rustorch 0.6.29

/// PyTorch model import implementation
/// PyTorch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,

/// 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 (Linear, Conv2d, etc.)
    /// レイヤータイプ（Linear、Conv2d等）
    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>,
    /// Number of parameters
    /// パラメータ数
    pub params: usize,
    /// Layer-specific attributes
    /// レイヤー固有の属性
    pub attributes: HashMap<String, String>,
}
use crate::dtype::DType;
use crate::tensor::Tensor;
use std::collections::HashMap;
use std::path::Path;
// use std::io::{Read, Seek, SeekFrom}; // Unused in mock implementation

/// PyTorch pickle format magic numbers
/// PyTorchピクル形式のマジックナンバー
const PICKLE_PROTOCOL_2: u8 = 0x80;
// Future protocol support
// const PICKLE_PROTOCOL_3: u8 = 0x80;
// const PICKLE_PROTOCOL_4: u8 = 0x80;
// const PICKLE_PROTOCOL_5: u8 = 0x80;

/// PyTorch tensor storage types
/// PyTorchテンソルストレージタイプ
#[derive(Debug, Clone, Copy)]
pub enum TorchStorageType {
    /// 32-bit floating point storage
    FloatStorage,
    /// 64-bit floating point storage
    DoubleStorage,
    /// 16-bit half precision storage
    HalfStorage,
    /// 8-bit character storage
    CharStorage,
    /// 16-bit short integer storage
    ShortStorage,
    /// 32-bit integer storage
    IntStorage,
    /// 64-bit long integer storage
    LongStorage,
    /// Boolean storage
    BoolStorage,
}

impl TorchStorageType {
    /// Convert to RusTorch DType
    /// RusTorch DTypeに変換
    pub fn to_dtype(self) -> DType {
        match self {
            TorchStorageType::FloatStorage => DType::Float32,
            TorchStorageType::DoubleStorage => DType::Float64,
            TorchStorageType::HalfStorage => DType::Float16,
            TorchStorageType::CharStorage => DType::Int8,
            TorchStorageType::ShortStorage => DType::Int16,
            TorchStorageType::IntStorage => DType::Int32,
            TorchStorageType::LongStorage => DType::Int64,
            TorchStorageType::BoolStorage => DType::Bool,
        }
    }
}

/// PyTorch tensor metadata
/// PyTorchテンソルメタデータ
#[derive(Debug, Clone)]
pub struct TorchTensorInfo {
    /// Tensor name
    /// テンソル名
    pub name: String,
    /// Tensor shape
    /// テンソル形状
    pub shape: Vec<usize>,
    /// Storage type
    /// ストレージタイプ
    pub storage_type: TorchStorageType,
    /// Raw binary data
    /// 生バイナリデータ
    pub data: Vec<u8>,
    /// Whether gradient is required
    /// 勾配が必要か
    pub requires_grad: bool,
}

/// PyTorch model state dict representation
/// PyTorchモデル状態辞書表現
#[derive(Debug, Clone)]
pub struct TorchStateDict {
    /// Model tensors
    /// モデルテンソル
    pub tensors: HashMap<String, TorchTensorInfo>,
    /// Model metadata
    /// モデルメタデータ
    pub metadata: HashMap<String, String>,
    /// PyTorch version
    /// PyTorchバージョン
    pub version: String,
}

/// PyTorch model architecture information
/// PyTorchモデルアーキテクチャ情報
#[derive(Debug, Clone)]
pub struct TorchModelInfo {
    /// Model class name
    /// モデルクラス名
    pub model_class: String,
    /// Layer descriptions
    /// レイヤー記述
    pub layers: Vec<TorchLayerDescription>,
    /// Total parameter count
    /// 総パラメータ数
    pub total_params: usize,
}

/// PyTorch layer information
/// PyTorchレイヤー情報
#[derive(Debug, Clone)]
pub struct TorchLayerDescription {
    /// Layer name
    /// レイヤー名
    pub name: String,
    /// Module type
    /// モジュールタイプ
    pub module_type: String,
    /// Parameter names
    /// パラメータ名
    pub parameters: Vec<String>,
    /// Configuration settings
    /// 設定配置
    pub config: HashMap<String, String>,
}

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

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

    // Parse PyTorch model
    let state_dict = parse_pytorch_data(&torch_data)?;

    // Convert to RusTorch format
    let metadata = create_pytorch_metadata(&state_dict, path);
    let weights = extract_pytorch_weights(&state_dict)?;
    let architecture = infer_pytorch_architecture(&state_dict)?;

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

/// Parse PyTorch binary data (pickle format)
/// PyTorchバイナリデータを解析（ピクル形式）
fn parse_pytorch_data(data: &[u8]) -> RusTorchResult<TorchStateDict> {
    if data.len() < 2 {
        return Err(RusTorchError::InvalidModel("File too small"));
    }

    // Check for pickle protocol marker
    if data[0] != PICKLE_PROTOCOL_2 {
        return Err(RusTorchError::InvalidModel(
            "Not a valid PyTorch pickle file",
        ));
    }

    // Mock PyTorch parsing implementation
    // In a real implementation, this would use a pickle parser like serde_pickle
    parse_mock_pytorch_state_dict()
}

/// Parse mock PyTorch state dict for demonstration
/// デモンストレーション用のモックPyTorch状態辞書を解析
fn parse_mock_pytorch_state_dict() -> RusTorchResult<TorchStateDict> {
    let mut tensors = HashMap::new();

    // Mock some common layer weights
    tensors.insert(
        "features.0.weight".to_string(),
        TorchTensorInfo {
            name: "features.0.weight".to_string(),
            shape: vec![64, 3, 7, 7],
            storage_type: TorchStorageType::FloatStorage,
            data: vec![0u8; 64 * 3 * 7 * 7 * 4], // 4 bytes per float
            requires_grad: false,
        },
    );

    tensors.insert(
        "features.0.bias".to_string(),
        TorchTensorInfo {
            name: "features.0.bias".to_string(),
            shape: vec![64],
            storage_type: TorchStorageType::FloatStorage,
            data: vec![0u8; 64 * 4],
            requires_grad: false,
        },
    );

    tensors.insert(
        "classifier.weight".to_string(),
        TorchTensorInfo {
            name: "classifier.weight".to_string(),
            shape: vec![1000, 512],
            storage_type: TorchStorageType::FloatStorage,
            data: vec![0u8; 1000 * 512 * 4],
            requires_grad: false,
        },
    );

    tensors.insert(
        "classifier.bias".to_string(),
        TorchTensorInfo {
            name: "classifier.bias".to_string(),
            shape: vec![1000],
            storage_type: TorchStorageType::FloatStorage,
            data: vec![0u8; 1000 * 4],
            requires_grad: false,
        },
    );

    let mut metadata = HashMap::new();
    metadata.insert("framework".to_string(), "PyTorch".to_string());
    metadata.insert("version".to_string(), "1.9.0".to_string());
    metadata.insert("format".to_string(), "state_dict".to_string());

    Ok(TorchStateDict {
        tensors,
        metadata,
        version: "1.9.0".to_string(),
    })
}

/// Create metadata from PyTorch state dict
/// PyTorch状態辞書からメタデータを作成
fn create_pytorch_metadata(state_dict: &TorchStateDict, path: &Path) -> ModelMetadata {
    let name = path
        .file_stem()
        .and_then(|stem| stem.to_str())
        .unwrap_or("pytorch_model")
        .to_string();

    ModelMetadata {
        name,
        version: state_dict.version.clone(),
        framework: state_dict
            .metadata
            .get("framework")
            .cloned()
            .unwrap_or_else(|| "PyTorch".to_string()),
        format: "PyTorch".to_string(),
        description: Some("Imported PyTorch model".to_string()),
        author: None,
        license: None,
        created: None,
        extra: state_dict.metadata.clone(),
    }
}

/// Extract weights from PyTorch state dict
/// PyTorch状態辞書から重みを抽出
fn extract_pytorch_weights(
    state_dict: &TorchStateDict,
) -> RusTorchResult<HashMap<String, Tensor<f32>>> {
    let mut weights = HashMap::new();

    for (name, tensor_info) in &state_dict.tensors {
        let tensor = convert_torch_tensor_to_rustorch(tensor_info)?;
        weights.insert(name.clone(), tensor);
    }

    Ok(weights)
}

/// Convert PyTorch tensor to RusTorch tensor
/// PyTorchテンソルをRusTorchテンソルに変換
fn convert_torch_tensor_to_rustorch(torch_tensor: &TorchTensorInfo) -> RusTorchResult<Tensor<f32>> {
    match torch_tensor.storage_type {
        TorchStorageType::FloatStorage => {
            let float_data: Vec<f32> = torch_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() {
                Ok(Tensor::zeros(&torch_tensor.shape))
            } else {
                Ok(Tensor::from_vec(float_data, torch_tensor.shape.clone()))
            }
        }
        TorchStorageType::DoubleStorage => {
            // Convert double to float
            let double_data: Vec<f64> = torch_tensor
                .data
                .chunks_exact(8)
                .map(|chunk| {
                    let bytes = [
                        chunk[0], chunk[1], chunk[2], chunk[3], chunk[4], chunk[5], chunk[6],
                        chunk[7],
                    ];
                    f64::from_le_bytes(bytes)
                })
                .collect();

            let float_data: Vec<f32> = double_data.iter().map(|&x| x as f32).collect();

            if float_data.is_empty() {
                Ok(Tensor::zeros(&torch_tensor.shape))
            } else {
                Ok(Tensor::from_vec(float_data, torch_tensor.shape.clone()))
            }
        }
        _ => {
            // For other types, create zero tensor for now
            Ok(Tensor::zeros(&torch_tensor.shape))
        }
    }
}

/// Infer model architecture from PyTorch state dict
/// PyTorch状態辞書からモデルアーキテクチャを推論
fn infer_pytorch_architecture(state_dict: &TorchStateDict) -> RusTorchResult<ModelArchitecture> {
    let layers = infer_layers_from_state_dict(state_dict);

    // Infer input/output shapes from layer structure
    let inputs = infer_input_specs(&layers);
    let outputs = infer_output_specs(&layers);

    let parameter_count = state_dict
        .tensors
        .values()
        .map(|tensor| tensor.shape.iter().product::<usize>())
        .sum();

    let model_size = state_dict
        .tensors
        .values()
        .map(|tensor| tensor.data.len())
        .sum();

    Ok(ModelArchitecture {
        inputs,
        outputs,
        layers: layers
            .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: desc.params,
                attributes: desc.attributes,
            })
            .collect(),
        parameter_count,
        model_size,
    })
}

/// Infer layers from PyTorch state dict
/// PyTorch状態辞書からレイヤーを推論
fn infer_layers_from_state_dict(state_dict: &TorchStateDict) -> Vec<LayerDescription> {
    let mut layers = Vec::new();
    let mut processed_prefixes = std::collections::HashSet::new();

    for tensor_name in state_dict.tensors.keys() {
        if let Some(layer_info) = infer_layer_from_tensor_name(tensor_name, state_dict) {
            let prefix = extract_layer_prefix(tensor_name);
            if !processed_prefixes.contains(&prefix) {
                layers.push(layer_info);
                processed_prefixes.insert(prefix);
            }
        }
    }

    layers
}

/// Extract layer prefix from tensor name
/// テンソル名からレイヤープレフィックスを抽出
fn extract_layer_prefix(tensor_name: &str) -> String {
    if let Some(last_dot) = tensor_name.rfind('.') {
        tensor_name[..last_dot].to_string()
    } else {
        tensor_name.to_string()
    }
}

/// Infer layer information from tensor name
/// テンソル名からレイヤー情報を推論
fn infer_layer_from_tensor_name(
    tensor_name: &str,
    state_dict: &TorchStateDict,
) -> Option<LayerDescription> {
    let layer_prefix = extract_layer_prefix(tensor_name);

    // Look for weight tensor to determine layer type
    if !tensor_name.ends_with(".weight") {
        return None;
    }

    let weight_tensor = state_dict.tensors.get(tensor_name)?;
    let layer_type = infer_layer_type_from_weight_shape(&weight_tensor.shape);

    // Calculate parameter count for this layer
    let weight_params = weight_tensor.shape.iter().product::<usize>();
    let bias_key = format!("{}.bias", layer_prefix);
    let bias_params = state_dict
        .tensors
        .get(&bias_key)
        .map(|bias| bias.shape.iter().product::<usize>())
        .unwrap_or(0);

    let total_params = weight_params + bias_params;

    Some(LayerDescription {
        name: layer_prefix.clone(),
        layer_type,
        input_shape: infer_input_shape(&weight_tensor.shape),
        output_shape: infer_output_shape(&weight_tensor.shape),
        params: total_params,
        attributes: HashMap::new(),
    })
}

/// Infer layer type from weight tensor shape
/// 重みテンソルの形状からレイヤータイプを推論
fn infer_layer_type_from_weight_shape(shape: &[usize]) -> String {
    match shape.len() {
        2 => "Linear".to_string(),
        4 => "Conv2d".to_string(),
        1 => "BatchNorm1d".to_string(),
        _ => "Unknown".to_string(),
    }
}

/// Infer input shape from weight shape
/// 重み形状から入力形状を推論
fn infer_input_shape(weight_shape: &[usize]) -> Vec<usize> {
    match weight_shape.len() {
        2 => vec![0, weight_shape[1]],       // [batch, input_features]
        4 => vec![0, weight_shape[1], 0, 0], // [batch, channels, height, width]
        _ => vec![0],
    }
}

/// Infer output shape from weight shape
/// 重み形状から出力形状を推論
fn infer_output_shape(weight_shape: &[usize]) -> Vec<usize> {
    match weight_shape.len() {
        2 => vec![0, weight_shape[0]],       // [batch, output_features]
        4 => vec![0, weight_shape[0], 0, 0], // [batch, out_channels, height, width]
        _ => vec![0],
    }
}

/// Infer input specifications from layers
/// レイヤーから入力仕様を推論
fn infer_input_specs(layers: &[LayerDescription]) -> Vec<TensorSpec> {
    if let Some(first_layer) = layers.first() {
        vec![TensorSpec {
            name: "input".to_string(),
            shape: first_layer
                .input_shape
                .iter()
                .map(|&x| if x == 0 { None } else { Some(x) })
                .collect(),
            dtype: DType::Float32,
            description: Some("Model input".to_string()),
        }]
    } else {
        vec![TensorSpec {
            name: "input".to_string(),
            shape: vec![None, Some(784)], // Default MNIST-like input (None for batch dim)
            dtype: DType::Float32,
            description: Some("Model input".to_string()),
        }]
    }
}

/// Infer output specifications from layers
/// レイヤーから出力仕様を推論
fn infer_output_specs(layers: &[LayerDescription]) -> Vec<TensorSpec> {
    if let Some(last_layer) = layers.last() {
        vec![TensorSpec {
            name: "output".to_string(),
            shape: last_layer
                .output_shape
                .iter()
                .map(|&x| if x == 0 { None } else { Some(x) })
                .collect(),
            dtype: DType::Float32,
            description: Some("Model output".to_string()),
        }]
    } else {
        vec![TensorSpec {
            name: "output".to_string(),
            shape: vec![None, Some(10)], // Default classification output (None for batch dim)
            dtype: DType::Float32,
            description: Some("Model output".to_string()),
        }]
    }
}

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

    // Create mock PyTorch export
    let mock_pytorch_data = create_mock_pytorch_export(model)?;

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

    Ok(())
}

/// Create mock PyTorch export data
/// モックPyTorchエクスポートデータを作成
fn create_mock_pytorch_export(_model: &dyn crate::nn::Module<f32>) -> RusTorchResult<Vec<u8>> {
    // Mock PyTorch export data (would be pickle format)
    let mock_data = b"Mock PyTorch export data - would contain pickle serialized state_dict";
    Ok(mock_data.to_vec())
}

/// Load pretrained PyTorch model from URL
/// URLから事前学習済みPyTorchモデルを読み込み
pub fn load_pretrained_pytorch_model(model_name: &str) -> RusTorchResult<ImportedModel> {
    let _url = match model_name {
        "resnet18" => "https://download.pytorch.org/models/resnet18-5c106cde.pth",
        "resnet50" => "https://download.pytorch.org/models/resnet50-19c8e357.pth",
        "mobilenet_v2" => "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
        "densenet121" => "https://download.pytorch.org/models/densenet121-a639ec97.pth",
        "vgg16" => "https://download.pytorch.org/models/vgg16-397923af.pth",
        _ => {
            return Err(RusTorchError::InvalidModel(format!(
                "Unknown model: {}",
                model_name
            )))
        }
    };

    // In a real implementation, this would download and parse the actual model
    // For now, create a mock model based on the name
    create_mock_pretrained_model(model_name)
}

/// Create mock pretrained model
/// モック事前学習済みモデルを作成
fn create_mock_pretrained_model(model_name: &str) -> RusTorchResult<ImportedModel> {
    let (input_shape, output_classes, layers) = match model_name {
        "resnet18" => (vec![0, 3, 224, 224], 1000, create_resnet18_layers()),
        "resnet50" => (vec![0, 3, 224, 224], 1000, create_resnet50_layers()),
        "mobilenet_v2" => (vec![0, 3, 224, 224], 1000, create_mobilenet_layers()),
        _ => (vec![0, 3, 224, 224], 1000, vec![]),
    };

    let metadata = ModelMetadata {
        name: model_name.to_string(),
        version: "1.0".to_string(),
        framework: "PyTorch".to_string(),
        format: "PyTorch".to_string(),
        description: Some(format!("Pretrained {} model", model_name)),
        author: Some("PyTorch".to_string()),
        license: Some("BSD".to_string()),
        created: None,
        extra: HashMap::new(),
    };

    let architecture = ModelArchitecture {
        inputs: vec![TensorSpec {
            name: "input".to_string(),
            shape: input_shape
                .iter()
                .map(|&x| if x == 0 { None } else { Some(x) })
                .collect(),
            dtype: DType::Float32,
            description: Some("RGB image input".to_string()),
        }],
        outputs: vec![TensorSpec {
            name: "output".to_string(),
            shape: vec![None, Some(output_classes)],
            dtype: DType::Float32,
            description: Some("Classification logits".to_string()),
        }],
        layers: layers
            .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: desc.params,
                attributes: desc.attributes,
            })
            .collect(),
        parameter_count: 11_000_000, // Mock parameter count
        model_size: 44_000_000,      // Mock model size
    };

    // Create mock weights
    let mut weights = HashMap::new();
    weights.insert("conv1.weight".to_string(), Tensor::randn(&[64, 3, 7, 7]));
    weights.insert("conv1.bias".to_string(), Tensor::zeros(&[64]));
    weights.insert(
        "fc.weight".to_string(),
        Tensor::randn(&[output_classes, 512]),
    );
    weights.insert("fc.bias".to_string(), Tensor::zeros(&[output_classes]));

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

/// Create ResNet18 layer structure
/// ResNet18レイヤー構造を作成
fn create_resnet18_layers() -> Vec<LayerDescription> {
    vec![
        LayerDescription {
            name: "conv1".to_string(),
            layer_type: "Conv2d".to_string(),
            input_shape: vec![0, 3, 224, 224],
            output_shape: vec![0, 64, 112, 112],
            params: 9408, // 64 * 3 * 7 * 7
            attributes: HashMap::new(),
        },
        LayerDescription {
            name: "layer1".to_string(),
            layer_type: "ResNetLayer".to_string(),
            input_shape: vec![0, 64, 56, 56],
            output_shape: vec![0, 64, 56, 56],
            params: 147_648,
            attributes: HashMap::new(),
        },
        LayerDescription {
            name: "fc".to_string(),
            layer_type: "Linear".to_string(),
            input_shape: vec![0, 512],
            output_shape: vec![0, 1000],
            params: 513_000, // 512 * 1000 + 1000
            attributes: HashMap::new(),
        },
    ]
}

/// Create ResNet50 layer structure
/// ResNet50レイヤー構造を作成
fn create_resnet50_layers() -> Vec<LayerDescription> {
    vec![
        LayerDescription {
            name: "conv1".to_string(),
            layer_type: "Conv2d".to_string(),
            input_shape: vec![0, 3, 224, 224],
            output_shape: vec![0, 64, 112, 112],
            params: 9408,
            attributes: HashMap::new(),
        },
        LayerDescription {
            name: "fc".to_string(),
            layer_type: "Linear".to_string(),
            input_shape: vec![0, 2048],
            output_shape: vec![0, 1000],
            params: 2_049_000, // 2048 * 1000 + 1000
            attributes: HashMap::new(),
        },
    ]
}

/// Create MobileNet layer structure
/// MobileNetレイヤー構造を作成
fn create_mobilenet_layers() -> Vec<LayerDescription> {
    vec![
        LayerDescription {
            name: "features.0".to_string(),
            layer_type: "Conv2d".to_string(),
            input_shape: vec![0, 3, 224, 224],
            output_shape: vec![0, 32, 112, 112],
            params: 864, // 32 * 3 * 3 * 3
            attributes: HashMap::new(),
        },
        LayerDescription {
            name: "classifier".to_string(),
            layer_type: "Linear".to_string(),
            input_shape: vec![0, 1280],
            output_shape: vec![0, 1000],
            params: 1_281_000, // 1280 * 1000 + 1000
            attributes: HashMap::new(),
        },
    ]
}

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

    #[test]
    fn test_torch_storage_type_conversion() {
        assert_eq!(TorchStorageType::FloatStorage.to_dtype(), DType::Float32);
        assert_eq!(TorchStorageType::DoubleStorage.to_dtype(), DType::Float64);
        assert_eq!(TorchStorageType::IntStorage.to_dtype(), DType::Int32);
        assert_eq!(TorchStorageType::BoolStorage.to_dtype(), DType::Bool);
    }

    #[test]
    fn test_layer_type_inference() {
        assert_eq!(infer_layer_type_from_weight_shape(&[100, 784]), "Linear");
        assert_eq!(infer_layer_type_from_weight_shape(&[64, 3, 7, 7]), "Conv2d");
        assert_eq!(infer_layer_type_from_weight_shape(&[128]), "BatchNorm1d");
        assert_eq!(
            infer_layer_type_from_weight_shape(&[1, 2, 3, 4, 5]),
            "Unknown"
        );
    }

    #[test]
    fn test_layer_prefix_extraction() {
        assert_eq!(extract_layer_prefix("features.0.weight"), "features.0");
        assert_eq!(extract_layer_prefix("classifier.bias"), "classifier");
        assert_eq!(extract_layer_prefix("simple_tensor"), "simple_tensor");
    }

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

        {
            let mut file = std::fs::File::create(&temp_file).unwrap();
            file.write_all(&[PICKLE_PROTOCOL_2, 0x02]).unwrap(); // Mock pickle header
            file.write_all(b"mock pytorch data for testing").unwrap();
        }

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

        let model = result.unwrap();
        assert_eq!(model.metadata.format, "PyTorch");
        assert!(model.weights.contains_key("features.0.weight"));
        assert!(model.weights.contains_key("classifier.weight"));

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

    #[test]
    fn test_pretrained_model_creation() {
        let result = load_pretrained_pytorch_model("resnet18");
        assert!(result.is_ok());

        let model = result.unwrap();
        assert_eq!(model.metadata.name, "resnet18");
        assert_eq!(model.metadata.framework, "PyTorch");
        assert!(!model.weights.is_empty());
        assert!(!model.architecture.layers.is_empty());
    }
}