trustformers-mobile 0.1.1

//! Core ML Model Converter and Optimization
//!
//! This module provides comprehensive model conversion from TrustformeRS to Core ML format,
//! including optimization, quantization, and hardware-specific tuning.

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

/// Core ML model format version
pub const COREML_VERSION: u32 = 5;

/// Core ML model converter
pub struct CoreMLModelConverter {
    config: CoreMLConverterConfig,
    optimization_passes: Vec<Box<dyn OptimizationPass>>,
    validation_rules: Vec<Box<dyn ValidationRule>>,
}

/// Core ML converter configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoreMLConverterConfig {
    /// Target iOS version
    pub target_ios_version: String,
    /// Optimization level
    pub optimization_level: OptimizationLevel,
    /// Enable model compression
    pub enable_compression: bool,
    /// Quantization configuration
    pub quantization: Option<CoreMLQuantizationConfig>,
    /// Model pruning configuration
    pub pruning: Option<PruningConfig>,
    /// Output format
    pub output_format: CoreMLFormat,
    /// Hardware target
    pub hardware_target: HardwareTarget,
}

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

/// Core ML output format
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CoreMLFormat {
    /// Standard .mlmodel format
    MLModel,
    /// Compiled .mlmodelc format
    MLModelC,
    /// Package format with metadata
    MLPackage,
}

/// Hardware optimization target
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum HardwareTarget {
    /// Optimize for all hardware
    All,
    /// Optimize for Neural Engine
    NeuralEngine,
    /// Optimize for GPU
    GPU,
    /// Optimize for CPU
    CPU,
}

/// Core ML quantization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoreMLQuantizationConfig {
    /// Weight quantization
    pub weight_bits: QuantizationBits,
    /// Activation quantization
    pub activation_bits: Option<QuantizationBits>,
    /// Quantization method
    pub method: QuantizationMethod,
    /// Calibration dataset size
    pub calibration_size: usize,
    /// Per-channel quantization
    pub per_channel: bool,
}

/// Quantization bit widths
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantizationBits {
    /// 1-bit (binary)
    Bit1,
    /// 2-bit
    Bit2,
    /// 4-bit
    Bit4,
    /// 8-bit
    Bit8,
    /// 16-bit
    Bit16,
}

/// Quantization methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantizationMethod {
    /// Linear quantization
    Linear,
    /// Lookup table quantization
    LookupTable,
    /// K-means quantization
    KMeans,
    /// Custom quantization
    Custom,
}

/// Pruning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PruningConfig {
    /// Target sparsity percentage
    pub target_sparsity: f32,
    /// Pruning method
    pub method: PruningMethod,
    /// Structured pruning
    pub structured: bool,
    /// Layers to exclude from pruning
    pub exclude_layers: Vec<String>,
}

/// Pruning methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PruningMethod {
    /// Magnitude-based pruning
    Magnitude,
    /// Gradient-based pruning
    Gradient,
    /// Random pruning
    Random,
    /// Structured pruning
    Structured,
}

/// Model optimization pass trait
pub trait OptimizationPass: Send + Sync {
    /// Name of the optimization pass
    fn name(&self) -> &str;

    /// Apply optimization to the model
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()>;

    /// Check if this pass should be applied
    fn should_apply(&self, config: &CoreMLConverterConfig) -> bool;
}

/// Model validation rule trait
pub trait ValidationRule: Send + Sync {
    /// Name of the validation rule
    fn name(&self) -> &str;

    /// Validate the model
    fn validate(&self, model: &CoreMLModelGraph) -> Result<()>;
}

/// Core ML model graph representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoreMLModelGraph {
    /// Model name
    pub name: String,
    /// Model version
    pub version: String,
    /// Input specifications
    pub inputs: Vec<TensorSpec>,
    /// Output specifications
    pub outputs: Vec<TensorSpec>,
    /// Model layers
    pub layers: Vec<CoreMLLayer>,
    /// Model weights
    pub weights: HashMap<String, WeightBlob>,
    /// Model metadata
    pub metadata: ModelMetadata,
}

/// Tensor specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorSpec {
    /// Tensor name
    pub name: String,
    /// Tensor shape
    pub shape: Vec<i64>,
    /// Data type
    pub dtype: CoreMLDataType,
    /// Description
    pub description: Option<String>,
}

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

/// Core ML layer representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoreMLLayer {
    /// Layer name
    pub name: String,
    /// Layer type
    pub layer_type: LayerType,
    /// Input names
    pub inputs: Vec<String>,
    /// Output names
    pub outputs: Vec<String>,
    /// Layer parameters
    pub params: LayerParams,
    /// Quantization info
    pub quantization: Option<LayerQuantization>,
}

/// Layer types supported by Core ML
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerType {
    Convolution,
    InnerProduct,
    BatchNorm,
    Activation,
    Pooling,
    Padding,
    Concat,
    Split,
    Reshape,
    Transpose,
    Reduce,
    Softmax,
    Embedding,
    LSTM,
    GRU,
    Attention,
    Custom(String),
}

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

/// Parameter value types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParamValue {
    Int(i64),
    Float(f32),
    String(String),
    IntArray(Vec<i64>),
    FloatArray(Vec<f32>),
    Bool(bool),
}

/// Weight blob
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WeightBlob {
    /// Weight shape
    pub shape: Vec<usize>,
    /// Weight data type
    pub dtype: CoreMLDataType,
    /// Quantization info
    pub quantization: Option<WeightQuantization>,
    /// Compressed data
    pub data: Vec<u8>,
}

/// Layer quantization info
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerQuantization {
    /// Number of bits
    pub bits: u8,
    /// Quantization scale
    pub scale: f32,
    /// Zero point
    pub zero_point: i32,
}

/// Weight quantization info
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WeightQuantization {
    /// Quantization type
    pub qtype: QuantizationType,
    /// Lookup table (if applicable)
    pub lookup_table: Option<Vec<f32>>,
    /// Scales for per-channel quantization
    pub scales: Option<Vec<f32>>,
    /// Zero points for per-channel quantization
    pub zero_points: Option<Vec<i32>>,
}

/// Quantization types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantizationType {
    Linear,
    LookupTable,
    PerChannel,
}

/// Model metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetadata {
    /// Model description
    pub description: String,
    /// Author
    pub author: String,
    /// License
    pub license: Option<String>,
    /// User-defined metadata
    pub user_defined: HashMap<String, String>,
    /// Performance hints
    pub performance_hints: PerformanceHints,
}

/// Performance hints for Core ML
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceHints {
    /// Preferred compute units
    pub compute_units: Vec<String>,
    /// Expected latency (ms)
    pub expected_latency_ms: Option<f32>,
    /// Memory footprint (MB)
    pub memory_footprint_mb: Option<f32>,
    /// Power efficiency rating
    pub power_efficiency: Option<String>,
}

impl CoreMLModelConverter {
    /// Create new model converter
    pub fn new(config: CoreMLConverterConfig) -> Self {
        let optimization_passes = Self::create_optimization_passes(&config);
        let validation_rules = Self::create_validation_rules();

        Self {
            config,
            optimization_passes,
            validation_rules,
        }
    }

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

        // Convert to Core ML graph
        let mut coreml_graph = self.convert_to_coreml_graph(trustformers_model)?;

        // Validate initial model
        self.validate_model(&coreml_graph)?;

        // Apply optimization passes
        self.apply_optimizations(&mut coreml_graph)?;

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

        // Apply pruning if configured
        if let Some(ref pruning_config) = self.config.pruning {
            self.apply_pruning(&mut coreml_graph, pruning_config)?;
        }

        // Final validation
        self.validate_model(&coreml_graph)?;

        // Write Core ML model
        let output_info = self.write_coreml_model(&coreml_graph, output_path)?;

        // Create conversion result
        Ok(ConversionResult {
            output_path: output_info.path,
            model_size_mb: output_info.size_mb,
            compression_ratio: output_info.compression_ratio,
            optimization_report: self.generate_optimization_report(&coreml_graph),
            validation_report: self.generate_validation_report(&coreml_graph),
        })
    }

    /// Create optimization passes based on configuration
    fn create_optimization_passes(
        config: &CoreMLConverterConfig,
    ) -> Vec<Box<dyn OptimizationPass>> {
        let mut passes: Vec<Box<dyn OptimizationPass>> = Vec::new();

        // Add passes based on optimization level
        match config.optimization_level {
            OptimizationLevel::None => {},
            OptimizationLevel::Basic => {
                passes.push(Box::new(ConstantFoldingPass));
                passes.push(Box::new(DeadCodeEliminationPass));
            },
            OptimizationLevel::Aggressive => {
                passes.push(Box::new(ConstantFoldingPass));
                passes.push(Box::new(DeadCodeEliminationPass));
                passes.push(Box::new(OperatorFusionPass));
                passes.push(Box::new(LayoutOptimizationPass));
            },
            OptimizationLevel::Maximum => {
                passes.push(Box::new(ConstantFoldingPass));
                passes.push(Box::new(DeadCodeEliminationPass));
                passes.push(Box::new(OperatorFusionPass));
                passes.push(Box::new(LayoutOptimizationPass));
                passes.push(Box::new(AggressiveFusionPass));
                passes.push(Box::new(PrecisionOptimizationPass));
            },
        }

        passes
    }

    /// Create validation rules
    fn create_validation_rules() -> Vec<Box<dyn ValidationRule>> {
        vec![
            Box::new(SupportedOperationsRule),
            Box::new(TensorShapeRule),
            Box::new(DataTypeRule),
            Box::new(MemoryLimitRule),
            Box::new(HardwareCompatibilityRule),
        ]
    }

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

    /// Convert to Core ML graph
    fn convert_to_coreml_graph(&self, model: TrustformersModel) -> Result<CoreMLModelGraph> {
        let mut layers = Vec::new();
        let mut weights = HashMap::new();

        // Convert each operation
        for op in model.graph {
            let layer = self.convert_operation(op)?;
            layers.push(layer);
        }

        // Convert weights
        for (name, tensor) in model.weights {
            let weight_blob = self.convert_weight(name.clone(), tensor)?;
            weights.insert(name, weight_blob);
        }

        Ok(CoreMLModelGraph {
            name: "TrustformersModel".to_string(),
            version: "1.0.0".to_string(),
            inputs: self.create_input_specs(),
            outputs: self.create_output_specs(),
            layers,
            weights,
            metadata: self.create_metadata(),
        })
    }

    /// Convert a single operation
    fn convert_operation(&self, op: Operation) -> Result<CoreMLLayer> {
        let layer_type = match op.op_type.as_str() {
            "Conv2d" => LayerType::Convolution,
            "Linear" => LayerType::InnerProduct,
            "BatchNorm2d" => LayerType::BatchNorm,
            "ReLU" => LayerType::Activation,
            "MaxPool2d" => LayerType::Pooling,
            _ => LayerType::Custom(op.op_type),
        };

        Ok(CoreMLLayer {
            name: op.name,
            layer_type,
            inputs: op.inputs,
            outputs: op.outputs,
            params: self.convert_params(op.params),
            quantization: None,
        })
    }

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

        for (key, value) in params {
            // Try to parse as different types
            if let Ok(int_val) = value.parse::<i64>() {
                converted.insert(key, ParamValue::Int(int_val));
            } else if let Ok(float_val) = value.parse::<f32>() {
                converted.insert(key, ParamValue::Float(float_val));
            } else if value == "true" || value == "false" {
                converted.insert(key, ParamValue::Bool(value == "true"));
            } else {
                converted.insert(key, ParamValue::String(value));
            }
        }

        LayerParams { params: converted }
    }

    /// Convert weight tensor
    fn convert_weight(&self, name: String, tensor: Tensor) -> Result<WeightBlob> {
        let shape = tensor.shape().to_vec();
        let dtype = CoreMLDataType::Float32; // Default

        // Compress weight data
        let tensor_data = tensor.data()?;
        let data = if self.config.enable_compression {
            self.compress_weight_data(&tensor_data)?
        } else {
            // Convert f32 to bytes without compression
            tensor_data.iter().flat_map(|&f| f.to_ne_bytes()).collect()
        };

        Ok(WeightBlob {
            shape,
            dtype,
            quantization: None,
            data,
        })
    }

    /// Compress weight data
    fn compress_weight_data(&self, data: &[f32]) -> Result<Vec<u8>> {
        // Simple compression (would use actual compression in production)
        let bytes: Vec<u8> = data.iter().flat_map(|&f| f.to_ne_bytes()).collect();
        Ok(bytes)
    }

    /// Create input specifications
    fn create_input_specs(&self) -> Vec<TensorSpec> {
        vec![TensorSpec {
            name: "input".to_string(),
            shape: vec![1, 3, 224, 224],
            dtype: CoreMLDataType::Float32,
            description: Some("Model input".to_string()),
        }]
    }

    /// Create output specifications
    fn create_output_specs(&self) -> Vec<TensorSpec> {
        vec![TensorSpec {
            name: "output".to_string(),
            shape: vec![1, 1000],
            dtype: CoreMLDataType::Float32,
            description: Some("Model output".to_string()),
        }]
    }

    /// Create metadata
    fn create_metadata(&self) -> ModelMetadata {
        ModelMetadata {
            description: "Model converted from TrustformeRS".to_string(),
            author: "TrustformeRS".to_string(),
            license: Some("MIT".to_string()),
            user_defined: HashMap::new(),
            performance_hints: PerformanceHints {
                compute_units: match self.config.hardware_target {
                    HardwareTarget::All => vec![
                        "cpu".to_string(),
                        "gpu".to_string(),
                        "neuralEngine".to_string(),
                    ],
                    HardwareTarget::NeuralEngine => vec!["neuralEngine".to_string()],
                    HardwareTarget::GPU => vec!["gpu".to_string()],
                    HardwareTarget::CPU => vec!["cpu".to_string()],
                },
                expected_latency_ms: None,
                memory_footprint_mb: None,
                power_efficiency: None,
            },
        }
    }

    /// Validate model
    fn validate_model(&self, model: &CoreMLModelGraph) -> Result<()> {
        for rule in &self.validation_rules {
            rule.validate(model)?;
        }
        Ok(())
    }

    /// Apply optimizations
    fn apply_optimizations(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        for pass in &self.optimization_passes {
            if pass.should_apply(&self.config) {
                pass.apply(model)?;
            }
        }
        Ok(())
    }

    /// Apply quantization
    fn apply_quantization(
        &self,
        model: &mut CoreMLModelGraph,
        config: &CoreMLQuantizationConfig,
    ) -> Result<()> {
        // Apply weight quantization
        for (name, weight) in &mut model.weights {
            if self.should_quantize_weight(name) {
                self.quantize_weight(weight, config)?;
            }
        }

        // Apply activation quantization if configured
        if config.activation_bits.is_some() {
            for layer in &mut model.layers {
                self.quantize_layer_activations(layer, config)?;
            }
        }

        Ok(())
    }

    /// Check if weight should be quantized
    fn should_quantize_weight(&self, name: &str) -> bool {
        // Skip certain layers from quantization
        !name.contains("final") && !name.contains("output")
    }

    /// Quantize weight
    fn quantize_weight(
        &self,
        weight: &mut WeightBlob,
        config: &CoreMLQuantizationConfig,
    ) -> Result<()> {
        let bits = match config.weight_bits {
            QuantizationBits::Bit1 => 1,
            QuantizationBits::Bit2 => 2,
            QuantizationBits::Bit4 => 4,
            QuantizationBits::Bit8 => 8,
            QuantizationBits::Bit16 => 16,
        };

        weight.quantization = Some(WeightQuantization {
            qtype: if config.per_channel {
                QuantizationType::PerChannel
            } else {
                QuantizationType::Linear
            },
            lookup_table: None,
            scales: None,
            zero_points: None,
        });

        Ok(())
    }

    /// Quantize layer activations
    fn quantize_layer_activations(
        &self,
        layer: &mut CoreMLLayer,
        config: &CoreMLQuantizationConfig,
    ) -> Result<()> {
        if let Some(bits) = config.activation_bits {
            let num_bits = match bits {
                QuantizationBits::Bit8 => 8,
                QuantizationBits::Bit16 => 16,
                _ => return Ok(()), // Only 8 and 16 bit activation quantization
            };

            layer.quantization = Some(LayerQuantization {
                bits: num_bits,
                scale: 1.0,
                zero_point: 0,
            });
        }

        Ok(())
    }

    /// Apply pruning
    fn apply_pruning(&self, model: &mut CoreMLModelGraph, config: &PruningConfig) -> Result<()> {
        for (name, weight) in &mut model.weights {
            if !config.exclude_layers.contains(name) {
                self.prune_weight(weight, config)?;
            }
        }

        Ok(())
    }

    /// Prune weight
    fn prune_weight(&self, weight: &mut WeightBlob, config: &PruningConfig) -> Result<()> {
        // Pruning implementation would go here
        println!(
            "Pruning weight to {}% sparsity",
            config.target_sparsity * 100.0
        );
        Ok(())
    }

    /// Write Core ML model
    fn write_coreml_model(
        &self,
        model: &CoreMLModelGraph,
        output_path: &Path,
    ) -> Result<OutputInfo> {
        let model_data = self.serialize_model(model)?;

        // 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 {
            CoreMLFormat::MLModel => output_path.with_extension("mlmodel"),
            CoreMLFormat::MLModelC => output_path.with_extension("mlmodelc"),
            CoreMLFormat::MLPackage => {
                let package_dir = output_path.with_extension("mlpackage");
                std::fs::create_dir_all(&package_dir)?;
                package_dir.join("Data").join("com.apple.CoreML").join("model.mlmodel")
            },
        };

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

        // Calculate size and compression
        let size_mb = model_data.len() as f32 / (1024.0 * 1024.0);
        let original_size_mb = self.calculate_original_size(model);
        let compression_ratio = original_size_mb / size_mb;

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

    /// Serialize model to binary format
    fn serialize_model(&self, model: &CoreMLModelGraph) -> Result<Vec<u8>> {
        // In production, would use protobuf serialization
        Ok(serde_json::to_vec(model)?)
    }

    /// Calculate original model size
    fn calculate_original_size(&self, model: &CoreMLModelGraph) -> f32 {
        let weight_size: usize = model.weights.values()
            .map(|w| w.shape.iter().product::<usize>() * 4) // Assume FP32
            .sum();

        weight_size as f32 / (1024.0 * 1024.0)
    }

    /// Generate optimization report
    fn generate_optimization_report(&self, model: &CoreMLModelGraph) -> OptimizationReport {
        OptimizationReport {
            passes_applied: self
                .optimization_passes
                .iter()
                .filter(|p| p.should_apply(&self.config))
                .map(|p| p.name().to_string())
                .collect(),
            compression_achieved: self.config.enable_compression,
            quantization_applied: self.config.quantization.is_some(),
            pruning_applied: self.config.pruning.is_some(),
            hardware_optimizations: match self.config.hardware_target {
                HardwareTarget::NeuralEngine => vec!["Neural Engine optimizations".to_string()],
                HardwareTarget::GPU => vec!["GPU optimizations".to_string()],
                _ => vec![],
            },
        }
    }

    /// Generate validation report
    fn generate_validation_report(&self, model: &CoreMLModelGraph) -> ValidationReport {
        ValidationReport {
            ios_version: self.config.target_ios_version.clone(),
            supported_devices: self.get_supported_devices(),
            warnings: vec![],
            info: vec![
                format!("Model has {} layers", model.layers.len()),
                format!("Model has {} weights", model.weights.len()),
            ],
        }
    }

    /// Get supported devices based on configuration
    fn get_supported_devices(&self) -> Vec<String> {
        match self.config.hardware_target {
            HardwareTarget::NeuralEngine => {
                vec!["iPhone 11+".to_string(), "iPad Pro 2018+".to_string()]
            },
            _ => vec!["All iOS devices".to_string()],
        }
    }
}

// Placeholder structures for loading
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,
    compression_ratio: 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,
    /// Compression ratio achieved
    pub compression_ratio: f32,
    /// Optimization report
    pub optimization_report: OptimizationReport,
    /// Validation report
    pub validation_report: ValidationReport,
}

/// Optimization report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationReport {
    /// Optimization passes applied
    pub passes_applied: Vec<String>,
    /// Whether compression was applied
    pub compression_achieved: bool,
    /// Whether quantization was applied
    pub quantization_applied: bool,
    /// Whether pruning was applied
    pub pruning_applied: bool,
    /// Hardware-specific optimizations
    pub hardware_optimizations: Vec<String>,
}

/// Validation report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ValidationReport {
    /// Target iOS version
    pub ios_version: String,
    /// Supported devices
    pub supported_devices: Vec<String>,
    /// Validation warnings
    pub warnings: Vec<String>,
    /// Validation info
    pub info: Vec<String>,
}

// Optimization passes

struct ConstantFoldingPass;
impl OptimizationPass for ConstantFoldingPass {
    fn name(&self) -> &str {
        "ConstantFolding"
    }
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
    fn should_apply(&self, _config: &CoreMLConverterConfig) -> bool {
        true
    }
}

struct DeadCodeEliminationPass;
impl OptimizationPass for DeadCodeEliminationPass {
    fn name(&self) -> &str {
        "DeadCodeElimination"
    }
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
    fn should_apply(&self, _config: &CoreMLConverterConfig) -> bool {
        true
    }
}

struct OperatorFusionPass;
impl OptimizationPass for OperatorFusionPass {
    fn name(&self) -> &str {
        "OperatorFusion"
    }
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
    fn should_apply(&self, config: &CoreMLConverterConfig) -> bool {
        matches!(
            config.optimization_level,
            OptimizationLevel::Aggressive | OptimizationLevel::Maximum
        )
    }
}

struct LayoutOptimizationPass;
impl OptimizationPass for LayoutOptimizationPass {
    fn name(&self) -> &str {
        "LayoutOptimization"
    }
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
    fn should_apply(&self, config: &CoreMLConverterConfig) -> bool {
        matches!(
            config.optimization_level,
            OptimizationLevel::Aggressive | OptimizationLevel::Maximum
        )
    }
}

struct AggressiveFusionPass;
impl OptimizationPass for AggressiveFusionPass {
    fn name(&self) -> &str {
        "AggressiveFusion"
    }
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
    fn should_apply(&self, config: &CoreMLConverterConfig) -> bool {
        matches!(config.optimization_level, OptimizationLevel::Maximum)
    }
}

struct PrecisionOptimizationPass;
impl OptimizationPass for PrecisionOptimizationPass {
    fn name(&self) -> &str {
        "PrecisionOptimization"
    }
    fn apply(&self, model: &mut CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
    fn should_apply(&self, config: &CoreMLConverterConfig) -> bool {
        matches!(config.optimization_level, OptimizationLevel::Maximum)
    }
}

// Validation rules

struct SupportedOperationsRule;
impl ValidationRule for SupportedOperationsRule {
    fn name(&self) -> &str {
        "SupportedOperations"
    }
    fn validate(&self, model: &CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
}

struct TensorShapeRule;
impl ValidationRule for TensorShapeRule {
    fn name(&self) -> &str {
        "TensorShape"
    }
    fn validate(&self, model: &CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
}

struct DataTypeRule;
impl ValidationRule for DataTypeRule {
    fn name(&self) -> &str {
        "DataType"
    }
    fn validate(&self, model: &CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
}

struct MemoryLimitRule;
impl ValidationRule for MemoryLimitRule {
    fn name(&self) -> &str {
        "MemoryLimit"
    }
    fn validate(&self, model: &CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
}

struct HardwareCompatibilityRule;
impl ValidationRule for HardwareCompatibilityRule {
    fn name(&self) -> &str {
        "HardwareCompatibility"
    }
    fn validate(&self, model: &CoreMLModelGraph) -> Result<()> {
        Ok(())
    }
}

impl Default for CoreMLConverterConfig {
    fn default() -> Self {
        Self {
            target_ios_version: "14.0".to_string(),
            optimization_level: OptimizationLevel::Basic,
            enable_compression: true,
            quantization: None,
            pruning: None,
            output_format: CoreMLFormat::MLModel,
            hardware_target: HardwareTarget::All,
        }
    }
}

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

    #[test]
    fn test_converter_creation() {
        let config = CoreMLConverterConfig::default();
        let converter = CoreMLModelConverter::new(config);
        assert!(!converter.optimization_passes.is_empty());
        assert!(!converter.validation_rules.is_empty());
    }

    #[test]
    fn test_quantization_config() {
        let config = CoreMLQuantizationConfig {
            weight_bits: QuantizationBits::Bit8,
            activation_bits: Some(QuantizationBits::Bit8),
            method: QuantizationMethod::Linear,
            calibration_size: 1000,
            per_channel: true,
        };

        assert_eq!(config.weight_bits, QuantizationBits::Bit8);
        assert!(config.per_channel);
    }

    #[test]
    fn test_pruning_config() {
        let config = PruningConfig {
            target_sparsity: 0.5,
            method: PruningMethod::Magnitude,
            structured: false,
            exclude_layers: vec!["output".to_string()],
        };

        assert_eq!(config.target_sparsity, 0.5);
        assert!(config.exclude_layers.contains(&"output".to_string()));
    }

    #[test]
    fn test_default_converter_config() {
        let config = CoreMLConverterConfig::default();
        assert!(!config.target_ios_version.is_empty());
        assert!(matches!(
            config.optimization_level,
            OptimizationLevel::Basic
        ));
        assert!(matches!(config.output_format, CoreMLFormat::MLModel));
    }

    #[test]
    fn test_optimization_level_variants() {
        let levels = vec![
            OptimizationLevel::None,
            OptimizationLevel::Basic,
            OptimizationLevel::Aggressive,
            OptimizationLevel::Maximum,
        ];
        assert_eq!(levels.len(), 4);
    }

    #[test]
    fn test_coreml_format_variants() {
        let formats = vec![
            CoreMLFormat::MLModel,
            CoreMLFormat::MLModelC,
            CoreMLFormat::MLPackage,
        ];
        assert_eq!(formats.len(), 3);
    }

    #[test]
    fn test_hardware_target_variants() {
        let targets = vec![
            HardwareTarget::All,
            HardwareTarget::NeuralEngine,
            HardwareTarget::GPU,
            HardwareTarget::CPU,
        ];
        assert_eq!(targets.len(), 4);
    }

    #[test]
    fn test_quantization_bits_variants() {
        let bits = vec![
            QuantizationBits::Bit1,
            QuantizationBits::Bit2,
            QuantizationBits::Bit4,
            QuantizationBits::Bit8,
            QuantizationBits::Bit16,
        ];
        assert_eq!(bits.len(), 5);
    }

    #[test]
    fn test_quantization_method_variants() {
        let methods = vec![QuantizationMethod::Linear, QuantizationMethod::KMeans];
        assert_eq!(methods.len(), 2);
    }

    #[test]
    fn test_pruning_method_variants() {
        let methods = vec![
            PruningMethod::Magnitude,
            PruningMethod::Gradient,
            PruningMethod::Random,
            PruningMethod::Structured,
        ];
        assert_eq!(methods.len(), 4);
    }

    #[test]
    fn test_converter_has_optimization_passes() {
        let config = CoreMLConverterConfig::default();
        let converter = CoreMLModelConverter::new(config);
        assert!(!converter.optimization_passes.is_empty());
    }

    #[test]
    fn test_converter_has_validation_rules() {
        let config = CoreMLConverterConfig::default();
        let converter = CoreMLModelConverter::new(config);
        assert!(converter.validation_rules.len() >= 3);
    }

    #[test]
    fn test_quantization_config_4bit() {
        let config = CoreMLQuantizationConfig {
            weight_bits: QuantizationBits::Bit4,
            activation_bits: None,
            method: QuantizationMethod::KMeans,
            calibration_size: 500,
            per_channel: false,
        };
        assert_eq!(config.weight_bits, QuantizationBits::Bit4);
        assert!(config.activation_bits.is_none());
        assert!(!config.per_channel);
    }

    #[test]
    fn test_pruning_config_structured() {
        let config = PruningConfig {
            target_sparsity: 0.9,
            method: PruningMethod::Structured,
            structured: true,
            exclude_layers: vec![],
        };
        assert_eq!(config.target_sparsity, 0.9);
        assert!(config.structured);
        assert!(config.exclude_layers.is_empty());
    }

    #[test]
    fn test_pruning_config_sparsity_bounds() {
        let config = PruningConfig {
            target_sparsity: 0.5,
            method: PruningMethod::Magnitude,
            structured: false,
            exclude_layers: vec!["output".to_string()],
        };
        assert!(config.target_sparsity >= 0.0);
        assert!(config.target_sparsity <= 1.0);
    }

    #[test]
    fn test_converter_config_with_compression() {
        let mut config = CoreMLConverterConfig::default();
        config.enable_compression = true;
        assert!(config.enable_compression);
    }

    #[test]
    fn test_converter_config_hardware_target_gpu() {
        let mut config = CoreMLConverterConfig::default();
        config.hardware_target = HardwareTarget::GPU;
        assert_eq!(config.hardware_target, HardwareTarget::GPU);
    }

    #[test]
    fn test_converter_config_aggressive_optimization() {
        let mut config = CoreMLConverterConfig::default();
        config.optimization_level = OptimizationLevel::Aggressive;
        assert_eq!(config.optimization_level, OptimizationLevel::Aggressive);
    }

    #[test]
    fn test_coreml_version_constant() {
        assert_eq!(COREML_VERSION, 5);
    }

    #[test]
    fn test_quantization_bits_equality() {
        assert_eq!(QuantizationBits::Bit8, QuantizationBits::Bit8);
        assert_ne!(QuantizationBits::Bit4, QuantizationBits::Bit8);
    }

    #[test]
    fn test_optimization_level_equality() {
        assert_eq!(OptimizationLevel::None, OptimizationLevel::None);
        assert_ne!(OptimizationLevel::None, OptimizationLevel::Maximum);
    }

    #[test]
    fn test_hardware_target_equality() {
        assert_eq!(HardwareTarget::All, HardwareTarget::All);
        assert_ne!(HardwareTarget::CPU, HardwareTarget::GPU);
    }

    #[test]
    fn test_converter_config_with_quantization_and_pruning() {
        let config = CoreMLConverterConfig {
            target_ios_version: "17.0".to_string(),
            optimization_level: OptimizationLevel::Maximum,
            enable_compression: true,
            quantization: Some(CoreMLQuantizationConfig {
                weight_bits: QuantizationBits::Bit4,
                activation_bits: Some(QuantizationBits::Bit8),
                method: QuantizationMethod::Linear,
                calibration_size: 2000,
                per_channel: true,
            }),
            pruning: Some(PruningConfig {
                target_sparsity: 0.7,
                method: PruningMethod::Magnitude,
                structured: false,
                exclude_layers: vec!["embed".to_string()],
            }),
            output_format: CoreMLFormat::MLPackage,
            hardware_target: HardwareTarget::NeuralEngine,
        };
        assert!(config.quantization.is_some());
        assert!(config.pruning.is_some());
    }
}