trustformers-models 0.1.1

use trustformers_core::{errors::Result, quantum::QuantumManager, tensor::Tensor};

use super::config::QuantumClassicalConfig;

/// Quantum optimizer for hybrid models
#[derive(Debug)]
pub struct QuantumOptimizer {
    /// Configuration
    pub config: QuantumClassicalConfig,
    /// Quantum manager
    pub quantum_manager: QuantumManager,
    /// Optimization parameters
    pub parameters: Vec<f64>,
    /// Gradient history
    pub gradient_history: Vec<Vec<f64>>,
    /// Learning rate schedule
    pub learning_rate_schedule: Vec<f64>,
    /// Current iteration
    pub current_iteration: usize,
}

impl QuantumOptimizer {
    /// Create a new quantum optimizer
    pub fn new(config: &QuantumClassicalConfig) -> Result<Self> {
        let quantum_manager = QuantumManager::simulator(config.num_qubits);
        let parameters = vec![0.1; config.get_quantum_parameters_count()];
        let gradient_history = Vec::new();
        let learning_rate_schedule =
            vec![config.quantum_learning_rate; config.max_quantum_iterations];

        Ok(Self {
            config: config.clone(),
            quantum_manager,
            parameters,
            gradient_history,
            learning_rate_schedule,
            current_iteration: 0,
        })
    }

    /// Optimize quantum parameters
    pub fn optimize(&mut self, input: &Tensor) -> Result<Tensor> {
        let mut best_parameters = self.parameters.clone();
        let mut best_loss = f64::INFINITY;

        // Quantum optimization loop
        for iteration in 0..self.config.max_quantum_iterations {
            // Compute gradients using parameter shift rule
            let gradients = self.compute_gradients(input)?;

            // Update parameters
            self.update_parameters(&gradients);

            // Compute loss
            let loss = self.compute_loss(input)?;

            // Update best parameters
            if loss < best_loss {
                best_loss = loss;
                best_parameters = self.parameters.clone();
            }

            // Check convergence
            if loss < self.config.quantum_optimization_tolerance {
                break;
            }

            // Store gradient history
            self.gradient_history.push(gradients);
            self.current_iteration = iteration;
        }

        // Apply best parameters
        self.parameters = best_parameters;

        // Return optimized output
        self.forward(input)
    }

    /// Forward pass with current parameters
    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Simplified forward pass - in practice would use quantum circuits
        let batch_size = input.shape()[0];
        let seq_len = input.shape()[1];
        let d_model = self.config.d_model;

        // Apply quantum transformation (simplified)
        let quantum_factor = self.parameters.iter().sum::<f64>() / self.parameters.len() as f64;
        let output_data: Vec<f32> =
            input.data()?.iter().map(|&x| x * quantum_factor as f32).collect();

        Tensor::from_vec(output_data, &[batch_size, seq_len, d_model])
    }

    /// Compute gradients using parameter shift rule
    fn compute_gradients(&self, input: &Tensor) -> Result<Vec<f64>> {
        let mut gradients = Vec::new();
        let shift = self.config.parameter_shift_stepsize;

        for i in 0..self.parameters.len() {
            // Forward pass with positive shift
            let mut params_plus = self.parameters.clone();
            params_plus[i] += shift;
            let optimizer_plus = QuantumOptimizer {
                parameters: params_plus,
                ..self.clone()
            };
            let output_plus = optimizer_plus.forward(input)?;

            // Forward pass with negative shift
            let mut params_minus = self.parameters.clone();
            params_minus[i] -= shift;
            let optimizer_minus = QuantumOptimizer {
                parameters: params_minus,
                ..self.clone()
            };
            let output_minus = optimizer_minus.forward(input)?;

            // Compute gradient
            let grad = (output_plus.sum(None, false)?.to_scalar()?
                - output_minus.sum(None, false)?.to_scalar()?) as f64
                / (2.0 * shift);
            gradients.push(grad);
        }

        Ok(gradients)
    }

    /// Update parameters using gradients
    fn update_parameters(&mut self, gradients: &[f64]) {
        let learning_rate = self.learning_rate_schedule
            [self.current_iteration.min(self.learning_rate_schedule.len() - 1)];

        for (param, &grad) in self.parameters.iter_mut().zip(gradients) {
            *param -= learning_rate * grad;
        }
    }

    /// Compute loss for optimization
    fn compute_loss(&self, input: &Tensor) -> Result<f64> {
        let output = self.forward(input)?;
        let loss = output.pow_scalar(2.0)?.sum(None, false)?.to_scalar()? as f64;
        Ok(loss)
    }

    /// Get parameter count
    pub fn parameter_count(&self) -> usize {
        self.parameters.len()
    }

    /// Get memory usage in MB
    pub fn memory_usage(&self) -> f32 {
        (self.parameters.len() * 8 + // f64 parameters
         self.gradient_history.len() * self.parameters.len() * 8 + // gradient history
         self.learning_rate_schedule.len() * 8) as f32
            / 1_000_000.0 // learning rate schedule
    }
}

impl Clone for QuantumOptimizer {
    fn clone(&self) -> Self {
        Self {
            config: self.config.clone(),
            quantum_manager: QuantumManager::simulator(self.config.num_qubits),
            parameters: self.parameters.clone(),
            gradient_history: self.gradient_history.clone(),
            learning_rate_schedule: self.learning_rate_schedule.clone(),
            current_iteration: self.current_iteration,
        }
    }
}