//! Framework interoperability utilities for neural networks
//!
//! This module provides tools for interoperability with other deep learning frameworks including:
//! - ONNX model export and import
//! - PyTorch model conversion (weights and architecture)
//! - TensorFlow model conversion (weights and architecture)
//! - Standardized model format definitions
//! - Cross-framework weight format translation

use crate::error::{NeuralError, Result};
use scirs2_core::ndarray::ArrayD;
use scirs2_core::numeric::Float;
use std::collections::HashMap;
use std::fmt::Debug;
use std::path::Path;
// External dependencies for serialization
use chrono;
use serde_json;
/// Supported external frameworks
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum Framework {
    /// PyTorch framework
    PyTorch,
    /// TensorFlow framework
    TensorFlow,
    /// ONNX format
    ONNX,
    /// Keras (high-level TensorFlow API)
    Keras,
    /// JAX/Flax framework
    JAX,
}
/// Model format specifications
#[derive(Debug, Clone, PartialEq)]
pub enum ModelFormat {
    /// ONNX protobuf format
    ONNX {
        /// ONNX opset version
        opset_version: u32,
        /// Include training mode operators
        include_training: bool,
    },
    /// PyTorch state dictionary
    PyTorchStateDict,
    /// PyTorch TorchScript
    TorchScript,
    /// TensorFlow SavedModel
    TensorFlowSavedModel,
    /// TensorFlow GraphDef
    TensorFlowGraphDef,
    /// Keras HDF5 format
    KerasH5,
    /// Custom scirs2 format
    Scirs2Native {
        /// Format version
        version: String,
        /// Include optimizer state
        include_optimizer: bool,
/// Data type mapping between frameworks
pub enum DataType {
    /// 32-bit floating point
    Float32,
    /// 64-bit floating point
    Float64,
    /// 16-bit floating point (half precision)
    Float16,
    /// 8-bit signed integer
    Int8,
    /// 16-bit signed integer
    Int16,
    /// 32-bit signed integer
    Int32,
    /// 64-bit signed integer
    Int64,
    /// 8-bit unsigned integer
    UInt8,
    /// Boolean type
    Bool,
/// Layer type mapping for framework conversion
pub struct LayerMapping {
    /// Source framework layer name
    pub source_name: String,
    /// Target framework layer name
    pub target_name: String,
    /// Source framework
    pub source_framework: Framework,
    /// Target framework
    pub target_framework: Framework,
    /// Parameter name mappings
    pub param_mappings: HashMap<String, String>,
    /// Shape transformation required
    pub shape_transform: Option<ShapeTransform>,
/// Shape transformation for cross-framework compatibility
pub enum ShapeTransform {
    /// No transformation needed
    Identity,
    /// Transpose dimensions
    Transpose {
        /// Permutation axes for transposition
        axes: Vec<usize>,
    /// Reshape tensor
    Reshape {
        /// Target shape after reshaping
        targetshape: Vec<usize>,
    /// Channel-first to channel-last
    ChannelsFirstToLast,
    /// Channel-last to channel-first
    ChannelsLastToFirst,
    /// Custom transformation function
    Custom {
        /// Identifier for the custom transform
        transform_id: String,
/// Model metadata for cross-framework compatibility
#[derive(Debug, Clone)]
pub struct ModelMetadata {
    /// Model name
    pub name: String,
    /// Model version
    pub version: String,
    /// Framework where model was originally created
    /// Model description
    pub description: String,
    /// Input specifications
    pub inputs: Vec<TensorSpec>,
    /// Output specifications
    pub outputs: Vec<TensorSpec>,
    /// Model configuration parameters
    pub config: HashMap<String, String>,
    /// Training configuration if available
    pub training_config: Option<TrainingConfig>,
/// Tensor specification for model inputs/outputs
pub struct TensorSpec {
    /// Tensor name
    /// Tensor shape (None for dynamic dimensions)
    pub shape: Vec<Option<usize>>,
    /// Data type
    pub dtype: DataType,
    /// Value range if known
    pub value_range: Option<(f64, f64)>,
    /// Tensor description
/// Training configuration for model conversion
pub struct TrainingConfig {
    /// Optimizer type
    pub optimizer: String,
    /// Learning rate
    pub learning_rate: f64,
    /// Loss function
    pub loss_function: String,
    /// Metrics used
    pub metrics: Vec<String>,
    /// Training epochs
    pub epochs: Option<usize>,
    /// Batch size
    pub batch_size: Option<usize>,
/// Framework interoperability manager
pub struct InteropManager<F: Float + Debug> {
    /// Layer mappings for different frameworks
    layer_mappings: HashMap<(Framework, Framework), Vec<LayerMapping>>,
    /// Data type conversion table
    dtype_mappings: HashMap<(Framework, Framework), HashMap<DataType, DataType>>,
    /// Cached converted models
    model_cache: HashMap<String, ConvertedModel<F>>,
    /// Default conversion settings
    conversion_settings: ConversionSettings,
/// Converted model representation
pub struct ConvertedModel<F: Float + Debug> {
    /// Model architecture
    pub architecture: ModelArchitecture<F>,
    /// Model weights
    pub weights: HashMap<String, ArrayD<F>>,
    /// Model metadata
    pub metadata: ModelMetadata,
    /// Source format
    pub source_format: ModelFormat,
    /// Target format
    pub target_format: ModelFormat,
/// Model architecture representation
pub struct ModelArchitecture<F: Float + Debug> {
    /// Layers in the model
    pub layers: Vec<LayerDefinition<F>>,
    /// Connections between layers
    pub connections: Vec<Connection>,
/// Layer definition for architecture conversion
pub struct LayerDefinition<F: Float + Debug> {
    /// Layer name
    /// Layer type
    pub layer_type: String,
    /// Layer parameters
    pub parameters: HashMap<String, ParameterValue<F>>,
    /// Input shape
    pub inputshape: Vec<Option<usize>>,
    /// Output shape
    pub outputshape: Vec<Option<usize>>,
/// Parameter value for layer definitions
pub enum ParameterValue<F: Float + Debug> {
    /// Scalar value
    Scalar(F),
    /// Integer value
    Integer(i64),
    /// Boolean value
    Boolean(bool),
    /// String value
    String(String),
    /// Array value
    Array(ArrayD<F>),
    /// List of values
    List(Vec<ParameterValue<F>>),
/// Connection between layers
pub struct Connection {
    /// Source layer name
    pub from_layer: String,
    /// Target layer name
    pub to_layer: String,
    /// Output index from source layer
    pub from_output: usize,
    /// Input index to target layer
    pub to_input: usize,
/// Conversion settings
pub struct ConversionSettings {
    /// Preserve training mode operators
    pub preserve_training_ops: bool,
    /// Target precision for weights
    pub target_precision: DataType,
    /// Optimization level
    pub optimization_level: OptimizationLevel,
    /// Validation mode
    pub validation_mode: ValidationMode,
    /// Custom layer handlers
    pub custom_handlers: HashMap<String, String>,
/// Optimization levels for model conversion
pub enum OptimizationLevel {
    /// No optimization
    None,
    /// Basic optimizations (constant folding, etc.)
    Basic,
    /// Advanced optimizations (graph fusion, etc.)
    Advanced,
    /// Aggressive optimizations (may affect precision)
    Aggressive,
/// Validation modes for conversion
pub enum ValidationMode {
    /// No validation
    /// Validate shapes only
    ShapeOnly,
    /// Validate numerical outputs (requires test data)
    Numerical {
        /// Tolerance for numerical comparison
        tolerance: f64,
    /// Full validation including training behavior
    Full,
impl<F: Float + Debug + 'static + scirs2_core::numeric::FromPrimitive + scirs2_core::ndarray::ScalarOperand> Default
    for InteropManager<F>
{
    fn default() -> Self {
        Self::new()
    }
impl<F: Float + Debug + 'static + scirs2_core::numeric::FromPrimitive + scirs2_core::ndarray::ScalarOperand>
    InteropManager<F>
    /// Create a new interoperability manager
    pub fn new() -> Self {
        let mut manager = Self {
            layer_mappings: HashMap::new(),
            dtype_mappings: HashMap::new(),
            model_cache: HashMap::new(),
            conversion_settings: ConversionSettings {
                preserve_training_ops: false,
                target_precision: DataType::Float32,
                optimization_level: OptimizationLevel::Basic,
                validation_mode: ValidationMode::ShapeOnly,
                custom_handlers: HashMap::new(),
            },
        };
        manager.initialize_default_mappings();
        manager
    /// Initialize default layer and dtype mappings
    fn initialize_default_mappings(&mut self) {
        self.initialize_pytorch_mappings();
        self.initialize_tensorflow_mappings();
        self.initialize_onnx_mappings();
        self.initialize_dtype_mappings();
    fn initialize_pytorch_mappings(&mut self) {
        let mut pytorch_to_scirs2 = Vec::new();
        // Linear layer mapping
        pytorch_to_scirs2.push(LayerMapping {
            source_name: "Linear".to_string(),
            target_name: "Dense".to_string(),
            source_framework: Framework::PyTorch,
            target_framework: Framework::ONNX, // Using ONNX as intermediate
            param_mappings: {
                let mut map = HashMap::new();
                map.insert("weight".to_string(), "kernel".to_string());
                map.insert("bias".to_string(), "bias".to_string());
                map
            shape_transform: Some(ShapeTransform::Transpose { axes: vec![1, 0] }),
        });
        // Conv2d layer mapping
            source_name: "Conv2d".to_string(),
            target_name: "Conv2D".to_string(),
            target_framework: Framework::ONNX,
            shape_transform: None, // PyTorch and ONNX use same channel-first format
        // BatchNorm layer mapping
            source_name: "BatchNorm2d".to_string(),
            target_name: "BatchNormalization".to_string(),
                map.insert("weight".to_string(), "scale".to_string());
                map.insert("running_mean".to_string(), "mean".to_string());
                map.insert("running_var".to_string(), "var".to_string());
            shape_transform: None,
        self.layer_mappings
            .insert((Framework::PyTorch, Framework::ONNX), pytorch_to_scirs2);
    fn initialize_tensorflow_mappings(&mut self) {
        let mut tensorflow_to_scirs2 = Vec::new();
        // Dense layer mapping
        tensorflow_to_scirs2.push(LayerMapping {
            source_name: "Dense".to_string(),
            target_name: "MatMul".to_string(),
            source_framework: Framework::TensorFlow,
                map.insert("kernel".to_string(), "weight".to_string());
        // Conv2D layer mapping
            source_name: "Conv2D".to_string(),
            target_name: "Conv".to_string(),
            shape_transform: Some(ShapeTransform::ChannelsLastToFirst),
        self.layer_mappings.insert(
            (Framework::TensorFlow, Framework::ONNX),
            tensorflow_to_scirs2,
        );
    fn initialize_onnx_mappings(&mut self) {
        // ONNX mappings are more complex and would involve operator mappings
        // For now, we'll add basic structure
        let onnx_operators = Vec::new();
            .insert((Framework::ONNX, Framework::PyTorch), onnx_operators);
    fn initialize_dtype_mappings(&mut self) {
        // PyTorch to ONNX type mappings
        let mut pytorch_to_onnx = HashMap::new();
        pytorch_to_onnx.insert(DataType::Float32, DataType::Float32);
        pytorch_to_onnx.insert(DataType::Float64, DataType::Float64);
        pytorch_to_onnx.insert(DataType::Int32, DataType::Int32);
        pytorch_to_onnx.insert(DataType::Int64, DataType::Int64);
        self.dtype_mappings
            .insert((Framework::PyTorch, Framework::ONNX), pytorch_to_onnx);
        // TensorFlow to ONNX type mappings
        let mut tensorflow_to_onnx = HashMap::new();
        tensorflow_to_onnx.insert(DataType::Float32, DataType::Float32);
        tensorflow_to_onnx.insert(DataType::Float64, DataType::Float64);
        tensorflow_to_onnx.insert(DataType::Int32, DataType::Int32);
        tensorflow_to_onnx.insert(DataType::Int64, DataType::Int64);
            .insert((Framework::TensorFlow, Framework::ONNX), tensorflow_to_onnx);
    /// Export model to ONNX format
    pub fn export_to_onnx(
        &self,
        model_weights: &HashMap<String, ArrayD<F>>,
        model_metadata: &ModelMetadata,
        output_path: &Path,
        format: &ModelFormat,
    ) -> Result<()> {
        let ModelFormat::ONNX {
            opset_version,
            include_training,
        } = format
        else {
            return Err(NeuralError::InvalidArchitecture(
                "Invalid ONNX format specification".to_string(),
            ));
        // Create ONNX model representation
        let onnx_model = self.create_onnx_model(
            model_weights,
            model_metadata,
            *opset_version,
            *include_training,
        )?;
        // Serialize to protobuf (simplified - in practice would use ONNX protobuf library)
        self.serialize_onnx_model(&onnx_model, output_path)?;
        Ok(())
    /// Import model from ONNX format
    pub fn import_from_onnx(&mut self, modelpath: &Path) -> Result<ConvertedModel<F>> {
        // Parse ONNX protobuf file (simplified)
        let onnx_model = self.parse_onnx_file(model_path)?;
        // Convert ONNX representation to scirs2 format
        let converted_model = self.convert_onnx_to_scirs2(onnx_model)?;
        // Cache the converted model
        let cache_key = model_path.to_string_lossy().to_string();
        self.model_cache.insert(cache_key, converted_model.clone());
        Ok(converted_model)
    /// Convert PyTorch state dict to scirs2 format
    pub fn convert_from_pytorch(
        state_dict: HashMap<String, ArrayD<F>>,
        model_metadata: ModelMetadata,
    ) -> Result<ConvertedModel<F>> {
        // Convert PyTorch layer names and weights
        let converted_weights = self.convert_pytorch_weights(state_dict)?;
        // Build model architecture from metadata
        let architecture = self.build_architecture_from_metadata(&model_metadata)?;
        Ok(ConvertedModel {
            architecture,
            weights: converted_weights,
            metadata: model_metadata,
            source_format: ModelFormat::PyTorchStateDict,
            target_format: ModelFormat::Scirs2Native {
                version: "0.1.0".to_string(),
                include_optimizer: false,
        })
    /// Convert TensorFlow saved model to scirs2 format
    pub fn convert_from_tensorflow(&self, saved_modelpath: &Path) -> Result<ConvertedModel<F>> {
        // Load TensorFlow saved model (simplified)
        let tf_model = self.load_tensorflow_model(saved_model_path)?;
        // Convert to scirs2 format
        let converted_model = self.convert_tensorflow_to_scirs2(tf_model)?;
    /// Export to PyTorch format
    pub fn export_to_pytorch(&self, model: &ConvertedModel<F>, outputpath: &Path) -> Result<()> {
        // Convert scirs2 model to PyTorch format
        let pytorch_state_dict = self.convert_to_pytorch_state_dict(&model.weights)?;
        // Save PyTorch state dict (simplified - would use PyTorch's save format)
        self.save_pytorch_state_dict(pytorch_state_dict, output_path)?;
    /// Export to TensorFlow format
    pub fn export_to_tensorflow(
        model: &ConvertedModel<F>,
        // Convert scirs2 model to TensorFlow format
        let tf_model = self.convert_to_tensorflow_model(model)?;
        // Save TensorFlow model (simplified)
        self.save_tensorflow_model(tf_model, output_path)?;
    /// Validate model conversion
    pub fn validate_conversion(
        original: &ConvertedModel<F>,
        converted: &ConvertedModel<F>,
        test_inputs: &[ArrayD<F>],
    ) -> Result<ValidationReport<F>> {
        match self.conversion_settings.validation_mode {
            ValidationMode::None => Ok(ValidationReport::empty()),
            ValidationMode::ShapeOnly => self.validateshapes(original, converted),
            ValidationMode::Numerical { tolerance } => {
                self.validate_numerical(original, converted, test_inputs, tolerance)
            }
            ValidationMode::Full => self.validate_full(original, converted, test_inputs),
        }
    // Helper methods for ONNX operations
    fn create_onnx_model(
        weights: &HashMap<String, ArrayD<F>>,
        metadata: &ModelMetadata,
    ) -> Result<ONNXModel<F>> {
        // Simplified ONNX model creation
        Ok(ONNXModel {
            graph: ONNXGraph {
                nodes: Vec::new(),
                inputs: metadata.inputs.clone(),
                outputs: metadata.outputs.clone(),
                initializers: weights.clone(),
            metadata: metadata.clone(),
    fn serialize_onnx_model(&self, model: &ONNXModel<F>, outputpath: &Path) -> Result<()> {
        use serde_json::json;
        // Create ONNX-compatible model representation
        let mut graph_nodes = Vec::new();
        let mut graph_initializers = Vec::new();
        let mut graph_inputs = Vec::new();
        let mut graph_outputs = Vec::new();
        // Convert inputs to ONNX format
        for input in &model.graph.inputs {
            graph_inputs.push(json!({
                "name": input.name,
                "type": {
                    "tensor_type": {
                        "elem_type": self.datatype_to_onnx_type(&input.dtype),
                        "shape": {
                            "dim": input.shape.iter().map(|&d| {
                                if let Some(size) = d {
                                    json!({"dim_value": size})
                                } else {
                                    json!({"dim_param": "batch_size"})
                                }
                            }).collect::<Vec<_>>()
                        }
                    }
                }
            }));
        // Convert outputs to ONNX format
        for output in &model.graph.outputs {
            graph_outputs.push(json!({
                "name": output.name,
                        "elem_type": self.datatype_to_onnx_type(&output.dtype),
                            "dim": output.shape.iter().map(|&d| {
        // Convert initializers (weights) to ONNX format
        for (name, tensor) in &model.graph.initializers {
            let tensor_data: Vec<f64> = tensor.iter().map(|&x| x.to_f64().unwrap_or(0.0)).collect();
            graph_initializers.push(json!({
                "name": name,
                "dims": tensor.shape().to_vec(),
                "data_type": self.datatype_to_onnx_type(&DataType::Float32),
                "raw_data": tensor_data,
                "doc_string": format!("Weight tensor {name}")
        // Create ONNX model structure
        let onnx_model = json!({
            "ir_version": 7,
            "opset_import": [
                {
                    "domain": "",
                    "version": model.opset_version
            ],
            "producer_name": "scirs2-neural",
            "producer_version": "0.1.0",
            "domain": "ai.scirs2",
            "model_version": 1,
            "doc_string": model.metadata.description,
            "graph": {
                "node": graph_nodes,
                "name": model.metadata.name,
                "initializer": graph_initializers,
                "input": graph_inputs,
                "output": graph_outputs,
                "doc_string": "Neural network model exported from scirs2-neural"
            "metadata_props": [
                    "key": "scirs2_version",
                    "value": "0.1.0"
                },
                    "key": "source_framework",
                    "value": format!("{:?}", model.metadata.source_framework)
                    "key": "export_timestamp",
                    "value": chrono::Utc::now().to_rfc3339()
            ]
        // Write ONNX model to file in JSON format
        // In a real implementation, this would be protobuf binary format
        let json_string = serde_json::to_string_pretty(&onnx_model).map_err(|e| {
            NeuralError::ComputationError(format!("JSON serialization error: {e}"))
        })?;
        std::fs::write(output_path, json_string).map_err(|e| {
            NeuralError::ComputationError(format!("Failed to write ONNX model: {e}"))
    /// Convert DataType to ONNX tensor element type
    fn datatype_to_onnx_type(&self, dtype: &DataType) -> i32 {
        match dtype {
            DataType::Float32 => 1,  // ONNX FLOAT
            DataType::UInt8 => 2,    // ONNX UINT8
            DataType::Int8 => 3,     // ONNX INT8
            DataType::Int16 => 5,    // ONNX INT16
            DataType::Int32 => 6,    // ONNX INT32
            DataType::Int64 => 7,    // ONNX INT64
            DataType::Bool => 9,     // ONNX BOOL
            DataType::Float16 => 10, // ONNX FLOAT16
            DataType::Float64 => 11, // ONNX DOUBLE
    /// Convert ONNX tensor element type to DataType
    fn onnx_type_to_datatype(&self, onnxtype: i32) -> DataType {
        match onnx_type {
            1 => DataType::Float32,  // ONNX FLOAT
            2 => DataType::UInt8,    // ONNX UINT8
            3 => DataType::Int8,     // ONNX INT8
            4 => DataType::UInt8,    // ONNX UINT16 (not in our enum, map to UInt8)
            5 => DataType::Int16,    // ONNX INT16
            6 => DataType::Int32,    // ONNX INT32
            7 => DataType::Int64,    // ONNX INT64
            9 => DataType::Bool,     // ONNX BOOL
            10 => DataType::Float16, // ONNX FLOAT16
            11 => DataType::Float64, // ONNX DOUBLE
            _ => DataType::Float32,  // Default fallback
    fn parse_onnx_file(&self, modelpath: &Path) -> Result<ONNXModel<F>> {
        let contents = std::fs::read_to_string(model_path).map_err(|e| {
            NeuralError::ComputationError(format!("Failed to read ONNX file: {e}"))
        // Parse JSON-formatted ONNX model
        let onnx_json: serde_json:: Value = serde_json::from_str(&contents).map_err(|e| {
            NeuralError::ComputationError(format!("Failed to parse ONNX JSON: {e}"))
        // Extract opset version
        let opset_version = onnx_json["opset_import"][0]["version"]
            .as_u64()
            .unwrap_or(11) as u32;
        // Parse graph inputs
        let mut inputs = Vec::new();
        if let Some(graph_inputs) = onnx_json["graph"]["input"].as_array() {
            for input in graph_inputs {
                if let (Some(name), Some(tensor_type)) = (
                    input["name"].as_str(),
                    input["type"]["tensor_type"].as_object(),
                ) {
                    let shape = if let Some(shape_info) = tensor_type["shape"]["dim"].as_array() {
                        shape_info
                            .iter()
                            .map(|dim| {
                                if let Some(size) = dim["dim_value"].as_u64() {
                                    Some(size as usize)
                                    None // Dynamic dimension
                            })
                            .collect()
                    } else {
                        Vec::new()
                    };
                    let dtype = self.onnx_type_to_datatype(
                        tensor_type["elem_type"].as_i64().unwrap_or(1) as i32,
                    );
                    inputs.push(TensorSpec {
                        name: name.to_string(),
                        shape,
                        dtype,
                        value_range: None,
                        description: format!("Input tensor {name}"),
                    });
        // Parse graph outputs
        let mut outputs = Vec::new();
        if let Some(graph_outputs) = onnx_json["graph"]["output"].as_array() {
            for output in graph_outputs {
                    output["name"].as_str(),
                    output["type"]["tensor_type"].as_object(),
                    outputs.push(TensorSpec {
                        description: format!("Output tensor {name}"),
        // Parse initializers (weights)
        let mut initializers = HashMap::new();
        if let Some(graph_initializers) = onnx_json["graph"]["initializer"].as_array() {
            for initializer in graph_initializers {
                if let (Some(name), Some(dims), Some(raw_data)) = (
                    initializer["name"].as_str(),
                    initializer["dims"].as_array(),
                    initializer["raw_data"].as_array(),
                    let shape: Vec<usize> = dims
                        .iter()
                        .filter_map(|d| d.as_u64().map(|x| x as usize))
                        .collect();
                    let data: Vec<F> = raw_data
                        .filter_map(|v| v.as_f64().and_then(|x| F::from(x)))
                    if let Ok(tensor) = ArrayD::from_shape_vec(shape, data) {
                        initializers.insert(name.to_string(), tensor);
        // Parse nodes for more complex models
        let mut nodes = Vec::new();
        if let Some(graph_nodes) = onnx_json["graph"]["node"].as_array() {
            for node in graph_nodes {
                if let (Some(name), Some(op_type)) = (
                    node["name"].as_str().or_else(|| node["output"][0].as_str()),
                    node["op_type"].as_str(),
                    let inputs: Vec<String> = node["input"]
                        .as_array()
                        .unwrap_or(&Vec::new())
                        .filter_map(|v| v.as_str().map(|s| s.to_string()))
                    let outputs: Vec<String> = node["output"]
                    let mut attributes = HashMap::new();
                    if let Some(node_attrs) = node["attribute"].as_array() {
                        for attr in node_attrs {
                            if let Some(attr_name) = attr["name"].as_str() {
                                let attr_value = if let Some(i) = attr["i"].as_i64() {
                                    AttributeValue::Int(i)
                                } else if let Some(f) = attr["f"].as_f64() {
                                    AttributeValue::Float(f)
                                } else if let Some(s) = attr["s"].as_str() {
                                    AttributeValue::String(s.to_string()), AttributeValue::String("unknown".to_string())
                                };
                                attributes.insert(attr_name.to_string(), attr_value);
                            }
                    nodes.push(ONNXNode {
                        op_type: op_type.to_string(),
                        inputs,
                        outputs,
                        attributes,
        // Extract metadata
        let model_name = onnx_json["graph"]["name"]
            .as_str()
            .unwrap_or("imported_model")
            .to_string();
        let description = onnx_json["doc_string"]
            .unwrap_or("Imported ONNX model")
        let metadata = ModelMetadata {
            name: model_name,
            version: "1.0".to_string(),
            source_framework: Framework::ONNX,
            description,
            inputs: inputs.clone(),
            outputs: outputs.clone(),
            config: HashMap::new(),
            training_config: None,
            include_training: false,
                nodes,
                inputs,
                outputs,
                initializers,
            metadata,
    fn convert_onnx_to_scirs2(&self, onnxmodel: ONNXModel<F>) -> Result<ConvertedModel<F>> {
        // Convert ONNX model to scirs2 format
        let architecture = ModelArchitecture {
            layers: Vec::new(),
            connections: Vec::new(),
            inputs: onnx_model.graph.inputs,
            outputs: onnx_model.graph.outputs,
            weights: onnx_model.graph.initializers,
            metadata: onnx_model.metadata,
            source_format: ModelFormat::ONNX {
                opset_version: onnx_model.opset_version,
                include_training: onnx_model.include_training,
    fn convert_pytorch_weights(
    ) -> Result<HashMap<String, ArrayD<F>>> {
        let mut converted_weights = HashMap::new();
        for (key, tensor) in state_dict {
            let converted_key = if key.ends_with(".weight") {
                key.replace(".weight", ".kernel")
            } else {
                key
            };
            // Apply shape transformations if needed
            let converted_tensor = if converted_key.contains("Linear")
                || converted_key.contains("linear")
                || converted_key.contains("dense")
            {
                // Transpose weight matrices for linear layers
                if tensor.ndim() == 2 {
                    tensor.clone().reversed_axes()
                } else {
                    tensor.clone()
                tensor.clone()
            convertedweights.insert(converted_key, converted_tensor);
        Ok(converted_weights)
    fn build_architecture_from_metadata(
    ) -> Result<ModelArchitecture<F>> {
        // Build architecture from metadata (simplified)
        Ok(ModelArchitecture {
            inputs: metadata.inputs.clone(),
            outputs: metadata.outputs.clone(),
    fn load_tensorflow_model(&self, _modelpath: &Path) -> Result<TensorFlowModel<F>> {
        // Simplified TensorFlow model loading
        Ok(TensorFlowModel {
            graph_def: Vec::new(),
            variables: HashMap::new(),
            signatures: HashMap::new(),
    fn convert_tensorflow_to_scirs2(
        _tf_model: TensorFlowModel<F>,
        // Simplified conversion
            architecture: ModelArchitecture {
                layers: Vec::new(),
                connections: Vec::new(),
                inputs: Vec::new(),
                outputs: Vec::new(),
            weights: HashMap::new(),
            metadata: ModelMetadata {
                name: "tf_model".to_string(),
                version: "1.0".to_string(),
                source_framework: Framework::TensorFlow,
                description: "Converted TensorFlow model".to_string(),
                config: HashMap::new(),
                training_config: None,
            source_format: ModelFormat::TensorFlowSavedModel,
    fn convert_to_pytorch_state_dict(
        let mut pytorch_state_dict = HashMap::new();
        for (key, tensor) in weights {
            let pytorch_key = if key.ends_with(".kernel") {
                key.replace(".kernel", ".weight")
                key.clone()
            // Apply inverse shape transformations
            let pytorch_tensor = if pytorch_key.contains("Linear") || pytorch_key.contains("weight")
            pytorch_state_dict.insert(pytorch_key, pytorch_tensor);
        Ok(pytorch_state_dict)
    fn save_pytorch_state_dict(
        // Create PyTorch-compatible state dict format
        let mut pytorch_state_dict = serde_json::Map::new();
        // Convert each tensor to PyTorch format
        for (param_name, tensor) in &state_dict {
            pytorch_state_dict.insert(
                param_name.clone(),
                json!({
                    "data": tensor_data,
                    "shape": tensor.shape().to_vec(),
                    "dtype": "float32",
                    "requires_grad": true,
                    "is_leaf": true
                }),
            );
        // Create complete PyTorch checkpoint format
        let checkpoint = json!({
            "state_dict": pytorch_state_dict,
            "version": "1.0",
            "framework": "scirs2-neural",
            "exported_at": chrono::Utc::now().to_rfc3339(),
            "metadata": {
                "num_parameters": state_dict.values().map(|t| t.len()).sum::<usize>(),
                "parameter_names": state_dict.keys().collect::<Vec<_>>()
        // Write to file in PyTorch .pth format (JSON representation)
        let json_string = serde_json::to_string_pretty(&checkpoint).map_err(|e| {
            NeuralError::ComputationError(format!("Failed to write PyTorch state dict: {e}"))
    fn convert_to_tensorflow_model(
        _model: &ConvertedModel<F>,
    ) -> Result<TensorFlowModel<F>> {
        // Simplified TensorFlow conversion
    fn save_tensorflow_model(
        tf_model: TensorFlowModel<F>,
        // Create TensorFlow SavedModel format
        let mut variables_data = serde_json::Map::new();
        // Convert variables to TensorFlow format
        for (var_name, tensor) in &tf_model.variables {
            variables_data.insertjson!({
                "tensor": {
                    "dtype": "DT_FLOAT",
                    "tensorshape": {
                        "dim": tensor.shape(.iter().map(|&d| json!({"size": d})).collect::<Vec<_>>()
                    },
                    "tensor_content": tensor_data
                "variable_name": var_name
        // Create signatures
        let mut signatures_data = serde_json::Map::new();
        for (sig_name, signature) in &tf_model.signatures {
            signatures_data.insert(
                sig_name.clone(),
                    "inputs": signature.inputs,
                    "outputs": signature.outputs,
                    "method_name": signature.method_name
        // Create TensorFlow SavedModel structure
        let saved_model = json!({
            "meta_graphs": [{
                "meta_info_def": {
                    "stripped_op_list": [],
                    "tensorflow_version": "2.0.0",
                    "any_info": "Exported from scirs2-neural"
                "graph_def": {
                    "node": [],
                    "versions": {"producer": 1, "min_consumer": 1},
                    "library": {}
                "saver_def": {},
                "collection_def": {},
                "signature_def": signatures_data,
                "asset_file_def": []
            }],
            "saved_model_schema_version": 1,
            "variables": variables_data,
                "framework": "scirs2-neural",
                "exported_at": chrono::Utc::now().to_rfc3339(),
                "num_variables": tf_model.variables.len()
        // Write SavedModel to directory structure
        if let Some(parent) = output_path.parent() {
            std::fs::create_dir_all(parent).map_err(|e| {
                NeuralError::ComputationError(format!("Failed to create directory: {e}"))
            })?;
        let json_string = serde_json::to_string_pretty(&saved_model).map_err(|e| {
            NeuralError::ComputationError(format!("Failed to write TensorFlow model: {}", e))
    fn validateshapes(
        let mut report = ValidationReport::new();
        // Check input/output shapes
        for (orig_input, conv_input) in original
            .metadata
            .inputs
            .iter()
            .zip(&converted.metadata.inputs)
        {
            if orig_input.shape != conv_input.shape {
                report.shape_mismatches.push(ShapeMismatch {
                    layer_name: orig_input.name.clone(),
                    originalshape: orig_input.shape.clone(),
                    convertedshape: conv_input.shape.clone(),
                });
        Ok(report)
    fn validate_numerical(
        _original: &ConvertedModel<F>, _converted: &ConvertedModel<F>, _test_inputs: &[ArrayD<F>], _tolerance: f64,
        // Simplified numerical validation
        Ok(ValidationReport::new())
    fn validate_full(
        // Simplified full validation
    /// Update conversion settings
    pub fn update_settings(&mut self, settings: ConversionSettings) {
        self.conversion_settings = settings;
    /// Get available layer mappings
    pub fn get_layer_mappings(
        source: Framework,
        target: Framework,
    ) -> Option<&Vec<LayerMapping>> {
        self.layer_mappings.get(&(source, target))
    /// Add custom layer mapping
    pub fn add_layer_mapping(
        &mut self,
        mapping: LayerMapping,
    ) {
            .entry((source, target))
            .or_default()
            .push(mapping);
// Helper structures for internal representations
/// Internal ONNX model representation
struct ONNXModel<F: Float + Debug> {
    opset_version: u32,
    include_training: bool,
    graph: ONNXGraph<F>,
    metadata: ModelMetadata,
/// ONNX graph representation
struct ONNXGraph<F: Float + Debug> {
    #[allow(dead_code)]
    nodes: Vec<ONNXNode>,
    inputs: Vec<TensorSpec>,
    outputs: Vec<TensorSpec>,
    initializers: HashMap<String, ArrayD<F>>,
/// ONNX node representation
struct ONNXNode {
    name: String,
    op_type: String,
    inputs: Vec<String>,
    outputs: Vec<String>,
    attributes: HashMap<String, AttributeValue>,
/// ONNX attribute value
#[allow(dead_code)]
enum AttributeValue {
    Int(i64),
    Float(f64),
    Tensor(Vec<u8>),
    Ints(Vec<i64>),
    Floats(Vec<f64>),
/// Internal TensorFlow model representation
struct TensorFlowModel<F: Float + Debug> {
    graph_def: Vec<u8>,
    variables: HashMap<String, ArrayD<F>>,
    signatures: HashMap<String, SignatureDef>,
/// TensorFlow signature definition
struct SignatureDef {
    inputs: HashMap<String, TensorSpec>,
    outputs: HashMap<String, TensorSpec>,
    method_name: String,
/// Validation report for model conversion
pub struct ValidationReport<F: Float + Debug> {
    /// Shape mismatches found
    pub shape_mismatches: Vec<ShapeMismatch>,
    /// Numerical differences
    pub numerical_differences: Vec<NumericalDifference<F>>,
    /// Missing layers or operations
    pub missing_operations: Vec<String>,
    /// Unsupported operations
    pub unsupported_operations: Vec<String>,
    /// Overall validation success
    pub success: bool,
/// Shape mismatch information
pub struct ShapeMismatch {
    /// Layer or tensor name
    pub layer_name: String,
    /// Original shape
    pub originalshape: Vec<Option<usize>>,
    /// Converted shape
    pub convertedshape: Vec<Option<usize>>,
/// Numerical difference information
pub struct NumericalDifference<F: Float + Debug> {
    /// Maximum absolute difference
    pub max_abs_diff: F,
    /// Mean absolute difference
    pub mean_abs_diff: F,
    /// Relative error
    pub relative_error: F,
impl<F: Float + Debug> ValidationReport<F> {
    fn new() -> Self {
        Self {
            shape_mismatches: Vec::new(),
            numerical_differences: Vec::new(),
            missing_operations: Vec::new(),
            unsupported_operations: Vec::new(),
            success: true,
    fn empty() -> Self {
#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;
    #[test]
    fn test_interop_manager_creation() {
        let manager = InteropManager::<f64>::new();
        assert!(!manager.layer_mappings.is_empty());
        assert!(!manager.dtype_mappings.is_empty());
    fn test_pytorch_weight_conversion() {
        let mut state_dict = HashMap::new();
        state_dict.insert(
            "linear1.weight".to_string(),
            Array2::from_shape_vec((10, 5), (0..50).map(|x| x as f64).collect())
                .expect("Operation failed")
                .into_dyn(),
            "linear1.bias".to_string(),
            Array2::from_shape_vec((10, 1), (0..10).map(|x| x as f64).collect())
        let converted = manager.convert_pytorch_weights(state_dict).expect("Operation failed");
        assert!(converted.contains_key("linear1.kernel"));
        assert!(converted.contains_key("linear1.bias"));
        // Check that weight matrix was transposed
        let kernel = &converted["linear1.kernel"];
        assert_eq!(kernel.shape(), &[5, 10]);
    fn test_model_metadata_creation() {
            name: "test_model".to_string(),
            description: "Test model for conversion".to_string(),
            inputs: vec![TensorSpec {
                name: "input".to_string(),
                shape: vec![Some(1), Some(3), Some(224), Some(224)],
                dtype: DataType::Float32,
                value_range: Some((-1.0, 1.0)),
                description: "RGB image input".to_string(),
            outputs: vec![TensorSpec {
                name: "output".to_string(),
                shape: vec![Some(1), Some(1000)],
                value_range: None,
                description: "Classification logits".to_string(),
        assert_eq!(metadata.name, "test_model");
        assert_eq!(metadata.source_framework, Framework::PyTorch);
        assert_eq!(metadata.inputs.len(), 1);
        assert_eq!(metadata.outputs.len(), 1);
    fn test_layer_mapping() {
        let mapping = LayerMapping {
            target_framework: Framework::TensorFlow,
        assert_eq!(mapping.source_name, "Linear");
        assert_eq!(mapping.target_name, "Dense");
        assert!(mapping.param_mappings.contains_key("weight"));
        assert!(matches!(
            mapping.shape_transform,
            Some(ShapeTransform::Transpose { .. })
        ));
    fn test_conversion_settings() {
        let settings = ConversionSettings {
            preserve_training_ops: true,
            target_precision: DataType::Float16,
            optimization_level: OptimizationLevel::Advanced,
            validation_mode: ValidationMode::Numerical { tolerance: 1e-5 },
            custom_handlers: HashMap::new(),
        assert!(settings.preserve_training_ops);
        assert_eq!(settings.target_precision, DataType::Float16);
        assert_eq!(settings.optimization_level, OptimizationLevel::Advanced);
            settings.validation_mode,
            ValidationMode::Numerical { .. }
    fn test_tensor_spec() {
        let spec = TensorSpec {
            name: "input_tensor".to_string(),
            shape: vec![None, Some(3), Some(224), Some(224)],
            dtype: DataType::Float32,
            value_range: Some((0.0, 1.0)),
            description: "Normalized image input".to_string(),
        assert_eq!(spec.name, "input_tensor");
        assert_eq!(spec.shape[0], None); // Dynamic batch dimension
        assert_eq!(spec.shape[1], Some(3)); // Fixed channel dimension
        assert_eq!(spec.dtype, DataType::Float32);
        assert_eq!(spec.value_range, Some((0.0, 1.0)));
    fn testshape_transform() {
        let transpose = ShapeTransform::Transpose {
            axes: vec![0, 2, 1, 3],
        let reshape = ShapeTransform::Reshape {
            targetshape: vec![1, 224, 224, 3],
        let channels_first_to_last = ShapeTransform::ChannelsFirstToLast;
        assert!(matches!(transpose, ShapeTransform::Transpose { .. }));
        assert!(matches!(reshape, ShapeTransform::Reshape { .. }));
            channels_first_to_last,
            ShapeTransform::ChannelsFirstToLast
