// Natural Policy Gradients
//
// This module implements natural policy gradient methods that use the Fisher information
// matrix to precondition policy gradients for more efficient optimization.

#[allow(dead_code)]

use super::{PolicyNetwork, RLOptimizationMetrics, RLOptimizerConfig, TrajectoryBatch};
use crate::error::Result;
use scirs2_core::ndarray::{Array1, Array2, ScalarOperand};
use scirs2_core::numeric::Float;
use std::fmt::Debug;

/// Natural gradient configuration
#[derive(Debug, Clone)]
pub struct NaturalGradientConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Base RL configuration
    pub base_config: RLOptimizerConfig<T>,

    /// Fisher information matrix estimation method
    pub fisher_method: FisherEstimationMethod,

    /// Damping parameter for Fisher matrix regularization
    pub damping: T,

    /// Fisher matrix update frequency
    pub fisher_update_freq: usize,

    /// Use empirical Fisher information matrix
    pub use_empirical_fisher: bool,

    /// Conjugate gradient parameters
    pub cg_iters: usize,
    pub cg_tolerance: T,

    /// Natural gradient scaling factor
    pub natural_grad_scale: T,

    /// Enable Fisher matrix preconditioning
    pub enable_preconditioning: bool,

    /// Diagonal Fisher approximation
    pub diagonal_fisher: bool,

    /// Block diagonal Fisher approximation
    pub block_diagonal_fisher: bool,

    /// Kronecker factored approximation (K-FAC style)
    pub kronecker_factored: bool,
}

/// Fisher information matrix estimation methods
#[derive(Debug, Clone, Copy)]
pub enum FisherEstimationMethod {
    /// Empirical Fisher Information Matrix
    Empirical,

    /// True Fisher Information Matrix (using log-likelihood Hessian)
    True,

    /// Diagonal approximation
    Diagonal,

    /// Block diagonal approximation
    BlockDiagonal,

    /// Kronecker factored approximation
    KroneckerFactored,

    /// Gauss-Newton approximation
    GaussNewton,

    /// BFGS quasi-Newton approximation
    BFGS,
}

impl<T: Float + Debug + Send + Sync + 'static> Default for NaturalGradientConfig<T> {
    fn default() -> Self {
        Self {
            base_config: RLOptimizerConfig::default(),
            fisher_method: FisherEstimationMethod::Empirical,
            damping: T::from(1e-4).unwrap_or_else(|| T::zero()),
            fisher_update_freq: 10,
            use_empirical_fisher: true,
            cg_iters: 10,
            cg_tolerance: T::from(1e-8).unwrap_or_else(|| T::zero()),
            natural_grad_scale: T::from(1.0).unwrap_or_else(|| T::zero()),
            enable_preconditioning: true,
            diagonal_fisher: false,
            block_diagonal_fisher: false,
            kronecker_factored: false,
        }
    }
}

/// Natural Policy Gradient optimizer
pub struct NaturalPolicyGradient<T: Float + Debug, P: PolicyNetwork<T>> {
    /// Configuration
    _config: NaturalGradientConfig<T>,

    /// Policy network
    policy: P,

    /// Fisher Information Matrix
    fisher_matrix: Option<Array2<T>>,

    /// Diagonal Fisher approximation
    fisher_diagonal: Option<Array1<T>>,

    /// Kronecker factors (for K-FAC style approximation)
    kronecker_factors: Option<KroneckerFactors<T>>,

    /// Empirical Fisher accumulator
    empirical_fisher_accumulator: FisherAccumulator<T>,

    /// Natural gradient state
    natural_grad_state: NaturalGradientState<T>,

    /// Update counter
    update_count: usize,

    /// Parameter dimension
    paramdim: usize,
}

/// Kronecker factorization components
#[derive(Debug, Clone)]
pub struct KroneckerFactors<T: Float + Debug + Send + Sync + 'static> {
    /// Input statistics (activation covariances)
    pub input_factors: Vec<Array2<T>>,

    /// Output statistics (gradient covariances)
    pub output_factors: Vec<Array2<T>>,

    /// Layer indices for factor mapping
    pub layer_indices: Vec<usize>,
}

/// Fisher information accumulator for empirical estimation
#[derive(Debug, Clone)]
pub struct FisherAccumulator<T: Float + Debug + Send + Sync + 'static> {
    /// Accumulated Fisher matrix
    pub fisher_sum: Array2<T>,

    /// Number of samples accumulated
    pub sample_count: usize,

    /// Gradient history for empirical Fisher
    pub gradient_history: Vec<Array1<T>>,

    /// Maximum history size
    pub max_history_size: usize,
}

/// Natural gradient optimization state
#[derive(Debug, Clone)]
pub struct NaturalGradientState<T: Float + Debug + Send + Sync + 'static> {
    /// Previous natural gradients
    pub prev_natural_grad: Option<Array1<T>>,

    /// Momentum for natural gradients
    pub momentum: T,

    /// Adaptive scaling factors
    pub adaptive_scales: Option<Array1<T>>,

    /// Trust region radius
    pub trust_radius: T,

    /// KL divergence history
    pub kl_history: Vec<T>,
}

impl<T: Float + Debug + ScalarOperand + std::ops::AddAssign + std::iter::Sum, P: PolicyNetwork<T + Send + Sync>>
    NaturalPolicyGradient<T, P>
{
    /// Create a new natural policy gradient optimizer
    pub fn new(_config: NaturalGradientConfig<T>, policy: P, paramdim: usize) -> Self {
        let fisher_accumulator = FisherAccumulator {
            fisher_sum: Array2::zeros((paramdim, paramdim)),
            sample_count: 0,
            gradient_history: Vec::new(),
            max_history_size: 1000,
        };

        let natural_grad_state = NaturalGradientState {
            prev_natural_grad: None,
            momentum: T::from(0.9).unwrap_or_else(|| T::zero()),
            adaptive_scales: None,
            trust_radius: T::from(1.0).unwrap_or_else(|| T::zero()),
            kl_history: Vec::new(),
        };

        Self {
            _config,
            policy,
            fisher_matrix: None,
            fisher_diagonal: None,
            kronecker_factors: None,
            empirical_fisher_accumulator: fisher_accumulator,
            natural_grad_state,
            update_count: 0,
            paramdim,
        }
    }

    /// Update using trajectory data
    pub fn update(
        &mut self,
        trajectory: TrajectoryBatch<T>,
        gradients: Array1<T>,
    ) -> Result<RLOptimizationMetrics<T>> {
        // Update Fisher information matrix
        if self.update_count % self._config.fisher_update_freq == 0 {
            self.update_fisher_information(&trajectory)?;
        }

        // Compute natural gradients
        let naturalgradients = self.compute_natural_gradients(&gradients)?;

        // Apply natural gradient update
        self.apply_natural_gradient_update(&naturalgradients)?;

        // Update state
        self.natural_grad_state.prev_natural_grad = Some(naturalgradients);
        self.update_count += 1;

        // Compute metrics
        let mut metrics = RLOptimizationMetrics::default();
        metrics.policy_grad_norm = self.vector_norm(&gradients);

        Ok(metrics)
    }

    /// Update Fisher Information Matrix
    fn update_fisher_information(&mut self, trajectory: &TrajectoryBatch<T>) -> Result<()> {
        match self._config.fisher_method {
            FisherEstimationMethod::Empirical => self.update_empirical_fisher(trajectory)?,
            FisherEstimationMethod::True => self.update_true_fisher(trajectory)?,
            FisherEstimationMethod::Diagonal => self.update_diagonal_fisher(trajectory)?,
            FisherEstimationMethod::BlockDiagonal => {
                self.update_block_diagonal_fisher(trajectory)?
            }
            FisherEstimationMethod::KroneckerFactored => {
                self.update_kronecker_factors(trajectory)?
            }
            _ => {
                // Fallback to empirical Fisher
                self.update_empirical_fisher(trajectory)?;
            }
        }

        Ok(())
    }

    /// Update empirical Fisher Information Matrix
    fn update_empirical_fisher(&mut self, trajectory: &TrajectoryBatch<T>) -> Result<()> {
        let batch_size = trajectory.observations.nrows();

        // Collect gradients for each sample
        for i in 0..batch_size {
            let obs = trajectory.observations.row(i).to_owned();
            let action = trajectory.actions.row(i).to_owned();

            // Compute log probability gradients
            let log_prob_grad = self.compute_log_prob_gradients(&obs, &action)?;

            // Add to empirical Fisher accumulator
            self.add_to_empirical_fisher(&log_prob_grad)?;
        }

        // Compute final empirical Fisher matrix
        self.finalize_empirical_fisher()?;

        Ok(())
    }

    /// Update true Fisher Information Matrix
    fn update_true_fisher(&mut self, trajectory: &TrajectoryBatch<T>) -> Result<()> {
        // True Fisher requires computing the Hessian of the log-likelihood
        // This is computationally expensive and often approximated
        self.update_empirical_fisher(trajectory) // Fallback for now
    }

    /// Update diagonal Fisher approximation
    fn update_diagonal_fisher(&mut self, trajectory: &TrajectoryBatch<T>) -> Result<()> {
        let mut diagonal = Array1::zeros(self.paramdim);
        let batch_size = trajectory.observations.nrows();

        for i in 0..batch_size {
            let obs = trajectory.observations.row(i).to_owned();
            let action = trajectory.actions.row(i).to_owned();

            let log_prob_grad = self.compute_log_prob_gradients(&obs, &action)?;
            diagonal = diagonal + log_prob_grad.mapv(|x| x * x);
        }

        diagonal = diagonal / T::from(batch_size).unwrap_or_else(|| T::zero());
        diagonal = diagonal + T::from(self._config.damping).unwrap_or_else(|| T::zero());

        self.fisher_diagonal = Some(diagonal);

        Ok(())
    }

    /// Update block diagonal Fisher approximation
    fn update_block_diagonal_fisher(&mut self, trajectory: &TrajectoryBatch<T>) -> Result<()> {
        // Block diagonal approximation groups parameters into blocks
        // and assumes independence between blocks
        Ok(())
    }

    /// Update Kronecker factorization
    fn update_kronecker_factors(&mut self, trajectory: &TrajectoryBatch<T>) -> Result<()> {
        // Kronecker factorization approximates the Fisher matrix as
        // a Kronecker product of smaller matrices (K-FAC style)
        Ok(())
    }

    /// Compute natural gradients
    fn compute_natural_gradients(&self, gradients: &Array1<T>) -> Result<Array1<T>> {
        if !self._config.enable_preconditioning {
            return Ok(gradients.clone());
        }

        let natural_grad = match self._config.fisher_method {
            FisherEstimationMethod::Diagonal => {
                if let Some(ref diag) = self.fisher_diagonal {
                    gradients / diag
                } else {
                    gradients.clone()
                }
            }
            _ => {
                if let Some(ref fisher) = self.fisher_matrix {
                    // Solve Fisher * natural_grad = gradients
                    self.solve_fisher_system(fisher, gradients)?
                } else {
                    gradients.clone()
                }
            }
        };

        // Apply scaling
        let scaled_natural_grad = natural_grad * self._config.natural_grad_scale;

        Ok(scaled_natural_grad)
    }

    /// Solve Fisher information system using conjugate gradient
    fn solve_fisher_system(&self, fisher: &Array2<T>, rhs: &Array1<T>) -> Result<Array1<T>> {
        let n = rhs.len();
        let mut x = Array1::zeros(n);
        let mut r = rhs.clone();
        let mut p = r.clone();
        let mut rsold = self.dot(&r, &r);

        for _i in 0..self._config.cg_iters {
            let ap = fisher.dot(&p);
            let alpha = rsold / self.dot(&p, &ap);

            x = &x + &(&p * alpha);
            r = &r - &(&ap * alpha);

            let rsnew = self.dot(&r, &r);

            if rsnew.sqrt() < self._config.cg_tolerance {
                break;
            }

            let beta = rsnew / rsold;
            p = &r + &(&p * beta);
            rsold = rsnew;
        }

        Ok(x)
    }

    /// Apply natural gradient update
    fn apply_natural_gradient_update(&mut self, naturalgradients: &Array1<T>) -> Result<()> {
        // In practice, this would update the policy network parameters
        // using the natural _gradients

        // Apply momentum if previous natural _gradients exist
        let update = if let Some(ref prev_ng) = self.natural_grad_state.prev_natural_grad {
            naturalgradients + &(prev_ng * self.natural_grad_state.momentum)
        } else {
            naturalgradients.clone()
        };

        // Apply trust region constraint
        let clipped_update = self.apply_trust_region_constraint(&update)?;

        // Update policy parameters (placeholder)
        self.update_policy_parameters(&clipped_update)?;

        Ok(())
    }

    /// Apply trust region constraint to natural gradient update
    fn apply_trust_region_constraint(&self, update: &Array1<T>) -> Result<Array1<T>> {
        let update_norm = self.vector_norm(update);
        let trust_radius = self.natural_grad_state.trust_radius;

        if update_norm <= trust_radius {
            Ok(update.clone())
        } else {
            Ok(update * (trust_radius / update_norm))
        }
    }

    /// Update policy network parameters
    fn update_policy_parameters(&mut self, update: &Array1<T>) -> Result<()> {
        // Placeholder for actual parameter _update
        // In practice, this would modify the policy network weights
        Ok(())
    }

    /// Compute log probability gradients
    fn compute_log_prob_gradients(
        &self,
        obs: &Array1<T>,
        _action: &Array1<T>,
    ) -> Result<Array1<T>> {
        // Placeholder for computing gradients of log probability
        // This would typically involve backpropagation through the policy network
        Ok(Array1::zeros(self.paramdim))
    }

    /// Add gradient to empirical Fisher accumulator
    fn add_to_empirical_fisher(&mut self, gradient: &Array1<T>) -> Result<()> {
        // Add outer product of gradient to Fisher sum
        for i in 0..self.paramdim {
            for j in 0..self.paramdim {
                self.empirical_fisher_accumulator.fisher_sum[[i, j]] += gradient[i] * gradient[j];
            }
        }

        self.empirical_fisher_accumulator.sample_count += 1;

        // Store gradient in history
        if self.empirical_fisher_accumulator.gradient_history.len()
            >= self.empirical_fisher_accumulator.max_history_size
        {
            self.empirical_fisher_accumulator.gradient_history.remove(0);
        }
        self.empirical_fisher_accumulator
            .gradient_history
            .push(gradient.clone());

        Ok(())
    }

    /// Finalize empirical Fisher matrix computation
    fn finalize_empirical_fisher(&mut self) -> Result<()> {
        if self.empirical_fisher_accumulator.sample_count == 0 {
            return Ok(());
        }

        // Normalize by sample count
        let fisher = &self.empirical_fisher_accumulator.fisher_sum
            / T::from(self.empirical_fisher_accumulator.sample_count).unwrap_or_else(|| T::zero());

        // Add damping for numerical stability
        let mut damped_fisher = fisher;
        for i in 0..self.paramdim {
            damped_fisher[[i, i]] += self._config.damping;
        }

        self.fisher_matrix = Some(damped_fisher);

        // Reset accumulator
        self.empirical_fisher_accumulator.fisher_sum.fill(T::zero());
        self.empirical_fisher_accumulator.sample_count = 0;

        Ok(())
    }

    /// Compute dot product
    fn dot(&self, a: &Array1<T>, b: &Array1<T>) -> T {
        a.iter().zip(b.iter()).map(|(&x, &y)| x * y).sum()
    }

    /// Compute vector norm
    fn vector_norm(&self, v: &Array1<T>) -> T {
        self.dot(v, v).sqrt()
    }

    /// Get current Fisher matrix
    pub fn get_fisher_matrix(&self) -> Option<&Array2<T>> {
        self.fisher_matrix.as_ref()
    }

    /// Get current natural gradient state
    pub fn get_natural_grad_state(&self) -> &NaturalGradientState<T> {
        &self.natural_grad_state
    }
}
