trustformers-mobile 0.1.1

//! iOS Inference Engine
//!
//! This module provides the main iOS inference engine with Core ML, Metal GPU,
//! and CPU backend support, along with iOS-specific optimizations.

use crate::{MobileBackend, MobileConfig, MobilePlatform, MobileStats};
use std::collections::HashMap;
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;

#[cfg(target_os = "ios")]
use objc::runtime::{Class, Object};

/// Load distribution strategies for multi-GPU systems
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LoadDistributionStrategy {
    RoundRobin,
    PerformanceBased,
    MemoryBased,
    Adaptive,
}

/// iOS-specific inference engine
pub struct iOSInferenceEngine {
    config: MobileConfig,
    stats: MobileStats,
    model_loaded: bool,
    #[cfg(target_os = "ios")]
    coreml_model: Option<*mut Object>,
    #[cfg(target_os = "ios")]
    metal_state: Option<MetalComputeState>,
    #[cfg(target_os = "ios")]
    multi_gpu_manager: Option<MultiGPUManager>,
}

/// Metal compute state for GPU inference
#[cfg(target_os = "ios")]
pub struct MetalComputeState {
    device: *mut super::metal::MTLDevice,
    command_queue: *mut super::metal::MTLCommandQueue,
    pipeline_cache: HashMap<String, *mut super::metal::MTLComputePipelineState>,
    buffer_pool: Vec<*mut super::metal::MTLBuffer>,
    current_command_buffer: Option<*mut super::metal::MTLCommandBuffer>,
}

/// Multi-GPU manager for devices with multiple GPUs
#[cfg(target_os = "ios")]
pub struct MultiGPUManager {
    devices: Vec<super::metal::MetalDevice>,
    distribution_strategy: LoadDistributionStrategy,
    device_utilization: Vec<f32>,
    current_device_index: usize,
    load_balancer: LoadBalancer,
}

/// Load balancer for multi-GPU execution
#[cfg(target_os = "ios")]
pub struct LoadBalancer {
    strategy: LoadDistributionStrategy,
    device_weights: Vec<f32>,
    performance_history: Vec<Vec<f32>>,
    memory_usage: Vec<f32>,
}

impl iOSInferenceEngine {
    /// Create new iOS inference engine
    pub fn new(config: MobileConfig) -> Result<Self> {
        if config.platform != MobilePlatform::Ios {
            return Err(TrustformersError::config_error {
                message: "iOS inference engine requires iOS platform configuration".to_string(),
                context: trustformers_core::error::ErrorContext::new(
                    trustformers_core::error::ErrorCode::E4001,
                    "new".to_string(),
                ),
            });
        }

        let stats = MobileStats::new(&config);

        Ok(Self {
            config,
            stats,
            model_loaded: false,
            #[cfg(target_os = "ios")]
            coreml_model: None,
            #[cfg(target_os = "ios")]
            metal_state: None,
            #[cfg(target_os = "ios")]
            multi_gpu_manager: None,
        })
    }

    /// Load model for iOS inference
    pub fn load_model(&mut self, model_path: &str) -> Result<()> {
        match self.config.backend {
            MobileBackend::CoreML => self.load_coreml_model(model_path),
            MobileBackend::CPU => self.load_cpu_model(model_path),
            MobileBackend::GPU => self.load_gpu_model(model_path),
            _ => Err(TrustformersError::runtime_error(format!(
                "Backend {:?} not supported on iOS",
                self.config.backend
            ))),
        }
    }

    /// Perform inference using iOS optimizations
    pub fn inference(&mut self, input: &Tensor) -> Result<Tensor> {
        if !self.model_loaded {
            return Err(TrustformersError::runtime_error("Model not loaded".into()).into());
        }

        let start_time = std::time::Instant::now();

        let result = match self.config.backend {
            MobileBackend::CoreML => self.coreml_inference(input),
            MobileBackend::CPU => self.cpu_inference(input),
            MobileBackend::GPU => self.gpu_inference(input),
            _ => Err(TrustformersError::runtime_error(
                "Unsupported backend".into(),
            )),
        };

        let inference_time = start_time.elapsed().as_millis() as f32;
        self.stats.update_inference(inference_time);

        result
    }

    /// Perform privacy-preserving inference using iOS optimizations
    pub fn privacy_preserving_inference(
        &mut self,
        input: &Tensor,
        privacy_config: &crate::privacy_preserving_inference::InferencePrivacyConfig,
    ) -> Result<crate::privacy_preserving_inference::PrivateInferenceResult> {
        use crate::privacy_preserving_inference::{
            InferenceUseCase, PrivacyPreservingInferenceEngine,
        };

        if !self.model_loaded {
            return Err(TrustformersError::runtime_error("Model not loaded".into()).into());
        }

        // Create privacy engine optimized for iOS
        let mut privacy_engine = PrivacyPreservingInferenceEngine::new(privacy_config.clone());

        // iOS-specific inference function that leverages platform optimizations
        let ios_inference_fn = |input: &Tensor| -> Result<Tensor> { self.inference(input) };

        // Perform privacy-preserving inference
        privacy_engine.private_inference(input, ios_inference_fn)
    }

    /// Initialize multi-GPU support if available
    #[cfg(target_os = "ios")]
    pub fn initialize_multi_gpu(&mut self, strategy: LoadDistributionStrategy) -> Result<()> {
        let devices = super::metal::MetalDevice::get_all_devices().map_err(|e| {
            TrustformersError::runtime_error(format!("Failed to get Metal devices: {}", e))
        })?;

        if devices.len() > 1 {
            self.multi_gpu_manager = Some(MultiGPUManager::new(devices, strategy)?);
            println!(
                "Initialized multi-GPU support with {} devices",
                devices.len()
            );
        }

        Ok(())
    }

    /// Get device information
    pub fn get_device_info(&self) -> IOsDeviceInfo {
        #[cfg(target_os = "ios")]
        {
            IOsDeviceInfo::detect()
        }
        #[cfg(not(target_os = "ios"))]
        {
            IOsDeviceInfo {
                device_model: "Unknown".to_string(),
                system_version: "Unknown".to_string(),
                processor_count: 1,
                physical_memory: 1024 * 1024 * 1024, // 1GB default
                thermal_state: ThermalState::Nominal,
                low_power_mode_enabled: false,
                metal_feature_set: "Unknown".to_string(),
                supports_neural_engine: false,
                supports_unified_memory: false,
                max_buffer_length: 1024 * 1024, // 1MB default
            }
        }
    }

    /// Get current statistics
    pub fn get_stats(&self) -> &MobileStats {
        &self.stats
    }

    /// Update statistics with memory usage
    pub fn update_memory_stats(&mut self, memory_mb: usize) {
        self.stats.update_memory(memory_mb);
    }

    // Private implementation methods

    #[cfg(target_os = "ios")]
    fn load_coreml_model(&mut self, model_path: &str) -> Result<()> {
        // Core ML model loading implementation
        // This would use Core ML APIs to load the model
        self.model_loaded = true;
        Ok(())
    }

    #[cfg(not(target_os = "ios"))]
    fn load_coreml_model(&mut self, _model_path: &str) -> Result<()> {
        Err(TrustformersError::runtime_error(
            "Core ML not available on this platform".into(),
        ))
    }

    fn load_cpu_model(&mut self, _model_path: &str) -> Result<()> {
        // CPU model loading implementation
        self.model_loaded = true;
        Ok(())
    }

    #[cfg(target_os = "ios")]
    fn load_gpu_model(&mut self, _model_path: &str) -> Result<()> {
        // Initialize Metal compute state
        let device = super::metal::MetalDevice::create_system_default().map_err(|e| {
            TrustformersError::runtime_error(format!("Failed to create Metal device: {}", e))
        })?;

        // Create compute state
        // This would set up Metal pipelines and buffers
        self.model_loaded = true;
        Ok(())
    }

    #[cfg(not(target_os = "ios"))]
    fn load_gpu_model(&mut self, _model_path: &str) -> Result<()> {
        Err(TrustformersError::runtime_error(
            "Metal GPU not available on this platform".into(),
        ))
    }

    #[cfg(target_os = "ios")]
    fn coreml_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        // Core ML inference implementation
        // This would use the loaded Core ML model to perform inference

        // For now, return a dummy result
        Ok(input.clone())
    }

    #[cfg(not(target_os = "ios"))]
    fn coreml_inference(&mut self, _input: &Tensor) -> Result<Tensor> {
        Err(TrustformersError::runtime_error(
            "Core ML not available on this platform".into(),
        ))
    }

    fn cpu_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        // CPU inference implementation
        // This would use CPU-optimized kernels for inference

        // For now, return a dummy result
        Ok(input.clone())
    }

    #[cfg(target_os = "ios")]
    fn gpu_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        // Metal GPU inference implementation
        // This would use Metal compute shaders for inference

        // Check if multi-GPU is available and should be used
        if let Some(ref mut multi_gpu) = self.multi_gpu_manager {
            return multi_gpu.distributed_inference(input);
        }

        // Single GPU inference
        Ok(input.clone())
    }

    #[cfg(not(target_os = "ios"))]
    fn gpu_inference(&mut self, _input: &Tensor) -> Result<Tensor> {
        Err(TrustformersError::runtime_error(
            "Metal GPU not available on this platform".into(),
        ))
    }
}

impl Drop for iOSInferenceEngine {
    fn drop(&mut self) {
        #[cfg(target_os = "ios")]
        {
            // Clean up Metal resources
            if let Some(ref mut metal_state) = self.metal_state {
                metal_state.cleanup();
            }
        }
    }
}

#[cfg(target_os = "ios")]
impl MetalComputeState {
    fn new() -> Result<Self> {
        let device = super::metal::MetalDevice::create_system_default().map_err(|e| {
            TrustformersError::runtime_error(format!("Failed to create Metal device: {}", e))
        })?;

        Ok(Self {
            device: std::ptr::null_mut(), // Would be properly initialized
            command_queue: std::ptr::null_mut(),
            pipeline_cache: HashMap::new(),
            buffer_pool: Vec::new(),
            current_command_buffer: None,
        })
    }

    fn cleanup(&mut self) {
        // Clean up Metal resources
        // This would properly release all Metal objects
    }
}

#[cfg(target_os = "ios")]
impl MultiGPUManager {
    fn new(
        devices: Vec<super::metal::MetalDevice>,
        strategy: LoadDistributionStrategy,
    ) -> Result<Self> {
        let device_count = devices.len();

        Ok(Self {
            devices,
            distribution_strategy: strategy,
            device_utilization: vec![0.0; device_count],
            current_device_index: 0,
            load_balancer: LoadBalancer::new(strategy, device_count),
        })
    }

    fn distributed_inference(&mut self, input: &Tensor) -> Result<Tensor> {
        // Select best device based on strategy
        let device_index = self.load_balancer.select_device(&self.device_utilization);

        // Perform inference on selected device
        self.inference_on_device(input, device_index)
    }

    fn inference_on_device(&mut self, input: &Tensor, device_index: usize) -> Result<Tensor> {
        // Update utilization
        self.device_utilization[device_index] += 1.0;

        // Perform actual inference on the specified device
        // This would use the Metal device at device_index

        // For now, return a dummy result
        Ok(input.clone())
    }
}

#[cfg(target_os = "ios")]
impl LoadBalancer {
    fn new(strategy: LoadDistributionStrategy, device_count: usize) -> Self {
        Self {
            strategy,
            device_weights: vec![1.0; device_count],
            performance_history: vec![Vec::new(); device_count],
            memory_usage: vec![0.0; device_count],
        }
    }

    fn select_device(&mut self, utilization: &[f32]) -> usize {
        match self.strategy {
            LoadDistributionStrategy::RoundRobin => {
                let current = self.current_device_index();
                self.advance_device_index();
                current
            },
            LoadDistributionStrategy::PerformanceBased => self.select_best_performance_device(),
            LoadDistributionStrategy::MemoryBased => self.select_lowest_memory_device(),
            LoadDistributionStrategy::Adaptive => self.select_adaptive_device(utilization),
        }
    }

    fn current_device_index(&self) -> usize {
        // Simple round-robin selection
        0 // Simplified implementation
    }

    fn advance_device_index(&mut self) {
        // Advance to next device in round-robin
    }

    fn select_best_performance_device(&self) -> usize {
        // Select device with best performance history
        self.performance_history
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| {
                let avg_a = a.iter().sum::<f32>() / a.len().max(1) as f32;
                let avg_b = b.iter().sum::<f32>() / b.len().max(1) as f32;
                avg_a.partial_cmp(&avg_b).unwrap_or(std::cmp::Ordering::Equal)
            })
            .map(|(i, _)| i)
            .unwrap_or(0)
    }

    fn select_lowest_memory_device(&self) -> usize {
        // Select device with lowest memory usage
        self.memory_usage
            .iter()
            .enumerate()
            .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(i, _)| i)
            .unwrap_or(0)
    }

    fn select_adaptive_device(&self, utilization: &[f32]) -> usize {
        // Adaptive selection based on multiple factors
        let mut best_device = 0;
        let mut best_score = f32::NEG_INFINITY;

        for (i, &util) in utilization.iter().enumerate() {
            let performance_score = self.get_performance_score(i);
            let memory_score = 1.0 - self.memory_usage[i];
            let utilization_score = 1.0 - util;

            let total_score =
                performance_score * 0.4 + memory_score * 0.3 + utilization_score * 0.3;

            if total_score > best_score {
                best_score = total_score;
                best_device = i;
            }
        }

        best_device
    }

    fn get_performance_score(&self, device_index: usize) -> f32 {
        if let Some(history) = self.performance_history.get(device_index) {
            if history.is_empty() {
                0.5 // Default score for new devices
            } else {
                history.iter().sum::<f32>() / history.len() as f32
            }
        } else {
            0.0
        }
    }
}

/// iOS device information
#[derive(Debug, Clone)]
pub struct IOsDeviceInfo {
    pub device_model: String,
    pub system_version: String,
    pub processor_count: usize,
    pub physical_memory: u64,
    pub thermal_state: ThermalState,
    pub low_power_mode_enabled: bool,
    pub metal_feature_set: String,
    pub supports_neural_engine: bool,
    pub supports_unified_memory: bool,
    pub max_buffer_length: usize,
}

/// iOS thermal state
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ThermalState {
    Nominal,
    Fair,
    Serious,
    Critical,
}

#[cfg(target_os = "ios")]
impl IOsDeviceInfo {
    /// Detect current iOS device information
    pub fn detect() -> Self {
        // This would use iOS APIs to detect device information
        // For now, return default values
        Self {
            device_model: "iPhone".to_string(),
            system_version: "15.0".to_string(),
            processor_count: 6,
            physical_memory: 6 * 1024 * 1024 * 1024, // 6GB
            thermal_state: ThermalState::Nominal,
            low_power_mode_enabled: false,
            metal_feature_set: "iOS_GPUFamily4_v1".to_string(),
            supports_neural_engine: true,
            supports_unified_memory: true,
            max_buffer_length: 256 * 1024 * 1024, // 256MB
        }
    }

    /// Check if device supports specific features
    pub fn supports_feature(&self, feature: iOSFeature) -> bool {
        match feature {
            iOSFeature::NeuralEngine => self.supports_neural_engine,
            iOSFeature::MetalPerformanceShaders => true, // Most modern iOS devices support MPS
            iOSFeature::CoreML => true,                  // Core ML is available on iOS 11+
            iOSFeature::UnifiedMemory => self.supports_unified_memory,
            iOSFeature::A12BionicOrNewer => {
                // This would check the actual processor model
                true // Simplified implementation
            },
            iOSFeature::MultipleGPUs => {
                // This would check for devices with multiple GPU cores
                self.device_model.contains("iPad Pro") // Simplified check
            },
        }
    }

    /// Get thermal state description
    pub fn thermal_state_description(&self) -> &'static str {
        match self.thermal_state {
            ThermalState::Nominal => "Normal temperature",
            ThermalState::Fair => "Slightly elevated temperature",
            ThermalState::Serious => "High temperature - performance may be reduced",
            ThermalState::Critical => "Very high temperature - significant throttling",
        }
    }

    /// Get memory pressure level
    pub fn get_memory_pressure(&self) -> f32 {
        // This would use iOS APIs to get actual memory pressure
        // For now, return a simulated value
        0.3 // 30% memory pressure
    }

    /// Get available memory in bytes
    pub fn get_available_memory(&self) -> u64 {
        // This would use iOS APIs to get actual available memory
        // For now, return a simulated value
        self.physical_memory / 2 // Assume 50% available
    }
}

/// iOS feature enumeration
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum iOSFeature {
    NeuralEngine,
    MetalPerformanceShaders,
    CoreML,
    UnifiedMemory,
    A12BionicOrNewer,
    MultipleGPUs,
}

impl std::fmt::Display for iOSFeature {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            iOSFeature::NeuralEngine => write!(f, "Neural Engine"),
            iOSFeature::MetalPerformanceShaders => write!(f, "Metal Performance Shaders"),
            iOSFeature::CoreML => write!(f, "Core ML"),
            iOSFeature::UnifiedMemory => write!(f, "Unified Memory"),
            iOSFeature::A12BionicOrNewer => write!(f, "A12 Bionic or newer"),
            iOSFeature::MultipleGPUs => write!(f, "Multiple GPUs"),
        }
    }
}

/// Multi-GPU information for iPad Pro and other multi-GPU devices
#[derive(Debug, Clone)]
pub struct MultiGPUInfo {
    pub gpu_count: usize,
    pub gpu_cores_per_device: Vec<usize>,
    pub total_gpu_cores: usize,
    pub supports_load_balancing: bool,
    pub supports_concurrent_execution: bool,
    pub max_concurrent_operations: usize,
}

impl MultiGPUInfo {
    /// Detect multi-GPU configuration
    pub fn detect() -> Option<Self> {
        // This would detect actual multi-GPU configuration
        // For now, return a simulated iPad Pro configuration
        Some(Self {
            gpu_count: 2,
            gpu_cores_per_device: vec![6, 6], // M3 iPad Pro has 12 GPU cores
            total_gpu_cores: 12,
            supports_load_balancing: true,
            supports_concurrent_execution: true,
            max_concurrent_operations: 4,
        })
    }

    /// Get optimal load distribution strategy
    pub fn get_optimal_strategy(&self) -> LoadDistributionStrategy {
        if self.supports_concurrent_execution && self.gpu_count > 1 {
            LoadDistributionStrategy::Adaptive
        } else {
            LoadDistributionStrategy::RoundRobin
        }
    }

    /// Calculate optimal work distribution
    pub fn calculate_work_distribution(&self, total_work: usize) -> Vec<usize> {
        if self.gpu_count == 0 {
            return vec![];
        }

        let work_per_core = total_work / self.total_gpu_cores;
        self.gpu_cores_per_device.iter().map(|&cores| cores * work_per_core).collect()
    }
}

// Non-iOS stub implementations
#[cfg(not(target_os = "ios"))]
impl IOsDeviceInfo {
    pub fn detect() -> Self {
        Self {
            device_model: "Unknown".to_string(),
            system_version: "Unknown".to_string(),
            processor_count: 1,
            physical_memory: 1024 * 1024 * 1024,
            thermal_state: ThermalState::Nominal,
            low_power_mode_enabled: false,
            metal_feature_set: "Unknown".to_string(),
            supports_neural_engine: false,
            supports_unified_memory: false,
            max_buffer_length: 1024 * 1024,
        }
    }

    pub fn supports_feature(&self, _feature: iOSFeature) -> bool {
        false
    }

    pub fn thermal_state_description(&self) -> &'static str {
        "Unknown"
    }

    pub fn get_memory_pressure(&self) -> f32 {
        0.0
    }

    pub fn get_available_memory(&self) -> u64 {
        0
    }
}