//! TensorBoard logger callback implementation

use crate::callbacks::{Callback, CallbackContext, CallbackTiming};
use crate::error::Result;
use scirs2_core::ndarray::ScalarOperand;
use scirs2_core::numeric::{Float, NumAssign};
use std::fmt::Debug;
use std::path::{Path, PathBuf};

/// TensorBoard logger callback that writes training metrics to TensorBoard.
///
/// Note: This is a placeholder implementation. A full implementation would
/// require integration with TensorBoard, which is beyond the scope of this example.
pub struct TensorBoardLogger<F: Float + Debug + ScalarOperand> {
    /// Directory to store TensorBoard logs
    log_dir: PathBuf,
    /// Whether to log histograms of model parameters
    log_histograms: bool,
    /// Frequency of logging (in batches)
    update_freq: usize,
    /// Phantom data for generic type
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + Debug + ScalarOperand + NumAssign> TensorBoardLogger<F> {
    /// Create a new TensorBoard logger callback
    ///
    /// # Arguments
    /// * `log_dir` - Directory to store TensorBoard logs
    /// * `log_histograms` - Whether to log histograms of model parameters
    /// * `update_freq` - Frequency of logging (in batches)
    pub fn new<P: AsRef<Path>>(log_dir: P, log_histograms: bool, update_freq: usize) -> Self {
        Self {
            log_dir: log_dir.as_ref().to_path_buf(),
            log_histograms,
            update_freq,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<F: Float + Debug + ScalarOperand + NumAssign> Callback<F> for TensorBoardLogger<F> {
    fn on_event(&mut self, timing: CallbackTiming, context: &mut CallbackContext<F>) -> Result<()> {
        match timing {
            CallbackTiming::BeforeTraining => {
                println!(
                    "TensorBoard: Initializing logger at {}",
                    self.log_dir.display()
                );
                // In a real implementation, we'd initialize the TensorBoard writer here
            }
            CallbackTiming::AfterBatch => {
                // Log batch metrics at specified frequency
                if context.batch.is_multiple_of(self.update_freq) {
                    if let Some(batch_loss) = context.batch_loss {
                        let global_step = context.epoch * context.total_batches + context.batch;
                        println!(
                            "TensorBoard: Logging batch {} loss: {:.6?}",
                            global_step, batch_loss
                        );
                        // In a real implementation, we'd log to TensorBoard here
                        // writer.add_scalar("train/batch_loss", batch_loss, global_step);
                    }
                }
            }
            CallbackTiming::AfterEpoch => {
                let epoch = context.epoch;

                // Log epoch metrics
                if let Some(epoch_loss) = context.epoch_loss {
                    println!(
                        "TensorBoard: Logging epoch {} train loss: {:.6?}",
                        epoch + 1,
                        epoch_loss
                    );
                    // writer.add_scalar("train/epoch_loss", epoch_loss, epoch);
                }

                if let Some(val_loss) = context.val_loss {
                    println!(
                        "TensorBoard: Logging epoch {} validation loss: {:.6?}",
                        epoch + 1,
                        val_loss
                    );
                    // writer.add_scalar("validation/loss", val_loss, epoch);
                }

                // Log metrics if available
                if !context.metrics.is_empty() {
                    println!(
                        "TensorBoard: Logging epoch {} metrics: {:.6?}",
                        epoch + 1,
                        context.metrics
                    );
                    // In a real implementation, we'd log each metric with a name
                    // For now just log the raw values
                    // for (i, metric) in context.metrics.iter().enumerate() {
                    //     writer.add_scalar(&format!("metrics/metric_{}", i), *metric, epoch);
                    // }
                }

                // Log model parameter histograms
                if self.log_histograms {
                    println!("TensorBoard: Logging model parameter histograms");
                    // In a real implementation, we'd log parameter histograms here
                    // for (name, param) in model.named_parameters() {
                    //     writer.add_histogram(&format!("parameters/{}", name), param, epoch);
                    // }
                }
            }
            CallbackTiming::AfterTraining => {
                println!("TensorBoard: Closing logger");
                // In a real implementation, we'd close the TensorBoard writer here
                // writer.close();
            }
            _ => {}
        }

        Ok(())
    }
}
