//! Documentation and integration guide content
//!
//! This module provides comprehensive documentation templates for mobile
//! platform integration including iOS integration guides, Android setup
//! instructions, and optimization best practices.

/// iOS integration guide content
pub const IOS_INTEGRATION_GUIDE: &str = r#"# iOS Integration Guide
## Prerequisites
- Xcode 14.0 or later
- iOS 12.0 or later
- Swift 5.0 or later
## Installation
### Using CocoaPods
```ruby
pod 'SciRS2Neural', '~> 1.0'
```
### Manual Installation
1. Download the SciRS2Neural.framework
2. Drag and drop it into your Xcode project
3. Ensure "Copy items if needed" is checked
4. Add the framework to "Embedded Binaries"
## Usage
### Swift
```swift
import SciRS2Neural
class ViewController: UIViewController {
    var model: SciRS2Model?
    
    override func viewDidLoad() {
        super.viewDidLoad()
        setupModel()
    }
    func setupModel() {
        model = SciRS2Model()
        
        guard let modelPath = Bundle.main.path(forResource: "model", ofType: "mlmodel") else {
            print("Model file not found")
            return
        }
        do {
            try model?.loadModel(from: modelPath)
            print("Model loaded successfully")
        } catch {
            print("Failed to load model: \(error)")
    func runInference(with inputData: [Float]) {
        guard let model = model else { return }
            // Convert input data to MLMultiArray
            let inputArray = try MLMultiArray(shape: [1, NSNumber(value: inputData.count)], dataType: .float32)
            for (index, value) in inputData.enumerated() {
                inputArray[index] = NSNumber(value: value)
            }
            
            // Run inference
            let output = try model.predict(input: inputArray)
            // Process output
            print("Prediction completed")
            print("Inference failed: \(error)")
}
### Objective-C
```objc
#import "SciRS2Model.h"
@interface ViewController ()
@property (nonatomic, strong) SciRS2Model *model;
@end
@implementation ViewController
- (void)viewDidLoad {
    [super viewDidLoad];
    [self setupModel];
- (void)setupModel {
    self.model = [[SciRS2Model alloc] init];
    NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model" ofType:@"mlmodel"];
    if (!modelPath) {
        NSLog(@"Model file not found");
        return;
    NSError *error;
    [self.model loadModelFromPath:modelPath error:&error];
    if (error) {
        NSLog(@"Failed to load model: %@", error.localizedDescription);
    } else {
        NSLog(@"Model loaded successfully");
## Performance Optimization
### Memory Management
- Use autorelease pools for batch processing
- Release model resources when not needed
- Monitor memory usage with Instruments
### Metal Performance Shaders
import MetalPerformanceShaders
// Enable Metal acceleration
let metalDevice = MTLCreateSystemDefaultDevice()
let commandQueue = metalDevice?.makeCommandQueue()
### Core ML Optimization
- Use Core ML Tools for model optimization
- Enable compute unit preferences
- Use asynchronous prediction for better performance
## Troubleshooting
### Common Issues
1. **Model not found**: Ensure the model file is included in the app bundle
2. **Memory issues**: Check model size and available memory
3. **Performance problems**: Profile with Instruments
### Debug Tips
- Enable verbose logging
- Use breakpoints for debugging
- Test on different device types
## Best Practices
1. Load models asynchronously
2. Cache prediction results when appropriate
3. Handle low memory warnings
4. Test on older devices
5. Monitor thermal state
"#;
/// Android integration guide content
pub const ANDROID_INTEGRATION_GUIDE: &str = r#"# Android Integration Guide
- Android Studio 4.0 or later
- Android API level 21 or later
- NDK (for native code)
### Using Gradle
```gradle
dependencies {
    implementation 'com.scirs2:neural:1.0.0'
1. Download the AAR file
2. Place it in your `libs` directory
3. Add to your `build.gradle`:
repositories {
    flatDir {
        dirs 'libs'
    implementation(name: 'scirs2-neural', version: '1.0.0', ext: 'aar')
### Kotlin
```kotlin
import com.scirs2.neural.SciRS2Model
class MainActivity : AppCompatActivity() {
    private var model: SciRS2Model? = null
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)
    private fun setupModel() {
        try {
            model = SciRS2Model(this, "scirs2_model.tflite")
            Log.d("SciRS2", "Model loaded successfully")
        } catch (e: IOException) {
            Log.e("SciRS2", "Failed to load model", e)
    private fun runInference(inputData: FloatArray) {
        model?.let { model ->
            try {
                val output = model.predict(inputData)
                Log.d("SciRS2", "Prediction: ${output.contentToString()}")
            } catch (e: Exception) {
                Log.e("SciRS2", "Inference failed", e)
    override fun onDestroy() {
        super.onDestroy()
        model?.close()
### Java
```java
import com.scirs2.neural.SciRS2Model;
public class MainActivity extends AppCompatActivity {
    private SciRS2Model model;
    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);
        setupModel();
    private void setupModel() {
            model = new SciRS2Model(this, "scirs2_model.tflite");
            Log.d("SciRS2", "Model loaded successfully");
        } catch (IOException e) {
            Log.e("SciRS2", "Failed to load model", e);
    private void runInference(float[] inputData) {
        if (model != null) {
                float[] output = model.predict(inputData);
                Log.d("SciRS2", "Prediction completed");
            } catch (Exception e) {
                Log.e("SciRS2", "Inference failed", e);
    protected void onDestroy() {
        super.onDestroy();
            model.close();
### NNAPI Acceleration
// Enable NNAPI acceleration
val options = Interpreter.Options()
options.setUseNNAPI(true)
val interpreter = Interpreter(modelBuffer, options)
### GPU Delegation
// Enable GPU acceleration
val gpuDelegate = GpuDelegate()
options.addDelegate(gpuDelegate)
### Multi-threading
// Use multiple threads
options.setNumThreads(4)
## ProGuard/R8 Configuration
```proguard
-keep class com.scirs2.neural.** { *; }
-keep class org.tensorflow.lite.** { *; }
-keepclassmembers class * {
    native <methods>;
1. **Model loading failed**: Check if model file is in assets
2. **Native library not found**: Ensure NDK is properly configured
3. **Out of memory**: Reduce model size or input batch size
### Performance Issues
- Profile with Android Profiler
- Use systrace for detailed analysis
- Monitor CPU and GPU usage
1. Load models on background threads
2. Use appropriate delegates for acceleration
3. Handle different screen densities
4. Test on various device configurations
5. Implement proper error handling
/// Optimization guide content
pub const OPTIMIZATION_GUIDE: &str = r#"# Mobile Optimization Guide
## Overview
This guide covers optimization techniques for deploying neural networks on mobile devices.
## Model Optimization Techniques
### Quantization
#### Post-Training Quantization
- Converts FP32 weights to INT8
- Minimal accuracy loss
- 4x size reduction
- 2-4x speed improvement
#### Quantization-Aware Training
- Training with simulated quantization
- Better accuracy preservation
- Requires retraining
### Pruning
#### Magnitude-Based Pruning
- Remove smallest weights
- Structured or unstructured
- 50-90% sparsity possible
#### Gradient-Based Pruning
- Use gradient information
- More sophisticated importance metrics
- Better accuracy retention
### Knowledge Distillation
#### Teacher-Student Framework
- Large teacher model
- Small student model
- Transfer knowledge through soft targets
### Layer Fusion
#### Common Patterns
- Conv + BatchNorm + ReLU
- Dense + Activation
- Reduces memory bandwidth
## Platform-Specific Optimizations
### iOS Optimizations
#### Core ML
- Automatic optimization
- Hardware-specific acceleration
- Neural Engine utilization
#### Metal Performance Shaders
- GPU acceleration
- Custom kernels
- Memory optimization
### Android Optimizations
#### TensorFlow Lite
- Optimized for mobile
- Multiple acceleration options
- Flexible deployment
#### NNAPI
- Hardware abstraction layer
- Vendor-optimized implementations
- Automatic fallbacks
## Performance Monitoring
### Key Metrics
1. **Latency**: Time per inference
2. **Throughput**: Inferences per second
3. **Memory**: Peak and average usage
4. **Power**: Energy consumption
5. **Thermal**: Temperature impact
### Profiling Tools
#### iOS
- Instruments
- Core ML Performance Reports
- Xcode Energy Gauge
#### Android
- Android Profiler
- Systrace
- GPU Profiler
## Memory Optimization
### Strategies
1. **Model Compression**: Reduce model size
2. **Memory Pooling**: Reuse allocations
3. **Lazy Loading**: Load on demand
4. **Memory Mapping**: Map instead of load
### Implementation
// iOS Memory Pool
class MemoryPool {
    private var buffers: [MLMultiArray] = []
    func getBuffer(shape: [Int]) -> MLMultiArray {
        // Reuse existing buffer or create new one
    func returnBuffer(_ buffer: MLMultiArray) {
        // Return buffer to pool
## Power Management
1. **Adaptive Inference**: Adjust based on battery level
2. **Thermal Throttling**: Reduce performance when hot
3. **Scheduling**: Run during charging
4. **Quality Scaling**: Lower quality for battery saving
// Android Power Management
class PowerManager {
    fun getOptimalInferenceMode(): InferenceMode {
        val batteryLevel = getBatteryLevel()
        val thermalState = getThermalState()
        return when {
            batteryLevel < 20 -> InferenceMode.POWER_SAVE
            thermalState == ThermalState.CRITICAL -> InferenceMode.THROTTLED
            else -> InferenceMode.NORMAL
### Development
1. **Profile Early**: Start optimization from day one
2. **Target Devices**: Test on representative hardware
3. **Measure Real Usage**: Monitor production metrics
4. **Iterative Optimization**: Gradual improvements
### Deployment
1. **A/B Testing**: Compare optimization variants
2. **Progressive Rollout**: Gradual feature deployment
3. **Monitoring**: Track performance metrics
4. **Fallbacks**: Handle edge cases gracefully
1. **High Memory Usage**
   - Check for memory leaks
   - Optimize buffer allocation
   - Use memory profiling tools
2. **Poor Performance**
   - Profile inference pipeline
   - Check hardware utilization
   - Optimize model architecture
3. **Battery Drain**
   - Monitor power consumption
   - Implement adaptive strategies
   - Optimize inference frequency
4. **Thermal Issues**
   - Monitor temperature
   - Implement throttling
   - Optimize workload distribution
1. Enable detailed logging
2. Use profiling tools extensively
3. Test on multiple devices
4. Monitor real-world usage
