use trustformers_core::errors::Result;

use super::config::{HybridTrainingStrategy, QuantumClassicalConfig};

/// Quantum training manager
#[derive(Debug)]
pub struct QuantumTrainingManager {
    /// Configuration
    pub config: QuantumClassicalConfig,
    /// Training strategy
    pub training_strategy: HybridTrainingStrategy,
    /// Classical learning rate
    pub classical_lr: f64,
    /// Quantum learning rate
    pub quantum_lr: f64,
    /// Training metrics
    pub training_metrics: QuantumTrainingMetrics,
    /// Current epoch
    pub current_epoch: usize,
    /// Training history
    pub training_history: Vec<QuantumTrainingMetrics>,
}

/// Quantum training metrics
#[derive(Debug, Clone)]
pub struct QuantumTrainingMetrics {
    /// Classical loss
    pub classical_loss: f64,
    /// Quantum loss
    pub quantum_loss: f64,
    /// Total loss
    pub total_loss: f64,
    /// Quantum fidelity
    pub quantum_fidelity: f64,
    /// Classical accuracy
    pub classical_accuracy: f64,
    /// Quantum advantage metric
    pub quantum_advantage: f64,
    /// Training time
    pub training_time: f64,
}

impl QuantumTrainingManager {
    /// Create a new quantum training manager
    pub fn new(config: &QuantumClassicalConfig) -> Result<Self> {
        let training_metrics = QuantumTrainingMetrics {
            classical_loss: 0.0,
            quantum_loss: 0.0,
            total_loss: 0.0,
            quantum_fidelity: 1.0,
            classical_accuracy: 0.0,
            quantum_advantage: 0.0,
            training_time: 0.0,
        };

        Ok(Self {
            config: config.clone(),
            training_strategy: config.hybrid_training_strategy.clone(),
            classical_lr: config.classical_learning_rate,
            quantum_lr: config.quantum_learning_rate,
            training_metrics,
            current_epoch: 0,
            training_history: Vec::new(),
        })
    }

    /// Train one epoch
    pub fn train_epoch(
        &mut self,
        classical_gradients: &[f32],
        quantum_gradients: &[f64],
    ) -> Result<QuantumTrainingMetrics> {
        let start_time = std::time::Instant::now();

        match self.training_strategy {
            HybridTrainingStrategy::Sequential => {
                self.train_sequential(classical_gradients, quantum_gradients)?;
            },
            HybridTrainingStrategy::Alternating => {
                self.train_alternating(classical_gradients, quantum_gradients)?;
            },
            HybridTrainingStrategy::Joint => {
                self.train_joint(classical_gradients, quantum_gradients)?;
            },
            HybridTrainingStrategy::Adaptive => {
                self.train_adaptive(classical_gradients, quantum_gradients)?;
            },
        }

        let training_time = start_time.elapsed().as_secs_f64();
        self.training_metrics.training_time = training_time;

        // Update training history
        self.training_history.push(self.training_metrics.clone());
        self.current_epoch += 1;

        Ok(self.training_metrics.clone())
    }

    /// Sequential training strategy
    fn train_sequential(
        &mut self,
        classical_gradients: &[f32],
        quantum_gradients: &[f64],
    ) -> Result<()> {
        // Train classical parameters first
        self.update_classical_parameters(classical_gradients)?;

        // Then train quantum parameters
        self.update_quantum_parameters(quantum_gradients)?;

        Ok(())
    }

    /// Alternating training strategy
    fn train_alternating(
        &mut self,
        classical_gradients: &[f32],
        quantum_gradients: &[f64],
    ) -> Result<()> {
        // Alternate between classical and quantum updates
        if self.current_epoch % 2 == 0 {
            self.update_classical_parameters(classical_gradients)?;
        } else {
            self.update_quantum_parameters(quantum_gradients)?;
        }

        Ok(())
    }

    /// Joint training strategy
    fn train_joint(
        &mut self,
        classical_gradients: &[f32],
        quantum_gradients: &[f64],
    ) -> Result<()> {
        // Update both classical and quantum parameters simultaneously
        self.update_classical_parameters(classical_gradients)?;
        self.update_quantum_parameters(quantum_gradients)?;

        Ok(())
    }

    /// Adaptive training strategy
    fn train_adaptive(
        &mut self,
        classical_gradients: &[f32],
        quantum_gradients: &[f64],
    ) -> Result<()> {
        // Decide based on current performance
        let classical_grad_norm =
            classical_gradients.iter().map(|&x| x.powi(2)).sum::<f32>().sqrt();
        let quantum_grad_norm = quantum_gradients.iter().map(|&x| x.powi(2)).sum::<f64>().sqrt();

        if classical_grad_norm as f64 > quantum_grad_norm {
            self.update_classical_parameters(classical_gradients)?;
        } else {
            self.update_quantum_parameters(quantum_gradients)?;
        }

        Ok(())
    }

    /// Update classical parameters
    fn update_classical_parameters(&mut self, gradients: &[f32]) -> Result<()> {
        // Simplified parameter update
        let classical_loss = gradients.iter().map(|&x| x.powi(2)).sum::<f32>() as f64;
        self.training_metrics.classical_loss = classical_loss;

        Ok(())
    }

    /// Update quantum parameters
    fn update_quantum_parameters(&mut self, gradients: &[f64]) -> Result<()> {
        // Simplified parameter update
        let quantum_loss = gradients.iter().map(|&x| x.powi(2)).sum::<f64>();
        self.training_metrics.quantum_loss = quantum_loss;

        // Update quantum fidelity
        self.training_metrics.quantum_fidelity = 1.0 - self.config.quantum_noise_variance;

        Ok(())
    }

    /// Get training statistics
    pub fn get_training_stats(&self) -> QuantumTrainingStats {
        let avg_classical_loss =
            self.training_history.iter().map(|m| m.classical_loss).sum::<f64>()
                / self.training_history.len() as f64;

        let avg_quantum_loss = self.training_history.iter().map(|m| m.quantum_loss).sum::<f64>()
            / self.training_history.len() as f64;

        let avg_quantum_fidelity =
            self.training_history.iter().map(|m| m.quantum_fidelity).sum::<f64>()
                / self.training_history.len() as f64;

        QuantumTrainingStats {
            total_epochs: self.current_epoch,
            avg_classical_loss,
            avg_quantum_loss,
            avg_quantum_fidelity,
            training_strategy: self.training_strategy.clone(),
            convergence_rate: self.compute_convergence_rate(),
        }
    }

    /// Compute convergence rate
    fn compute_convergence_rate(&self) -> f64 {
        if self.training_history.len() < 2 {
            return 0.0;
        }

        let first_loss = self.training_history[0].total_loss;
        let last_loss = self.training_history.last().unwrap().total_loss;

        if first_loss > 0.0 {
            (first_loss - last_loss) / first_loss
        } else {
            0.0
        }
    }

    /// Reset training state
    pub fn reset(&mut self) {
        self.current_epoch = 0;
        self.training_history.clear();
        self.training_metrics = QuantumTrainingMetrics {
            classical_loss: 0.0,
            quantum_loss: 0.0,
            total_loss: 0.0,
            quantum_fidelity: 1.0,
            classical_accuracy: 0.0,
            quantum_advantage: 0.0,
            training_time: 0.0,
        };
    }
}

/// Quantum training statistics
#[derive(Debug, Clone)]
pub struct QuantumTrainingStats {
    /// Total epochs trained
    pub total_epochs: usize,
    /// Average classical loss
    pub avg_classical_loss: f64,
    /// Average quantum loss
    pub avg_quantum_loss: f64,
    /// Average quantum fidelity
    pub avg_quantum_fidelity: f64,
    /// Training strategy used
    pub training_strategy: HybridTrainingStrategy,
    /// Convergence rate
    pub convergence_rate: f64,
}

impl Default for QuantumTrainingMetrics {
    fn default() -> Self {
        Self {
            classical_loss: 0.0,
            quantum_loss: 0.0,
            total_loss: 0.0,
            quantum_fidelity: 1.0,
            classical_accuracy: 0.0,
            quantum_advantage: 0.0,
            training_time: 0.0,
        }
    }
}
