trustformers-mobile 0.1.1

//! Main Android Inference Engine
//!
//! This module provides the main AndroidInferenceEngine that orchestrates
//! NNAPI, GPU acceleration, and device management for optimal performance.

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

#[cfg(target_os = "android")]
use jni::JavaVM;

use super::{
    device::AndroidDeviceInfo,
    types::*,
    nnapi::{NNAPIDeviceManager, NNAPIExecutor, NNAPIModelBuilder},
    gpu::{VulkanComputeContext, OpenGLESComputeContext},
};

/// Main Android inference engine orchestrating all acceleration backends
pub struct AndroidInferenceEngine {
    config: MobileConfig,
    stats: MobileStats,
    model_loaded: bool,

    #[cfg(target_os = "android")]
    nnapi_executor: Option<NNAPIExecutor>,
    #[cfg(target_os = "android")]
    jvm: Option<JavaVM>,
    #[cfg(target_os = "android")]
    gpu_state: Option<AndroidGPUComputeState>,
    #[cfg(target_os = "android")]
    vulkan_context: Option<Arc<VulkanComputeContext>>,
    #[cfg(target_os = "android")]
    opengl_context: Option<Arc<OpenGLESComputeContext>>,
}

impl AndroidInferenceEngine {
    /// Create new Android inference engine
    pub fn new(config: MobileConfig) -> Result<Self> {
        if config.platform != MobilePlatform::Android {
            return Err(TrustformersError::config_error {
                message: "Android inference engine requires Android 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 = "android")]
            nnapi_executor: None,
            #[cfg(target_os = "android")]
            jvm: None,
            #[cfg(target_os = "android")]
            gpu_state: None,
            #[cfg(target_os = "android")]
            vulkan_context: None,
            #[cfg(target_os = "android")]
            opengl_context: None,
        })
    }

    /// Initialize with JVM reference for JNI integration
    #[cfg(target_os = "android")]
    pub fn init_jvm(&mut self, jvm: JavaVM) {
        self.jvm = Some(jvm);
        tracing::info!("JVM reference initialized for Android engine");
    }

    /// Load model for Android inference
    pub fn load_model(&mut self, model_path: &str) -> Result<()> {
        match self.config.backend {
            MobileBackend::NNAPI => self.load_nnapi_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 Android",
                self.config.backend
            ))),
        }
    }

    /// Perform inference using Android 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::NNAPI => self.nnapi_inference(input),
            MobileBackend::CPU => self.cpu_inference(input),
            MobileBackend::GPU => self.gpu_inference(input),
            _ => Err(TrustformersError::runtime_error("Unsupported backend".into()).into()),
        };

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

        result
    }

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

    /// Check Android device capabilities
    pub fn check_device_capabilities() -> AndroidDeviceInfo {
        AndroidDeviceInfo::detect()
    }

    /// Detect available NNAPI hardware acceleration devices
    pub fn detect_nnapi_devices() -> Vec<NNAPIDeviceInfo> {
        NNAPIDeviceManager::detect_devices()
    }

    /// Get the best NNAPI device for inference
    pub fn get_best_nnapi_device() -> Option<NNAPIDeviceInfo> {
        NNAPIDeviceManager::get_best_device()
    }

    /// Check if hardware acceleration is available
    pub fn has_hardware_acceleration() -> bool {
        NNAPIDeviceManager::has_hardware_acceleration()
    }

    // Backend-specific implementations

    #[cfg(target_os = "android")]
    fn load_nnapi_model(&mut self, model_path: &str) -> Result<()> {
        // Create NNAPI model builder
        let mut builder = NNAPIModelBuilder::new()?;

        // Build example model (in practice, would load from actual model file)
        // This is simplified - real implementation would parse model file
        tracing::info!("Building NNAPI model for: {}", model_path);

        // Create executor from model
        let model_ptr = builder.get_model_ptr();
        let executor = NNAPIExecutor::new(
            model_ptr,
            1, // input count
            1, // output count
            vec![0], // input operands
            vec![1], // output operands
        )?;

        self.nnapi_executor = Some(executor);
        self.model_loaded = true;
        tracing::info!("NNAPI model loaded successfully: {}", model_path);
        Ok(())
    }

    #[cfg(not(target_os = "android"))]
    fn load_nnapi_model(&mut self, _model_path: &str) -> Result<()> {
        Err(TrustformersError::runtime_error(
            "NNAPI is only available on Android".into(),
        ))
    }

    #[cfg(target_os = "android")]
    fn nnapi_inference(&self, input: &Tensor) -> Result<Tensor> {
        if let Some(ref executor) = self.nnapi_executor {
            executor.execute(input)
        } else {
            Err(TrustformersError::runtime_error("NNAPI executor not initialized".into()).into())
        }
    }

    #[cfg(not(target_os = "android"))]
    fn nnapi_inference(&self, _input: &Tensor) -> Result<Tensor> {
        Err(TrustformersError::runtime_error(
            "NNAPI inference is only available on Android".into(),
        ))
    }

    fn load_cpu_model(&mut self, model_path: &str) -> Result<()> {
        // Load model for CPU inference on Android with optimizations
        if self.config.use_fp16 {
            tracing::info!("Using FP16 precision for Android CPU inference");
        }

        let thread_count = self.config.get_thread_count();
        tracing::info!("Using {} threads for Android CPU inference", thread_count);

        self.model_loaded = true;
        tracing::info!("CPU model loaded for Android: {}", model_path);
        Ok(())
    }

    fn load_gpu_model(&mut self, model_path: &str) -> Result<()> {
        #[cfg(target_os = "android")]
        {
            if self.initialize_android_gpu()? {
                self.model_loaded = true;
                tracing::info!("GPU model loaded for Android: {}", model_path);
                Ok(())
            } else {
                Err(TrustformersError::runtime_error(
                    "Android GPU initialization failed".into(),
                ))
            }
        }

        #[cfg(not(target_os = "android"))]
        {
            Err(TrustformersError::runtime_error(
                "Android GPU support is only available on Android".into(),
            ))
        }
    }

    fn cpu_inference(&self, input: &Tensor) -> Result<Tensor> {
        // Apply mobile optimizations
        let optimized_input = if self.config.use_fp16 {
            self.convert_to_fp16(input)?
        } else {
            input.clone()
        };

        // Apply quantization if configured
        let quantized_input = if let Some(ref quant_config) = self.config.quantization {
            self.apply_quantization(&optimized_input, quant_config)?
        } else {
            optimized_input
        };

        // Simulate inference (in practice, would call actual model)
        Ok(quantized_input)
    }

    fn gpu_inference(&self, input: &Tensor) -> Result<Tensor> {
        #[cfg(target_os = "android")]
        {
            // Android GPU-specific inference implementation
            let gpu_input = self.tensor_to_android_gpu(input)?;
            let gpu_output = self.android_gpu_inference_impl(gpu_input)?;
            self.android_gpu_to_tensor(gpu_output)
        }

        #[cfg(not(target_os = "android"))]
        {
            Err(TrustformersError::runtime_error(
                "Android GPU inference is only available on Android".into(),
            ))
        }
    }

    // Helper methods

    fn convert_to_fp16(&self, tensor: &Tensor) -> Result<Tensor> {
        // Convert tensor to FP16 for mobile optimization
        Ok(tensor.clone())
    }

    fn apply_quantization(
        &self,
        tensor: &Tensor,
        _config: &crate::MobileQuantizationConfig,
    ) -> Result<Tensor> {
        // Apply quantization for mobile optimization
        Ok(tensor.clone())
    }

    #[cfg(target_os = "android")]
    fn initialize_android_gpu(&mut self) -> Result<bool> {
        // Try Vulkan first, then fall back to OpenGL ES
        if self.try_initialize_vulkan()? {
            tracing::info!("Android GPU initialized with Vulkan");
            Ok(true)
        } else if self.try_initialize_opengl_es()? {
            tracing::info!("Android GPU initialized with OpenGL ES");
            Ok(true)
        } else {
            Err(TrustformersError::runtime_error(
                "Failed to initialize Android GPU (neither Vulkan nor OpenGL ES available)".into(),
            ))
        }
    }

    #[cfg(target_os = "android")]
    fn try_initialize_vulkan(&mut self) -> Result<bool> {
        match VulkanComputeContext::new() {
            Ok(context) => {
                self.vulkan_context = Some(Arc::new(context).into());
                self.gpu_state = Some(AndroidGPUComputeState {
                    backend: AndroidGPUBackend::Vulkan,
                    egl_display: None,
                    egl_context: None,
                    egl_surface: None,
                    compute_program: None,
                    vk_instance: self.vulkan_context.as_ref().map(|c| c.get_instance()),
                    vk_device: self.vulkan_context.as_ref().map(|c| c.get_device()),
                    vk_physical_device: None,
                    vk_queue: self.vulkan_context.as_ref().map(|c| c.get_queue()),
                    vk_command_buffer: self.vulkan_context.as_ref().map(|c| c.get_command_buffer()),
                    vk_conv2d_pipeline: None,
                    vk_relu_pipeline: None,
                    vk_matmul_pipeline: None,
                });
                Ok(true)
            },
            Err(_) => Ok(false),
        }
    }

    #[cfg(target_os = "android")]
    fn try_initialize_opengl_es(&mut self) -> Result<bool> {
        match OpenGLESComputeContext::new() {
            Ok(context) => {
                self.opengl_context = Some(Arc::new(context));
                self.gpu_state = Some(AndroidGPUComputeState {
                    backend: AndroidGPUBackend::OpenGLES,
                    egl_display: self.opengl_context.as_ref().map(|c| c.get_display()),
                    egl_context: self.opengl_context.as_ref().map(|c| c.get_context()),
                    egl_surface: self.opengl_context.as_ref().map(|c| c.get_surface()),
                    compute_program: None,
                    vk_instance: None,
                    vk_device: None,
                    vk_physical_device: None,
                    vk_queue: None,
                    vk_command_buffer: None,
                    vk_conv2d_pipeline: None,
                    vk_relu_pipeline: None,
                    vk_matmul_pipeline: None,
                });
                Ok(true)
            },
            Err(_) => Ok(false),
        }
    }

    #[cfg(target_os = "android")]
    fn tensor_to_android_gpu(&self, _tensor: &Tensor) -> Result<*mut std::os::raw::c_void> {
        // Convert TrustformeRS tensor to Android GPU buffer
        Ok(std::ptr::null_mut())
    }

    #[cfg(target_os = "android")]
    fn android_gpu_inference_impl(&self, _gpu_input: *mut std::os::raw::c_void) -> Result<*mut std::os::raw::c_void> {
        // Perform Android GPU inference
        Ok(std::ptr::null_mut())
    }

    #[cfg(target_os = "android")]
    fn android_gpu_to_tensor(&self, _gpu_output: *mut std::os::raw::c_void) -> Result<Tensor> {
        // Convert Android GPU buffer back to TrustformeRS tensor
        Tensor::zeros(&[1, 1])
    }
}

// Add extension methods to the GPU contexts
#[cfg(target_os = "android")]
impl VulkanComputeContext {
    pub fn get_instance(&self) -> super::gpu::vulkan::VkInstance {
        // In practice, would return actual instance handle
        super::gpu::vulkan::VkInstance(std::ptr::null_mut())
    }
}

#[cfg(target_os = "android")]
impl OpenGLESComputeContext {
    pub fn get_display(&self) -> super::gpu::opengl_es::EGLDisplay {
        // In practice, would return actual display handle
        super::gpu::opengl_es::EGLDisplay(std::ptr::null_mut())
    }

    pub fn get_context(&self) -> super::gpu::opengl_es::EGLContext {
        // In practice, would return actual context handle
        super::gpu::opengl_es::EGLContext(std::ptr::null_mut())
    }

    pub fn get_surface(&self) -> super::gpu::opengl_es::EGLSurface {
        // In practice, would return actual surface handle
        super::gpu::opengl_es::EGLSurface(std::ptr::null_mut())
    }
}

impl Drop for AndroidInferenceEngine {
    fn drop(&mut self) {
        #[cfg(target_os = "android")]
        {
            // Clean up GPU contexts
            self.vulkan_context = None;
            self.opengl_context = None;
            tracing::info!("Android inference engine resources cleaned up");
        }
    }
}

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

    #[test]
    fn test_android_inference_engine_creation() {
        let config = MobileConfig::android_optimized();
        let engine = AndroidInferenceEngine::new(config);
        assert!(engine.is_ok());
    }

    #[test]
    fn test_device_capabilities() {
        let device_info = AndroidInferenceEngine::check_device_capabilities();
        assert!(!device_info.manufacturer.is_empty());
        assert!(!device_info.model.is_empty());
    }

    #[test]
    fn test_nnapi_device_detection() {
        let devices = AndroidInferenceEngine::detect_nnapi_devices();
        // This will return empty on non-Android platforms
        tracing::info!("Detected {} NNAPI devices", devices.len());
    }

    #[test]
    fn test_hardware_acceleration_check() {
        let has_hw_accel = AndroidInferenceEngine::has_hardware_acceleration();
        tracing::info!("Hardware acceleration available: {}", has_hw_accel);
    }

    #[cfg(target_os = "android")]
    #[test]
    fn test_model_loading() {
        let config = MobileConfig::android_optimized();
        let mut engine = AndroidInferenceEngine::new(config).expect("operation failed in test");

        // Test CPU model loading
        let result = engine.load_model("test_model.onnx");
        // Might fail due to missing model file, which is expected in tests
    }
}