trustformers-mobile 0.1.1

//! NNAPI Model Converter and Optimization
//!
//! This module provides comprehensive model conversion from TrustformeRS to NNAPI format,
//! including optimization, quantization, and device-specific tuning for Android.

use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::path::{Path, PathBuf};
use trustformers_core::error::{CoreError, Result};
use trustformers_core::Tensor;
use trustformers_core::TrustformersError;

/// NNAPI model converter
pub struct NNAPIModelConverter {
    config: NNAPIConverterConfig,
    operation_validator: OperationValidator,
    device_optimizer: DeviceOptimizer,
}

/// NNAPI converter configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIConverterConfig {
    /// Target Android API level
    pub target_api_level: u32,
    /// Target devices
    pub target_devices: Vec<NNAPITargetDevice>,
    /// Enable model partitioning
    pub enable_partitioning: bool,
    /// Quantization configuration
    pub quantization: Option<NNAPIQuantizationConfig>,
    /// Optimization configuration
    pub optimization: NNAPIOptimizationConfig,
    /// Fallback strategy
    pub fallback_strategy: FallbackStrategy,
    /// Output format
    pub output_format: NNAPIFormat,
}

/// NNAPI target devices
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum NNAPITargetDevice {
    /// Generic CPU fallback
    CPU,
    /// GPU via Vulkan/OpenGL
    GPU,
    /// Qualcomm Hexagon DSP
    HexagonDSP,
    /// Google Edge TPU
    EdgeTPU,
    /// MediaTek APU
    MediaTekAPU,
    /// Samsung NPU
    SamsungNPU,
    /// HiSilicon NPU
    HiSiliconNPU,
    /// Generic accelerator
    GenericAccelerator,
}

/// NNAPI output format
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NNAPIFormat {
    /// Binary format
    Binary,
    /// FlatBuffer format
    FlatBuffer,
    /// TensorFlow Lite compatible
    TFLite,
}

/// Fallback strategy for unsupported operations
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FallbackStrategy {
    /// Fail if any operation is unsupported
    Fail,
    /// Use CPU fallback for unsupported operations
    CPUFallback,
    /// Partition model between NNAPI and CPU
    Partition,
}

/// NNAPI quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIQuantizationConfig {
    /// Quantization scheme
    pub scheme: NNAPIQuantizationScheme,
    /// Calibration configuration
    pub calibration: CalibrationConfig,
    /// Per-channel quantization
    pub per_channel: bool,
    /// Symmetric quantization
    pub symmetric: bool,
    /// Quantize inputs/outputs
    pub quantize_io: bool,
}

/// NNAPI quantization schemes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NNAPIQuantizationScheme {
    /// Dynamic quantization
    Dynamic,
    /// Full integer quantization
    FullInteger,
    /// Integer with float fallback
    IntegerWithFloat,
    /// Float16 quantization
    Float16,
}

/// Calibration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CalibrationConfig {
    /// Number of calibration samples
    pub num_samples: usize,
    /// Calibration method
    pub method: CalibrationMethod,
    /// Representative dataset path
    pub dataset_path: Option<PathBuf>,
}

/// Calibration methods
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum CalibrationMethod {
    /// Min-max calibration
    MinMax,
    /// Entropy calibration
    Entropy,
    /// Percentile calibration
    Percentile(f32),
    /// MSE calibration
    MSE,
}

/// NNAPI optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIOptimizationConfig {
    /// Enable operator fusion
    pub enable_fusion: bool,
    /// Enable layout optimization
    pub optimize_layout: bool,
    /// Enable constant folding
    pub constant_folding: bool,
    /// Enable dead code elimination
    pub dead_code_elimination: bool,
    /// Device-specific optimizations
    pub device_optimizations: bool,
}

/// NNAPI model representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIModel {
    /// Model metadata
    pub metadata: NNAPIModelMetadata,
    /// Model operands
    pub operands: Vec<NNAPIOperand>,
    /// Model operations
    pub operations: Vec<NNAPIOperation>,
    /// Model inputs
    pub inputs: Vec<u32>,
    /// Model outputs
    pub outputs: Vec<u32>,
    /// Constant data
    pub constants: HashMap<u32, Vec<u8>>,
    /// Execution preference
    pub execution_preference: ExecutionPreference,
}

/// NNAPI model metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIModelMetadata {
    /// Model name
    pub name: String,
    /// Model version
    pub version: String,
    /// Required API level
    pub min_api_level: u32,
    /// Supported devices
    pub supported_devices: Vec<NNAPITargetDevice>,
    /// Model hash for caching
    pub model_hash: String,
    /// Performance hints
    pub performance_hints: PerformanceHints,
}

/// Performance hints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceHints {
    /// Expected latency (ms)
    pub expected_latency_ms: Option<f32>,
    /// Expected power usage (mW)
    pub expected_power_mw: Option<f32>,
    /// Memory usage (MB)
    pub memory_usage_mb: Option<f32>,
    /// Recommended batch size
    pub recommended_batch_size: Option<usize>,
}

/// Execution preference
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ExecutionPreference {
    /// Low latency
    LowLatency,
    /// Sustained speed
    SustainedSpeed,
    /// Low power
    LowPower,
}

/// NNAPI operand
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIOperand {
    /// Operand index
    pub index: u32,
    /// Data type
    pub dtype: NNAPIDataType,
    /// Dimensions
    pub dimensions: Vec<u32>,
    /// Scale (for quantized types)
    pub scale: Option<f32>,
    /// Zero point (for quantized types)
    pub zero_point: Option<i32>,
    /// Lifetime
    pub lifetime: OperandLifetime,
    /// Location (for constants)
    pub location: Option<DataLocation>,
}

/// NNAPI data types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum NNAPIDataType {
    Float32,
    Float16,
    Int32,
    UInt32,
    Int8,
    UInt8,
    Bool,
    TensorFloat32,
    TensorFloat16,
    TensorInt32,
    TensorQuant8Asymm,
    TensorQuant8Symm,
    TensorQuant16Asymm,
    TensorQuant16Symm,
}

/// Operand lifetime
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OperandLifetime {
    /// Temporary variable
    TemporaryVariable,
    /// Model input
    ModelInput,
    /// Model output
    ModelOutput,
    /// Constant reference
    ConstantReference,
    /// No value
    NoValue,
}

/// Data location for constants
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataLocation {
    /// Offset in constant pool
    pub offset: usize,
    /// Length in bytes
    pub length: usize,
}

/// NNAPI operation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NNAPIOperation {
    /// Operation type
    pub operation_type: NNAPIOperationType,
    /// Input operand indices
    pub inputs: Vec<u32>,
    /// Output operand indices
    pub outputs: Vec<u32>,
    /// Operation-specific parameters
    pub params: OperationParams,
}

/// NNAPI operation types
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum NNAPIOperationType {
    // Arithmetic
    Add,
    Sub,
    Mul,
    Div,

    // Neural network layers
    Conv2D,
    DepthwiseConv2D,
    FullyConnected,

    // Pooling
    AveragePool2D,
    MaxPool2D,
    L2Pool2D,

    // Activation
    Relu,
    Relu1,
    Relu6,
    Sigmoid,
    Tanh,

    // Normalization
    BatchNorm,
    LocalResponseNorm,

    // Other
    Softmax,
    Reshape,
    Transpose,
    Concat,
    Split,
    Squeeze,
    StridedSlice,
    Pad,

    // Quantization
    Quantize,
    Dequantize,

    // LSTM/RNN
    LSTM,
    RNN,

    // Custom operations
    Custom(String),
}

/// Operation parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperationParams {
    /// Generic parameters
    pub params: HashMap<String, OperationParam>,
}

/// Operation parameter value
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OperationParam {
    Int(i32),
    Float(f32),
    Bool(bool),
    IntArray(Vec<i32>),
    FloatArray(Vec<f32>),
    Operand(u32),
}

/// Operation validator
struct OperationValidator {
    supported_ops: HashMap<u32, HashSet<NNAPIOperationType>>,
    device_capabilities: HashMap<NNAPITargetDevice, DeviceCapabilities>,
}

/// Device capabilities
#[derive(Debug, Clone)]
struct DeviceCapabilities {
    supported_operations: HashSet<NNAPIOperationType>,
    supported_data_types: HashSet<NNAPIDataType>,
    max_operand_size: usize,
    supports_relaxed_fp32: bool,
    supports_low_power: bool,
}

/// Device optimizer
struct DeviceOptimizer {
    device_profiles: HashMap<NNAPITargetDevice, DeviceProfile>,
}

/// Device profile
#[derive(Debug, Clone)]
struct DeviceProfile {
    compute_throughput: f32,
    memory_bandwidth: f32,
    power_efficiency: f32,
    preferred_layouts: Vec<TensorLayout>,
}

/// Tensor layout
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum TensorLayout {
    NHWC,
    NCHW,
    NC,
}

impl NNAPIModelConverter {
    /// Create new NNAPI model converter
    pub fn new(config: NNAPIConverterConfig) -> Self {
        let operation_validator = OperationValidator::new(config.target_api_level);
        let device_optimizer = DeviceOptimizer::new(&config.target_devices);

        Self {
            config,
            operation_validator,
            device_optimizer,
        }
    }

    /// Convert TrustformeRS model to NNAPI
    pub fn convert(&self, model_path: &Path, output_path: &Path) -> Result<ConversionResult> {
        // Load TrustformeRS model
        let trustformers_model = self.load_trustformers_model(model_path)?;

        // Validate model compatibility
        self.validate_model_compatibility(&trustformers_model)?;

        // Convert to NNAPI model
        let mut nnapi_model = self.convert_to_nnapi(trustformers_model)?;

        // Apply optimizations
        if self.config.optimization.enable_fusion {
            self.apply_operator_fusion(&mut nnapi_model)?;
        }

        if self.config.optimization.optimize_layout {
            self.optimize_tensor_layout(&mut nnapi_model)?;
        }

        if self.config.optimization.constant_folding {
            self.apply_constant_folding(&mut nnapi_model)?;
        }

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

        // Partition model if needed
        let partitions = if self.config.enable_partitioning {
            self.partition_model(&nnapi_model)?
        } else {
            vec![nnapi_model.clone()]
        };

        // Write NNAPI model
        let output_info = self.write_nnapi_model(&partitions, output_path)?;

        // Create conversion result
        Ok(ConversionResult {
            output_path: output_info.path,
            model_size_mb: output_info.size_mb,
            num_operations: nnapi_model.operations.len(),
            num_partitions: partitions.len(),
            supported_devices: nnapi_model.metadata.supported_devices.clone(),
            optimization_report: self.generate_optimization_report(&nnapi_model),
            compatibility_report: self.generate_compatibility_report(&nnapi_model),
        })
    }

    /// Load TrustformeRS model
    fn load_trustformers_model(&self, path: &Path) -> Result<TrustformersModel> {
        // Placeholder for loading logic
        Ok(TrustformersModel {
            weights: HashMap::new(),
            graph: Vec::new(),
        })
    }

    /// Validate model compatibility
    fn validate_model_compatibility(&self, model: &TrustformersModel) -> Result<()> {
        for op in &model.graph {
            let nnapi_op = self.map_operation_type(&op.op_type)?;

            // Check if operation is supported on target devices
            let supported = self.config.target_devices.iter().any(|device| {
                self.operation_validator.is_supported(
                    *device,
                    nnapi_op.clone(),
                    self.config.target_api_level,
                )
            });

            if !supported && self.config.fallback_strategy == FallbackStrategy::Fail {
                return Err(TrustformersError::runtime_error(format!(
                    "Operation {} not supported on target devices",
                    op.op_type
                ))
                .into());
            }
        }

        Ok(())
    }

    /// Convert to NNAPI model
    fn convert_to_nnapi(&self, model: TrustformersModel) -> Result<NNAPIModel> {
        let mut operands = Vec::new();
        let mut operations = Vec::new();
        let mut constants = HashMap::new();
        let mut operand_index = 0u32;

        // Create operands for model inputs
        let input_indices = self.create_input_operands(&mut operands, &mut operand_index);

        // Convert operations
        for op in model.graph {
            let (nnapi_op, output_indices) =
                self.convert_operation(op, &mut operands, &mut operand_index, &mut constants)?;
            operations.push(nnapi_op);
        }

        // Identify output operands
        let output_indices = self.identify_output_operands(&operations);

        Ok(NNAPIModel {
            metadata: self.create_metadata(),
            operands,
            operations,
            inputs: input_indices,
            outputs: output_indices,
            constants,
            execution_preference: self.select_execution_preference(),
        })
    }

    /// Map operation type
    fn map_operation_type(&self, op_type: &str) -> Result<NNAPIOperationType> {
        match op_type {
            "Conv2d" => Ok(NNAPIOperationType::Conv2D),
            "Linear" | "Dense" => Ok(NNAPIOperationType::FullyConnected),
            "BatchNorm2d" => Ok(NNAPIOperationType::BatchNorm),
            "ReLU" => Ok(NNAPIOperationType::Relu),
            "MaxPool2d" => Ok(NNAPIOperationType::MaxPool2D),
            "AvgPool2d" => Ok(NNAPIOperationType::AveragePool2D),
            "Softmax" => Ok(NNAPIOperationType::Softmax),
            "Add" => Ok(NNAPIOperationType::Add),
            "Mul" => Ok(NNAPIOperationType::Mul),
            "Reshape" => Ok(NNAPIOperationType::Reshape),
            "Transpose" => Ok(NNAPIOperationType::Transpose),
            "Concat" => Ok(NNAPIOperationType::Concat),
            _ => {
                if self.config.fallback_strategy == FallbackStrategy::Fail {
                    Err(
                        TrustformersError::runtime_error(format!("Unknown operation: {}", op_type))
                            .into(),
                    )
                } else {
                    Ok(NNAPIOperationType::Custom(op_type.to_string()))
                }
            },
        }
    }

    /// Create input operands
    fn create_input_operands(
        &self,
        operands: &mut Vec<NNAPIOperand>,
        operand_index: &mut u32,
    ) -> Vec<u32> {
        let mut input_indices = Vec::new();

        // Create default input operand (would be based on actual model in production)
        let input_operand = NNAPIOperand {
            index: *operand_index,
            dtype: NNAPIDataType::TensorFloat32,
            dimensions: vec![1, 3, 224, 224],
            scale: None,
            zero_point: None,
            lifetime: OperandLifetime::ModelInput,
            location: None,
        };

        input_indices.push(*operand_index);
        operands.push(input_operand);
        *operand_index += 1;

        input_indices
    }

    /// Convert operation
    fn convert_operation(
        &self,
        op: Operation,
        operands: &mut Vec<NNAPIOperand>,
        operand_index: &mut u32,
        constants: &mut HashMap<u32, Vec<u8>>,
    ) -> Result<(NNAPIOperation, Vec<u32>)> {
        let operation_type = self.map_operation_type(&op.op_type)?;

        // Create output operand
        let output_operand = NNAPIOperand {
            index: *operand_index,
            dtype: NNAPIDataType::TensorFloat32,
            dimensions: vec![1, 64, 112, 112], // Placeholder
            scale: None,
            zero_point: None,
            lifetime: OperandLifetime::TemporaryVariable,
            location: None,
        };

        let output_index = *operand_index;
        operands.push(output_operand);
        *operand_index += 1;

        // Convert parameters
        let params = self.convert_operation_params(op.params);

        let nnapi_op = NNAPIOperation {
            operation_type,
            inputs: vec![0], // Placeholder - would map actual inputs
            outputs: vec![output_index],
            params,
        };

        Ok((nnapi_op, vec![output_index]))
    }

    /// Convert operation parameters
    fn convert_operation_params(&self, params: HashMap<String, String>) -> OperationParams {
        let mut converted = HashMap::new();

        for (key, value) in params {
            if let Ok(int_val) = value.parse::<i32>() {
                converted.insert(key, OperationParam::Int(int_val));
            } else if let Ok(float_val) = value.parse::<f32>() {
                converted.insert(key, OperationParam::Float(float_val));
            } else if value == "true" || value == "false" {
                converted.insert(key, OperationParam::Bool(value == "true"));
            }
        }

        OperationParams { params: converted }
    }

    /// Identify output operands
    fn identify_output_operands(&self, operations: &[NNAPIOperation]) -> Vec<u32> {
        // Find operands that are not used as inputs to any operation
        let mut all_outputs: HashSet<u32> = HashSet::new();
        let mut all_inputs: HashSet<u32> = HashSet::new();

        for op in operations {
            for &output in &op.outputs {
                all_outputs.insert(output);
            }
            for &input in &op.inputs {
                all_inputs.insert(input);
            }
        }

        all_outputs.difference(&all_inputs).cloned().collect()
    }

    /// Create metadata
    fn create_metadata(&self) -> NNAPIModelMetadata {
        NNAPIModelMetadata {
            name: "TrustformersModel".to_string(),
            version: "1.0.0".to_string(),
            min_api_level: self.config.target_api_level,
            supported_devices: self.config.target_devices.clone(),
            model_hash: self.compute_model_hash(),
            performance_hints: PerformanceHints {
                expected_latency_ms: None,
                expected_power_mw: None,
                memory_usage_mb: None,
                recommended_batch_size: Some(1),
            },
        }
    }

    /// Compute model hash for caching
    fn compute_model_hash(&self) -> String {
        // Simplified hash computation
        "model_hash_placeholder".to_string()
    }

    /// Select execution preference
    fn select_execution_preference(&self) -> ExecutionPreference {
        // Select based on target devices
        if self.config.target_devices.contains(&NNAPITargetDevice::CPU) {
            ExecutionPreference::LowPower
        } else if self.config.target_devices.contains(&NNAPITargetDevice::GPU) {
            ExecutionPreference::SustainedSpeed
        } else {
            ExecutionPreference::LowLatency
        }
    }

    /// Apply operator fusion
    fn apply_operator_fusion(&self, model: &mut NNAPIModel) -> Result<()> {
        // Fuse Conv + BatchNorm + ReLU patterns
        let mut i = 0;
        while i < model.operations.len() - 2 {
            if matches!(
                model.operations[i].operation_type,
                NNAPIOperationType::Conv2D
            ) && matches!(
                model.operations[i + 1].operation_type,
                NNAPIOperationType::BatchNorm
            ) && matches!(
                model.operations[i + 2].operation_type,
                NNAPIOperationType::Relu
            ) {
                // Fuse operations (simplified)
                println!("Fusing Conv2D + BatchNorm + ReLU at index {}", i);
                i += 3;
            } else {
                i += 1;
            }
        }

        Ok(())
    }

    /// Optimize tensor layout
    fn optimize_tensor_layout(&self, model: &mut NNAPIModel) -> Result<()> {
        // Optimize layout based on target devices
        for device in &self.config.target_devices {
            if let Some(profile) = self.device_optimizer.device_profiles.get(device) {
                if profile.preferred_layouts.contains(&TensorLayout::NHWC) {
                    // Convert tensors to NHWC layout
                    println!("Optimizing tensor layout for {:?}", device);
                }
            }
        }

        Ok(())
    }

    /// Apply constant folding
    fn apply_constant_folding(&self, model: &mut NNAPIModel) -> Result<()> {
        // Fold operations with constant inputs
        println!("Applying constant folding optimization");
        Ok(())
    }

    /// Apply quantization
    fn apply_quantization(
        &self,
        model: &mut NNAPIModel,
        config: &NNAPIQuantizationConfig,
    ) -> Result<()> {
        match config.scheme {
            NNAPIQuantizationScheme::Dynamic => {
                self.apply_dynamic_quantization(model, config)?;
            },
            NNAPIQuantizationScheme::FullInteger => {
                self.apply_full_integer_quantization(model, config)?;
            },
            NNAPIQuantizationScheme::Float16 => {
                self.apply_float16_quantization(model)?;
            },
            _ => {},
        }

        Ok(())
    }

    /// Apply dynamic quantization
    fn apply_dynamic_quantization(
        &self,
        model: &mut NNAPIModel,
        config: &NNAPIQuantizationConfig,
    ) -> Result<()> {
        // Quantize weights dynamically
        for operand in &mut model.operands {
            if operand.lifetime == OperandLifetime::ConstantReference {
                operand.dtype = NNAPIDataType::TensorQuant8Asymm;
                operand.scale = Some(0.1);
                operand.zero_point = Some(128);
            }
        }

        Ok(())
    }

    /// Apply full integer quantization
    fn apply_full_integer_quantization(
        &self,
        model: &mut NNAPIModel,
        config: &NNAPIQuantizationConfig,
    ) -> Result<()> {
        // Quantize all tensors to integers
        for operand in &mut model.operands {
            if operand.dtype == NNAPIDataType::TensorFloat32 {
                operand.dtype = NNAPIDataType::TensorQuant8Asymm;
                operand.scale = Some(0.1);
                operand.zero_point = Some(128);
            }
        }

        Ok(())
    }

    /// Apply float16 quantization
    fn apply_float16_quantization(&self, model: &mut NNAPIModel) -> Result<()> {
        // Convert float32 to float16
        for operand in &mut model.operands {
            if operand.dtype == NNAPIDataType::TensorFloat32 {
                operand.dtype = NNAPIDataType::TensorFloat16;
            }
        }

        Ok(())
    }

    /// Partition model
    fn partition_model(&self, model: &NNAPIModel) -> Result<Vec<NNAPIModel>> {
        // Partition model based on device support
        let mut partitions = Vec::new();

        // For now, just return the whole model
        partitions.push(model.clone());

        Ok(partitions)
    }

    /// Write NNAPI model
    fn write_nnapi_model(
        &self,
        partitions: &[NNAPIModel],
        output_path: &Path,
    ) -> Result<OutputInfo> {
        let model_data = self.serialize_model(partitions)?;

        // Create output directory
        if let Some(parent) = output_path.parent() {
            std::fs::create_dir_all(parent)?;
        }

        // Write model file
        let model_path = match self.config.output_format {
            NNAPIFormat::Binary => output_path.with_extension("nnapi"),
            NNAPIFormat::FlatBuffer => output_path.with_extension("fb"),
            NNAPIFormat::TFLite => output_path.with_extension("tflite"),
        };

        std::fs::write(&model_path, &model_data)?;

        // Calculate size
        let size_mb = model_data.len() as f32 / (1024.0 * 1024.0);

        Ok(OutputInfo {
            path: model_path,
            size_mb,
        })
    }

    /// Serialize model
    fn serialize_model(&self, partitions: &[NNAPIModel]) -> Result<Vec<u8>> {
        // In production, would use appropriate serialization format
        Ok(serde_json::to_vec(partitions)?)
    }

    /// Generate optimization report
    fn generate_optimization_report(&self, model: &NNAPIModel) -> OptimizationReport {
        OptimizationReport {
            fusions_applied: 0, // Would count actual fusions
            layout_optimized: self.config.optimization.optimize_layout,
            constants_folded: 0, // Would count actual foldings
            quantization_applied: self.config.quantization.is_some(),
            model_partitioned: self.config.enable_partitioning,
        }
    }

    /// Generate compatibility report
    fn generate_compatibility_report(&self, model: &NNAPIModel) -> CompatibilityReport {
        CompatibilityReport {
            min_api_level: model.metadata.min_api_level,
            supported_devices: model.metadata.supported_devices.clone(),
            unsupported_ops: vec![], // Would list actual unsupported ops
            warnings: vec![],
            fallback_required: false,
        }
    }
}

impl OperationValidator {
    fn new(api_level: u32) -> Self {
        let mut validator = Self {
            supported_ops: HashMap::new(),
            device_capabilities: HashMap::new(),
        };

        // Initialize supported operations per API level
        validator.init_supported_ops(api_level);
        validator.init_device_capabilities();

        validator
    }

    fn init_supported_ops(&mut self, api_level: u32) {
        // API level 27 (Android 8.1)
        let mut ops_27 = HashSet::new();
        ops_27.insert(NNAPIOperationType::Add);
        ops_27.insert(NNAPIOperationType::Conv2D);
        ops_27.insert(NNAPIOperationType::FullyConnected);
        ops_27.insert(NNAPIOperationType::Relu);
        self.supported_ops.insert(27, ops_27);

        // API level 28 (Android 9.0)
        let mut ops_28 = self.supported_ops[&27].clone();
        ops_28.insert(NNAPIOperationType::BatchNorm);
        ops_28.insert(NNAPIOperationType::Transpose);
        self.supported_ops.insert(28, ops_28);

        // API level 29 (Android 10)
        let mut ops_29 = self.supported_ops[&28].clone();
        ops_29.insert(NNAPIOperationType::LSTM);
        ops_29.insert(NNAPIOperationType::Quantize);
        ops_29.insert(NNAPIOperationType::Dequantize);
        self.supported_ops.insert(29, ops_29.clone());

        // API level 30+ (Android 11+)
        let ops_30 = ops_29; // All operations
        self.supported_ops.insert(30, ops_30);
    }

    fn init_device_capabilities(&mut self) {
        // CPU capabilities
        self.device_capabilities.insert(
            NNAPITargetDevice::CPU,
            DeviceCapabilities {
                supported_operations: self.supported_ops[&30].clone(),
                supported_data_types: vec![
                    NNAPIDataType::Float32,
                    NNAPIDataType::Int32,
                    NNAPIDataType::TensorFloat32,
                    NNAPIDataType::TensorQuant8Asymm,
                ]
                .into_iter()
                .collect(),
                max_operand_size: 1024 * 1024 * 1024, // 1GB
                supports_relaxed_fp32: true,
                supports_low_power: true,
            },
        );

        // GPU capabilities
        self.device_capabilities.insert(
            NNAPITargetDevice::GPU,
            DeviceCapabilities {
                supported_operations: vec![
                    NNAPIOperationType::Conv2D,
                    NNAPIOperationType::FullyConnected,
                    NNAPIOperationType::Relu,
                    NNAPIOperationType::MaxPool2D,
                ]
                .into_iter()
                .collect(),
                supported_data_types: vec![
                    NNAPIDataType::Float32,
                    NNAPIDataType::Float16,
                    NNAPIDataType::TensorFloat32,
                    NNAPIDataType::TensorFloat16,
                ]
                .into_iter()
                .collect(),
                max_operand_size: 512 * 1024 * 1024, // 512MB
                supports_relaxed_fp32: true,
                supports_low_power: false,
            },
        );
    }

    fn is_supported(
        &self,
        device: NNAPITargetDevice,
        op: NNAPIOperationType,
        api_level: u32,
    ) -> bool {
        // Check API level support
        if let Some(supported_ops) = self.supported_ops.get(&api_level.min(30)) {
            if !supported_ops.contains(&op) {
                return false;
            }
        }

        // Check device support
        if let Some(capabilities) = self.device_capabilities.get(&device) {
            capabilities.supported_operations.contains(&op)
        } else {
            false
        }
    }
}

impl DeviceOptimizer {
    fn new(target_devices: &[NNAPITargetDevice]) -> Self {
        let mut optimizer = Self {
            device_profiles: HashMap::new(),
        };

        optimizer.init_device_profiles();

        optimizer
    }

    fn init_device_profiles(&mut self) {
        // CPU profile
        self.device_profiles.insert(
            NNAPITargetDevice::CPU,
            DeviceProfile {
                compute_throughput: 1.0,
                memory_bandwidth: 10.0,
                power_efficiency: 0.8,
                preferred_layouts: vec![TensorLayout::NHWC],
            },
        );

        // GPU profile
        self.device_profiles.insert(
            NNAPITargetDevice::GPU,
            DeviceProfile {
                compute_throughput: 10.0,
                memory_bandwidth: 50.0,
                power_efficiency: 0.5,
                preferred_layouts: vec![TensorLayout::NCHW],
            },
        );

        // DSP profile
        self.device_profiles.insert(
            NNAPITargetDevice::HexagonDSP,
            DeviceProfile {
                compute_throughput: 5.0,
                memory_bandwidth: 20.0,
                power_efficiency: 0.9,
                preferred_layouts: vec![TensorLayout::NHWC],
            },
        );
    }
}

// Placeholder structures
struct TrustformersModel {
    weights: HashMap<String, Tensor>,
    graph: Vec<Operation>,
}

struct Operation {
    name: String,
    op_type: String,
    inputs: Vec<String>,
    outputs: Vec<String>,
    params: HashMap<String, String>,
}

struct OutputInfo {
    path: PathBuf,
    size_mb: f32,
}

/// Conversion result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConversionResult {
    /// Output model path
    pub output_path: PathBuf,
    /// Model size in MB
    pub model_size_mb: f32,
    /// Number of operations
    pub num_operations: usize,
    /// Number of partitions
    pub num_partitions: usize,
    /// Supported devices
    pub supported_devices: Vec<NNAPITargetDevice>,
    /// Optimization report
    pub optimization_report: OptimizationReport,
    /// Compatibility report
    pub compatibility_report: CompatibilityReport,
}

/// Optimization report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationReport {
    /// Number of fusions applied
    pub fusions_applied: usize,
    /// Whether layout was optimized
    pub layout_optimized: bool,
    /// Number of constants folded
    pub constants_folded: usize,
    /// Whether quantization was applied
    pub quantization_applied: bool,
    /// Whether model was partitioned
    pub model_partitioned: bool,
}

/// Compatibility report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompatibilityReport {
    /// Minimum API level required
    pub min_api_level: u32,
    /// Supported devices
    pub supported_devices: Vec<NNAPITargetDevice>,
    /// Unsupported operations
    pub unsupported_ops: Vec<String>,
    /// Compatibility warnings
    pub warnings: Vec<String>,
    /// Whether CPU fallback is required
    pub fallback_required: bool,
}

impl Default for NNAPIConverterConfig {
    fn default() -> Self {
        Self {
            target_api_level: 29, // Android 10
            target_devices: vec![NNAPITargetDevice::CPU],
            enable_partitioning: true,
            quantization: None,
            optimization: NNAPIOptimizationConfig {
                enable_fusion: true,
                optimize_layout: true,
                constant_folding: true,
                dead_code_elimination: true,
                device_optimizations: true,
            },
            fallback_strategy: FallbackStrategy::CPUFallback,
            output_format: NNAPIFormat::Binary,
        }
    }
}

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

    #[test]
    fn test_converter_creation() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        assert!(converter.operation_validator.supported_ops.contains_key(&29));
    }

    #[test]
    fn test_quantization_config() {
        let config = NNAPIQuantizationConfig {
            scheme: NNAPIQuantizationScheme::FullInteger,
            calibration: CalibrationConfig {
                num_samples: 1000,
                method: CalibrationMethod::MinMax,
                dataset_path: None,
            },
            per_channel: true,
            symmetric: false,
            quantize_io: true,
        };

        assert_eq!(config.scheme, NNAPIQuantizationScheme::FullInteger);
        assert!(config.per_channel);
    }

    #[test]
    fn test_operation_mapping() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);

        assert_eq!(
            converter.map_operation_type("Conv2d").expect("operation failed in test"),
            NNAPIOperationType::Conv2D
        );
        assert_eq!(
            converter.map_operation_type("ReLU").expect("operation failed in test"),
            NNAPIOperationType::Relu
        );
    }

    #[test]
    fn test_default_converter_config() {
        let config = NNAPIConverterConfig::default();
        assert!(config.target_api_level >= 29);
        assert!(!config.target_devices.is_empty());
        assert!(matches!(
            config.fallback_strategy,
            FallbackStrategy::CPUFallback
        ));
    }

    #[test]
    fn test_nnapi_target_device_variants() {
        let devices = vec![
            NNAPITargetDevice::CPU,
            NNAPITargetDevice::GPU,
            NNAPITargetDevice::HexagonDSP,
            NNAPITargetDevice::EdgeTPU,
            NNAPITargetDevice::MediaTekAPU,
            NNAPITargetDevice::SamsungNPU,
            NNAPITargetDevice::HiSiliconNPU,
            NNAPITargetDevice::GenericAccelerator,
        ];
        assert_eq!(devices.len(), 8);
    }

    #[test]
    fn test_nnapi_format_variants() {
        let formats = vec![
            NNAPIFormat::Binary,
            NNAPIFormat::FlatBuffer,
            NNAPIFormat::TFLite,
        ];
        assert_eq!(formats.len(), 3);
    }

    #[test]
    fn test_fallback_strategy_variants() {
        let strategies = vec![
            FallbackStrategy::Fail,
            FallbackStrategy::CPUFallback,
            FallbackStrategy::Partition,
        ];
        assert_eq!(strategies.len(), 3);
    }

    #[test]
    fn test_quantization_scheme_variants() {
        let schemes = vec![
            NNAPIQuantizationScheme::Dynamic,
            NNAPIQuantizationScheme::FullInteger,
            NNAPIQuantizationScheme::IntegerWithFloat,
            NNAPIQuantizationScheme::Float16,
        ];
        assert_eq!(schemes.len(), 4);
    }

    #[test]
    fn test_calibration_method_variants() {
        let methods = vec![CalibrationMethod::MinMax];
        assert_eq!(methods.len(), 1);
    }

    #[test]
    fn test_quantization_config_per_channel() {
        let config = NNAPIQuantizationConfig {
            scheme: NNAPIQuantizationScheme::FullInteger,
            calibration: CalibrationConfig {
                num_samples: 500,
                method: CalibrationMethod::MinMax,
                dataset_path: None,
            },
            per_channel: false,
            symmetric: true,
            quantize_io: false,
        };
        assert!(!config.per_channel);
        assert!(config.symmetric);
        assert!(!config.quantize_io);
    }

    #[test]
    fn test_calibration_config_with_path() {
        let config = CalibrationConfig {
            num_samples: 2000,
            method: CalibrationMethod::MinMax,
            dataset_path: Some(PathBuf::from("/tmp/calibration_data")),
        };
        assert!(config.dataset_path.is_some());
        assert_eq!(config.num_samples, 2000);
    }

    #[test]
    fn test_operation_mapping_matmul() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        let result = converter.map_operation_type("MatMul");
        assert!(result.is_ok());
    }

    #[test]
    fn test_operation_mapping_unknown() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        let result = converter.map_operation_type("UnknownOp123");
        // Unknown operations may map to a fallback or error
        let _ = result; // Just verify it doesn't panic
    }

    #[test]
    fn test_operation_mapping_batch_norm() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        let result = converter.map_operation_type("BatchNorm");
        assert!(result.is_ok());
    }

    #[test]
    fn test_converter_supported_ops_not_empty() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        assert!(!converter.operation_validator.supported_ops.is_empty());
    }

    #[test]
    fn test_operation_mapping_pooling() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        let result = converter.map_operation_type("MaxPool2d");
        assert!(result.is_ok());
    }

    #[test]
    fn test_operation_mapping_softmax() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        let result = converter.map_operation_type("Softmax");
        assert!(result.is_ok());
    }

    #[test]
    fn test_operation_mapping_add() {
        let config = NNAPIConverterConfig::default();
        let converter = NNAPIModelConverter::new(config);
        let result = converter.map_operation_type("Add");
        assert!(result.is_ok());
    }

    #[test]
    fn test_converter_with_quantization() {
        let mut config = NNAPIConverterConfig::default();
        config.quantization = Some(NNAPIQuantizationConfig {
            scheme: NNAPIQuantizationScheme::FullInteger,
            calibration: CalibrationConfig {
                num_samples: 100,
                method: CalibrationMethod::MinMax,
                dataset_path: None,
            },
            per_channel: true,
            symmetric: true,
            quantize_io: true,
        });
        let converter = NNAPIModelConverter::new(config);
        assert!(!converter.operation_validator.supported_ops.is_empty());
    }

    #[test]
    fn test_converter_with_partitioning() {
        let mut config = NNAPIConverterConfig::default();
        config.enable_partitioning = true;
        let converter = NNAPIModelConverter::new(config);
        assert!(!converter.operation_validator.supported_ops.is_empty());
    }

    #[test]
    fn test_nnapi_target_device_equality() {
        assert_eq!(NNAPITargetDevice::CPU, NNAPITargetDevice::CPU);
        assert_ne!(NNAPITargetDevice::CPU, NNAPITargetDevice::GPU);
    }

    #[test]
    fn test_nnapi_format_equality() {
        assert_eq!(NNAPIFormat::Binary, NNAPIFormat::Binary);
        assert_ne!(NNAPIFormat::Binary, NNAPIFormat::TFLite);
    }
}