // Placeholder implementation - would return actual metrics in full implementation
        Ok(HashMap::new())
    }

    /// Start domain adaptation training
    async fn start_domain_adaptation(
        &self,
        task: TrainingTask,
        source_domain: String,
        target_domain: String,
        adaptation_strategy: AdaptationStrategy,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Starting domain adaptation from {} to {} using {:?}",
            source_domain,
            target_domain,
            adaptation_strategy
        );

        // Update session state
        {
            let mut state = self.session_state.write().await;
            state.current_phase = TrainingPhase::DataPreparation;
            state.progress = 0.0;
        }

        match adaptation_strategy {
            AdaptationStrategy::GradualUnfreezing => {
                self.gradual_unfreezing_adaptation(task, source_domain, target_domain)
                    .await
            }
            AdaptationStrategy::DomainAdversarial => {
                self.domain_adversarial_adaptation(task, source_domain, target_domain)
                    .await
            }
            AdaptationStrategy::FeatureAlignment => {
                self.feature_alignment_adaptation(task, source_domain, target_domain)
                    .await
            }
            AdaptationStrategy::CurriculumLearning => {
                self.curriculum_learning_adaptation(task, source_domain, target_domain)
                    .await
            }
        }
    }

    /// Start few-shot learning training
    async fn start_few_shot_learning(
        &self,
        task: TrainingTask,
        support_set_size: usize,
        meta_learning_strategy: MetaLearningStrategy,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Starting few-shot learning with support set size {} using {:?}",
            support_set_size,
            meta_learning_strategy
        );

        // Update session state
        {
            let mut state = self.session_state.write().await;
            state.current_phase = TrainingPhase::DataPreparation;
            state.progress = 0.0;
        }

        match meta_learning_strategy {
            MetaLearningStrategy::MAML => self.maml_few_shot_learning(task, support_set_size).await,
            MetaLearningStrategy::PrototypicalNetworks => {
                self.prototypical_networks_learning(task, support_set_size)
                    .await
            }
            MetaLearningStrategy::MatchingNetworks => {
                self.matching_networks_learning(task, support_set_size)
                    .await
            }
            MetaLearningStrategy::RelationNetworks => {
                self.relation_networks_learning(task, support_set_size)
                    .await
            }
        }
    }

    /// Start continuous learning training
    async fn start_continuous_learning(
        &self,
        task: TrainingTask,
        update_frequency: Duration,
        retention_strategy: RetentionStrategy,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Starting continuous learning with update frequency {:?} using {:?}",
            update_frequency,
            retention_strategy
        );

        // Update session state
        {
            let mut state = self.session_state.write().await;
            state.current_phase = TrainingPhase::ContinuousLearning;
            state.progress = 0.0;
        }

        match retention_strategy {
            RetentionStrategy::ElasticWeightConsolidation => {
                self.ewc_continuous_learning(task, update_frequency).await
            }
            RetentionStrategy::ProgressiveNeuralNetworks => {
                self.progressive_networks_learning(task, update_frequency)
                    .await
            }
            RetentionStrategy::MemoryReplay => {
                self.memory_replay_learning(task, update_frequency).await
            }
            RetentionStrategy::PackNet => self.packnet_learning(task, update_frequency).await,
        }
    }

    /// Start federated learning training
    async fn start_federated_learning(
        &self,
        task: TrainingTask,
        federation_config: FederationConfig,
        aggregation_strategy: AggregationStrategy,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Starting federated learning with {} clients",
            federation_config.num_clients
        );

        // Update session state
        {
            let mut state = self.session_state.write().await;
            state.current_phase = TrainingPhase::Optimization;
            state.progress = 0.0;
        }

        self.federated_training_loop(task, federation_config, aggregation_strategy)
            .await
    }

    // Domain adaptation implementation methods
    async fn gradual_unfreezing_adaptation(
        &self,
        task: TrainingTask,
        source_domain: String,
        target_domain: String,
    ) -> Result<String, RecognitionError> {
        // Simulate gradual unfreezing domain adaptation
        for epoch in 1..=task.hyperparameters.epochs {
            {
                let mut state = self.session_state.write().await;
                state.current_epoch = epoch;
                state.progress = epoch as f32 / task.hyperparameters.epochs as f32;
                state.current_phase = TrainingPhase::DomainAdaptation;
            }

            tracing::info!(
                "Domain adaptation epoch {}/{}: Gradual unfreezing from {} to {}",
                epoch,
                task.hyperparameters.epochs,
                source_domain,
                target_domain
            );

            // Simulate training delay
            tokio::time::sleep(Duration::from_millis(100)).await;
        }

        // Mark training as completed
        {
            let mut state = self.session_state.write().await;
            state.status = TrainingStatus::Completed;
            state.progress = 1.0;
        }

        tracing::info!("Domain adaptation training completed successfully");
        Ok(task.task_id)
    }

    async fn domain_adversarial_adaptation(
        &self,
        task: TrainingTask,
        source_domain: String,
        target_domain: String,
    ) -> Result<String, RecognitionError> {
        // Placeholder implementation for domain adversarial training
        tracing::info!(
            "Domain adversarial adaptation from {} to {}",
            source_domain,
            target_domain
        );
        self.gradual_unfreezing_adaptation(task, source_domain, target_domain)
            .await
    }

    async fn feature_alignment_adaptation(
        &self,
        task: TrainingTask,
        source_domain: String,
        target_domain: String,
    ) -> Result<String, RecognitionError> {
        // Placeholder implementation for feature alignment
        tracing::info!(
            "Feature alignment adaptation from {} to {}",
            source_domain,
            target_domain
        );
        self.gradual_unfreezing_adaptation(task, source_domain, target_domain)
            .await
    }

    async fn curriculum_learning_adaptation(
        &self,
        task: TrainingTask,
        source_domain: String,
        target_domain: String,
    ) -> Result<String, RecognitionError> {
        // Placeholder implementation for curriculum learning
        tracing::info!(
            "Curriculum learning adaptation from {} to {}",
            source_domain,
            target_domain
        );
        self.gradual_unfreezing_adaptation(task, source_domain, target_domain)
            .await
    }

    // Few-shot learning implementation methods
    async fn maml_few_shot_learning(
        &self,
        task: TrainingTask,
        support_set_size: usize,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "MAML few-shot learning with support set size {}",
            support_set_size
        );

        for epoch in 1..=task.hyperparameters.epochs {
            {
                let mut state = self.session_state.write().await;
                state.current_epoch = epoch;
                state.progress = epoch as f32 / task.hyperparameters.epochs as f32;
                state.current_phase = TrainingPhase::FewShotOptimization;
            }

            tracing::info!("MAML epoch {}/{}", epoch, task.hyperparameters.epochs);
            tokio::time::sleep(Duration::from_millis(100)).await;
        }

        {
            let mut state = self.session_state.write().await;
            state.status = TrainingStatus::Completed;
            state.progress = 1.0;
        }

        Ok(task.task_id)
    }

    async fn prototypical_networks_learning(
        &self,
        task: TrainingTask,
        support_set_size: usize,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Prototypical networks learning with support set size {}",
            support_set_size
        );
        self.maml_few_shot_learning(task, support_set_size).await
    }

    async fn matching_networks_learning(
        &self,
        task: TrainingTask,
        support_set_size: usize,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Matching networks learning with support set size {}",
            support_set_size
        );
        self.maml_few_shot_learning(task, support_set_size).await
    }

    async fn relation_networks_learning(
        &self,
        task: TrainingTask,
        support_set_size: usize,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Relation networks learning with support set size {}",
            support_set_size
        );
        self.maml_few_shot_learning(task, support_set_size).await
    }

    // Continuous learning implementation methods
    async fn ewc_continuous_learning(
        &self,
        task: TrainingTask,
        update_frequency: Duration,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "EWC continuous learning with update frequency {:?}",
            update_frequency
        );

        for epoch in 1..=task.hyperparameters.epochs {
            {
                let mut state = self.session_state.write().await;
                state.current_epoch = epoch;
                state.progress = epoch as f32 / task.hyperparameters.epochs as f32;
                state.current_phase = TrainingPhase::ContinuousLearning;
            }

            tracing::info!(
                "EWC continuous learning epoch {}/{}",
                epoch,
                task.hyperparameters.epochs
            );
            tokio::time::sleep(update_frequency).await;
        }

        {
            let mut state = self.session_state.write().await;
            state.status = TrainingStatus::Completed;
            state.progress = 1.0;
        }

        Ok(task.task_id)
    }

    async fn progressive_networks_learning(
        &self,
        task: TrainingTask,
        update_frequency: Duration,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Progressive networks learning with update frequency {:?}",
            update_frequency
        );
        self.ewc_continuous_learning(task, update_frequency).await
    }

    async fn memory_replay_learning(
        &self,
        task: TrainingTask,
        update_frequency: Duration,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "Memory replay learning with update frequency {:?}",
            update_frequency
        );
        self.ewc_continuous_learning(task, update_frequency).await
    }

    async fn packnet_learning(
        &self,
        task: TrainingTask,
        update_frequency: Duration,
    ) -> Result<String, RecognitionError> {
        tracing::info!(
            "PackNet learning with update frequency {:?}",
            update_frequency
        );
        self.ewc_continuous_learning(task, update_frequency).await
    }

    // Federated learning implementation
    async fn federated_training_loop(
        &self,
        task: TrainingTask,
        federation_config: FederationConfig,
        aggregation_strategy: AggregationStrategy,
    ) -> Result<String, RecognitionError> {
        for round in 1..=federation_config.communication_rounds {
            {
                let mut state = self.session_state.write().await;
                state.current_epoch = round;
                state.progress = round as f32 / federation_config.communication_rounds as f32;
                state.current_phase = TrainingPhase::DomainAdaptation;
            }

            tracing::info!(
                "Federated learning round {}/{} with {} clients",
                round,
                federation_config.communication_rounds,
                federation_config.num_clients
            );

            // Simulate client selection and training
            let selected_clients = self.select_clients(&federation_config).await?;
            self.aggregate_client_updates(
                &federation_config,
                &aggregation_strategy,
                &selected_clients,
            )
            .await?;

            tokio::time::sleep(Duration::from_millis(200)).await;
        }

        {
            let mut state = self.session_state.write().await;
            state.status = TrainingStatus::Completed;
            state.progress = 1.0;
        }

        tracing::info!("Federated learning completed");
        Ok(task.task_id)
    }

    async fn select_clients(
        &self,
        config: &FederationConfig,
    ) -> Result<Vec<String>, RecognitionError> {
        // Simulate client selection based on strategy
        let client_count = (config.num_clients as f32 * 0.5) as usize; // Use 50% as default fraction
        let selected_clients: Vec<String> =
            (0..client_count).map(|i| format!("client_{i}")).collect();

        tracing::info!(
            "Selected {} clients using {:?} strategy",
            selected_clients.len(),
            config.client_selection
        );
        Ok(selected_clients)
    }

    async fn aggregate_client_updates(
        &self,
        config: &FederationConfig,
        aggregation_strategy: &AggregationStrategy,
        clients: &[String],
    ) -> Result<(), RecognitionError> {
        tracing::info!(
            "Aggregating updates from {} clients using {:?}",
            clients.len(),
            aggregation_strategy
        );
        // Simulate aggregation delay
        tokio::time::sleep(Duration::from_millis(50)).await;
        Ok(())
    }
}

/// Error types specific to training
#[derive(Debug, thiserror::Error)]
pub enum TrainingError {
    /// Configuration error occurred during training setup
    #[error("Configuration error: {message}")]
    ConfigurationError {
        /// Error message
        message: String,
    },

    /// Data loading error occurred during training
    #[error("Data loading error: {message}")]
    DataLoadingError {
        /// Error message
        message: String,
    },

    /// Model error occurred during training
    #[error("Model error: {message}")]
    ModelError {
        /// Error message
        message: String,
    },

    /// Training failed
    #[error("Training failed: {message}")]
    TrainingFailed {
        /// Error message
        message: String,
    },

    /// Validation error occurred during training
    #[error("Validation error: {message}")]
    ValidationError {
        /// Error message
        message: String,
    },

    /// Export error occurred during model export
    #[error("Export error: {message}")]
    ExportError {
        /// Error message
        message: String,
    },
}

impl From<TrainingError> for RecognitionError {
    fn from(error: TrainingError) -> Self {
        RecognitionError::TrainingError {
            message: error.to_string(),
            source: Some(Box::new(error)),
        }
    }
}