trustformers-mobile 0.1.1

//! Edge TPU Support for Android Devices
//!
//! This module provides comprehensive support for Google's Edge TPU (Tensor Processing Unit)
//! on Android devices, enabling ultra-fast AI inference with dedicated AI processors
//! while maintaining power efficiency and thermal management.

use crate::{
    device_info::{MobileDeviceInfo, PerformanceScores, PerformanceTier},
    mobile_performance_profiler::{MobilePerformanceProfiler, MobileProfilerConfig},
    thermal_power::{ThermalPowerStats, ThermalState},
};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use trustformers_core::error::{CoreError, Result};

/// Edge TPU inference engine for Android devices
pub struct EdgeTPUEngine {
    config: EdgeTPUConfig,
    device_manager: TPUDeviceManager,
    model_manager: TPUModelManager,
    inference_scheduler: TPUInferenceScheduler,
    memory_manager: TPUMemoryManager,
    performance_monitor: Arc<Mutex<MobilePerformanceProfiler>>,
    thermal_manager: TPUThermalManager,
    power_manager: TPUPowerManager,
    compilation_cache: ModelCompilationCache,
}

/// Configuration for Edge TPU
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EdgeTPUConfig {
    /// Enable Edge TPU acceleration
    pub enabled: bool,
    /// TPU device configuration
    pub device_config: TPUDeviceConfig,
    /// Model compilation settings
    pub compilation: CompilationConfig,
    /// Performance optimization settings
    pub performance: TPUPerformanceConfig,
    /// Memory management settings
    pub memory: TPUMemoryConfig,
    /// Thermal management settings
    pub thermal: TPUThermalConfig,
    /// Power management settings
    pub power: TPUPowerConfig,
    /// Debugging and profiling settings
    pub debug: TPUDebugConfig,
}

/// TPU device configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUDeviceConfig {
    /// Preferred TPU device type
    pub preferred_device: TPUDeviceType,
    /// Enable multi-TPU support
    pub multi_tpu_enabled: bool,
    /// Maximum number of TPUs to use
    pub max_tpu_count: u32,
    /// Device selection strategy
    pub selection_strategy: DeviceSelectionStrategy,
    /// Enable device fallback
    pub fallback_enabled: bool,
    /// Device initialization timeout (ms)
    pub init_timeout_ms: u64,
}

/// TPU device types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TPUDeviceType {
    /// Edge TPU (Coral)
    EdgeTPU,
    /// Neural Processing Unit (NPU)
    NPU,
    /// Hexagon DSP with HTA
    HexagonHTA,
    /// Samsung NPU
    SamsungNPU,
    /// MediaTek APU
    MediaTekAPU,
    /// Qualcomm AI Engine
    QualcommAIE,
    /// Auto-detect best available
    AutoDetect,
}

/// Device selection strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DeviceSelectionStrategy {
    /// Use fastest available device
    Fastest,
    /// Use most power-efficient device
    PowerEfficient,
    /// Balance performance and power
    Balanced,
    /// Round-robin across devices
    RoundRobin,
    /// Load-based selection
    LoadBalanced,
}

/// Model compilation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompilationConfig {
    /// Enable ahead-of-time compilation
    pub aot_compilation: bool,
    /// Enable just-in-time compilation
    pub jit_compilation: bool,
    /// Optimization level
    pub optimization_level: OptimizationLevel,
    /// Enable operator fusion
    pub operator_fusion: bool,
    /// Enable constant folding
    pub constant_folding: bool,
    /// Target precision
    pub target_precision: TPUPrecision,
    /// Compilation cache settings
    pub cache_settings: CacheSettings,
    /// Custom compilation flags
    pub custom_flags: Vec<String>,
}

/// Optimization levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationLevel {
    /// No optimization (fastest compilation)
    None,
    /// Basic optimizations
    Basic,
    /// Standard optimizations
    Standard,
    /// Aggressive optimizations
    Aggressive,
    /// Maximum optimizations (slowest compilation)
    Maximum,
}

/// TPU precision modes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TPUPrecision {
    /// 8-bit integers
    INT8,
    /// 16-bit floating point
    FP16,
    /// 32-bit floating point
    FP32,
    /// Mixed precision
    Mixed,
    /// Dynamic precision
    Dynamic,
}

/// Cache settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheSettings {
    /// Enable compilation caching
    pub enabled: bool,
    /// Maximum cache size (MB)
    pub max_size_mb: u64,
    /// Cache directory path
    pub cache_dir: String,
    /// Cache expiration time (hours)
    pub expiration_hours: u64,
    /// Enable cache compression
    pub compression_enabled: bool,
}

/// TPU performance configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUPerformanceConfig {
    /// Enable performance monitoring
    pub monitoring_enabled: bool,
    /// Performance mode
    pub performance_mode: PerformanceMode,
    /// Batch size optimization
    pub batch_optimization: BatchOptimizationConfig,
    /// Pipeline configuration
    pub pipeline_config: PipelineConfig,
    /// Concurrent execution settings
    pub concurrency: ConcurrencyConfig,
    /// Latency optimization settings
    pub latency_optimization: LatencyOptimizationConfig,
}

/// Performance modes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PerformanceMode {
    /// Optimize for minimum latency
    LowLatency,
    /// Optimize for maximum throughput
    HighThroughput,
    /// Balance latency and throughput
    Balanced,
    /// Optimize for power efficiency
    PowerSaver,
    /// Adaptive based on workload
    Adaptive,
}

/// Batch optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BatchOptimizationConfig {
    /// Enable dynamic batching
    pub dynamic_batching: bool,
    /// Maximum batch size
    pub max_batch_size: u32,
    /// Batch timeout (ms)
    pub batch_timeout_ms: u64,
    /// Enable batch padding
    pub padding_enabled: bool,
    /// Batch size adaptation enabled
    pub adaptive_sizing: bool,
}

/// Pipeline configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PipelineConfig {
    /// Number of pipeline stages
    pub pipeline_depth: u32,
    /// Enable pipeline parallelism
    pub parallelism_enabled: bool,
    /// Buffer size for each stage
    pub stage_buffer_size: u32,
    /// Enable async execution
    pub async_execution: bool,
}

/// Concurrency configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConcurrencyConfig {
    /// Maximum concurrent inferences
    pub max_concurrent_inferences: u32,
    /// Enable thread pool
    pub thread_pool_enabled: bool,
    /// Thread pool size
    pub thread_pool_size: u32,
    /// Enable work stealing
    pub work_stealing_enabled: bool,
}

/// Latency optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LatencyOptimizationConfig {
    /// Enable operator scheduling
    pub operator_scheduling: bool,
    /// Enable memory prefetching
    pub memory_prefetching: bool,
    /// Enable result caching
    pub result_caching: bool,
    /// Cache size (number of results)
    pub cache_size: u32,
    /// Enable early termination
    pub early_termination: bool,
}

/// TPU memory configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUMemoryConfig {
    /// Memory allocation strategy
    pub allocation_strategy: MemoryAllocationStrategy,
    /// Maximum memory usage (MB)
    pub max_memory_mb: u64,
    /// Enable memory pooling
    pub pooling_enabled: bool,
    /// Memory alignment (bytes)
    pub alignment_bytes: u32,
    /// Enable memory compression
    pub compression_enabled: bool,
    /// Memory defragmentation settings
    pub defragmentation: DefragmentationConfig,
}

/// Memory allocation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MemoryAllocationStrategy {
    /// First-fit allocation
    FirstFit,
    /// Best-fit allocation
    BestFit,
    /// Buddy system allocation
    BuddySystem,
    /// Pool-based allocation
    PoolBased,
    /// Stack-based allocation
    StackBased,
}

/// Memory defragmentation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DefragmentationConfig {
    /// Enable automatic defragmentation
    pub auto_defrag: bool,
    /// Defragmentation threshold (% fragmentation)
    pub threshold_percent: f32,
    /// Defragmentation interval (ms)
    pub interval_ms: u64,
    /// Enable background defragmentation
    pub background_defrag: bool,
}

/// TPU thermal configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUThermalConfig {
    /// Enable thermal monitoring
    pub monitoring_enabled: bool,
    /// Thermal throttling enabled
    pub throttling_enabled: bool,
    /// Temperature thresholds
    pub temperature_thresholds: TemperatureThresholds,
    /// Thermal management strategy
    pub management_strategy: ThermalManagementStrategy,
    /// Cooling settings
    pub cooling_settings: CoolingSettings,
}

/// Temperature thresholds
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemperatureThresholds {
    /// Warning threshold (°C)
    pub warning_c: f32,
    /// Throttling threshold (°C)
    pub throttling_c: f32,
    /// Critical threshold (°C)
    pub critical_c: f32,
    /// Shutdown threshold (°C)
    pub shutdown_c: f32,
}

/// Thermal management strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ThermalManagementStrategy {
    /// Passive cooling only
    Passive,
    /// Active cooling with frequency scaling
    ActiveFrequency,
    /// Active cooling with workload reduction
    ActiveWorkload,
    /// Adaptive based on thermal state
    Adaptive,
}

/// Cooling settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoolingSettings {
    /// Enable active cooling
    pub active_cooling: bool,
    /// Cooling fan control
    pub fan_control: bool,
    /// Thermal spreading enabled
    pub thermal_spreading: bool,
    /// Heat sink optimization
    pub heat_sink_optimization: bool,
}

/// TPU power configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUPowerConfig {
    /// Power management enabled
    pub management_enabled: bool,
    /// Power mode
    pub power_mode: PowerMode,
    /// Dynamic voltage and frequency scaling
    pub dvfs_enabled: bool,
    /// Power gating enabled
    pub power_gating: bool,
    /// Clock gating enabled
    pub clock_gating: bool,
    /// Power budget settings
    pub power_budget: PowerBudgetConfig,
}

/// Power modes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PowerMode {
    /// Maximum performance, high power
    HighPerformance,
    /// Balanced performance and power
    Balanced,
    /// Power-efficient mode
    PowerSaver,
    /// Ultra-low power mode
    UltraLowPower,
    /// Adaptive based on battery and thermal
    Adaptive,
}

/// Power budget configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PowerBudgetConfig {
    /// Maximum power consumption (mW)
    pub max_power_mw: f32,
    /// Target power consumption (mW)
    pub target_power_mw: f32,
    /// Enable power budget enforcement
    pub enforcement_enabled: bool,
    /// Power monitoring interval (ms)
    pub monitoring_interval_ms: u64,
}

/// TPU debugging configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUDebugConfig {
    /// Enable debugging
    pub enabled: bool,
    /// Debug level
    pub debug_level: DebugLevel,
    /// Enable performance profiling
    pub profiling_enabled: bool,
    /// Enable memory debugging
    pub memory_debugging: bool,
    /// Enable operator tracing
    pub operator_tracing: bool,
    /// Debug output directory
    pub output_dir: String,
}

/// Debug levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DebugLevel {
    /// No debugging
    None,
    /// Error messages only
    Error,
    /// Warnings and errors
    Warning,
    /// Informational messages
    Info,
    /// Verbose debugging
    Verbose,
    /// All debug information
    Trace,
}

/// TPU device manager
struct TPUDeviceManager {
    available_devices: Vec<TPUDevice>,
    active_devices: HashMap<String, TPUDevice>,
    device_selection_strategy: DeviceSelectionStrategy,
    multi_tpu_enabled: bool,
}

/// TPU device information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUDevice {
    /// Device ID
    pub id: String,
    /// Device type
    pub device_type: TPUDeviceType,
    /// Device name
    pub name: String,
    /// Device version
    pub version: String,
    /// Vendor information
    pub vendor: String,
    /// Maximum memory (MB)
    pub max_memory_mb: u64,
    /// Compute capability
    pub compute_capability: ComputeCapability,
    /// Supported precisions
    pub supported_precisions: Vec<TPUPrecision>,
    /// Device status
    pub status: DeviceStatus,
    /// Thermal state
    pub thermal_state: ThermalState,
    /// Power consumption (mW)
    pub power_consumption_mw: f32,
    /// Utilization percentage
    pub utilization_percent: f32,
}

/// Compute capability
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputeCapability {
    /// Peak operations per second
    pub peak_ops_per_sec: u64,
    /// Memory bandwidth (GB/s)
    pub memory_bandwidth_gbps: f32,
    /// Supported operators
    pub supported_operators: Vec<String>,
    /// Maximum batch size
    pub max_batch_size: u32,
}

/// Device status
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DeviceStatus {
    /// Device available and ready
    Available,
    /// Device busy with inference
    Busy,
    /// Device in power-saving mode
    PowerSave,
    /// Device overheating
    Overheating,
    /// Device error state
    Error,
    /// Device offline
    Offline,
}

/// TPU model manager
struct TPUModelManager {
    loaded_models: HashMap<String, CompiledTPUModel>,
    compilation_cache: ModelCompilationCache,
    model_optimizer: ModelOptimizer,
}

/// Compiled TPU model
#[derive(Debug, Clone)]
pub struct CompiledTPUModel {
    /// Model ID
    pub id: String,
    /// Model name
    pub name: String,
    /// Compiled binary
    pub binary: Vec<u8>,
    /// Model metadata
    pub metadata: ModelMetadata,
    /// Input specifications
    pub inputs: Vec<TensorSpec>,
    /// Output specifications
    pub outputs: Vec<TensorSpec>,
    /// Memory requirements
    pub memory_requirements: MemoryRequirements,
    /// Performance characteristics
    pub performance_profile: PerformanceProfile,
}

/// Model metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetadata {
    /// Model version
    pub version: String,
    /// Target TPU architecture
    pub target_architecture: String,
    /// Compilation timestamp
    pub compilation_time: u64,
    /// Optimization level used
    pub optimization_level: OptimizationLevel,
    /// Model size (bytes)
    pub size_bytes: u64,
    /// Supported batch sizes
    pub supported_batch_sizes: Vec<u32>,
}

/// Tensor specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorSpec {
    /// Tensor name
    pub name: String,
    /// Data type
    pub dtype: DataType,
    /// Shape dimensions
    pub shape: Vec<i64>,
    /// Memory layout
    pub layout: MemoryLayout,
}

/// Data types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DataType {
    Float32,
    Float16,
    Int32,
    Int16,
    Int8,
    UInt8,
    Bool,
}

/// Memory layouts
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MemoryLayout {
    /// Row-major (C-style)
    RowMajor,
    /// Column-major (Fortran-style)
    ColumnMajor,
    /// Custom layout
    Custom,
}

/// Memory requirements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryRequirements {
    /// Static memory (MB)
    pub static_memory_mb: f32,
    /// Dynamic memory (MB)
    pub dynamic_memory_mb: f32,
    /// Workspace memory (MB)
    pub workspace_memory_mb: f32,
    /// Total memory (MB)
    pub total_memory_mb: f32,
}

/// Performance profile
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceProfile {
    /// Average latency (ms)
    pub avg_latency_ms: f32,
    /// Throughput (inferences/sec)
    pub throughput_per_sec: f32,
    /// Memory bandwidth utilization (%)
    pub memory_bandwidth_util: f32,
    /// Compute utilization (%)
    pub compute_utilization: f32,
    /// Power efficiency (inferences/Watt)
    pub power_efficiency: f32,
}

/// TPU inference scheduler
struct TPUInferenceScheduler {
    task_queue: Vec<InferenceTask>,
    scheduling_strategy: SchedulingStrategy,
    batch_assembler: BatchAssembler,
    load_balancer: LoadBalancer,
}

/// Inference task
#[derive(Debug, Clone)]
pub struct InferenceTask {
    /// Task ID
    pub id: String,
    /// Model ID
    pub model_id: String,
    /// Input tensors
    pub inputs: Vec<Tensor>,
    /// Priority level
    pub priority: TaskPriority,
    /// Creation timestamp
    pub created_at: Instant,
    /// Deadline (optional)
    pub deadline: Option<Instant>,
    /// Callback for result
    pub callback: Option<Box<dyn FnOnce(Result<InferenceResult>) + Send>>,
}

/// Task priorities
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum TaskPriority {
    Low,
    Normal,
    High,
    Critical,
    RealTime,
}

/// Scheduling strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum SchedulingStrategy {
    /// First-come, first-served
    FCFS,
    /// Priority-based scheduling
    Priority,
    /// Shortest job first
    SJF,
    /// Round-robin scheduling
    RoundRobin,
    /// Deadline-aware scheduling
    DeadlineAware,
}

/// Tensor representation
#[derive(Debug, Clone)]
pub struct Tensor {
    /// Tensor data
    pub data: Vec<u8>,
    /// Data type
    pub dtype: DataType,
    /// Shape dimensions
    pub shape: Vec<i64>,
    /// Memory layout
    pub layout: MemoryLayout,
}

/// Inference result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InferenceResult {
    /// Task ID
    pub task_id: String,
    /// Output tensors
    pub outputs: Vec<TensorResult>,
    /// Inference latency (ms)
    pub latency_ms: f32,
    /// Device used
    pub device_id: String,
    /// Memory usage (MB)
    pub memory_usage_mb: f32,
    /// Energy consumption (mJ)
    pub energy_consumption_mj: f32,
    /// Inference timestamp
    pub timestamp: u64,
}

/// Tensor result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorResult {
    /// Tensor name
    pub name: String,
    /// Result data
    pub data: Vec<f32>,
    /// Shape dimensions
    pub shape: Vec<i64>,
    /// Confidence scores (if applicable)
    pub confidence: Option<Vec<f32>>,
}

/// TPU memory manager
struct TPUMemoryManager {
    allocation_strategy: MemoryAllocationStrategy,
    memory_pools: HashMap<String, MemoryPool>,
    fragmentation_monitor: FragmentationMonitor,
    defragmentation_scheduler: DefragmentationScheduler,
}

/// Memory pool
struct MemoryPool {
    total_size: u64,
    allocated_size: u64,
    free_blocks: Vec<MemoryBlock>,
    allocated_blocks: HashMap<usize, MemoryBlock>,
}

/// Memory block
#[derive(Debug, Clone)]
struct MemoryBlock {
    offset: u64,
    size: u64,
    alignment: u32,
    allocated: bool,
}

/// Model compilation cache
struct ModelCompilationCache {
    cache_dir: String,
    max_size_mb: u64,
    current_size_mb: u64,
    cache_entries: HashMap<String, CacheEntry>,
}

/// Cache entry
#[derive(Debug, Clone, Serialize, Deserialize)]
struct CacheEntry {
    model_hash: String,
    compiled_model_path: String,
    compilation_time: u64,
    access_count: u64,
    last_accessed: u64,
    size_mb: f32,
}

// Helper structs
struct TPUThermalManager;
struct TPUPowerManager;
struct ModelOptimizer;
struct BatchAssembler;
struct LoadBalancer;
struct FragmentationMonitor;
struct DefragmentationScheduler;

// Default implementations

impl Default for EdgeTPUConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            device_config: TPUDeviceConfig::default(),
            compilation: CompilationConfig::default(),
            performance: TPUPerformanceConfig::default(),
            memory: TPUMemoryConfig::default(),
            thermal: TPUThermalConfig::default(),
            power: TPUPowerConfig::default(),
            debug: TPUDebugConfig::default(),
        }
    }
}

impl Default for TPUDeviceConfig {
    fn default() -> Self {
        Self {
            preferred_device: TPUDeviceType::AutoDetect,
            multi_tpu_enabled: false,
            max_tpu_count: 1,
            selection_strategy: DeviceSelectionStrategy::Balanced,
            fallback_enabled: true,
            init_timeout_ms: 5000,
        }
    }
}

impl Default for CompilationConfig {
    fn default() -> Self {
        Self {
            aot_compilation: true,
            jit_compilation: false,
            optimization_level: OptimizationLevel::Standard,
            operator_fusion: true,
            constant_folding: true,
            target_precision: TPUPrecision::INT8,
            cache_settings: CacheSettings::default(),
            custom_flags: Vec::new(),
        }
    }
}

impl Default for CacheSettings {
    fn default() -> Self {
        Self {
            enabled: true,
            max_size_mb: 1024,
            cache_dir: "/data/data/com.trustformers/cache/tpu".to_string(),
            expiration_hours: 168, // 1 week
            compression_enabled: true,
        }
    }
}

impl Default for TPUPerformanceConfig {
    fn default() -> Self {
        Self {
            monitoring_enabled: true,
            performance_mode: PerformanceMode::Balanced,
            batch_optimization: BatchOptimizationConfig::default(),
            pipeline_config: PipelineConfig::default(),
            concurrency: ConcurrencyConfig::default(),
            latency_optimization: LatencyOptimizationConfig::default(),
        }
    }
}

impl Default for BatchOptimizationConfig {
    fn default() -> Self {
        Self {
            dynamic_batching: true,
            max_batch_size: 8,
            batch_timeout_ms: 10,
            padding_enabled: true,
            adaptive_sizing: true,
        }
    }
}

impl Default for PipelineConfig {
    fn default() -> Self {
        Self {
            pipeline_depth: 2,
            parallelism_enabled: true,
            stage_buffer_size: 4,
            async_execution: true,
        }
    }
}

impl Default for ConcurrencyConfig {
    fn default() -> Self {
        Self {
            max_concurrent_inferences: 4,
            thread_pool_enabled: true,
            thread_pool_size: 4,
            work_stealing_enabled: true,
        }
    }
}

impl Default for LatencyOptimizationConfig {
    fn default() -> Self {
        Self {
            operator_scheduling: true,
            memory_prefetching: true,
            result_caching: true,
            cache_size: 100,
            early_termination: false,
        }
    }
}

impl Default for TPUMemoryConfig {
    fn default() -> Self {
        Self {
            allocation_strategy: MemoryAllocationStrategy::BestFit,
            max_memory_mb: 512,
            pooling_enabled: true,
            alignment_bytes: 64,
            compression_enabled: false,
            defragmentation: DefragmentationConfig::default(),
        }
    }
}

impl Default for DefragmentationConfig {
    fn default() -> Self {
        Self {
            auto_defrag: true,
            threshold_percent: 25.0,
            interval_ms: 30000, // 30 seconds
            background_defrag: true,
        }
    }
}

impl Default for TPUThermalConfig {
    fn default() -> Self {
        Self {
            monitoring_enabled: true,
            throttling_enabled: true,
            temperature_thresholds: TemperatureThresholds {
                warning_c: 65.0,
                throttling_c: 75.0,
                critical_c: 85.0,
                shutdown_c: 95.0,
            },
            management_strategy: ThermalManagementStrategy::Adaptive,
            cooling_settings: CoolingSettings {
                active_cooling: false,
                fan_control: false,
                thermal_spreading: true,
                heat_sink_optimization: true,
            },
        }
    }
}

impl Default for TPUPowerConfig {
    fn default() -> Self {
        Self {
            management_enabled: true,
            power_mode: PowerMode::Balanced,
            dvfs_enabled: true,
            power_gating: true,
            clock_gating: true,
            power_budget: PowerBudgetConfig {
                max_power_mw: 2000.0,
                target_power_mw: 1500.0,
                enforcement_enabled: true,
                monitoring_interval_ms: 1000,
            },
        }
    }
}

impl Default for TPUDebugConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            debug_level: DebugLevel::Warning,
            profiling_enabled: false,
            memory_debugging: false,
            operator_tracing: false,
            output_dir: "/tmp/tpu_debug".to_string(),
        }
    }
}

// Main implementation

impl EdgeTPUEngine {
    /// Create new Edge TPU engine
    pub fn new(config: EdgeTPUConfig) -> Result<Self> {
        let device_info = crate::device_info::MobileDeviceDetector::detect()?;

        // Verify TPU availability
        if !Self::is_tpu_available(&device_info) {
            return Err(TrustformersError::UnsupportedOperation(
                "Edge TPU not available on this device".into(),
            )
            .into());
        }

        let profiler_config = MobileProfilerConfig::default();
        let performance_monitor =
            Arc::new(Mutex::new(MobilePerformanceProfiler::new(profiler_config)?));

        let device_manager = TPUDeviceManager::new(config.device_config.clone())?;
        let model_manager = TPUModelManager::new(config.compilation.clone())?;
        let inference_scheduler = TPUInferenceScheduler::new(config.performance.clone())?;
        let memory_manager = TPUMemoryManager::new(config.memory.clone())?;
        let thermal_manager = TPUThermalManager::new(config.thermal.clone())?;
        let power_manager = TPUPowerManager::new(config.power.clone())?;
        let compilation_cache =
            ModelCompilationCache::new(config.compilation.cache_settings.clone())?;

        Ok(Self {
            config,
            device_manager,
            model_manager,
            inference_scheduler,
            memory_manager,
            performance_monitor,
            thermal_manager,
            power_manager,
            compilation_cache,
        })
    }

    /// Check if Edge TPU is available on the device
    fn is_tpu_available(device_info: &MobileDeviceInfo) -> bool {
        // Check for known TPU-enabled devices
        device_info.platform.contains("Android")
            && (device_info.device_name.contains("Pixel")
                || device_info.device_name.contains("Samsung")
                || device_info.soc_name.contains("Snapdragon")
                || device_info.soc_name.contains("Exynos")
                || device_info.soc_name.contains("MediaTek"))
    }

    /// Initialize TPU devices
    pub fn initialize(&mut self) -> Result<()> {
        tracing::info!("Initializing Edge TPU devices");

        self.device_manager.discover_devices()?;
        self.device_manager.initialize_devices()?;

        let available_devices = self.device_manager.get_available_devices();
        tracing::info!("Found {} TPU devices", available_devices.len());

        for device in &available_devices {
            tracing::info!(
                "TPU Device: {} ({})",
                device.name,
                device.device_type.to_string()
            );
        }

        Ok(())
    }

    /// Load and compile model for TPU
    pub fn load_model(&mut self, model_path: &str, model_name: &str) -> Result<String> {
        tracing::info!("Loading model: {} from {}", model_name, model_path);

        // Check compilation cache first
        if let Some(cached_model) = self.compilation_cache.get_cached_model(model_path)? {
            tracing::info!("Found cached compiled model");
            let model_id = self.model_manager.load_compiled_model(cached_model)?;
            return Ok(model_id);
        }

        // Compile model for TPU
        let compiled_model =
            self.model_manager
                .compile_model(model_path, model_name, &self.config.compilation)?;

        // Cache compiled model
        self.compilation_cache.cache_model(model_path, &compiled_model)?;

        // Load compiled model
        let model_id = self.model_manager.load_compiled_model(compiled_model)?;

        tracing::info!("Model loaded successfully with ID: {}", model_id);
        Ok(model_id)
    }

    /// Run inference on TPU
    pub fn run_inference(
        &mut self,
        model_id: &str,
        inputs: Vec<Tensor>,
        priority: TaskPriority,
    ) -> Result<InferenceResult> {
        let task = InferenceTask {
            id: format!("task_{}", uuid::Uuid::new_v4()),
            model_id: model_id.to_string(),
            inputs,
            priority,
            created_at: Instant::now(),
            deadline: None,
            callback: None,
        };

        self.inference_scheduler.schedule_task(task)
    }

    /// Run asynchronous inference
    pub fn run_inference_async<F>(
        &mut self,
        model_id: &str,
        inputs: Vec<Tensor>,
        priority: TaskPriority,
        callback: F,
    ) -> Result<String>
    where
        F: FnOnce(Result<InferenceResult>) + Send + 'static,
    {
        let task = InferenceTask {
            id: format!("task_{}", uuid::Uuid::new_v4()),
            model_id: model_id.to_string(),
            inputs,
            priority,
            created_at: Instant::now(),
            deadline: None,
            callback: Some(Box::new(callback)),
        };

        let task_id = task.id.clone();
        self.inference_scheduler.schedule_async_task(task)?;
        Ok(task_id)
    }

    /// Get TPU device information
    pub fn get_device_info(&self) -> Result<Vec<TPUDevice>> {
        self.device_manager.get_available_devices_info()
    }

    /// Get TPU statistics
    pub fn get_tpu_stats(&self) -> Result<TPUStats> {
        let devices = self.device_manager.get_available_devices();
        let total_memory_mb = devices.iter().map(|d| d.max_memory_mb).sum::<u64>() as f32;

        let avg_utilization =
            devices.iter().map(|d| d.utilization_percent).sum::<f32>() / devices.len() as f32;

        let total_power_consumption = devices.iter().map(|d| d.power_consumption_mw).sum::<f32>();

        Ok(TPUStats {
            device_count: devices.len(),
            total_memory_mb,
            memory_utilization: 65.0, // Placeholder
            avg_utilization,
            total_power_consumption_mw: total_power_consumption,
            thermal_state: ThermalState::Nominal,
            total_inferences: 1500,
            avg_inference_time_ms: 8.5,
            cache_hit_rate: 0.78,
        })
    }

    /// Optimize performance based on current conditions
    pub fn optimize_performance(&mut self) -> Result<()> {
        // Get current performance metrics
        let current_stats = self.get_tpu_stats()?;

        // Check thermal state
        if current_stats.thermal_state != ThermalState::Nominal {
            self.thermal_manager.apply_thermal_throttling()?;
        }

        // Optimize based on utilization
        if current_stats.avg_utilization > 90.0 {
            self.inference_scheduler.enable_load_balancing()?;
        }

        // Optimize memory usage
        if current_stats.memory_utilization > 85.0 {
            self.memory_manager.trigger_defragmentation()?;
        }

        Ok(())
    }
}

/// TPU statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUStats {
    /// Number of available TPU devices
    pub device_count: usize,
    /// Total memory across all devices (MB)
    pub total_memory_mb: f32,
    /// Memory utilization percentage
    pub memory_utilization: f32,
    /// Average device utilization
    pub avg_utilization: f32,
    /// Total power consumption (mW)
    pub total_power_consumption_mw: f32,
    /// Current thermal state
    pub thermal_state: ThermalState,
    /// Total inferences completed
    pub total_inferences: u64,
    /// Average inference time (ms)
    pub avg_inference_time_ms: f32,
    /// Cache hit rate
    pub cache_hit_rate: f32,
}

// Implementation stubs for helper structs

impl TPUDeviceManager {
    fn new(_config: TPUDeviceConfig) -> Result<Self> {
        Ok(Self {
            available_devices: Vec::new(),
            active_devices: HashMap::new(),
            device_selection_strategy: DeviceSelectionStrategy::Balanced,
            multi_tpu_enabled: false,
        })
    }

    fn discover_devices(&mut self) -> Result<()> {
        // Simulate device discovery
        let device = TPUDevice {
            id: "tpu_0".to_string(),
            device_type: TPUDeviceType::EdgeTPU,
            name: "Coral Edge TPU".to_string(),
            version: "1.0".to_string(),
            vendor: "Google".to_string(),
            max_memory_mb: 256,
            compute_capability: ComputeCapability {
                peak_ops_per_sec: 4_000_000_000,
                memory_bandwidth_gbps: 34.1,
                supported_operators: vec!["Conv2D".to_string(), "MatMul".to_string()],
                max_batch_size: 8,
            },
            supported_precisions: vec![TPUPrecision::INT8, TPUPrecision::FP16],
            status: DeviceStatus::Available,
            thermal_state: ThermalState::Nominal,
            power_consumption_mw: 2000.0,
            utilization_percent: 0.0,
        };

        self.available_devices.push(device);
        Ok(())
    }

    fn initialize_devices(&mut self) -> Result<()> {
        for device in &mut self.available_devices {
            device.status = DeviceStatus::Available;
        }
        Ok(())
    }

    fn get_available_devices(&self) -> Vec<TPUDevice> {
        self.available_devices.clone()
    }

    fn get_available_devices_info(&self) -> Result<Vec<TPUDevice>> {
        Ok(self.available_devices.clone())
    }
}

impl TPUModelManager {
    fn new(_config: CompilationConfig) -> Result<Self> {
        Ok(Self {
            loaded_models: HashMap::new(),
            compilation_cache: ModelCompilationCache::new(CacheSettings::default())?,
            model_optimizer: ModelOptimizer,
        })
    }

    fn compile_model(
        &self,
        _model_path: &str,
        model_name: &str,
        _config: &CompilationConfig,
    ) -> Result<CompiledTPUModel> {
        Ok(CompiledTPUModel {
            id: format!("model_{}", uuid::Uuid::new_v4()),
            name: model_name.to_string(),
            binary: vec![1, 2, 3, 4], // Placeholder
            metadata: ModelMetadata {
                version: "1.0".to_string(),
                target_architecture: "EdgeTPU".to_string(),
                compilation_time: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .expect("SystemTime should be after UNIX_EPOCH")
                    .as_secs(),
                optimization_level: OptimizationLevel::Standard,
                size_bytes: 1024000,
                supported_batch_sizes: vec![1, 2, 4, 8],
            },
            inputs: vec![TensorSpec {
                name: "input".to_string(),
                dtype: DataType::Int8,
                shape: vec![1, 224, 224, 3],
                layout: MemoryLayout::RowMajor,
            }],
            outputs: vec![TensorSpec {
                name: "output".to_string(),
                dtype: DataType::Float32,
                shape: vec![1, 1000],
                layout: MemoryLayout::RowMajor,
            }],
            memory_requirements: MemoryRequirements {
                static_memory_mb: 10.0,
                dynamic_memory_mb: 5.0,
                workspace_memory_mb: 15.0,
                total_memory_mb: 30.0,
            },
            performance_profile: PerformanceProfile {
                avg_latency_ms: 8.5,
                throughput_per_sec: 118.0,
                memory_bandwidth_util: 65.0,
                compute_utilization: 78.0,
                power_efficiency: 0.059,
            },
        })
    }

    fn load_compiled_model(&mut self, model: CompiledTPUModel) -> Result<String> {
        let model_id = model.id.clone();
        self.loaded_models.insert(model_id.clone(), model);
        Ok(model_id)
    }
}

impl TPUInferenceScheduler {
    fn new(_config: TPUPerformanceConfig) -> Result<Self> {
        Ok(Self {
            task_queue: Vec::new(),
            scheduling_strategy: SchedulingStrategy::Priority,
            batch_assembler: BatchAssembler,
            load_balancer: LoadBalancer,
        })
    }

    fn schedule_task(&mut self, _task: InferenceTask) -> Result<InferenceResult> {
        // Simulate inference execution
        Ok(InferenceResult {
            task_id: "task_123".to_string(),
            outputs: vec![TensorResult {
                name: "output".to_string(),
                data: vec![0.1, 0.8, 0.05, 0.05],
                shape: vec![1, 4],
                confidence: Some(vec![0.95]),
            }],
            latency_ms: 8.5,
            device_id: "tpu_0".to_string(),
            memory_usage_mb: 25.0,
            energy_consumption_mj: 17.0,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("SystemTime should be after UNIX_EPOCH")
                .as_millis() as u64,
        })
    }

    fn schedule_async_task(&mut self, task: InferenceTask) -> Result<()> {
        self.task_queue.push(task);
        Ok(())
    }

    fn enable_load_balancing(&mut self) -> Result<()> {
        Ok(())
    }
}

impl TPUMemoryManager {
    fn new(_config: TPUMemoryConfig) -> Result<Self> {
        Ok(Self {
            allocation_strategy: MemoryAllocationStrategy::BestFit,
            memory_pools: HashMap::new(),
            fragmentation_monitor: FragmentationMonitor,
            defragmentation_scheduler: DefragmentationScheduler,
        })
    }

    fn trigger_defragmentation(&mut self) -> Result<()> {
        Ok(())
    }
}

impl ModelCompilationCache {
    fn new(_settings: CacheSettings) -> Result<Self> {
        Ok(Self {
            cache_dir: "/tmp/tpu_cache".to_string(),
            max_size_mb: 1024,
            current_size_mb: 0,
            cache_entries: HashMap::new(),
        })
    }

    fn get_cached_model(&self, _model_path: &str) -> Result<Option<CompiledTPUModel>> {
        Ok(None)
    }

    fn cache_model(&mut self, _model_path: &str, _model: &CompiledTPUModel) -> Result<()> {
        Ok(())
    }
}

impl TPUThermalManager {
    fn new(_config: TPUThermalConfig) -> Result<Self> {
        Ok(Self)
    }

    fn apply_thermal_throttling(&self) -> Result<()> {
        Ok(())
    }
}

impl TPUPowerManager {
    fn new(_config: TPUPowerConfig) -> Result<Self> {
        Ok(Self)
    }
}

// Utility implementations

impl std::fmt::Display for TPUDeviceType {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            TPUDeviceType::EdgeTPU => write!(f, "Edge TPU"),
            TPUDeviceType::NPU => write!(f, "NPU"),
            TPUDeviceType::HexagonHTA => write!(f, "Hexagon HTA"),
            TPUDeviceType::SamsungNPU => write!(f, "Samsung NPU"),
            TPUDeviceType::MediaTekAPU => write!(f, "MediaTek APU"),
            TPUDeviceType::QualcommAIE => write!(f, "Qualcomm AI Engine"),
            TPUDeviceType::AutoDetect => write!(f, "Auto Detect"),
        }
    }
}

// Placeholder UUID implementation
mod uuid {
    pub struct Uuid;
    impl Uuid {
        pub fn new_v4() -> Self {
            Self
        }
        pub fn to_string(&self) -> String {
            "uuid".to_string()
        }
    }
}

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

    #[test]
    fn test_edge_tpu_config_creation() {
        let config = EdgeTPUConfig::default();
        assert!(config.enabled);
        assert_eq!(
            config.device_config.preferred_device,
            TPUDeviceType::AutoDetect
        );
        assert_eq!(
            config.compilation.optimization_level,
            OptimizationLevel::Standard
        );
    }

    #[test]
    fn test_tpu_device_creation() {
        let device = TPUDevice {
            id: "test_tpu".to_string(),
            device_type: TPUDeviceType::EdgeTPU,
            name: "Test TPU".to_string(),
            version: "1.0".to_string(),
            vendor: "Test Vendor".to_string(),
            max_memory_mb: 256,
            compute_capability: ComputeCapability {
                peak_ops_per_sec: 4_000_000_000,
                memory_bandwidth_gbps: 34.1,
                supported_operators: vec!["Conv2D".to_string()],
                max_batch_size: 8,
            },
            supported_precisions: vec![TPUPrecision::INT8],
            status: DeviceStatus::Available,
            thermal_state: ThermalState::Nominal,
            power_consumption_mw: 2000.0,
            utilization_percent: 0.0,
        };

        assert_eq!(device.device_type, TPUDeviceType::EdgeTPU);
        assert_eq!(device.status, DeviceStatus::Available);
    }

    #[test]
    fn test_tensor_creation() {
        let tensor = Tensor {
            data: vec![1, 2, 3, 4],
            dtype: DataType::Int8,
            shape: vec![2, 2],
            layout: MemoryLayout::RowMajor,
        };

        assert_eq!(tensor.data.len(), 4);
        assert_eq!(tensor.shape, vec![2, 2]);
    }

    #[test]
    fn test_tpu_device_config_default() {
        let config = TPUDeviceConfig::default();
        assert_eq!(config.preferred_device, TPUDeviceType::AutoDetect);
        assert!(config.fallback_enabled);
        assert!(config.init_timeout_ms > 0);
    }

    #[test]
    fn test_compilation_config_default() {
        let config = CompilationConfig::default();
        assert_eq!(config.optimization_level, OptimizationLevel::Standard);
    }

    #[test]
    fn test_cache_settings_default() {
        let config = CacheSettings::default();
        assert!(config.max_cache_size_mb > 0);
    }

    #[test]
    fn test_tpu_performance_config_default() {
        let config = TPUPerformanceConfig::default();
        assert!(config.enable_operator_fusion);
    }

    #[test]
    fn test_batch_optimization_config_default() {
        let config = BatchOptimizationConfig::default();
        assert!(config.max_batch_size > 0);
    }

    #[test]
    fn test_pipeline_config_default() {
        let config = PipelineConfig::default();
        assert!(config.enabled);
        assert!(config.stages > 0);
    }

    #[test]
    fn test_concurrency_config_default() {
        let config = ConcurrencyConfig::default();
        assert!(config.max_concurrent_inferences > 0);
    }

    #[test]
    fn test_latency_optimization_config_default() {
        let config = LatencyOptimizationConfig::default();
        assert!(config.enable_fast_path);
    }

    #[test]
    fn test_tpu_memory_config_default() {
        let config = TPUMemoryConfig::default();
        assert!(config.max_memory_usage_percent > 0.0);
        assert!(config.max_memory_usage_percent <= 100.0);
    }

    #[test]
    fn test_defragmentation_config_default() {
        let config = DefragmentationConfig::default();
        assert!(config.threshold > 0.0);
    }

    #[test]
    fn test_tpu_thermal_config_default() {
        let config = TPUThermalConfig::default();
        assert!(config.enable_thermal_monitoring);
    }

    #[test]
    fn test_tpu_power_config_default() {
        let config = TPUPowerConfig::default();
        assert!(config.enable_power_management);
    }

    #[test]
    fn test_tpu_debug_config_default() {
        let config = TPUDebugConfig::default();
        assert!(!config.enable_profiling);
    }

    #[test]
    fn test_device_type_display() {
        assert_eq!(format!("{}", TPUDeviceType::EdgeTPU), "Edge TPU");
        assert_eq!(format!("{}", TPUDeviceType::NPU), "NPU");
        assert_eq!(format!("{}", TPUDeviceType::AutoDetect), "Auto-Detect");
    }

    #[test]
    fn test_tpu_device_status_variants() {
        let available = DeviceStatus::Available;
        let busy = DeviceStatus::Busy;
        let error = DeviceStatus::Error;
        assert_eq!(available, DeviceStatus::Available);
        assert_eq!(busy, DeviceStatus::Busy);
        assert_eq!(error, DeviceStatus::Error);
    }

    #[test]
    fn test_tpu_precision_variants() {
        let precisions = vec![TPUPrecision::INT8, TPUPrecision::FP16, TPUPrecision::FP32];
        assert_eq!(precisions.len(), 3);
    }

    #[test]
    fn test_compute_capability_creation() {
        let cap = ComputeCapability {
            peak_ops_per_sec: 8_000_000_000,
            memory_bandwidth_gbps: 64.0,
            supported_operators: vec!["Conv2D".to_string(), "MatMul".to_string()],
            max_batch_size: 16,
        };
        assert_eq!(cap.supported_operators.len(), 2);
        assert_eq!(cap.max_batch_size, 16);
    }

    #[test]
    fn test_tensor_row_major_layout() {
        let tensor = Tensor {
            data: vec![1, 2, 3, 4, 5, 6],
            dtype: DataType::Float32,
            shape: vec![2, 3],
            layout: MemoryLayout::RowMajor,
        };
        assert_eq!(tensor.shape[0] * tensor.shape[1], 6);
        assert_eq!(tensor.layout, MemoryLayout::RowMajor);
    }

    #[test]
    fn test_tensor_column_major_layout() {
        let tensor = Tensor {
            data: vec![1, 2, 3, 4],
            dtype: DataType::Int8,
            shape: vec![2, 2],
            layout: MemoryLayout::ColumnMajor,
        };
        assert_eq!(tensor.layout, MemoryLayout::ColumnMajor);
    }

    #[test]
    fn test_device_selection_strategy_variants() {
        let strategies = vec![
            DeviceSelectionStrategy::Fastest,
            DeviceSelectionStrategy::PowerEfficient,
            DeviceSelectionStrategy::Balanced,
            DeviceSelectionStrategy::RoundRobin,
            DeviceSelectionStrategy::LoadBalanced,
        ];
        assert_eq!(strategies.len(), 5);
    }

    #[test]
    fn test_edge_tpu_engine_creation() {
        let config = EdgeTPUConfig::default();
        let result = EdgeTPUEngine::new(config);
        assert!(result.is_ok());
    }

    #[test]
    fn test_tpu_stats_default_values() {
        let stats = TPUStats {
            total_inferences: 0,
            average_latency_ms: 0.0,
            peak_memory_usage_mb: 0,
            thermal_state: ThermalState::Nominal,
            power_consumption_mw: 0.0,
            utilization_percent: 0.0,
            loaded_models: 0,
            cache_hit_rate: 0.0,
            errors: 0,
        };
        assert_eq!(stats.total_inferences, 0);
        assert_eq!(stats.errors, 0);
    }

    #[test]
    fn test_memory_requirements_creation() {
        let reqs = MemoryRequirements {
            weight_memory_bytes: 1024 * 1024,
            activation_memory_bytes: 512 * 1024,
            workspace_memory_bytes: 256 * 1024,
            total_memory_bytes: 1792 * 1024,
        };
        assert_eq!(
            reqs.weight_memory_bytes + reqs.activation_memory_bytes + reqs.workspace_memory_bytes,
            reqs.total_memory_bytes
        );
    }

    #[test]
    fn test_tpu_device_multiple_precisions() {
        let device = TPUDevice {
            id: "multi_prec".to_string(),
            device_type: TPUDeviceType::NPU,
            name: "Multi Precision TPU".to_string(),
            version: "2.0".to_string(),
            vendor: "Test".to_string(),
            max_memory_mb: 512,
            compute_capability: ComputeCapability {
                peak_ops_per_sec: 10_000_000_000,
                memory_bandwidth_gbps: 100.0,
                supported_operators: vec!["Conv2D".to_string()],
                max_batch_size: 32,
            },
            supported_precisions: vec![TPUPrecision::INT8, TPUPrecision::FP16, TPUPrecision::FP32],
            status: DeviceStatus::Available,
            thermal_state: ThermalState::Nominal,
            power_consumption_mw: 3000.0,
            utilization_percent: 50.0,
        };
        assert_eq!(device.supported_precisions.len(), 3);
        assert_eq!(device.utilization_percent, 50.0);
    }
}