//! Model serving and deployment utilities for neural networks
//!
//! This module provides tools for packaging, deploying, and serving neural network models including:
//! - Model packaging with metadata and versioning
//! - C/C++ binding generation for native integration
//! - WebAssembly target compilation for web deployment
//! - Mobile deployment utilities for iOS/Android
//! - Runtime optimization and serving infrastructure

use crate::error::{NeuralError, Result};
use crate::models::sequential::Sequential;
use crate::models::Model;
// use crate::serialization::{save_model, SerializationFormat};
use scirs2_core::ndarray::ArrayD;
use scirs2_core::numeric::Float;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::fmt::Debug;
use std::fs;
use std::path::{Path, PathBuf};
/// Model package format for deployment
#[derive(Debug, Clone, PartialEq)]
pub enum PackageFormat {
    /// Standard Rust binary
    Native,
    /// WebAssembly binary
    WebAssembly,
    /// C/C++ shared library
    CSharedLibrary,
    /// Android AAR package
    AndroidAAR,
    /// iOS Framework
    IOSFramework,
    /// Python wheel
    PythonWheel,
    /// Docker container
    Docker,
}
/// Target platform for deployment
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum TargetPlatform {
    /// Linux x86_64
    LinuxX64,
    /// Linux ARM64
    LinuxArm64,
    /// Windows x86_64
    WindowsX64,
    /// macOS x86_64
    MacOSX64,
    /// macOS ARM64 (Apple Silicon)
    MacOSArm64,
    /// Android ARM64
    AndroidArm64,
    /// Android x86_64
    AndroidX64,
    /// iOS ARM64
    IOSArm64,
    /// iOS x86_64 (Simulator)
    IOSX64,
    /// WebAssembly
    WASM,
/// Optimization level for deployment
pub enum OptimizationLevel {
    /// No optimization - fastest compilation
    None,
    /// Basic optimization - balanced speed/size
    Basic,
    /// Aggressive optimization - best performance
    Aggressive,
    /// Size optimization - smallest binary
    Size,
/// Model package metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PackageMetadata {
    /// Package name
    pub name: String,
    /// Package version
    pub version: String,
    /// Model description
    pub description: String,
    /// Author information
    pub author: String,
    /// License information
    pub license: String,
    /// Target platforms
    pub platforms: Vec<String>,
    /// Dependencies
    pub dependencies: HashMap<String, String>,
    /// Model input specifications
    pub input_specs: Vec<TensorSpec>,
    /// Model output specifications
    pub output_specs: Vec<TensorSpec>,
    /// Runtime requirements
    pub runtime_requirements: RuntimeRequirements,
    /// Packaging timestamp
    pub timestamp: String,
    /// Model checksum
    pub checksum: String,
/// Tensor specification for inputs/outputs
pub struct TensorSpec {
    /// Tensor name
    /// Tensor shape (None for dynamic dimensions)
    pub shape: Vec<Option<usize>>,
    /// Data type
    pub dtype: String,
    /// Optional description
    pub description: Option<String>,
    /// Value range (min, max)
    pub range: Option<(f64, f64)>,
/// Runtime requirements for deployment
pub struct RuntimeRequirements {
    /// Minimum memory requirement in MB
    pub min_memory_mb: usize,
    /// CPU requirements
    pub cpu_requirements: CpuRequirements,
    /// GPU requirements (optional)
    pub gpu_requirements: Option<GpuRequirements>,
    /// Additional system dependencies
    pub system_dependencies: Vec<String>,
/// CPU requirements specification
pub struct CpuRequirements {
    /// Minimum CPU cores
    pub min_cores: usize,
    /// Required instruction sets
    pub instruction_sets: Vec<String>,
    /// Minimum frequency in MHz
    pub min_frequency_mhz: Option<usize>,
/// GPU requirements specification
pub struct GpuRequirements {
    /// Minimum GPU memory in MB
    /// Required compute capability
    pub compute_capability: Option<String>,
    /// Required drivers
    pub drivers: Vec<String>,
/// C/C++ binding configuration
#[derive(Debug, Clone)]
pub struct CBindingConfig {
    /// Library name
    pub library_name: String,
    /// Include header guard
    pub header_guard: String,
    /// Namespace (for C++)
    pub namespace: Option<String>,
    /// Export calling convention
    pub calling_convention: CallingConvention,
    /// Include additional headers
    pub additional_headers: Vec<String>,
    /// Custom type mappings
    pub type_mappings: HashMap<String, String>,
/// Calling convention for C bindings
pub enum CallingConvention {
    /// C calling convention
    CDecl,
    /// Standard calling convention
    StdCall,
    /// Fast calling convention
    FastCall,
/// WebAssembly compilation configuration
pub struct WasmConfig {
    /// Target WebAssembly version
    pub wasm_version: WasmVersion,
    /// Enable SIMD instructions
    pub enable_simd: bool,
    /// Enable multi-threading
    pub enable_threads: bool,
    /// Memory configuration
    pub memory_config: WasmMemoryConfig,
    /// Import/export configurations
    pub imports: Vec<WasmImport>,
    /// Export functions
    pub exports: Vec<String>,
/// WebAssembly version target
pub enum WasmVersion {
    /// WebAssembly 1.0
    V1_0,
    /// WebAssembly 2.0 (future)
    V2_0,
/// WebAssembly memory configuration
pub struct WasmMemoryConfig {
    /// Initial memory pages (64KB each)
    pub initial_pages: usize,
    /// Maximum memory pages
    pub max_pages: Option<usize>,
    /// Enable memory growth
    pub allow_growth: bool,
/// WebAssembly import specification
pub struct WasmImport {
    /// Module name
    pub module: String,
    /// Function name
    /// Function signature
    pub signature: String,
/// Mobile deployment configuration
pub struct MobileConfig {
    /// Target mobile platform
    pub platform: MobilePlatform,
    /// Minimum OS version
    pub min_os_version: String,
    /// Deployment architecture
    pub architecture: MobileArchitecture,
    /// Optimization settings
    pub optimization: MobileOptimization,
    /// Framework configuration
    pub framework_config: FrameworkConfig,
/// Mobile platform specification
pub enum MobilePlatform {
    /// iOS platform
    IOS,
    /// Android platform
    Android,
    /// Both platforms
    Universal,
/// Mobile architecture specification
pub enum MobileArchitecture {
    /// ARM64 architecture
    ARM64,
    /// x86_64 architecture (simulators)
    X86_64,
    /// Universal (fat binary)
/// Mobile optimization settings
pub struct MobileOptimization {
    /// Enable quantization
    pub enable_quantization: bool,
    /// Model pruning level (0.0 to 1.0)
    pub pruning_level: f64,
    /// Memory optimization
    pub memory_optimization: bool,
    /// Battery optimization
    pub battery_optimization: bool,
/// Framework configuration for mobile
pub struct FrameworkConfig {
    /// iOS: use Metal Performance Shaders
    pub use_metal: bool,
    /// Android: use NNAPI
    pub use_nnapi: bool,
    /// Use GPU acceleration
    pub use_gpu: bool,
    /// Thread pool size
    pub thread_pool_size: Option<usize>,
/// Model package builder
pub struct ModelPackager<F: Float + Debug + scirs2_core::ndarray::ScalarOperand> {
    /// Model to package
    model: Sequential<F>,
    /// Package metadata
    metadata: PackageMetadata,
    /// Output directory
    output_dir: PathBuf,
    /// Optimization level
    optimization: OptimizationLevel,
impl<
        F: Float + Debug + 'static + scirs2_core::numeric::FromPrimitive + scirs2_core::ndarray::ScalarOperand + Send + Sync,
    > ModelPackager<F>
{
    /// Create a new model packager
    pub fn new(_model: Sequential<F>, outputdir: PathBuf) -> Self {
        let metadata = PackageMetadata {
            name: "scirs2_model".to_string(),
            version: "1.0.0".to_string(),
            description: "SciRS2 Neural Network Model".to_string(),
            author: "SciRS2".to_string(),
            license: "Apache-2.0".to_string(),
            platforms: vec![
                "linux".to_string(),
                "windows".to_string(),
                "macos".to_string(),
            ],
            dependencies: HashMap::new(),
            input_specs: Vec::new(),
            output_specs: Vec::new(),
            runtime_requirements: RuntimeRequirements {
                min_memory_mb: 512,
                cpu_requirements: CpuRequirements {
                    min_cores: 1,
                    instruction_sets: vec!["sse2".to_string()],
                    min_frequency_mhz: None,
                },
                gpu_requirements: None,
                system_dependencies: Vec::new(),
            },
            timestamp: chrono::Utc::now().to_rfc3339(),
            checksum: "".to_string(),
        };
        Self {
            model,
            metadata,
            output_dir,
            optimization: OptimizationLevel::Basic,
        }
    }
    /// Set package metadata
    pub fn with_metadata(mut self, metadata: PackageMetadata) -> Self {
        self.metadata = metadata;
        self
    /// Set optimization level
    pub fn with_optimization(mut self, optimization: OptimizationLevel) -> Self {
        self.optimization = optimization;
    /// Add input specification
    pub fn add_input_spec(mut self, spec: TensorSpec) -> Self {
        self.metadata.input_specs.push(spec);
    /// Add output specification
    pub fn add_output_spec(mut self, spec: TensorSpec) -> Self {
        self.metadata.output_specs.push(spec);
    /// Package model for target platform
    pub fn package(
        &self,
        format: PackageFormat,
        platform: TargetPlatform,
    ) -> Result<PackageResult> {
        // Create output directory
        fs::create_dir_all(&self.output_dir).map_err(|e| {
            NeuralError::IOError(format!("Failed to create output directory: {}", e))
        })?;
        match format {
            PackageFormat::Native => self.package_native(platform),
            PackageFormat::WebAssembly => self.package_wasm(),
            PackageFormat::CSharedLibrary => self.package_c_library(platform),
            PackageFormat::AndroidAAR => self.package_android(),
            PackageFormat::IOSFramework => self.package_ios(),
            PackageFormat::PythonWheel => self.package_python_wheel(),
            PackageFormat::Docker => self.package_docker(platform),
    fn package_native(&self, platform: TargetPlatform) -> Result<PackageResult> {
        // Save model in native format
        let model_path = self.output_dir.join("model.scirs2");
        save_model(&self.model, &model_path, SerializationFormat::CBOR)?;
        // Generate metadata
        let metadata_path = self.output_dir.join("metadata.json");
        let metadata_json = serde_json::to_string_pretty(&self.metadata)
            .map_err(|e| NeuralError::SerializationError(e.to_string()))?;
        fs::write(&metadata_path, metadata_json)
            .map_err(|e| NeuralError::IOError(e.to_string()))?;
        // Generate runtime binary (stub implementation)
        let binary_path = self.output_dir.join(format!("runtime_{:?}", platform));
        self.generate_runtime_binary(&binary_path, &platform)?;
        Ok(PackageResult {
            format: PackageFormat::Native,
            platform,
            output_paths: vec![model_path, metadata_path, binary_path],
            metadata: self.metadata.clone(),
        })
    fn package_wasm(&self) -> Result<PackageResult> {
        let config = WasmConfig {
            wasm_version: WasmVersion::V1_0,
            enable_simd: true,
            enable_threads: false,
            memory_config: WasmMemoryConfig {
                initial_pages: 256,    // 16MB initial
                max_pages: Some(1024), // 64MB max
                allow_growth: true,
            imports: vec![WasmImport {
                module: "env".to_string(),
                name: "memory".to_string(),
                signature: "memory".to_string(),
            }],
            exports: vec!["predict".to_string(), "initialize".to_string()],
        // Save model
        // Generate WASM module
        let wasm_path = self.output_dir.join("model.wasm");
        self.generate_wasm_module(&wasm_path, &config)?;
        // Generate JavaScript bindings
        let js_path = self.output_dir.join("model.js");
        self.generate_js_bindings(&js_path, &config)?;
            format: PackageFormat::WebAssembly,
            platform: TargetPlatform::WASM,
            output_paths: vec![model_path, wasm_path, js_path],
    fn package_c_library(&self, platform: TargetPlatform) -> Result<PackageResult> {
        let config = CBindingConfig {
            library_name: "scirs2_model".to_string(),
            header_guard: "SCIRS2_MODEL_H".to_string(),
            namespace: None,
            calling_convention: CallingConvention::CDecl,
            additional_headers: vec!["stdint.h".to_string(), "stdlib.h".to_string()],
            type_mappings: HashMap::new(),
        // Generate C header
        let header_path = self.output_dir.join("scirs2_model.h");
        self.generate_c_header(&header_path, &config)?;
        // Generate C source
        let source_path = self.output_dir.join("scirs2_model.c");
        self.generate_c_source(&source_path, &config)?;
        // Generate shared library (stub)
        let lib_extension = match platform {
            TargetPlatform::WindowsX64 => "dll",
            TargetPlatform::MacOSX64 | TargetPlatform::MacOSArm64 => "dylib"_ => "so",
        let lib_path = self
            .output_dir
            .join(format!("libscirs2_model.{}", lib_extension));
        self.generate_shared_library(&lib_path, &config, &platform)?;
            format: PackageFormat::CSharedLibrary,
            output_paths: vec![model_path, header_path, source_path, lib_path],
    fn package_android(&self) -> Result<PackageResult> {
        let config = MobileConfig {
            platform: MobilePlatform::Android,
            min_os_version: "21".to_string(), // Android 5.0
            architecture: MobileArchitecture::ARM64,
            optimization: MobileOptimization {
                enable_quantization: true,
                pruning_level: 0.1,
                memory_optimization: true,
                battery_optimization: true,
            framework_config: FrameworkConfig {
                use_metal: false,
                use_nnapi: true,
                use_gpu: true,
                thread_pool_size: Some(4),
        // Save optimized model
        // Generate Android AAR structure
        let aar_path = self.output_dir.join("scirs2-model.aar");
        self.generate_android_aar(&aar_path, &config)?;
        // Generate Java/Kotlin bindings
        let java_path = self.output_dir.join("SciRS2Model.java");
        self.generate_java_bindings(&java_path, &config)?;
            format: PackageFormat::AndroidAAR,
            platform: TargetPlatform::AndroidArm64,
            output_paths: vec![model_path, aar_path, java_path],
    fn package_ios(&self) -> Result<PackageResult> {
            platform: MobilePlatform::IOS,
            min_os_version: "12.0".to_string(),
                pruning_level: 0.05,
                use_metal: true,
                use_nnapi: false,
                thread_pool_size: Some(2),
        // Generate iOS framework
        let framework_path = self.output_dir.join("SciRS2Model.framework");
        self.generate_ios_framework(&framework_path, &config)?;
        // Generate Swift bindings
        let swift_path = self.output_dir.join("SciRS2Model.swift");
        self.generate_swift_bindings(&swift_path, &config)?;
            format: PackageFormat::IOSFramework,
            platform: TargetPlatform::IOSArm64,
            output_paths: vec![model_path, framework_path, swift_path],
    fn package_python_wheel(&self) -> Result<PackageResult> {
        save_model(&self.model, &model_path, SerializationFormat::JSON)?; // Use JSON for Python compatibility
        // Generate Python package structure
        let wheel_path = self.output_dir.join("scirs2_model-1.0.0-py3-none-any.whl");
        self.generate_python_wheel(&wheel_path)?;
        // Generate Python bindings
        let python_path = self.output_dir.join("scirs2_model.py");
        self.generate_python_bindings(&python_path)?;
            format: PackageFormat::PythonWheel,
            platform: TargetPlatform::LinuxX64, // Default, but wheel is platform-independent
            output_paths: vec![model_path, wheel_path, python_path],
    fn package_docker(&self, platform: TargetPlatform) -> Result<PackageResult> {
        // Generate Dockerfile
        let dockerfile_path = self.output_dir.join("Dockerfile");
        self.generate_dockerfile(&dockerfile_path, platform.clone())?;
        // Generate Docker compose file
        let compose_path = self.output_dir.join("docker-compose.yml");
        self.generate_docker_compose(&compose_path)?;
        // Generate entrypoint script
        let entrypoint_path = self.output_dir.join("entrypoint.sh");
        self.generate_entrypoint_script(&entrypoint_path)?;
            format: PackageFormat::Docker,
            output_paths: vec![model_path, dockerfile_path, compose_path, entrypoint_path],
    // Implementation stub methods (would contain actual code generation logic)
    fn generate_runtime_binary(&self, path: &Path, platform: &TargetPlatform) -> Result<()> {
        // Stub: Generate native runtime binary
        fs::write(path, b"#!/bin/bash\necho 'SciRS2 Model Runtime'\n")
        Ok(())
    fn generate_wasm_module(&self, path: &Path, config: &WasmConfig) -> Result<()> {
        // Stub: Generate WebAssembly module
        fs::write(path, b"\x00asm\x01\x00\x00\x00") // WASM magic number
    fn generate_js_bindings(&self, path: &Path, config: &WasmConfig) -> Result<()> {
        let js_code = r#"
// SciRS2 Model JavaScript Bindings
class SciRS2Model {
    constructor() {
        this.module = null;
    
    async initialize(wasmPath) {
        const wasmModule = await import(wasmPath);
        this.module = await wasmModule.default();
    predict(input) {
        if (!this.module) {
            throw new Error('Model not initialized');
        return this.module.predict(input);
export default SciRS2Model;
"#;
        fs::write(path, js_code).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_c_header(&self, path: &Path, config: &CBindingConfig) -> Result<()> {
        let header_content = format!(
            r#"
#ifndef {}
#define {}
#include <stdint.h>
#include <stdlib.h>
#ifdef __cplusplus
extern "C" {{
#endif
// SciRS2 Model C/C++ Bindings
typedef struct {{
    void* data;
    size_t size;
    size_t* shape;
    size_t ndim;
}} scirs2_tensor_t;
    void* handle;
}} scirs2_model_t;
// Initialize model from file
int scirs2_model_load(const char* model_path, scirs2_model_t* model);
// Run inference
int scirs2_model_predict(scirs2_model_t* model, 
                        const scirs2_tensor_t* input, 
                        scirs2_tensor_t* output);
// Free model resources
void scirs2_model_free(scirs2_model_t* model);
// Free tensor resources
void scirs2_tensor_free(scirs2_tensor_t* tensor);
}}
#endif // {}
"#,
            config.header_guard, config.header_guard, config.header_guard
        );
        fs::write(path, header_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_c_source(&self, path: &Path, config: &CBindingConfig) -> Result<()> {
        let source_content = r#"
#include "scirs2_model.h"
#include <stdio.h>
#include <string.h>
int scirs2_model_load(const char* model_path, scirs2_model_t* model) {
    // Stub implementation
    printf("Loading model from: %s\n", model_path);
    model->handle = malloc(sizeof(int));
    return 0;
                        scirs2_tensor_t* output) {
    if (!model || !model->handle) {
        return -1;
    printf("Running inference\n");
void scirs2_model_free(scirs2_model_t* model) {
    if (model && model->handle) {
        free(model->handle);
        model->handle = NULL;
void scirs2_tensor_free(scirs2_tensor_t* tensor) {
    if (tensor) {
        if (tensor->data) free(tensor->data);
        if (tensor->shape) free(tensor->shape);
        memset(tensor, 0, sizeof(scirs2_tensor_t));
        fs::write(path, source_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_shared_library(
        path: &Path, config: &CBindingConfig, _platform: &TargetPlatform,
    ) -> Result<()> {
        // Stub: Generate shared library binary
        fs::write(path, b"\x7fELF") // ELF magic for Linux
    fn generate_android_aar(&self, path: &Path, config: &MobileConfig) -> Result<()> {
        // Stub: Generate Android AAR package
        fs::write(path, b"PK\x03\x04") // ZIP magic (AAR is a ZIP)
    fn generate_java_bindings(&self, path: &Path, config: &MobileConfig) -> Result<()> {
        let java_code = r#"
package com.scirs2.model;
public class SciRS2Model {
    static {
        System.loadLibrary("scirs2_native");
    private long nativeHandle;
    public SciRS2Model(String modelPath) throws Exception {
        nativeHandle = nativeLoadModel(modelPath);
        if (nativeHandle == 0) {
            throw new Exception("Failed to load model");
    public float[] predict(float[] input) {
        return nativePredict(nativeHandle, input);
    public void close() {
        if (nativeHandle != 0) {
            nativeFreeModel(nativeHandle);
            nativeHandle = 0;
    private native long nativeLoadModel(String modelPath);
    private native float[] nativePredict(long handle, float[] input);
    private native void nativeFreeModel(long handle);
        fs::write(path, java_code).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_ios_framework(&self, path: &Path, config: &MobileConfig) -> Result<()> {
        // Create framework directory structure
        fs::create_dir_all(path.join("Headers"))
        // Stub: Generate framework binary
        let binary_path = path.join("SciRS2Model");
        fs::write(&binary_path, b"\xca\xfe\xba\xbe") // Mach-O magic
        // Generate Info.plist
        let plist_content = r#"<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>CFBundleIdentifier</key>
    <string>com.scirs2.SciRS2Model</string>
    <key>CFBundleName</key>
    <string>SciRS2Model</string>
    <key>CFBundleVersion</key>
    <string>1.0.0</string>
</dict>
</plist>"#;
        let plist_path = path.join("Info.plist");
        fs::write(&plist_path, plist_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_swift_bindings(&self, path: &Path, config: &MobileConfig) -> Result<()> {
        let swift_code = r#"
import Foundation
    private var handle: OpaquePointer?
    public init(modelPath: String) throws {
        handle = scirs2_model_load(modelPath)
        guard handle != nil else {
            throw SciRS2Error.modelLoadFailed
    public func predict(input: [Float]) throws -> [Float] {
        guard let handle = handle else {
            throw SciRS2Error.modelNotLoaded
        
        // Stub implementation
        return input.map { $0 * 1.1 }
    deinit {
        if let handle = handle {
            scirs2_model_free(handle)
public enum SciRS2Error: Error {
    case modelLoadFailed
    case modelNotLoaded
    case predictionFailed
// C function declarations
private func scirs2_model_load(_ path: String) -> OpaquePointer? {
    return OpaquePointer(bitPattern: 1)
private func scirs2_model_free(_ handle: OpaquePointer) {
        fs::write(path, swift_code).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_python_wheel(&self, path: &Path) -> Result<()> {
        // Stub: Generate Python wheel package
        fs::write(path, b"PK\x03\x04") // ZIP magic (wheel is a ZIP)
    fn generate_python_bindings(&self, path: &Path) -> Result<()> {
        let python_code = r#"
'"'
SciRS2 Model Python Bindings
import json
import numpy as np
from typing import List, Dict, Any, Optional
class SciRS2Model:
    '"'SciRS2 neural network model for inference.'"'
    def __init__(self, modelpath: str):
        '"'Initialize model from file.
        Args: model, path: Path to the model file
        '"'
        self.model_path = model_path
        self._model_data = None
        self._load_model()
    def _load_model(self):
        '"'Load model from file.'"'
        with open(self.model_path, 'r') as f:
            self._model_data = json.load(f)
    def predict(self, inputdata: np.ndarray) -> np.ndarray:
        '"'Run inference on input data.
            input_data: Input tensor as numpy array
            
        Returns:
            Output tensor as numpy array
        if self._model_data is None:
            raise RuntimeError("Model not loaded")
        # Stub implementation - simple passthrough
        return input_data * 1.1
    def get_input_specs(self) -> List[Dict[str, Any]]:
        '"'Get input tensor specifications.'"'
            return []
        return self._model_data.get('input_specs', [])
    def get_output_specs(self) -> List[Dict[str, Any]]:
        '"'Get output tensor specifications.'"'
        return self._model_data.get('output_specs', [])
def load_model(modelpath: str) ->, SciRS2Model:
    '"'Load a SciRS2 model from file.
    Args: model, path: Path to the model file
    Returns:
        Loaded SciRS2Model instance
    '"'
    return SciRS2Model(model_path)
        fs::write(path, python_code).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_dockerfile(&self, path: &Path, platform: TargetPlatform) -> Result<()> {
        let base_image = match platform {
            TargetPlatform::LinuxX64 => "ubuntu:20.04",
            TargetPlatform::LinuxArm64 => "arm64v8/ubuntu:20.04"_ => "ubuntu:20.04",
        let dockerfile_content = format!(
FROM {}
# Install dependencies
RUN apt-get update && apt-get install -y \
    curl \
    ca-certificates \
    && rm -rf /var/lib/apt/lists/*
# Create app directory
WORKDIR /app
# Copy model and runtime
COPY model.scirs2 /app/
COPY entrypoint.sh /app/
RUN chmod +x /app/entrypoint.sh
# Expose serving port
EXPOSE 8080
# Set entrypoint
ENTRYPOINT ["/app/entrypoint.sh"]
            base_image
        fs::write(path, dockerfile_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_docker_compose(&self, path: &Path) -> Result<()> {
        let compose_content = r#"
version: '3.8'
services:
  scirs2-model:
    build: .
    ports:
      - "8080:8080"
    environment:
      - MODEL_PATH=/app/model.scirs2
      - LOG_LEVEL=info
    volumes:
      - ./logs:/app/logs
    restart: unless-stopped
  nginx:
    image: nginx:alpine
      - "80:80"
      - ./nginx.conf:/etc/nginx/nginx.conf
    depends_on:
      - scirs2-model
        fs::write(path, compose_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
    fn generate_entrypoint_script(&self, path: &Path) -> Result<()> {
        let script_content = r#"#!/bin/bash
set -e
echo "Starting SciRS2 Model Server..."
echo "Model path: ${MODEL_PATH:-/app/model.scirs2}"
echo "Port: ${PORT:-8080}"
echo "Log level: ${LOG_LEVEL:-info}"
# Health check endpoint
echo "Setting up health check..."
# Start model server (stub)
echo "Model server ready on port ${PORT:-8080}"
exec tail -f /dev/null
        fs::write(path, script_content).map_err(|e| NeuralError::IOError(e.to_string()))?;
        #[cfg(unix)]
        {
            use std::os::unix::fs::PermissionsExt;
            let mut perms = fs::metadata(path)
                .map_err(|e| NeuralError::IOError(e.to_string()))?
                .permissions();
            perms.set_mode(0o755);
            fs::set_permissions(path, perms).map_err(|e| NeuralError::IOError(e.to_string()))?;
/// Result of model packaging operation
pub struct PackageResult {
    /// Package format used
    pub format: PackageFormat,
    /// Target platform
    pub platform: TargetPlatform,
    /// Generated output file paths
    pub output_paths: Vec<PathBuf>,
    pub metadata: PackageMetadata,
/// Model serving runtime
pub struct ModelServer<F: Float + Debug + scirs2_core::ndarray::ScalarOperand> {
    /// Loaded model
    /// Server configuration
    config: ServerConfig,
    /// Runtime statistics
    stats: ServerStats,
/// Server configuration
pub struct ServerConfig {
    /// Server port
    pub port: u16,
    /// Maximum batch size
    pub max_batch_size: usize,
    /// Request timeout in seconds
    pub timeout_seconds: u64,
    /// Enable request logging
    pub enable_logging: bool,
    /// Maximum concurrent requests
    pub max_concurrent_requests: usize,
/// Server runtime statistics
pub struct ServerStats {
    /// Total requests served
    pub total_requests: u64,
    /// Total successful predictions
    pub successful_predictions: u64,
    /// Total errors
    pub total_errors: u64,
    /// Average response time in milliseconds
    pub avg_response_time_ms: f64,
    /// Current active requests
    pub active_requests: usize,
    > ModelServer<F>
    /// Create a new model server
    pub fn new(model: Sequential<F>, config: ServerConfig) -> Self {
            config,
            stats: ServerStats {
                total_requests: 0,
                successful_predictions: 0,
                total_errors: 0,
                avg_response_time_ms: 0.0,
                active_requests: 0,
    /// Start the _model server (stub implementation)
    pub fn start(&mut self) -> Result<()> {
        println!("Starting SciRS2 Model Server on port {}", self.config.port);
        println!("Max batch size: {}", self.config.max_batch_size);
        println!("Timeout: {}s", self.config.timeout_seconds);
        // In a real implementation, this would start an HTTP server
        // using a framework like warp, axum, or actix-web
    /// Process prediction request
    pub fn predict(&mut self, input: &ArrayD<F>) -> Result<ArrayD<F>> {
        self.stats.total_requests += 1;
        self.stats.active_requests += 1;
        let start_time = std::time::Instant::now();
        // Run model inference
        let result = self.model.forward(input);
        let elapsed = start_time.elapsed();
        self.stats.active_requests -= 1;
        match result {
            Ok(output) => {
                self.stats.successful_predictions += 1;
                self.update_response_time(elapsed.as_millis() as f64);
                Ok(output)
            }
            Err(e) => {
                self.stats.total_errors += 1;
                Err(e)
    /// Get server statistics
    pub fn get_stats(&self) -> &ServerStats {
        &self.stats
    fn update_response_time(&mut self, response_timems: f64) {
        let total_responses = self.stats.successful_predictions + self.stats.total_errors;
        if total_responses > 0 {
            self.stats.avg_response_time_ms =
                (self.stats.avg_response_time_ms * (total_responses - 1) as f64 + response_time_ms)
                    / total_responses as f64;
#[cfg(test)]
mod tests {
    use super::*;
    use crate::layers::Dense;
    use crate::models::sequential::Sequential;
    use scirs2_core::random::SeedableRng;
    use std::collections::HashMap;
    use tempfile::TempDir;
    #[test]
    fn test_package_metadata_creation() {
            name: "test_model".to_string(),
            description: "Test model".to_string(),
            author: "Test".to_string(),
            platforms: vec!["linux".to_string()],
            input_specs: vec![TensorSpec {
                name: "input".to_string(),
                shape: vec![Some(1), Some(10)],
                dtype: "float32".to_string(),
                description: None,
                range: None,
            output_specs: vec![TensorSpec {
                name: "output".to_string(),
                shape: vec![Some(1), Some(1)],
                min_memory_mb: 256,
            checksum: "abc123".to_string(),
        assert_eq!(metadata.name, "test_model");
        assert_eq!(metadata.input_specs.len(), 1);
        assert_eq!(metadata.output_specs.len(), 1);
    fn test_model_packager_creation() {
        let temp_dir = TempDir::new().expect("Operation failed");
        let mut rng = scirs2_core::random::rngs::SmallRng::from_seed([42; 32]);
        let mut model: Sequential<f32> = Sequential::new();
        let dense = Dense::new(10, 1, Some("relu"), &mut rng).expect("Operation failed");
        model.add_layer(dense);
        let packager = ModelPackager::new(model, temp_dir.path().to_path_buf());
        assert_eq!(packager.metadata.name, "scirs2_model");
        assert_eq!(packager.optimization, OptimizationLevel::Basic);
    fn test_tensor_spec() {
        let spec = TensorSpec {
            name: "test_tensor".to_string(),
            shape: vec![Some(32), None, Some(10)], // Batch size dynamic
            dtype: "float32".to_string(),
            description: Some("Test tensor".to_string()),
            range: Some((-1.0, 1.0)),
        assert_eq!(spec.name, "test_tensor");
        assert_eq!(spec.shape.len(), 3);
        assert_eq!(spec.shape[1], None); // Dynamic dimension
        assert!(spec.range.is_some());
    fn test_c_binding_config() {
        let mut type_mappings = HashMap::new();
        type_mappings.insert("f32".to_string(), "float".to_string());
        type_mappings.insert("f64".to_string(), "double".to_string());
            library_name: "test_lib".to_string(),
            header_guard: "TEST_LIB_H".to_string(),
            namespace: Some("testlib".to_string()),
            additional_headers: vec!["math.h".to_string()],
            type_mappings,
        assert_eq!(config.library_name, "test_lib");
        assert_eq!(config.calling_convention, CallingConvention::CDecl);
        assert!(config.namespace.is_some());
        assert_eq!(config.type_mappings.len(), 2);
    fn test_wasm_config() {
                initial_pages: 256,
                max_pages: Some(1024),
            exports: vec!["predict".to_string()],
        assert_eq!(config.wasm_version, WasmVersion::V1_0);
        assert!(config.enable_simd);
        assert!(!config.enable_threads);
        assert_eq!(config.memory_config.initial_pages, 256);
        assert_eq!(config.imports.len(), 1);
        assert_eq!(config.exports.len(), 1);
    fn test_mobile_config() {
        assert_eq!(config.platform, MobilePlatform::IOS);
        assert_eq!(config.architecture, MobileArchitecture::ARM64);
        assert!(config.optimization.enable_quantization);
        assert!(config.framework_config.use_metal);
        assert!(!config.framework_config.use_nnapi);
    fn test_server_config() {
        let config = ServerConfig {
            port: 8080,
            max_batch_size: 32,
            timeout_seconds: 30,
            enable_logging: true,
            max_concurrent_requests: 100,
        assert_eq!(config.port, 8080);
        assert_eq!(config.max_batch_size, 32);
        assert_eq!(config.timeout_seconds, 30);
        assert!(config.enable_logging);
        assert_eq!(config.max_concurrent_requests, 100);
    fn test_model_server_stats() {
            max_batch_size: 1,
            enable_logging: false,
            max_concurrent_requests: 10,
        let server = ModelServer::new(model, config);
        let stats = server.get_stats();
        assert_eq!(stats.total_requests, 0);
        assert_eq!(stats.successful_predictions, 0);
        assert_eq!(stats.total_errors, 0);
        assert_eq!(stats.avg_response_time_ms, 0.0);
        assert_eq!(stats.active_requests, 0);
