//! Variable Selection Network for TFT
//!
//! Implements learnable feature selection using gated linear units and
//! soft feature selection weights for improved interpretability.

use std::collections::HashMap;

use candle_core::{Device, Module, Tensor};
use candle_nn::{linear, Linear, VarBuilder};

use super::GatedResidualNetwork;
use crate::MLError;

/// Variable Selection Network for feature importance learning
#[derive(Debug, Clone)]
pub struct VariableSelectionNetwork {
    pub input_size: usize,
    pub hidden_size: usize,
    // Gated Linear Units for variable selection
    flattened_grn: GatedResidualNetwork,
    single_var_grns: Vec<GatedResidualNetwork>,
    // Soft attention weights
    attention_weights: Linear,
    // Feature importance tracking
    importance_scores: HashMap<usize, f64>,
    device: Device,
}

impl VariableSelectionNetwork {
    pub fn new(input_size: usize, hidden_size: usize, vs: VarBuilder<'_>) -> Result<Self, MLError> {
        let device = vs.device().clone();

        // Create GRN for flattened inputs
        let flattened_grn =
            GatedResidualNetwork::new(input_size, hidden_size, vs.pp("flattened_grn"))?;

        // Create individual GRNs for each variable
        let mut single_var_grns = Vec::new();
        for i in 0..input_size {
            let grn = GatedResidualNetwork::new(
                1, // Single variable
                hidden_size,
                vs.pp(format!("single_var_grn_{}", i)),
            )?;
            single_var_grns.push(grn);
        }

        // Attention layer for variable selection
        let attention_weights = linear(
            hidden_size * input_size,
            input_size,
            vs.pp("attention_weights"),
        )?;

        Ok(Self {
            input_size,
            hidden_size,
            flattened_grn,
            single_var_grns,
            attention_weights,
            importance_scores: HashMap::new(),
            device,
        })
    }

    pub fn forward(
        &mut self,
        inputs: &Tensor,
        context: Option<&Tensor>,
    ) -> Result<Tensor, MLError> {
        let batch_size = inputs.dim(0)?;
        let input_dims = inputs.dims();

        // Normalize inputs to 3D format for uniform processing
        // For 2D: create reshaped view once (reused across all variables)
        // For 3D: use input reference directly to avoid 2.88MB clone per forward pass
        let is_2d = input_dims.len() == 2;
        let seq_len = if is_2d { 1 } else { input_dims[1] };

        // Pre-reshape 2D inputs outside loop to avoid repeated operations
        let reshaped_2d = if is_2d {
            Some(inputs.unsqueeze(1)?) // [batch_size, 1, input_size]
        } else {
            None
        };

        if !is_2d && input_dims.len() != 3 {
            return Err(MLError::InvalidInput(format!(
                "Input must be 2D or 3D, got {:?}",
                input_dims
            )));
        }

        // Process individual variables
        let mut var_outputs = Vec::new();
        for (i, grn) in self.single_var_grns.iter_mut().enumerate() {
            // Extract variable i from all time steps
            // Use pre-reshaped tensor for 2D, or direct input reference for 3D
            let var_data = if let Some(ref reshaped) = reshaped_2d {
                reshaped.narrow(2, i, 1)? // [batch_size, 1, 1]
            } else {
                inputs.narrow(2, i, 1)? // [batch_size, seq_len, 1]
            };
            let var_flattened = var_data.flatten(1, 2)?; // [batch_size, seq_len]
            let var_reshaped = var_flattened.unsqueeze(2)?; // [batch_size, seq_len, 1]
            let var_flat_2d = var_reshaped.flatten(0, 1)?; // [batch_size * seq_len, 1]

            let var_output = grn.forward(&var_flat_2d, context)?; // [batch_size * seq_len, hidden_size]
            let var_output_3d = var_output.reshape((batch_size, seq_len, self.hidden_size))?;
            var_outputs.push(var_output_3d);
        }

        // Stack variable outputs
        let stacked_vars = Tensor::stack(&var_outputs, 3)?; // [batch_size, seq_len, hidden_size, input_size]
        let vars_flattened = stacked_vars.flatten(2, 3)?; // [batch_size, seq_len, hidden_size * input_size]

        // Compute attention weights for variable selection
        let attention_input = vars_flattened.flatten(0, 1)?; // [batch_size * seq_len, hidden_size * input_size]
        let raw_weights = self.attention_weights.forward(&attention_input)?; // [batch_size * seq_len, input_size]
        let attention_weights = candle_nn::ops::softmax(&raw_weights, 1)?;
        let attention_3d = attention_weights.reshape((batch_size, seq_len, self.input_size))?;

        // Update importance scores
        self.update_importance_scores(&attention_3d)?;

        // Apply variable selection weights
        let weighted_vars = self.apply_variable_selection(&stacked_vars, &attention_3d)?;

        Ok(weighted_vars)
    }

    fn update_importance_scores(&mut self, attention_weights: &Tensor) -> Result<(), MLError> {
        // Compute mean attention weights across batch and time
        let mean_weights = attention_weights.mean_keepdim(0)?.mean_keepdim(1)?; // [1, 1, input_size]
        let weights_vec = mean_weights.flatten_all()?.to_vec1::<f32>()?;

        // Clear previous scores to prevent memory growth (HashMap maintains capacity but releases entries)
        self.importance_scores.clear();

        // Update importance scores
        for (i, weight) in weights_vec.iter().copied().enumerate() {
            self.importance_scores.insert(i, weight as f64);
        }

        Ok(())
    }

    fn apply_variable_selection(
        &self,
        stacked_vars: &Tensor,
        attention_weights: &Tensor,
    ) -> Result<Tensor, MLError> {
        // Expand attention weights to match stacked_vars dimensions
        let expanded_weights = attention_weights.unsqueeze(2)?; // [batch_size, seq_len, 1, input_size]
        let broadcast_weights = expanded_weights.broadcast_as(stacked_vars.shape())?;

        // Apply weights
        let weighted = (stacked_vars * &broadcast_weights)?;

        // Sum over variables dimension
        let selected = weighted.sum(3)?; // [batch_size, seq_len, hidden_size]

        Ok(selected)
    }

    pub fn get_importance_scores(&self) -> Result<Vec<f64>, MLError> {
        let mut scores = vec![0.0; self.input_size];
        for (i, &score) in &self.importance_scores {
            if *i < self.input_size {
                scores[*i] = score;
            }
        }

        // Normalize to sum to 1.0 if all scores are zero (uniform distribution)
        let sum: f64 = scores.iter().sum();
        if sum == 0.0 {
            let uniform_score = 1.0 / self.input_size as f64;
            scores.fill(uniform_score);
        }

        Ok(scores)
    }

    pub fn get_top_features(&self, k: usize) -> Vec<(usize, f64)> {
        let mut features: Vec<(usize, f64)> = self
            .importance_scores
            .iter()
            .map(|(&idx, &score)| (idx, score))
            .collect();

        features.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        features.truncate(k);
        features
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use candle_core::DType;


    #[test]
    fn test_variable_selection_network_creation() -> Result<(), MLError> {
        let device = Device::Cpu;
        let vs = VarBuilder::zeros(DType::F32, &device);

        let vsn = VariableSelectionNetwork::new(10, 64, vs.pp("test"))?;
        assert_eq!(vsn.input_size, 10);
        assert_eq!(vsn.hidden_size, 64);
        Ok(())
    }

    #[test]
    fn test_variable_selection_forward_2d() -> Result<(), MLError> {
        let device = Device::Cpu;
        let vs = VarBuilder::zeros(DType::F32, &device);

        let mut vsn = VariableSelectionNetwork::new(5, 32, vs.pp("test"))?;

        // Create test input [batch_size=2, input_size=5]
        let input_data = vec![1.0f32, 2.0, 3.0, 4.0, 5.0, 2.0, 3.0, 4.0, 5.0, 6.0];
        let inputs = Tensor::from_slice(&input_data, (2, 5), &device)?;

        let output = vsn.forward(&inputs, None)?;

        // Output should have shape [batch_size=2, seq_len=1, hidden_size=32]
        assert_eq!(output.dims(), &[2, 1, 32]);
        Ok(())
    }

    #[test]
    fn test_variable_selection_forward_3d() -> Result<(), MLError> {
        let device = Device::Cpu;
        let vs = VarBuilder::zeros(DType::F32, &device);

        let mut vsn = VariableSelectionNetwork::new(3, 16, vs.pp("test"))?;

        // Create test input [batch_size=2, seq_len=4, input_size=3]
        let input_data = vec![1.0f32; 24]; // 2 * 4 * 3
        let inputs = Tensor::from_slice(&input_data, (2, 4, 3), &device)?;

        let output = vsn.forward(&inputs, None)?;

        // Output should have shape [batch_size=2, seq_len=4, hidden_size=16]
        assert_eq!(output.dims(), &[2, 4, 16]);
        Ok(())
    }

    #[test]
    fn test_variable_selection_with_context() -> Result<(), MLError> {
        let device = Device::Cpu;
        let vs = VarBuilder::zeros(DType::F32, &device);

        let mut vsn = VariableSelectionNetwork::new(4, 24, vs.pp("test"))?;

        // Create test input and context
        let input_data = vec![1.0f32; 8]; // 2 * 4
        let inputs = Tensor::from_slice(&input_data, (2, 4), &device)?;

        let context_data = vec![0.5f32; 48]; // 2 * 24
        let context = Tensor::from_slice(&context_data, (2, 24), &device)?;

        let output = vsn.forward(&inputs, Some(&context))?;

        // Output should have shape [batch_size=2, seq_len=1, hidden_size=24]
        assert_eq!(output.dims(), &[2, 1, 24]);
        Ok(())
    }

    #[test]
    fn test_importance_scores() -> Result<(), MLError> {
        let device = Device::Cpu;
        let vs = VarBuilder::zeros(DType::F32, &device);

        let vsn = VariableSelectionNetwork::new(5, 32, vs.pp("test"))?;
        let scores = vsn.get_importance_scores()?;

        assert_eq!(scores.len(), 5);
        // Should sum to 1.0 (uniform distribution)
        let sum: f64 = scores.iter().sum();
        assert!((sum - 1.0).abs() < 1e-6);
        Ok(())
    }
}