rustorch 0.6.29

//! Simplified PyTorch to RusTorch conversion for demonstration
//! デモンストレーション用の簡略化PyTorchからRusTorch変換

use crate::formats::pytorch::{PyTorchModel, StateDict};
use crate::tensor::Tensor;
use std::collections::HashMap;
use std::error::Error;
use std::fmt;

/// 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
    pub name: String,
    /// Type of layer
    pub layer_type: String,
    /// Input tensor shape
    pub input_shape: Vec<usize>,
    /// Output tensor shape
    pub output_shape: Vec<usize>,
}

/// Simplified conversion error
/// 簡略化変換エラー
#[derive(Debug)]
pub enum SimpleConversionError {
    /// Layer not supported in simplified version
    /// 簡略版でサポートされていないレイヤー
    UnsupportedLayer(String),
    /// Missing parameter
    /// パラメータが見つからない
    MissingParameter(String),
    /// Invalid parameter format
    /// 無効なパラメータ形式
    InvalidParameter(String),
}

impl fmt::Display for SimpleConversionError {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            SimpleConversionError::UnsupportedLayer(layer) => {
                write!(f, "Unsupported layer: {}", layer)
            }
            SimpleConversionError::MissingParameter(param) => {
                write!(f, "Missing parameter: {}", param)
            }
            SimpleConversionError::InvalidParameter(msg) => write!(f, "Invalid parameter: {}", msg),
        }
    }
}

impl Error for SimpleConversionError {}

/// Simplified layer information
/// 簡略化レイヤー情報
#[derive(Debug, Clone)]
pub struct SimpleLayerDescription {
    /// Layer name
    /// レイヤー名
    pub name: String,
    /// Layer type as string
    /// 文字列としてのレイヤータイプ
    pub layer_type: String,
    /// Parameter shapes
    /// パラメータ形状
    pub parameter_shapes: HashMap<String, Vec<usize>>,
    /// Total number of parameters
    /// 総パラメータ数
    pub num_parameters: usize,
    /// Converted tensors
    /// 変換されたテンソル
    pub tensors: HashMap<String, Tensor<f32>>,
}

/// Simplified PyTorch model representation
/// 簡略化PyTorchモデル表現
#[derive(Debug)]
pub struct SimplifiedPyTorchModel {
    /// Model layers
    /// モデルレイヤー
    pub layers: HashMap<String, SimpleLayerDescription>,
    /// Execution order
    /// 実行順序
    pub execution_order: Vec<String>,
    /// Model statistics
    /// モデル統計
    pub total_parameters: usize,
}

/// Simplified converter
/// 簡略化変換器
pub struct SimplePyTorchConverter;

impl SimplePyTorchConverter {
    /// Convert PyTorch model to simplified representation
    /// PyTorchモデルを簡略表現に変換
    pub fn convert(
        pytorch_model: &PyTorchModel,
    ) -> Result<SimplifiedPyTorchModel, SimpleConversionError> {
        let mut layers = HashMap::new();
        let mut total_parameters = 0;

        // Group parameters by layer
        let layer_params = Self::group_parameters_by_layer(&pytorch_model.state_dict)?;

        // Convert each layer
        for (layer_name, params) in layer_params {
            let layer_info = Self::convert_layer(&layer_name, &params)?;
            total_parameters += layer_info.num_parameters;
            layers.insert(layer_name.clone(), layer_info);
        }

        // Create execution order (simplified - just sort by name)
        let mut execution_order: Vec<String> = layers.keys().cloned().collect();
        execution_order.sort();

        Ok(SimplifiedPyTorchModel {
            layers,
            execution_order,
            total_parameters,
        })
    }

    /// Group state dict parameters by layer name
    /// ステートディクトパラメータをレイヤー名でグループ化
    fn group_parameters_by_layer(
        state_dict: &StateDict,
    ) -> Result<
        HashMap<String, HashMap<String, &crate::formats::pytorch::TensorData>>,
        SimpleConversionError,
    > {
        let mut layer_params = HashMap::new();

        for (param_name, tensor_data) in &state_dict.tensors {
            let (layer_name, param_type) = Self::parse_parameter_name(param_name)?;

            layer_params
                .entry(layer_name)
                .or_insert_with(HashMap::new)
                .insert(param_type, tensor_data);
        }

        Ok(layer_params)
    }

    /// Parse parameter name
    /// パラメータ名を解析
    fn parse_parameter_name(param_name: &str) -> Result<(String, String), SimpleConversionError> {
        let parts: Vec<&str> = param_name.split('.').collect();

        if parts.len() < 2 {
            return Err(SimpleConversionError::InvalidParameter(format!(
                "Invalid parameter name: {}",
                param_name
            )));
        }

        let param_type = parts.last().unwrap().to_string();
        let layer_name = parts[..parts.len() - 1].join(".");

        Ok((layer_name, param_type))
    }

    /// Convert single layer
    /// 単一レイヤーを変換
    fn convert_layer(
        layer_name: &str,
        params: &HashMap<String, &crate::formats::pytorch::TensorData>,
    ) -> Result<SimpleLayerDescription, SimpleConversionError> {
        // Infer layer type from parameters
        let layer_type = Self::infer_layer_type(layer_name, params);

        // Convert parameters to tensors
        let mut tensors = HashMap::new();
        let mut parameter_shapes = HashMap::new();
        let mut num_parameters = 0;

        for (param_name, tensor_data) in params {
            let tensor = Self::convert_tensor_data(tensor_data);
            let param_count: usize = tensor_data.shape.iter().product();

            tensors.insert(param_name.clone(), tensor);
            parameter_shapes.insert(param_name.clone(), tensor_data.shape.clone());
            num_parameters += param_count;
        }

        Ok(SimpleLayerDescription {
            name: layer_name.to_string(),
            layer_type,
            parameter_shapes,
            num_parameters,
            tensors,
        })
    }

    /// Infer layer type from name and parameters
    /// 名前とパラメータからレイヤータイプを推論
    fn infer_layer_type(
        layer_name: &str,
        params: &HashMap<String, &crate::formats::pytorch::TensorData>,
    ) -> String {
        // Check common naming patterns
        if layer_name.contains("linear")
            || layer_name.contains("fc")
            || layer_name.contains("classifier")
        {
            return "Linear".to_string();
        }
        if layer_name.contains("conv") && !layer_name.contains("transpose") {
            return "Conv2d".to_string();
        }
        if layer_name.contains("bn") || layer_name.contains("batch_norm") {
            return "BatchNorm2d".to_string();
        }

        // Infer from parameter shapes
        if let Some(weight) = params.get("weight") {
            match weight.shape.len() {
                2 => "Linear".to_string(),
                4 => "Conv2d".to_string(),
                1 => "BatchNorm2d".to_string(),
                _ => format!("Unknown_{}D", weight.shape.len()),
            }
        } else {
            "Unknown".to_string()
        }
    }

    /// Convert tensor data to RusTorch tensor
    /// テンソルデータをRusTorchテンソルに変換
    fn convert_tensor_data(tensor_data: &crate::formats::pytorch::TensorData) -> Tensor<f32> {
        let data: Vec<f32> = tensor_data.data.iter().map(|&x| x as f32).collect();

        Tensor::from_vec(data, tensor_data.shape.clone())
    }
}

/// Display implementation for SimplifiedPyTorchModel
/// SimplifiedPyTorchModelの表示実装
impl SimplifiedPyTorchModel {
    /// Print model summary
    /// モデル要約を表示
    pub fn print_summary(&self) {
        println!("🤖 Simplified PyTorch Model Summary");
        println!("==================================");
        println!("Total layers: {}", self.layers.len());
        println!("Total parameters: {}", self.total_parameters);
        println!();

        println!("📋 Layer Details:");
        for layer_name in &self.execution_order {
            if let Some(layer) = self.layers.get(layer_name) {
                println!("  📦 {}: {}", layer_name, layer.layer_type);
                println!("     Parameters: {}", layer.num_parameters);

                for (param_name, shape) in &layer.parameter_shapes {
                    println!("     - {}: {:?}", param_name, shape);
                }
                println!();
            }
        }
    }

    /// Get layer by name
    /// 名前でレイヤーを取得
    pub fn get_layer(&self, name: &str) -> Option<&SimpleLayerDescription> {
        self.layers.get(name)
    }

    /// Get all layer names
    /// 全レイヤー名を取得
    pub fn layer_names(&self) -> Vec<&String> {
        self.execution_order.iter().collect()
    }

    /// Simulate forward pass (placeholder)
    /// 順伝播のシミュレーション（プレースホルダー）
    pub fn simulate_forward(
        &self,
        input_shape: Vec<usize>,
    ) -> Result<Vec<usize>, SimpleConversionError> {
        let mut current_shape = input_shape;

        for layer_name in &self.execution_order {
            if let Some(layer) = self.layers.get(layer_name) {
                current_shape = self.simulate_layer_forward(layer, current_shape)?;
                println!("After {}: {:?}", layer_name, current_shape);
            }
        }

        Ok(current_shape)
    }

    /// Simulate single layer forward pass
    /// 単一レイヤーの順伝播をシミュレーション
    fn simulate_layer_forward(
        &self,
        layer: &SimpleLayerDescription,
        input_shape: Vec<usize>,
    ) -> Result<Vec<usize>, SimpleConversionError> {
        match layer.layer_type.as_str() {
            "Linear" => {
                if let Some(weight_shape) = layer.parameter_shapes.get("weight") {
                    if weight_shape.len() == 2 {
                        // Linear: [batch, in_features] -> [batch, out_features]
                        let out_features = weight_shape[0];
                        let mut output_shape = input_shape;
                        let last_idx = output_shape.len() - 1;
                        output_shape[last_idx] = out_features;
                        return Ok(output_shape);
                    }
                }
                Err(SimpleConversionError::InvalidParameter(
                    "Invalid Linear layer".to_string(),
                ))
            }
            "Conv2d" => {
                if let Some(weight_shape) = layer.parameter_shapes.get("weight") {
                    if weight_shape.len() == 4 {
                        // Conv2d: [batch, in_channels, H, W] -> [batch, out_channels, H', W']
                        let out_channels = weight_shape[0];
                        let mut output_shape = input_shape;
                        if output_shape.len() >= 4 {
                            let channel_idx = output_shape.len() - 3;
                            output_shape[channel_idx] = out_channels;
                            // Simplified: assume same spatial dimensions (would need stride/padding for exact calculation)
                        }
                        return Ok(output_shape);
                    }
                }
                Err(SimpleConversionError::InvalidParameter(
                    "Invalid Conv2d layer".to_string(),
                ))
            }
            "BatchNorm2d" => {
                // BatchNorm doesn't change shape
                Ok(input_shape)
            }
            _ => {
                // Unknown layer - assume no shape change
                Ok(input_shape)
            }
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::formats::pytorch::{StateDict, TensorData};

    fn create_simple_test_model() -> PyTorchModel {
        let mut state_dict = StateDict::new();

        // Linear layer
        state_dict.tensors.insert(
            "fc.weight".to_string(),
            TensorData {
                shape: vec![10, 5],
                data: vec![0.1; 50],
                dtype: "f32".to_string(),
            },
        );
        state_dict.tensors.insert(
            "fc.bias".to_string(),
            TensorData {
                shape: vec![10],
                data: vec![0.0; 10],
                dtype: "f32".to_string(),
            },
        );

        crate::formats::pytorch::PyTorchModel::from_state_dict(state_dict)
    }

    #[test]
    fn test_simple_conversion() {
        let pytorch_model = create_simple_test_model();
        let converted = SimplePyTorchConverter::convert(&pytorch_model).unwrap();

        assert_eq!(converted.layers.len(), 1);
        assert!(converted.layers.contains_key("fc"));
        assert_eq!(converted.total_parameters, 60); // 50 weights + 10 biases
    }

    #[test]
    fn test_layer_type_inference() {
        let layer_type = SimplePyTorchConverter::infer_layer_type("fc", &HashMap::new());
        assert_eq!(layer_type, "Linear");

        let layer_type = SimplePyTorchConverter::infer_layer_type("conv1", &HashMap::new());
        assert_eq!(layer_type, "Conv2d");
    }

    #[test]
    fn test_parameter_parsing() {
        let (layer_name, param_type) =
            SimplePyTorchConverter::parse_parameter_name("features.0.weight").unwrap();
        assert_eq!(layer_name, "features.0");
        assert_eq!(param_type, "weight");
    }
}