trustformers-mobile 0.1.1

//! Mobile Optimization Suite
//!
//! This module provides comprehensive mobile-specific optimizations including:
//! - Quantization (INT4, INT8, FP16, Dynamic)
//! - Operator fusion
//! - Memory pooling
//! - Graph optimization
//! - Kernel optimization
//! - Power-aware scheduling
//! - Cache optimization

pub mod adaptive_cache_manager;
pub mod adaptive_inference;
pub mod advanced_profiler;
pub mod advanced_quantization;
pub mod ai_powered_optimizer;
pub mod cache_optimizer;
pub mod efficient_training;
pub mod enhanced_int4_quantization;
pub mod enhanced_memory_manager;
pub mod fusion;
pub mod gguf_mobile;
pub mod graph_optimizer;
pub mod intelligent_config_optimizer;
pub mod kernel_optimizer;
pub mod knowledge_distillation;
pub mod memory_pool;
pub mod multimodal_optimizer;
pub mod performance_analytics;
pub mod power_scheduler;
pub mod quantization;
pub mod simd_optimizer;
pub mod size_optimizer;
pub mod streaming_ai;
pub mod sustainable_ai;

use crate::{MemoryOptimization, MobileConfig};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;
use trustformers_core::TrustformersError;

// Re-export key types
pub use quantization::{
    CalibrationMethod, DynamicQuantizer, FP16Quantizer, Int4Quantizer, Int8Quantizer,
    MobileQuantizer, QuantizationCalibration, QuantizationContext, QuantizationScheme,
};

pub use gguf_mobile::{
    MobileGGUFConfig, MobileGGUFQuantizer, MobileGGUFStats, MobileGGUFType, MobileGGUFUtils,
};

pub use enhanced_int4_quantization::{
    BlockSize, EnhancedInt4Config, EnhancedInt4Quantizer, Int4Block, QuantizedInt4Stats,
    QuantizedInt4Tensor,
};

pub use fusion::{
    AttentionFusion, ConvBatchNormFusion, FusedOperator, FusionContext, FusionPattern,
    LinearActivationFusion, OperatorFusion,
};

pub use memory_pool::{
    AllocationStrategy, MemoryAllocation, MemoryPoolConfig, MobileMemoryPool, PoolStats,
};

pub use graph_optimizer::{
    AlgebraicSimplification, ConstantFolding, DeadCodeElimination, GraphOptimizer, GraphRewriter,
    OptimizationPass,
};

pub use kernel_optimizer::{
    KernelConfig, KernelOptimizer, MetalKernel, NeonKernel,
    OptimizedKernel as KernelOptimizerOptimizedKernel, SimdKernel, VulkanKernel,
};

pub use power_scheduler::{
    BatteryConstraints, PowerAwareScheduler, PowerProfile,
    SchedulingDecision as PowerSchedulingDecision, SchedulingPolicy, ThermalConstraints,
};

pub use adaptive_cache_manager::{
    AdaptiveCacheConfig, AdaptiveCacheManager, CacheEntry, CacheStats as AdaptiveCacheStats,
    EvictionStrategy,
};

pub use cache_optimizer::{
    AccessPattern as CacheAccessPattern, CacheHints, CacheOptimizer, CacheStrategy, DataLayout,
    TilingConfig,
};

pub use simd_optimizer::{
    AdvSimdOptimizations, NeonOptimizations, SimdInstructions, SimdOptimizer, VectorizationStrategy,
};

pub use knowledge_distillation::{
    DistillationConfig, DistillationStats, DistillationStrategy, KnowledgeDistiller, StudentModel,
    TeacherModel,
};

pub use adaptive_inference::{
    AdaptiveConfig, AdaptiveInferenceEngine, AdaptiveStats, DeviceCapabilities, InferenceContext,
    InferenceResult, InferenceStrategy, NetworkState, PowerSource, Priority, ThermalState,
};

pub use advanced_quantization::{
    CalibrationDataset, DynamicQuantizationStrategy, MobilePrecision, MobileQuantizationEngine,
    QuantizationBenchmarks, QuantizationConfig, QuantizationGranularity, QuantizationMethod,
    QuantizationParameters, QuantizationScheme as AdvancedQuantizationScheme, QuantizedModel,
    QuantizedTensor,
};

pub use efficient_training::{
    BatchStrategy, BiasType, CheckpointStrategy, GradientStrategy, LearningRateSchedule,
    MemoryOptimizationLevel, MobileTrainingConfig, MobileTrainingEngine, MobileTrainingMethod,
    PauseReason, PromptInitStrategy, ScheduleType, TargetModules, TrainingResult, TrainingSample,
    TrainingState,
};

pub use advanced_profiler::{
    AdvancedProfiler, AdvancedProfilerConfig, BottleneckType, ImplementationDifficulty,
    MemoryAnalysis, OperationProfile, OperationType,
    OptimizationRecommendation as ProfilerOptimizationRecommendation, PerformanceBottleneck,
    PerformanceMetrics, PowerAnalysis, ProfilerOutputFormat, ProfilingReport,
    RecommendationPriority, RecommendationType, TemperatureTrend, ThermalAnalysis,
    ThermalRecommendation,
};

pub use enhanced_memory_manager::{
    AccessPattern as MemoryAccessPattern, AccessType, AdvancedAllocationStrategy,
    AllocationMetadata, AllocationPriority, AllocationStats, EnhancedMemoryConfig,
    EnhancedMemoryManager, LifetimeHint, MemoryPressure, MemoryStats, MemoryType, PrefetchStrategy,
};

pub use performance_analytics::{
    DataPoint, ExportFormat, MetricType,
    OptimizationRecommendation as AnalyticsOptimizationRecommendation, PerformanceAnalyticsConfig,
    PerformanceAnalyticsEngine, PerformanceAnomaly, PerformanceForecast, PerformanceInsights,
    PerformanceTrend,
};

pub use intelligent_config_optimizer::{
    ConfigurationRecommendation, DeviceCapabilityAssessment, IntelligentConfigOptimizer,
    OptimizationGoals, OptimizationPriorities, OptimizationStrategy,
};

pub use ai_powered_optimizer::{
    ActivationType, ArchitectureMetrics, ConnectionType, DeviceConstraints, DeviceEnvironment,
    EarlyStoppingConfig, LayerConfig, LayerType, MobileArchitecture, MobileNAS, NASConfig,
    OptimizationTarget, PerformanceRecord, QualityTradeoffs,
    QuantizationConfig as NASQuantizationConfig, QuantizationScheme as NASQuantizationScheme,
    ReinforcementLearningAgent, SearchStrategy, SkipConnection, UsagePattern, UserContext,
    UserPreferences,
};

pub use multimodal_optimizer::{
    CrossModalAttentionOptimizer, CrossModalKnowledgeDistillation,
    DistillationConfig as MultiModalDistillationConfig, FrameType, Modality, MultiModalConfig,
    MultiModalFrame, ProcessingStats, RealTimeVideoProcessor, StreamingMultiModalInference,
    TeacherModel as MultiModalTeacherModel, VideoFrame, VideoMetadata,
};

pub use sustainable_ai::{
    BatchProcessingResult, BatchProcessingStats, BatchTask, CarbonFootprintTracker, CarbonImpact,
    CarbonMetrics, CompressionEvent, CompressionTechnique, EnergyForecast, EnergyMeasurement,
    EnergyOptimalBatchProcessor, EnergyOptimizationLevel, GridLocation, HourlyReport,
    OperationType as SustainableOperationType, RenewableEnergyScheduler, ScheduledTask,
    SchedulingDecision as SustainableSchedulingDecision, SustainableAIConfig,
    SustainableCompressionResult, SustainableModelCompression,
    TaskPriority as SustainableTaskPriority, TaskType as SustainableTaskType,
};

pub use streaming_ai::{
    AdaptationAction, AdaptationEvent, AdaptationStrategy as StreamingAdaptationStrategy,
    AdaptationTrigger, AdaptationType, AttentionCache, AttentionPattern, AttentionResult,
    CacheStats, KVCache, LatencyTracker, OptimizationLevel as StreamingOptimizationLevel,
    OptimizedKernel as StreamingOptimizedKernel, PerformanceTracker, PipelineData, PipelineStage,
    ProcessingPipeline, RealTimeModelAdaptation, StageType, StreamingAIConfig, StreamingBuffer,
    StreamingResult, StreamingToken, StreamingTokenResult, StreamingTransformerOptimizer,
    UltraLowLatencyEngine,
};

pub use size_optimizer::{
    FrameworkSizeResults, SizeMetrics, SizeOptimizationStrategy, SizeOptimizer, SizeOptimizerConfig,
};

/// Comprehensive mobile optimization engine
pub struct MobileOptimizationEngine {
    config: MobileConfig,
    quantizer: Arc<dyn MobileQuantizer>,
    fusion_engine: OperatorFusion,
    memory_pool: MobileMemoryPool,
    graph_optimizer: GraphOptimizer,
    kernel_optimizer: KernelOptimizer,
    power_scheduler: PowerAwareScheduler,
    cache_optimizer: CacheOptimizer,
    simd_optimizer: SimdOptimizer,
    knowledge_distiller: Option<KnowledgeDistiller>,
    adaptive_engine: Option<AdaptiveInferenceEngine>,
    stats: OptimizationStats,
}

impl std::fmt::Debug for MobileOptimizationEngine {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("MobileOptimizationEngine")
            .field("config", &"<MobileConfig>")
            .field("quantizer", &"<Arc<dyn MobileQuantizer>>")
            .field("fusion_engine", &"<OperatorFusion>")
            .field("memory_pool", &"<MobileMemoryPool>")
            .field("graph_optimizer", &"<GraphOptimizer>")
            .field("kernel_optimizer", &"<KernelOptimizer>")
            .field("power_scheduler", &"<PowerAwareScheduler>")
            .field("cache_optimizer", &"<CacheOptimizer>")
            .field("simd_optimizer", &"<SimdOptimizer>")
            .field("knowledge_distiller", &self.knowledge_distiller.is_some())
            .field("adaptive_engine", &self.adaptive_engine.is_some())
            .field("stats", &"<OptimizationStats>")
            .finish()
    }
}

impl MobileOptimizationEngine {
    /// Create new optimization engine with mobile configuration
    pub fn new(config: MobileConfig) -> Result<Self> {
        config.validate()?;

        // Create quantizer based on configuration
        let quantizer: Arc<dyn MobileQuantizer> =
            if let Some(ref quant_config) = config.quantization {
                match quant_config.scheme {
                    crate::MobileQuantizationScheme::Int4 => Arc::new(Int4Quantizer::new()),
                    crate::MobileQuantizationScheme::Int8 => Arc::new(Int8Quantizer::new()),
                    crate::MobileQuantizationScheme::FP16 => Arc::new(FP16Quantizer::new()),
                    crate::MobileQuantizationScheme::Dynamic => Arc::new(DynamicQuantizer::new()),
                }
            } else {
                Arc::new(FP16Quantizer::new()) // Default to FP16
            };

        // Create memory pool with platform-specific configuration
        let memory_pool = MobileMemoryPool::new(MemoryPoolConfig {
            max_memory_bytes: config.max_memory_mb * 1024 * 1024,
            allocation_strategy: match config.memory_optimization {
                MemoryOptimization::Minimal => AllocationStrategy::FirstFit,
                MemoryOptimization::Balanced => AllocationStrategy::BestFit,
                MemoryOptimization::Maximum => AllocationStrategy::BuddySystem,
            },
            enable_defragmentation: config.memory_optimization == MemoryOptimization::Maximum,
        })?;

        // Create other optimizers
        let fusion_engine = OperatorFusion::new(config.backend);
        let graph_optimizer = GraphOptimizer::new();
        let kernel_optimizer = KernelOptimizer::new(config.backend);
        let power_scheduler = PowerAwareScheduler::new(config.clone());
        let cache_optimizer = CacheOptimizer::new(config.platform);
        let simd_optimizer = SimdOptimizer::new(config.platform);

        Ok(Self {
            config,
            quantizer,
            fusion_engine,
            memory_pool,
            graph_optimizer,
            kernel_optimizer,
            power_scheduler,
            cache_optimizer,
            simd_optimizer,
            knowledge_distiller: None,
            adaptive_engine: None,
            stats: OptimizationStats::default(),
        })
    }

    /// Optimize model weights for mobile deployment
    pub fn optimize_model_weights(
        &mut self,
        weights: &HashMap<String, Tensor>,
    ) -> Result<HashMap<String, Tensor>> {
        // Create a temporary model from weights for optimization
        let mut model = MobileModel {
            graph: ComputationGraph {
                operators: Vec::new(),
                edges: Vec::new(),
            },
            weights: weights.clone(),
            metadata: ModelMetadata::default(),
            execution_schedule: None,
            memory_pool: None,
        };

        // Apply quantization to the weights
        self.apply_quantization(&mut model)?;

        Ok(model.weights)
    }

    /// Estimate memory footprint for given number of parameters
    pub fn estimate_memory_footprint(&self, total_params: usize) -> MemoryFootprint {
        // Estimate based on quantization scheme and platform
        let bytes_per_param = match &self.config.quantization {
            Some(quant_config) => match quant_config.scheme {
                crate::MobileQuantizationScheme::Int4 => 0.5, // 4 bits per parameter
                crate::MobileQuantizationScheme::Int8 => 1.0, // 8 bits per parameter
                crate::MobileQuantizationScheme::FP16 => 2.0, // 16 bits per parameter
                crate::MobileQuantizationScheme::Dynamic => 1.5, // Mixed precision average
            },
            None => 4.0, // Default FP32
        };

        let original_memory = total_params * 4; // Original FP32 size
        let optimized_memory = (total_params as f32 * bytes_per_param) as usize;
        let savings_percent = if original_memory > 0 {
            ((original_memory - optimized_memory) as f32 / original_memory as f32) * 100.0
        } else {
            0.0
        };

        MemoryFootprint {
            total_memory_bytes: optimized_memory,
            memory_savings_percent: savings_percent,
            model_memory_bytes: optimized_memory,
            runtime_overhead_bytes: optimized_memory / 10, // Estimate 10% overhead
        }
    }

    /// Optimize tensor for mobile inference
    pub fn optimize_tensor(&self, tensor: &Tensor) -> Result<Tensor> {
        // Apply basic optimizations like layout transformation
        // For now, just return the tensor as-is
        // In a real implementation, this would apply layout optimizations, etc.
        Ok(tensor.clone())
    }

    /// Optimize batch of tensors for mobile inference
    pub fn optimize_batch(&self, tensors: &[Tensor]) -> Result<Vec<Tensor>> {
        // Apply batch-level optimizations
        // For now, just apply tensor optimization to each tensor
        tensors.iter().map(|tensor| self.optimize_tensor(tensor)).collect()
    }

    /// Optimize model for mobile deployment
    pub fn optimize_model(&mut self, model: &mut MobileModel) -> Result<OptimizationReport> {
        let start_time = std::time::Instant::now();
        let initial_size = model.estimate_size();

        // Phase 1: Graph-level optimizations
        self.apply_graph_optimizations(model)?;

        // Phase 2: Operator fusion
        self.apply_operator_fusion(model)?;

        // Phase 3: Quantization
        self.apply_quantization(model)?;

        // Phase 4: Kernel optimization
        self.apply_kernel_optimizations(model)?;

        // Phase 5: Memory optimization
        self.apply_memory_optimizations(model)?;

        // Phase 6: Cache optimization
        self.apply_cache_optimizations(model)?;

        // Phase 7: SIMD optimization
        self.apply_simd_optimizations(model)?;

        // Phase 8: Power-aware scheduling
        self.apply_power_scheduling(model)?;

        let final_size = model.estimate_size();
        let optimization_time = start_time.elapsed();

        Ok(OptimizationReport {
            initial_size_bytes: initial_size,
            final_size_bytes: final_size,
            size_reduction_percent: ((initial_size - final_size) as f32 / initial_size as f32)
                * 100.0,
            optimization_time_ms: optimization_time.as_millis() as u64,
            applied_optimizations: self.get_applied_optimizations(),
            performance_improvement: self.estimate_performance_improvement(),
            power_savings: self.estimate_power_savings(),
            memory_savings: self.estimate_memory_savings(),
        })
    }

    /// Apply graph-level optimizations
    fn apply_graph_optimizations(&mut self, model: &mut MobileModel) -> Result<()> {
        // Constant folding
        self.graph_optimizer.apply_constant_folding(&mut model.graph)?;

        // Dead code elimination
        self.graph_optimizer.apply_dead_code_elimination(&mut model.graph)?;

        // Algebraic simplification
        self.graph_optimizer.apply_algebraic_simplification(&mut model.graph)?;

        // Common subexpression elimination
        self.graph_optimizer.apply_cse(&mut model.graph)?;

        self.stats.graph_optimizations_applied += 1;
        Ok(())
    }

    /// Apply operator fusion optimizations
    fn apply_operator_fusion(&mut self, model: &mut MobileModel) -> Result<()> {
        let fusion_patterns = self.fusion_engine.detect_fusion_opportunities(&model.graph)?;

        for pattern in fusion_patterns {
            match pattern {
                FusionPattern::ConvBatchNorm => {
                    self.fusion_engine.fuse_conv_batchnorm(&mut model.graph)?;
                },
                FusionPattern::LinearActivation => {
                    self.fusion_engine.fuse_linear_activation(&mut model.graph)?;
                },
                FusionPattern::MultiHeadAttention => {
                    self.fusion_engine.fuse_attention(&mut model.graph)?;
                },
                _ => {},
            }
        }

        self.stats.fusion_operations_applied += 1;
        Ok(())
    }

    /// Apply quantization optimizations
    fn apply_quantization(&mut self, model: &mut MobileModel) -> Result<()> {
        // Calibrate quantization if needed
        if self.quantizer.requires_calibration() {
            let calibration_data = model.get_calibration_data()?;
            self.quantizer.calibrate(&calibration_data)?;
        }

        // Quantize weights
        for (name, weight) in &mut model.weights {
            let quantized = self.quantizer.quantize_tensor(weight)?;
            *weight = quantized;
            self.stats.tensors_quantized += 1;
        }

        // Update model metadata
        model.metadata.quantization_scheme = Some(self.quantizer.get_scheme());

        Ok(())
    }

    /// Apply kernel-level optimizations
    fn apply_kernel_optimizations(&mut self, model: &mut MobileModel) -> Result<()> {
        for operator in &mut model.graph.operators {
            let optimized_kernel = self.kernel_optimizer.optimize_kernel(
                &operator.kernel,
                &operator.input_shapes,
                &operator.output_shape,
            )?;

            operator.kernel = optimized_kernel.kernel_type;
            self.stats.kernels_optimized += 1;
        }

        Ok(())
    }

    /// Apply memory optimizations
    fn apply_memory_optimizations(&mut self, model: &mut MobileModel) -> Result<()> {
        // Analyze memory access patterns
        let access_patterns = self.analyze_memory_patterns(model)?;

        // Optimize tensor layouts
        for (tensor_name, pattern) in access_patterns {
            if let Some(tensor) = model.weights.get_mut(&tensor_name) {
                let optimized_layout = self.cache_optimizer.optimize_layout(tensor, &pattern)?;
                *tensor = optimized_layout;
            }
        }

        // Set up memory pooling
        model.enable_memory_pooling(Arc::new(self.memory_pool.clone()));

        self.stats.memory_optimizations_applied += 1;
        Ok(())
    }

    /// Apply cache optimizations
    fn apply_cache_optimizations(&mut self, model: &mut MobileModel) -> Result<()> {
        // Optimize data layout for cache efficiency
        for operator in &mut model.graph.operators {
            let cache_hints =
                self.cache_optimizer.generate_hints(&operator.kernel, &operator.input_shapes)?;

            operator.cache_hints = Some(cache_hints);
        }

        // Apply loop tiling where beneficial
        self.cache_optimizer.apply_tiling(&mut model.graph)?;

        self.stats.cache_optimizations_applied += 1;
        Ok(())
    }

    /// Apply SIMD optimizations
    fn apply_simd_optimizations(&mut self, model: &mut MobileModel) -> Result<()> {
        for operator in &mut model.graph.operators {
            if self.simd_optimizer.can_vectorize(&operator.kernel) {
                let vectorized = self
                    .simd_optimizer
                    .vectorize_kernel(&operator.kernel, &operator.input_shapes)?;

                operator.kernel = vectorized;
                self.stats.simd_optimizations_applied += 1;
            }
        }

        Ok(())
    }

    /// Apply power-aware scheduling
    fn apply_power_scheduling(&mut self, model: &mut MobileModel) -> Result<()> {
        let schedule = self.power_scheduler.create_schedule(&model.graph)?;
        model.execution_schedule = Some(schedule);

        self.stats.power_optimizations_applied += 1;
        Ok(())
    }

    /// Analyze memory access patterns
    fn analyze_memory_patterns(
        &self,
        model: &MobileModel,
    ) -> Result<HashMap<String, CacheAccessPattern>> {
        let mut patterns = HashMap::new();

        for operator in &model.graph.operators {
            for input in &operator.inputs {
                let pattern = CacheAccessPattern::analyze(input, &operator.kernel)?;
                patterns.insert(input.clone(), pattern);
            }
        }

        Ok(patterns)
    }

    /// Get list of applied optimizations
    fn get_applied_optimizations(&self) -> Vec<String> {
        let mut optimizations = Vec::new();

        if self.stats.graph_optimizations_applied > 0 {
            optimizations.push("Graph Optimization".to_string());
        }
        if self.stats.fusion_operations_applied > 0 {
            optimizations.push("Operator Fusion".to_string());
        }
        if self.stats.tensors_quantized > 0 {
            optimizations.push(format!("Quantization ({})", self.quantizer.get_scheme()));
        }
        if self.stats.kernels_optimized > 0 {
            optimizations.push("Kernel Optimization".to_string());
        }
        if self.stats.memory_optimizations_applied > 0 {
            optimizations.push("Memory Optimization".to_string());
        }
        if self.stats.cache_optimizations_applied > 0 {
            optimizations.push("Cache Optimization".to_string());
        }
        if self.stats.simd_optimizations_applied > 0 {
            optimizations.push("SIMD Optimization".to_string());
        }
        if self.stats.power_optimizations_applied > 0 {
            optimizations.push("Power-aware Scheduling".to_string());
        }
        if self.stats.knowledge_distillation_applied > 0 {
            optimizations.push("Knowledge Distillation".to_string());
        }
        if self.stats.adaptive_inferences_applied > 0 {
            optimizations.push("Adaptive Inference".to_string());
        }

        optimizations
    }

    /// Estimate performance improvement
    fn estimate_performance_improvement(&self) -> f32 {
        let mut improvement = 1.0;

        // Each optimization contributes to performance
        if self.stats.fusion_operations_applied > 0 {
            improvement *= 1.2; // 20% from fusion
        }
        if self.stats.tensors_quantized > 0 {
            improvement *= 1.5; // 50% from quantization
        }
        if self.stats.kernels_optimized > 0 {
            improvement *= 1.3; // 30% from kernel optimization
        }
        if self.stats.cache_optimizations_applied > 0 {
            improvement *= 1.15; // 15% from cache optimization
        }
        if self.stats.simd_optimizations_applied > 0 {
            improvement *= 1.4; // 40% from SIMD
        }
        if self.stats.knowledge_distillation_applied > 0 {
            improvement *= 1.8; // 80% from model compression
        }
        if self.stats.adaptive_inferences_applied > 0 {
            improvement *= 1.6; // 60% from adaptive strategies
        }

        (improvement - 1.0) * 100.0 // Return as percentage
    }

    /// Estimate power savings
    fn estimate_power_savings(&self) -> f32 {
        let mut savings: f32 = 0.0;

        // Quantization reduces computation power
        if self.stats.tensors_quantized > 0 {
            savings += match self.quantizer.get_scheme() {
                QuantizationScheme::Int4 => 60.0,
                QuantizationScheme::Int8 => 40.0,
                QuantizationScheme::FP16 => 20.0,
                QuantizationScheme::Dynamic => 30.0,
                QuantizationScheme::GGUF_Q2_K => 70.0, // Highest savings
                QuantizationScheme::GGUF_Q3_K => 65.0,
                QuantizationScheme::GGUF_Q4_K => 55.0,
                QuantizationScheme::GGUF_Q5_0 => 45.0,
                QuantizationScheme::GGUF_Q6_K => 35.0,
            };
        }

        // Power scheduling contributes
        if self.stats.power_optimizations_applied > 0 {
            savings += 15.0;
        }

        // Memory optimizations reduce power
        if self.stats.memory_optimizations_applied > 0 {
            savings += 10.0;
        }

        // Knowledge distillation reduces computation power
        if self.stats.knowledge_distillation_applied > 0 {
            savings += 35.0; // Smaller model = less computation
        }

        // Adaptive inference optimizes power usage
        if self.stats.adaptive_inferences_applied > 0 {
            savings += 25.0; // Smart resource allocation
        }

        savings.min(80.0) // Cap at 80% savings
    }

    /// Estimate memory savings
    fn estimate_memory_savings(&self) -> f32 {
        let mut savings: f32 = 0.0;

        // Quantization is the primary memory saver
        if self.stats.tensors_quantized > 0 {
            savings += match self.quantizer.get_scheme() {
                QuantizationScheme::Int4 => 87.5,      // 4-bit vs 32-bit
                QuantizationScheme::Int8 => 75.0,      // 8-bit vs 32-bit
                QuantizationScheme::FP16 => 50.0,      // 16-bit vs 32-bit
                QuantizationScheme::Dynamic => 60.0,   // Average
                QuantizationScheme::GGUF_Q2_K => 92.0, // 2.5625-bit vs 32-bit (highest savings)
                QuantizationScheme::GGUF_Q3_K => 89.3, // 3.4375-bit vs 32-bit
                QuantizationScheme::GGUF_Q4_K => 85.9, // 4.5-bit vs 32-bit
                QuantizationScheme::GGUF_Q5_0 => 82.8, // 5.5-bit vs 32-bit
                QuantizationScheme::GGUF_Q6_K => 79.7, // 6.5-bit vs 32-bit
            };
        }

        // Memory pooling helps
        if self.stats.memory_optimizations_applied > 0 {
            savings += 10.0;
        }

        // Knowledge distillation reduces model size significantly
        if self.stats.knowledge_distillation_applied > 0 {
            savings += 60.0; // Smaller student model
        }

        // Adaptive inference optimizes memory usage
        if self.stats.adaptive_inferences_applied > 0 {
            savings += 15.0; // Dynamic memory allocation and caching
        }

        savings.min(90.0) // Cap at 90% savings
    }

    /// Enable knowledge distillation with configuration
    pub fn enable_knowledge_distillation(
        &mut self,
        distillation_config: DistillationConfig,
    ) -> Result<()> {
        let distiller = KnowledgeDistiller::new(distillation_config, self.config.backend);
        self.knowledge_distiller = Some(distiller);
        Ok(())
    }

    /// Perform knowledge distillation to create a compressed student model
    pub fn distill_model(
        &mut self,
        teacher_model: TeacherModel,
        student_model: StudentModel,
        training_data: &[knowledge_distillation::DistillationSample],
    ) -> Result<StudentModel> {
        let distiller = self.knowledge_distiller.as_mut().ok_or_else(|| {
            TrustformersError::invalid_input("Knowledge distillation not enabled".to_string())
        })?;

        distiller.set_teacher_model(teacher_model)?;
        distiller.set_student_model(student_model)?;

        let optimized_student = distiller.distill(training_data)?;

        // Update stats
        self.stats.knowledge_distillation_applied += 1;

        Ok(optimized_student)
    }

    /// Get knowledge distillation statistics
    pub fn get_distillation_stats(&self) -> Option<&DistillationStats> {
        self.knowledge_distiller.as_ref().map(|d| d.get_stats())
    }

    /// Enable adaptive inference with configuration
    pub fn enable_adaptive_inference(&mut self, adaptive_config: AdaptiveConfig) -> Result<()> {
        let adaptive_engine = AdaptiveInferenceEngine::new(adaptive_config, self.config.backend)?;
        self.adaptive_engine = Some(adaptive_engine);
        Ok(())
    }

    /// Perform adaptive inference based on device capabilities and context
    pub fn adaptive_infer(
        &mut self,
        input: &Tensor,
        context: InferenceContext,
    ) -> Result<InferenceResult> {
        let adaptive_engine = self.adaptive_engine.as_mut().ok_or_else(|| {
            TrustformersError::invalid_input("Adaptive inference not enabled".to_string())
        })?;

        let result = adaptive_engine.infer(input, context)?;

        // Update stats
        self.stats.adaptive_inferences_applied += 1;

        Ok(result)
    }

    /// Get adaptive inference statistics
    pub fn get_adaptive_stats(&self) -> Option<&AdaptiveStats> {
        self.adaptive_engine.as_ref().map(|a| a.get_stats())
    }
}

/// Mobile model representation
#[derive(Debug, Clone)]
pub struct MobileModel {
    pub graph: ComputationGraph,
    pub weights: HashMap<String, Tensor>,
    pub metadata: ModelMetadata,
    pub execution_schedule: Option<ExecutionSchedule>,
    memory_pool: Option<Arc<MobileMemoryPool>>,
}

impl MobileModel {
    /// Create a new mobile model
    pub fn new(
        graph: ComputationGraph,
        weights: HashMap<String, Tensor>,
        metadata: ModelMetadata,
        execution_schedule: Option<ExecutionSchedule>,
    ) -> Self {
        Self {
            graph,
            weights,
            metadata,
            execution_schedule,
            memory_pool: None,
        }
    }

    /// Enable memory pooling for the model
    pub fn enable_memory_pooling(&mut self, pool: Arc<MobileMemoryPool>) {
        self.memory_pool = Some(pool);
    }

    /// Estimate model size in bytes
    pub fn estimate_size(&self) -> usize {
        self.weights
            .values()
            .map(|t| t.data().unwrap_or_default().len() * std::mem::size_of::<f32>())
            .sum()
    }

    /// Get calibration data for quantization
    pub fn get_calibration_data(&self) -> Result<Vec<Tensor>> {
        // This would normally return representative input data
        // For now, return empty vec
        Ok(Vec::new())
    }
}

/// Computation graph representation
#[derive(Debug, Clone)]
pub struct ComputationGraph {
    pub operators: Vec<GraphOperator>,
    pub edges: Vec<Edge>,
}

/// Graph operator
#[derive(Debug, Clone)]
pub struct GraphOperator {
    pub id: usize,
    pub kernel: KernelType,
    pub inputs: Vec<String>,
    pub outputs: Vec<String>,
    pub input_shapes: Vec<Vec<usize>>,
    pub output_shape: Vec<usize>,
    pub cache_hints: Option<CacheHints>,
}

/// Kernel type
#[derive(Debug, Clone, PartialEq)]
pub enum KernelType {
    Conv2d,
    Linear,
    BatchNorm,
    Activation,
    Attention,
    Pooling,
    Custom(String),
}

/// Graph edge
#[derive(Debug, Clone)]
pub struct Edge {
    pub from: usize,
    pub to: usize,
    pub tensor_name: String,
}

/// Model metadata
#[derive(Debug, Clone, Default)]
pub struct ModelMetadata {
    pub name: String,
    pub version: String,
    pub quantization_scheme: Option<QuantizationScheme>,
    pub optimization_level: Option<OptimizationLevel>,
}

/// Memory footprint information
#[derive(Debug, Clone)]
pub struct MemoryFootprint {
    pub total_memory_bytes: usize,
    pub memory_savings_percent: f32,
    pub model_memory_bytes: usize,
    pub runtime_overhead_bytes: usize,
}

impl MemoryFootprint {
    pub fn memory_usage_mb(&self) -> f32 {
        self.total_memory_bytes as f32 / (1024.0 * 1024.0)
    }
}

/// Execution schedule
#[derive(Debug, Clone)]
pub struct ExecutionSchedule {
    pub operator_order: Vec<usize>,
    pub power_hints: Vec<PowerHint>,
}

/// Power hint for execution
#[derive(Debug, Clone)]
pub struct PowerHint {
    pub operator_id: usize,
    pub power_mode: PowerMode,
}

/// Power mode
#[derive(Debug, Clone, Copy)]
pub enum PowerMode {
    HighPerformance,
    Balanced,
    PowerSaving,
}

/// Optimization level
#[derive(Debug, Clone, Copy)]
pub enum OptimizationLevel {
    O0, // No optimization
    O1, // Basic optimizations
    O2, // Aggressive optimizations
    O3, // Maximum optimizations
}

/// Optimization statistics
#[derive(Debug, Clone, Default)]
struct OptimizationStats {
    graph_optimizations_applied: usize,
    fusion_operations_applied: usize,
    tensors_quantized: usize,
    kernels_optimized: usize,
    memory_optimizations_applied: usize,
    cache_optimizations_applied: usize,
    simd_optimizations_applied: usize,
    power_optimizations_applied: usize,
    knowledge_distillation_applied: usize,
    adaptive_inferences_applied: usize,
}

/// Optimization report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationReport {
    pub initial_size_bytes: usize,
    pub final_size_bytes: usize,
    pub size_reduction_percent: f32,
    pub optimization_time_ms: u64,
    pub applied_optimizations: Vec<String>,
    pub performance_improvement: f32,
    pub power_savings: f32,
    pub memory_savings: f32,
}

impl OptimizationReport {
    /// Get human-readable summary
    pub fn summary(&self) -> String {
        format!(
            "Mobile Optimization Report:\n\
             - Size Reduction: {:.1}% ({} MB → {} MB)\n\
             - Performance Improvement: {:.1}%\n\
             - Power Savings: {:.1}%\n\
             - Memory Savings: {:.1}%\n\
             - Optimization Time: {} ms\n\
             - Applied: {}",
            self.size_reduction_percent,
            self.initial_size_bytes / (1024 * 1024),
            self.final_size_bytes / (1024 * 1024),
            self.performance_improvement,
            self.power_savings,
            self.memory_savings,
            self.optimization_time_ms,
            self.applied_optimizations.join(", ")
        )
    }
}

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

    #[test]
    fn test_optimization_engine_creation() {
        let config = MobileConfig::default();
        let engine = MobileOptimizationEngine::new(config);
        assert!(engine.is_ok());
    }

    #[test]
    fn test_optimization_report() {
        let report = OptimizationReport {
            initial_size_bytes: 100 * 1024 * 1024,
            final_size_bytes: 25 * 1024 * 1024,
            size_reduction_percent: 75.0,
            optimization_time_ms: 500,
            applied_optimizations: vec![
                "Graph Optimization".to_string(),
                "Quantization (INT8)".to_string(),
            ],
            performance_improvement: 150.0,
            power_savings: 40.0,
            memory_savings: 75.0,
        };

        let summary = report.summary();
        assert!(summary.contains("75.0%"));
        assert!(summary.contains("100 MB → 25 MB"));
    }
}