rustorch 0.6.29

use crate::dtype::DType;
/// ONNX model import implementation
/// ONNXモデルインポート実装
use crate::error::{RusTorchError, RusTorchResult};
use crate::model_import::{ImportedModel, LayerInfo, ModelArchitecture, ModelMetadata, TensorSpec}; // ModelStructure,
use crate::tensor::Tensor;
use std::collections::HashMap;
use std::path::Path;

/// Layer description for model conversion
/// モデル変換用レイヤー記述
#[derive(Debug, Clone)]
pub struct LayerDescription {
    /// Layer name
    /// レイヤー名
    pub name: String,
    /// Layer type
    /// レイヤータイプ
    pub layer_type: String,
    /// Input shape
    /// 入力形状
    pub input_shape: Vec<usize>,
    /// Output shape
    /// 出力形状
    pub output_shape: Vec<usize>,
}

/// ONNX data types mapping
/// ONNXデータ型マッピング
#[derive(Debug, Clone, Copy)]
pub enum OnnxDataType {
    /// Undefined data type
    Undefined = 0,
    /// 32-bit floating point
    Float = 1,
    /// 8-bit unsigned integer
    Uint8 = 2,
    /// 8-bit signed integer
    Int8 = 3,
    /// 16-bit unsigned integer
    Uint16 = 4,
    /// 16-bit signed integer
    Int16 = 5,
    /// 32-bit signed integer
    Int32 = 6,
    /// 64-bit signed integer
    Int64 = 7,
    /// String data type
    String = 8,
    /// Boolean data type
    Bool = 9,
    /// 16-bit floating point
    Float16 = 10,
    /// 64-bit floating point
    Double = 11,
    /// 32-bit unsigned integer
    Uint32 = 12,
    /// 64-bit unsigned integer
    Uint64 = 13,
    /// 64-bit complex number
    Complex64 = 14,
    /// 128-bit complex number
    Complex128 = 15,
}

impl OnnxDataType {
    /// Convert ONNX data type to RusTorch DType
    /// ONNXデータ型をRusTorch DTypeに変換
    pub fn to_dtype(self) -> DType {
        match self {
            OnnxDataType::Float => DType::Float32,
            OnnxDataType::Double => DType::Float64,
            OnnxDataType::Float16 => DType::Float16,
            OnnxDataType::Int8 => DType::Int8,
            OnnxDataType::Uint8 => DType::UInt8,
            OnnxDataType::Int16 => DType::Int16,
            OnnxDataType::Uint16 => DType::UInt16,
            OnnxDataType::Int32 => DType::Int32,
            OnnxDataType::Uint32 => DType::UInt32,
            OnnxDataType::Int64 => DType::Int64,
            OnnxDataType::Uint64 => DType::UInt64,
            OnnxDataType::Bool => DType::Bool,
            OnnxDataType::Complex64 => DType::Complex64,
            OnnxDataType::Complex128 => DType::Complex128,
            _ => DType::Float32, // Default fallback
        }
    }
}

/// ONNX tensor information
/// ONNXテンソル情報
#[derive(Debug, Clone)]
pub struct OnnxTensorInfo {
    /// Tensor name identifier
    pub name: String,
    /// Tensor dimensions
    pub shape: Vec<i64>,
    /// ONNX data type
    pub data_type: OnnxDataType,
    /// Raw tensor data
    pub data: Vec<u8>,
}

/// ONNX node/operation information
/// ONNXノード/操作情報
#[derive(Debug, Clone)]
pub struct OnnxNode {
    /// Node name
    pub name: String,
    /// Operation type
    pub op_type: String,
    /// Input tensor names
    pub inputs: Vec<String>,
    /// Output tensor names
    pub outputs: Vec<String>,
    /// Node attributes
    pub attributes: HashMap<String, String>,
}

/// ONNX model representation
/// ONNXモデル表現
#[derive(Debug, Clone)]
pub struct OnnxModel {
    /// ONNX IR version
    pub ir_version: i64,
    /// Model producer name
    pub producer_name: String,
    /// Producer version
    pub producer_version: String,
    /// Model domain
    pub domain: String,
    /// Model version number
    pub model_version: i64,
    /// Documentation string
    pub doc_string: String,
    /// Model graph
    pub graph: OnnxGraph,
    /// Additional metadata properties
    pub metadata_props: HashMap<String, String>,
}

/// ONNX graph representation
/// ONNXグラフ表現
#[derive(Debug, Clone)]
pub struct OnnxGraph {
    /// Graph name
    pub name: String,
    /// Computation nodes
    pub nodes: Vec<OnnxNode>,
    /// Weight initializers
    pub initializers: Vec<OnnxTensorInfo>,
    /// Input tensors
    pub inputs: Vec<OnnxTensorInfo>,
    /// Output tensors
    pub outputs: Vec<OnnxTensorInfo>,
    /// Intermediate value information
    pub value_info: Vec<OnnxTensorInfo>,
}

/// Import ONNX model from file
/// ファイルからONNXモデルをインポート
pub fn import_onnx_model<P: AsRef<Path>>(path: P) -> RusTorchResult<ImportedModel> {
    let path = path.as_ref();

    // Read ONNX file
    let onnx_data = std::fs::read(path).map_err(|e| RusTorchError::FileNotFound(e.to_string()))?;

    // Parse ONNX model (mock implementation)
    let onnx_model = parse_onnx_data(&onnx_data)?;

    // Convert to RusTorch format
    let metadata = create_metadata(&onnx_model);
    let weights = extract_weights(&onnx_model)?;
    let architecture = create_architecture(&onnx_model)?;

    Ok(ImportedModel {
        metadata,
        weights,
        architecture,
    })
}

/// Parse ONNX binary data
/// ONNXバイナリデータを解析
fn parse_onnx_data(data: &[u8]) -> RusTorchResult<OnnxModel> {
    // Mock ONNX parsing implementation
    // In a real implementation, this would use protobuf to parse the ONNX format

    if data.len() < 10 {
        return Err(RusTorchError::InvalidModel(
            "File too small to be valid ONNX",
        ));
    }

    // Create a mock ONNX model for demonstration
    let mock_model = OnnxModel {
        ir_version: 7,
        producer_name: "pytorch".to_string(),
        producer_version: "1.9.0".to_string(),
        domain: "".to_string(),
        model_version: 1,
        doc_string: "Mock ONNX model for demonstration".to_string(),
        graph: OnnxGraph {
            name: "main_graph".to_string(),
            nodes: vec![
                OnnxNode {
                    name: "linear1".to_string(),
                    op_type: "MatMul".to_string(),
                    inputs: vec!["input".to_string(), "linear1.weight".to_string()],
                    outputs: vec!["linear1_output".to_string()],
                    attributes: HashMap::new(),
                },
                OnnxNode {
                    name: "add1".to_string(),
                    op_type: "Add".to_string(),
                    inputs: vec!["linear1_output".to_string(), "linear1.bias".to_string()],
                    outputs: vec!["output".to_string()],
                    attributes: HashMap::new(),
                },
            ],
            initializers: vec![
                OnnxTensorInfo {
                    name: "linear1.weight".to_string(),
                    shape: vec![784, 128],
                    data_type: OnnxDataType::Float,
                    data: vec![0u8; 784 * 128 * 4], // Mock weight data
                },
                OnnxTensorInfo {
                    name: "linear1.bias".to_string(),
                    shape: vec![128],
                    data_type: OnnxDataType::Float,
                    data: vec![0u8; 128 * 4], // Mock bias data
                },
            ],
            inputs: vec![OnnxTensorInfo {
                name: "input".to_string(),
                shape: vec![-1, 784], // -1 for dynamic batch size
                data_type: OnnxDataType::Float,
                data: vec![],
            }],
            outputs: vec![OnnxTensorInfo {
                name: "output".to_string(),
                shape: vec![-1, 128],
                data_type: OnnxDataType::Float,
                data: vec![],
            }],
            value_info: vec![],
        },
        metadata_props: HashMap::new(),
    };

    Ok(mock_model)
}

/// Create model metadata from ONNX model
/// ONNXモデルからモデルメタデータを作成
fn create_metadata(onnx_model: &OnnxModel) -> ModelMetadata {
    ModelMetadata {
        name: onnx_model.graph.name.clone(),
        version: onnx_model.model_version.to_string(),
        framework: onnx_model.producer_name.clone(),
        format: "ONNX".to_string(),
        description: if onnx_model.doc_string.is_empty() {
            None
        } else {
            Some(onnx_model.doc_string.clone())
        },
        author: Some(onnx_model.producer_name.clone()),
        license: None,
        created: None,
        extra: onnx_model.metadata_props.clone(),
    }
}

/// Extract weights from ONNX model
/// ONNXモデルから重みを抽出
fn extract_weights(onnx_model: &OnnxModel) -> RusTorchResult<HashMap<String, Tensor<f32>>> {
    let mut weights = HashMap::new();

    for initializer in &onnx_model.graph.initializers {
        let tensor = convert_onnx_tensor_to_rustorch(initializer)?;
        weights.insert(initializer.name.clone(), tensor);
    }

    Ok(weights)
}

/// Convert ONNX tensor to RusTorch tensor
/// ONNXテンソルをRusTorchテンソルに変換
fn convert_onnx_tensor_to_rustorch(onnx_tensor: &OnnxTensorInfo) -> RusTorchResult<Tensor<f32>> {
    // Convert shape from i64 to usize, handling dynamic dimensions
    let shape: Vec<usize> = onnx_tensor
        .shape
        .iter()
        .map(|&dim| if dim < 0 { 1 } else { dim as usize })
        .collect();

    match onnx_tensor.data_type {
        OnnxDataType::Float => {
            // Convert bytes to f32 values
            let float_data: Vec<f32> = onnx_tensor
                .data
                .chunks_exact(4)
                .map(|chunk| {
                    let bytes = [chunk[0], chunk[1], chunk[2], chunk[3]];
                    f32::from_le_bytes(bytes)
                })
                .collect();

            if float_data.is_empty() {
                // Create zero tensor for empty data
                Ok(Tensor::zeros(&shape))
            } else {
                Ok(Tensor::from_vec(float_data, shape))
            }
        }
        _ => {
            // For other data types, create zero tensor for now
            // In a real implementation, this would handle type conversion
            Ok(Tensor::zeros(&shape))
        }
    }
}

/// Create model architecture from ONNX model
/// ONNXモデルからモデルアーキテクチャを作成
fn create_architecture(onnx_model: &OnnxModel) -> RusTorchResult<ModelArchitecture> {
    let inputs = onnx_model
        .graph
        .inputs
        .iter()
        .map(create_tensor_spec)
        .collect();

    let outputs = onnx_model
        .graph
        .outputs
        .iter()
        .map(create_tensor_spec)
        .collect();

    let layer_descs: Vec<LayerDescription> = onnx_model
        .graph
        .nodes
        .iter()
        .map(create_layer_info)
        .collect();

    let layers = layer_descs
        .into_iter()
        .map(|desc| LayerInfo {
            name: desc.name,
            layer_type: desc.layer_type,
            input_shape: desc
                .input_shape
                .iter()
                .map(|&x| if x == 0 { None } else { Some(x) })
                .collect(),
            output_shape: desc
                .output_shape
                .iter()
                .map(|&x| if x == 0 { None } else { Some(x) })
                .collect(),
            params: 0, // Would need to calculate from initializers
            attributes: std::collections::HashMap::new(),
        })
        .collect();

    let parameter_count = onnx_model
        .graph
        .initializers
        .iter()
        .map(|init| {
            init.shape
                .iter()
                .map(|&dim| dim.max(1) as usize)
                .product::<usize>()
        })
        .sum();

    let model_size = onnx_model
        .graph
        .initializers
        .iter()
        .map(|init| init.data.len())
        .sum();

    Ok(ModelArchitecture {
        inputs,
        outputs,
        layers,
        parameter_count,
        model_size,
    })
}

/// Create tensor specification from ONNX tensor info
/// ONNXテンソル情報からテンソル仕様を作成
fn create_tensor_spec(onnx_tensor: &OnnxTensorInfo) -> TensorSpec {
    let shape = onnx_tensor
        .shape
        .iter()
        .map(|&dim| if dim < 0 { None } else { Some(dim as usize) })
        .collect();

    TensorSpec {
        name: onnx_tensor.name.clone(),
        shape,
        dtype: onnx_tensor.data_type.to_dtype(),
        description: None,
    }
}

/// Create layer information from ONNX node
/// ONNXノードからレイヤー情報を作成
fn create_layer_info(node: &OnnxNode) -> LayerDescription {
    LayerDescription {
        name: node.name.clone(),
        layer_type: map_onnx_op_to_layer_type(&node.op_type),
        input_shape: vec![0],  // Would need to infer from graph
        output_shape: vec![0], // Would need to infer from graph
    }
}

/// Map ONNX operation type to RusTorch layer type
/// ONNX操作タイプをRusTorchレイヤータイプにマップ
fn map_onnx_op_to_layer_type(op_type: &str) -> String {
    match op_type {
        "MatMul" => "Linear".to_string(),
        "Conv" => "Conv2d".to_string(),
        "MaxPool" => "MaxPool2d".to_string(),
        "AveragePool" => "AvgPool2d".to_string(),
        "BatchNormalization" => "BatchNorm2d".to_string(),
        "Relu" => "ReLU".to_string(),
        "Sigmoid" => "Sigmoid".to_string(),
        "Tanh" => "Tanh".to_string(),
        "Softmax" => "Softmax".to_string(),
        "Add" => "Add".to_string(),
        "Mul" => "Mul".to_string(),
        "Reshape" => "Reshape".to_string(),
        "Transpose" => "Transpose".to_string(),
        _ => op_type.to_string(),
    }
}

/// Export RusTorch model to ONNX format
/// RusTorchモデルをONNX形式にエクスポート
pub fn export_to_onnx<P: AsRef<Path>>(
    model: &dyn crate::nn::Module<f32>,
    path: P,
    input_shape: &[usize],
) -> RusTorchResult<()> {
    let path = path.as_ref();

    // Create mock ONNX export
    // In a real implementation, this would serialize the model to ONNX format

    let mock_onnx_data = create_mock_onnx_export(model, input_shape)?;

    std::fs::write(path, mock_onnx_data)
        .map_err(|e| RusTorchError::SerializationError(e.to_string()))?;

    Ok(())
}

/// Create mock ONNX export data
/// モックONNXエクスポートデータを作成
fn create_mock_onnx_export(
    _model: &dyn crate::nn::Module<f32>,
    _input_shape: &[usize],
) -> RusTorchResult<Vec<u8>> {
    // Mock ONNX export data
    let mock_data = b"Mock ONNX export data - would contain protobuf serialized model";
    Ok(mock_data.to_vec())
}

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

    #[test]
    fn test_onnx_data_type_conversion() {
        assert_eq!(OnnxDataType::Float.to_dtype(), DType::Float32);
        assert_eq!(OnnxDataType::Double.to_dtype(), DType::Float64);
        assert_eq!(OnnxDataType::Int32.to_dtype(), DType::Int32);
        assert_eq!(OnnxDataType::Bool.to_dtype(), DType::Bool);
    }

    #[test]
    fn test_op_type_mapping() {
        assert_eq!(map_onnx_op_to_layer_type("MatMul"), "Linear");
        assert_eq!(map_onnx_op_to_layer_type("Conv"), "Conv2d");
        assert_eq!(map_onnx_op_to_layer_type("Relu"), "ReLU");
        assert_eq!(map_onnx_op_to_layer_type("UnknownOp"), "UnknownOp");
    }

    #[test]
    fn test_onnx_import_mock() {
        // Create a temporary mock ONNX file
        let temp_dir = std::env::temp_dir();
        let temp_file = temp_dir.join("test_model.onnx");

        {
            let mut file = std::fs::File::create(&temp_file).unwrap();
            file.write_all(b"mock onnx data for testing").unwrap();
        }

        // Test import
        let result = import_onnx_model(&temp_file);
        assert!(result.is_ok());

        let model = result.unwrap();
        assert_eq!(model.metadata.format, "ONNX");
        assert!(model.weights.contains_key("linear1.weight"));
        assert!(model.weights.contains_key("linear1.bias"));

        // Cleanup
        std::fs::remove_file(temp_file).ok();
    }
}