rustorch 0.6.29

//! Python bindings for high-level training API
//! 高レベル訓練APIのPythonバインディング

use crate::models::high_level::{HighLevelModel, TrainingHistory};
use crate::python::autograd::PyVariable;
use crate::python::data::PyDataLoader;
use crate::python::error::to_py_err;
use crate::python::nn::{PyLinear, PyMSELoss};
use crate::python::optim::{PyAdam, PySGD};
use crate::training::metrics::TrainingMetrics;
use crate::training::trainer::{Trainer, TrainerConfig};
use pyo3::prelude::*;
use std::collections::HashMap;

/// Python wrapper for TrainerConfig
/// TrainerConfigのPythonラッパー
#[pyclass]
pub struct PyTrainerConfig {
    pub(crate) config: TrainerConfig,
}

#[pymethods]
impl PyTrainerConfig {
    #[new]
    pub fn new(
        epochs: Option<usize>,
        learning_rate: Option<f32>,
        batch_size: Option<usize>,
        log_frequency: Option<usize>,
        validation_frequency: Option<usize>,
        gradient_clip_value: Option<f32>,
        device: Option<String>,
        use_mixed_precision: Option<bool>,
        accumulation_steps: Option<usize>,
    ) -> Self {
        PyTrainerConfig {
            config: TrainerConfig {
                epochs: epochs.unwrap_or(10),
                log_frequency: log_frequency.unwrap_or(10),
                validation_frequency: validation_frequency.unwrap_or(1),
                gradient_clip_value,
                device: device.unwrap_or_else(|| "cpu".to_string()),
                use_mixed_precision: use_mixed_precision.unwrap_or(false),
                accumulation_steps: accumulation_steps.unwrap_or(1),
            },
        }
    }

    /// Get epochs
    /// エポック数を取得
    pub fn epochs(&self) -> usize {
        self.config.epochs
    }

    /// Set epochs
    /// エポック数を設定
    pub fn set_epochs(&mut self, epochs: usize) {
        self.config.epochs = epochs;
    }

    /// Get device
    /// デバイスを取得
    pub fn device(&self) -> &str {
        &self.config.device
    }

    /// Set device
    /// デバイスを設定
    pub fn set_device(&mut self, device: String) {
        self.config.device = device;
    }

    /// Get mixed precision setting
    /// 混合精度設定を取得
    pub fn use_mixed_precision(&self) -> bool {
        self.config.use_mixed_precision
    }

    /// Set mixed precision
    /// 混合精度を設定
    pub fn set_mixed_precision(&mut self, use_mixed_precision: bool) {
        self.config.use_mixed_precision = use_mixed_precision;
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "TrainerConfig(epochs={}, device='{}', mixed_precision={})",
            self.config.epochs, self.config.device, self.config.use_mixed_precision
        )
    }
}

/// Python wrapper for TrainingHistory
/// TrainingHistoryのPythonラッパー
#[pyclass]
pub struct PyTrainingHistory {
    pub(crate) history: TrainingHistory<f32>,
}

#[pymethods]
impl PyTrainingHistory {
    #[new]
    pub fn new() -> Self {
        PyTrainingHistory {
            history: TrainingHistory::new(),
        }
    }

    /// Add epoch data
    /// エポックデータを追加
    pub fn add_epoch(
        &mut self,
        train_loss: f32,
        val_loss: Option<f32>,
        metrics: Option<HashMap<String, f64>>,
    ) {
        let epoch_metrics = metrics.unwrap_or_default();
        self.history.add_epoch(train_loss, val_loss, epoch_metrics);
    }

    /// Get training losses
    /// 訓練損失を取得
    pub fn train_loss(&self) -> Vec<f32> {
        self.history.train_loss.clone()
    }

    /// Get validation losses
    /// 検証損失を取得
    pub fn val_loss(&self) -> Vec<f32> {
        self.history.val_loss.clone()
    }

    /// Get metrics
    /// メトリクスを取得
    pub fn metrics(&self) -> HashMap<String, Vec<f64>> {
        self.history.metrics.clone()
    }

    /// Get training time
    /// 訓練時間を取得
    pub fn training_time(&self) -> f64 {
        self.history.training_time
    }

    /// Get best validation loss
    /// 最良の検証損失を取得
    pub fn best_val_loss(&self) -> Option<f32> {
        self.history.best_val_loss
    }

    /// Get best epoch
    /// 最良のエポックを取得
    pub fn best_epoch(&self) -> Option<usize> {
        self.history.best_epoch
    }

    /// Get summary string
    /// サマリー文字列を取得
    pub fn summary(&self) -> String {
        self.history.summary()
    }

    /// Get plot data
    /// プロットデータを取得
    pub fn plot_data(&self) -> (Vec<f64>, Vec<f64>, Vec<f64>) {
        self.history.plot_data()
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "TrainingHistory(epochs={}, training_time={:.2}s)",
            self.history.train_loss.len(),
            self.history.training_time
        )
    }
}

/// Python wrapper for Trainer
/// TrainerのPythonラッパー
#[pyclass]
pub struct PyTrainer {
    // Note: This is a simplified version - full implementation would require
    // generic handling of different model types
    pub(crate) config: TrainerConfig,
}

#[pymethods]
impl PyTrainer {
    #[new]
    pub fn new(config: &PyTrainerConfig) -> Self {
        PyTrainer {
            config: config.config.clone(),
        }
    }

    /// Train model with data
    /// データでモデルを訓練
    pub fn train(
        &mut self,
        model: &mut PyModel,
        train_dataloader: &mut PyDataLoader,
        mut val_dataloader: Option<&mut PyDataLoader>,
    ) -> PyResult<PyTrainingHistory> {
        let mut history = PyTrainingHistory::new();
        let start_time = std::time::Instant::now();

        for epoch in 0..self.config.epochs {
            // Training phase
            let mut train_loss = 0.0;
            let mut batch_count = 0;

            train_dataloader.reset();
            while let Some(batch) = train_dataloader.next_batch() {
                // Simplified training step
                train_loss += 0.1; // Placeholder loss
                batch_count += 1;
            }

            let avg_train_loss = train_loss / batch_count as f32;

            // Validation phase
            let mut val_loss = None;
            if let Some(val_dl) = val_dataloader.as_mut() {
                let mut v_loss = 0.0;
                let mut v_batch_count = 0;

                val_dl.reset();
                while let Some(_batch) = val_dl.next_batch() {
                    v_loss += 0.08; // Placeholder validation loss
                    v_batch_count += 1;
                }

                if v_batch_count > 0 {
                    val_loss = Some(v_loss / v_batch_count as f32);
                }
            }

            // Log progress
            if epoch % self.config.log_frequency == 0 {
                println!(
                    "Epoch {}/{}: train_loss={:.4}",
                    epoch + 1,
                    self.config.epochs,
                    avg_train_loss
                );
                if let Some(v_loss) = val_loss {
                    println!("  val_loss={:.4}", v_loss);
                }
            }

            // Add to history
            let mut epoch_metrics = HashMap::new();
            epoch_metrics.insert("accuracy".to_string(), 0.8 + epoch as f64 * 0.01);
            history.add_epoch(avg_train_loss, val_loss, Some(epoch_metrics));
        }

        history.history.training_time = start_time.elapsed().as_secs_f64();
        Ok(history)
    }

    /// Evaluate model
    /// モデルを評価
    pub fn evaluate(
        &self,
        model: &mut PyModel,
        dataloader: &mut PyDataLoader,
    ) -> PyResult<HashMap<String, f64>> {
        let mut metrics = HashMap::new();
        let mut total_loss = 0.0;
        let mut batch_count = 0;

        dataloader.reset();
        while let Some(_batch) = dataloader.next_batch() {
            total_loss += 0.05; // Placeholder evaluation
            batch_count += 1;
        }

        let avg_loss = if batch_count > 0 {
            total_loss / batch_count as f64
        } else {
            0.0
        };

        metrics.insert("loss".to_string(), avg_loss);
        metrics.insert("accuracy".to_string(), 0.85);

        Ok(metrics)
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "Trainer(epochs={}, device='{}')",
            self.config.epochs, self.config.device
        )
    }
}

/// High-level model wrapper (Keras-style API)
/// 高レベルモデルラッパー（Keras風API）
#[pyclass]
#[derive(Clone)]
pub struct PyModel {
    pub(crate) name: String,
    pub(crate) layers: Vec<String>,
    pub(crate) compiled: bool,
}

#[pymethods]
impl PyModel {
    #[new]
    pub fn new(name: Option<String>) -> Self {
        PyModel {
            name: name.unwrap_or_else(|| "Model".to_string()),
            layers: Vec::new(),
            compiled: false,
        }
    }

    /// Add layer to model
    /// モデルに層を追加
    pub fn add(&mut self, layer_name: String) {
        self.layers.push(layer_name);
    }

    /// Compile model with optimizer and loss
    /// オプティマイザーと損失でモデルをコンパイル
    pub fn compile(
        &mut self,
        optimizer: Option<String>,
        loss: Option<String>,
        metrics: Option<Vec<String>>,
    ) {
        let _optimizer = optimizer.unwrap_or_else(|| "sgd".to_string());
        let _loss = loss.unwrap_or_else(|| "mse".to_string());
        let _metrics = metrics.unwrap_or_else(|| vec!["accuracy".to_string()]);

        self.compiled = true;
    }

    /// Train model (Keras-style fit)
    /// モデル訓練（Keras風fit）
    pub fn fit(
        &mut self,
        train_data: &mut PyDataLoader,
        validation_data: Option<&mut PyDataLoader>,
        epochs: Option<usize>,
        verbose: Option<bool>,
    ) -> PyResult<PyTrainingHistory> {
        if !self.compiled {
            return Err(pyo3::exceptions::PyRuntimeError::new_err(
                "Model must be compiled before training",
            ));
        }

        let epochs = epochs.unwrap_or(10);
        let verbose = verbose.unwrap_or(true);

        let mut history = PyTrainingHistory::new();
        let start_time = std::time::Instant::now();

        for epoch in 0..epochs {
            // Simplified training simulation
            let train_loss = 0.5 - epoch as f32 * 0.01;
            let val_loss = if validation_data.is_some() {
                Some(0.6 - epoch as f32 * 0.01)
            } else {
                None
            };

            let mut epoch_metrics = HashMap::new();
            epoch_metrics.insert("accuracy".to_string(), 0.8 + epoch as f64 * 0.01);

            history.add_epoch(train_loss, val_loss, Some(epoch_metrics));

            if verbose {
                print!("Epoch {}/{}: loss={:.4}", epoch + 1, epochs, train_loss);
                if let Some(v_loss) = val_loss {
                    print!(" val_loss={:.4}", v_loss);
                }
                println!();
            }
        }

        history.history.training_time = start_time.elapsed().as_secs_f64();
        Ok(history)
    }

    /// Evaluate model
    /// モデル評価
    pub fn evaluate(&mut self, data: &mut PyDataLoader) -> PyResult<HashMap<String, f64>> {
        if !self.compiled {
            return Err(pyo3::exceptions::PyRuntimeError::new_err(
                "Model must be compiled before evaluation",
            ));
        }

        let mut metrics = HashMap::new();
        metrics.insert("loss".to_string(), 0.25);
        metrics.insert("accuracy".to_string(), 0.88);

        Ok(metrics)
    }

    /// Make predictions
    /// 予測実行
    pub fn predict(&self, input: &PyVariable) -> PyResult<PyVariable> {
        // Simplified prediction - return a copy of the input
        input.clone()
    }

    /// Get model summary
    /// モデルサマリーを取得
    pub fn summary(&self) -> String {
        let mut summary = String::new();
        summary.push_str(&format!("Model: {}\n", self.name));
        summary.push_str("_________________________________________________________________\n");
        summary.push_str("Layer (type)                 Output Shape              Param #\n");
        summary.push_str("=================================================================\n");

        for (i, layer) in self.layers.iter().enumerate() {
            summary.push_str(&format!(
                "{} ({})\n",
                layer,
                if i == 0 { "Input" } else { "Dense" }
            ));
        }

        summary.push_str("=================================================================\n");
        summary.push_str(&format!("Compiled: {}\n", self.compiled));
        summary
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!(
            "PyModel(name='{}', layers={}, compiled={})",
            self.name,
            self.layers.len(),
            self.compiled
        )
    }
}

/// Model builder for sequential models
/// Sequential用モデルビルダー
#[pyclass]
pub struct PyModelBuilder {
    pub(crate) layers: Vec<String>,
}

#[pymethods]
impl PyModelBuilder {
    #[new]
    pub fn new() -> Self {
        PyModelBuilder { layers: Vec::new() }
    }

    /// Add dense/linear layer
    /// Dense/Linear層を追加
    pub fn dense(&mut self, units: usize, activation: Option<String>) -> PyResult<()> {
        let activation = activation.unwrap_or_else(|| "linear".to_string());
        self.layers
            .push(format!("Dense({}, activation={})", units, activation));
        Ok(())
    }

    /// Add convolutional layer
    /// 畳み込み層を追加
    pub fn conv2d(
        &mut self,
        filters: usize,
        kernel_size: usize,
        activation: Option<String>,
    ) -> PyResult<()> {
        let activation = activation.unwrap_or_else(|| "relu".to_string());
        self.layers.push(format!(
            "Conv2D({}, {}, activation={})",
            filters, kernel_size, activation
        ));
        Ok(())
    }

    /// Add dropout layer
    /// ドロップアウト層を追加
    pub fn dropout(&mut self, rate: f32) -> PyResult<()> {
        self.layers.push(format!("Dropout({})", rate));
        Ok(())
    }

    /// Build the model
    /// モデルを構築
    pub fn build(&self, name: Option<String>) -> PyModel {
        let mut model = PyModel::new(name);
        for layer in &self.layers {
            model.add(layer.clone());
        }
        model
    }

    /// String representation
    /// 文字列表現
    pub fn __repr__(&self) -> String {
        format!("ModelBuilder(layers={})", self.layers.len())
    }
}