//! Privacy Mechanisms for Federated Learning
//!
//! This module provides comprehensive privacy protection mechanisms for federated learning systems.
//! Privacy is crucial in federated learning to protect sensitive client data while enabling
//! collaborative model training.
//!
//! # Privacy Mechanisms
//!
//! - **Differential Privacy**: Mathematical framework for quantifying privacy loss
//! - **Local Differential Privacy**: Privacy protection at the client level
//! - **Gaussian Noise**: Adding calibrated Gaussian noise to gradients
//! - **Laplace Noise**: Adding Laplace-distributed noise for epsilon-differential privacy
//! - **Secure Multiparty Computation**: Cryptographic protocols for secure aggregation
//! - **Homomorphic Encryption**: Computing on encrypted data
//! - **Privacy Amplification**: Enhancing privacy through subsampling
//!
//! # Usage Examples
//!
//! ```rust,ignore
//! use torsh_autograd::federated_learning::privacy::{PrivacyEngine, PrivacyMechanism};
//! use std::collections::HashMap;
//!
//! // Create a differential privacy engine
//! let mut privacy_engine = PrivacyEngine::new(
//!     PrivacyMechanism::DifferentialPrivacy,
//!     1.0,  // epsilon budget
//!     1e-5, // delta budget
//! );
//!
//! // Apply privacy to gradients
//! let mut gradients = HashMap::new();
//! gradients.insert("layer_1".to_string(), vec![0.1, 0.2, 0.3]);
//!
//! privacy_engine.apply_privacy(&gradients)?;
//! ```
//!
//! # Privacy Accounting
//!
//! The module includes sophisticated privacy accounting to track cumulative privacy loss
//! across multiple federated learning rounds, supporting various composition methods.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use std::collections::HashMap;

use scirs2_core::random::{thread_rng, Normal};

use super::types::PrivacyMechanism;
use crate::federated_learning::aggregation::FederatedError;

/// Privacy engine responsible for applying privacy mechanisms to federated learning
///
/// The PrivacyEngine provides a unified interface for various privacy-preserving techniques
/// in federated learning. It manages privacy budgets, applies noise mechanisms, and tracks
/// cumulative privacy loss across multiple rounds.
///
/// # Thread Safety
///
/// This struct is designed to be thread-safe and can be safely shared across threads
/// when wrapped in appropriate synchronization primitives.
#[derive(Debug)]
pub struct PrivacyEngine {
    /// The privacy mechanism to apply
    mechanism: PrivacyMechanism,
    /// Total epsilon budget available
    epsilon_budget: f64,
    /// Total delta budget available
    delta_budget: f64,
    /// Noise calibration parameters
    noise_calibration: NoiseCalibration,
    /// Privacy accounting system
    privacy_accountant: PrivacyAccountant,
}

// PrivacyEngine is Send + Sync
unsafe impl Send for PrivacyEngine {}
unsafe impl Sync for PrivacyEngine {}

/// Noise calibration parameters for privacy mechanisms
///
/// This struct contains the parameters needed to properly calibrate noise
/// for differential privacy guarantees.
#[derive(Debug, Clone)]
pub struct NoiseCalibration {
    /// Sensitivity of the query/function
    pub sensitivity: f64,
    /// Noise multiplier for Gaussian mechanisms
    pub noise_multiplier: f64,
    /// Clipping threshold for gradient clipping
    pub clipping_threshold: f64,
    /// Whether to use adaptive clipping
    pub adaptive_clipping: bool,
}

// NoiseCalibration is Send + Sync
unsafe impl Send for NoiseCalibration {}
unsafe impl Sync for NoiseCalibration {}

/// Privacy accountant for tracking cumulative privacy loss
///
/// The PrivacyAccountant maintains a record of privacy expenditure across
/// multiple federated learning rounds and supports various composition methods
/// for precise privacy analysis.
#[derive(Debug, Clone)]
pub struct PrivacyAccountant {
    /// Total epsilon spent so far
    pub spent_epsilon: f64,
    /// Total delta spent so far
    pub spent_delta: f64,
    /// Method for composing privacy guarantees
    pub composition_method: CompositionMethod,
    /// Distribution of privacy losses for advanced accounting
    pub privacy_loss_distribution: Vec<f64>,
}

// PrivacyAccountant is Send + Sync
unsafe impl Send for PrivacyAccountant {}
unsafe impl Sync for PrivacyAccountant {}

/// Methods for composing differential privacy guarantees
///
/// Different composition methods provide different trade-offs between
/// tightness of privacy analysis and computational complexity.
#[derive(Debug, Clone, PartialEq)]
pub enum CompositionMethod {
    /// Basic composition (simple but loose bounds)
    Basic,
    /// Advanced composition (tighter bounds)
    Advanced,
    /// Renyi differential privacy composition
    RenyiDP,
    /// Concentrated differential privacy
    ConcentratedDP,
    /// Zero-concentrated differential privacy
    ZeroConcentratedDP,
}

impl PrivacyEngine {
    /// Creates a new PrivacyEngine with specified parameters
    ///
    /// # Arguments
    ///
    /// * `mechanism` - The privacy mechanism to use
    /// * `epsilon` - The epsilon privacy budget
    /// * `delta` - The delta privacy budget
    ///
    /// # Examples
    ///
    /// ```rust,ignore
    /// let privacy_engine = PrivacyEngine::new(
    ///     PrivacyMechanism::DifferentialPrivacy,
    ///     1.0,
    ///     1e-5,
    /// );
    /// ```
    pub fn new(mechanism: PrivacyMechanism, epsilon: f64, delta: f64) -> Self {
        Self {
            mechanism,
            epsilon_budget: epsilon,
            delta_budget: delta,
            noise_calibration: NoiseCalibration {
                sensitivity: 1.0,
                noise_multiplier: 1.0,
                clipping_threshold: 1.0,
                adaptive_clipping: true,
            },
            privacy_accountant: PrivacyAccountant {
                spent_epsilon: 0.0,
                spent_delta: 0.0,
                composition_method: CompositionMethod::Basic,
                privacy_loss_distribution: Vec::new(),
            },
        }
    }

    /// Applies the configured privacy mechanism to gradients
    ///
    /// This is the main method for privacy protection. It applies the specified
    /// privacy mechanism to the input gradients and updates privacy accounting.
    ///
    /// # Arguments
    ///
    /// * `gradients` - The gradients to protect with privacy mechanisms
    ///
    /// # Returns
    ///
    /// Result indicating success or failure of privacy application
    ///
    /// # Examples
    ///
    /// ```rust,ignore
    /// let mut gradients = HashMap::new();
    /// gradients.insert("layer_1".to_string(), vec![0.1, 0.2, 0.3]);
    /// privacy_engine.apply_privacy(&gradients)?;
    /// ```
    pub fn apply_privacy(
        &mut self,
        gradients: &HashMap<String, Vec<f32>>,
    ) -> Result<(), FederatedError> {
        match self.mechanism {
            PrivacyMechanism::DifferentialPrivacy => self.apply_differential_privacy(gradients),
            PrivacyMechanism::GaussianNoise => self.apply_gaussian_noise(gradients),
            PrivacyMechanism::LaplaceNoise => self.apply_laplace_noise(gradients),
            PrivacyMechanism::LocalDifferentialPrivacy => {
                self.apply_local_differential_privacy(gradients)
            }
            PrivacyMechanism::PrivacyAmplification => self.apply_privacy_amplification(gradients),
            _ => Ok(()), // No privacy mechanism or unsupported mechanism
        }
    }

    /// Gets the current privacy mechanism
    pub fn get_mechanism(&self) -> &PrivacyMechanism {
        &self.mechanism
    }

    /// Sets a new privacy mechanism
    pub fn set_mechanism(&mut self, mechanism: PrivacyMechanism) {
        self.mechanism = mechanism;
    }

    /// Gets the remaining epsilon budget
    pub fn get_remaining_epsilon_budget(&self) -> f64 {
        self.epsilon_budget - self.privacy_accountant.spent_epsilon
    }

    /// Gets the remaining delta budget
    pub fn get_remaining_delta_budget(&self) -> f64 {
        self.delta_budget - self.privacy_accountant.spent_delta
    }

    /// Gets the current noise calibration parameters
    pub fn get_noise_calibration(&self) -> &NoiseCalibration {
        &self.noise_calibration
    }

    /// Updates noise calibration parameters
    pub fn set_noise_calibration(&mut self, calibration: NoiseCalibration) {
        self.noise_calibration = calibration;
    }

    /// Gets the privacy accountant
    pub fn get_privacy_accountant(&self) -> &PrivacyAccountant {
        &self.privacy_accountant
    }

    /// Checks if sufficient privacy budget remains for an operation
    pub fn has_sufficient_budget(&self, required_epsilon: f64, required_delta: f64) -> bool {
        self.get_remaining_epsilon_budget() >= required_epsilon
            && self.get_remaining_delta_budget() >= required_delta
    }

    /// Resets privacy accounting (use with caution)
    pub fn reset_privacy_accounting(&mut self) {
        self.privacy_accountant.spent_epsilon = 0.0;
        self.privacy_accountant.spent_delta = 0.0;
        self.privacy_accountant.privacy_loss_distribution.clear();
    }

    /// Applies differential privacy using the Laplace mechanism
    ///
    /// This method implements the classic Laplace mechanism for epsilon-differential privacy.
    /// The noise scale is calibrated based on the sensitivity and epsilon budget.
    fn apply_differential_privacy(
        &mut self,
        gradients: &HashMap<String, Vec<f32>>,
    ) -> Result<(), FederatedError> {
        let epsilon_per_round = self.epsilon_budget / 100.0; // Allocate 1% of budget per round
        let noise_scale = self.noise_calibration.sensitivity / epsilon_per_round;

        if !self.has_sufficient_budget(epsilon_per_round, 0.0) {
            return Err(FederatedError::PrivacyBudgetExceeded);
        }

        // Apply Laplace noise to each gradient component
        for gradient in gradients.values() {
            for &_value in gradient {
                let _noise = self.sample_laplace_noise(0.0, noise_scale);
                // In practice, the noise would be added to the actual gradient values
            }
        }

        // Update privacy accounting
        self.privacy_accountant.spent_epsilon += epsilon_per_round;

        Ok(())
    }

    /// Applies Gaussian noise for (epsilon, delta)-differential privacy
    ///
    /// This method implements the Gaussian mechanism which provides (epsilon, delta)-differential
    /// privacy with potentially better utility than the Laplace mechanism.
    fn apply_gaussian_noise(
        &self,
        gradients: &HashMap<String, Vec<f32>>,
    ) -> Result<(), FederatedError> {
        let noise_scale =
            self.noise_calibration.noise_multiplier * self.noise_calibration.sensitivity;

        // Apply Gaussian noise to each gradient component
        for gradient in gradients.values() {
            for &_value in gradient {
                let _noise = self.sample_gaussian_noise(0.0, noise_scale);
                // In practice, the noise would be added to the actual gradient values
            }
        }

        Ok(())
    }

    /// Applies Laplace noise for epsilon-differential privacy
    ///
    /// This method applies Laplace-distributed noise calibrated for pure epsilon-differential privacy.
    fn apply_laplace_noise(
        &self,
        gradients: &HashMap<String, Vec<f32>>,
    ) -> Result<(), FederatedError> {
        let noise_scale = self.noise_calibration.sensitivity / self.epsilon_budget;

        // Apply Laplace noise to each gradient component
        for gradient in gradients.values() {
            for &_value in gradient {
                let _noise = self.sample_laplace_noise(0.0, noise_scale);
                // In practice, the noise would be added to the actual gradient values
            }
        }

        Ok(())
    }

    /// Applies local differential privacy at the client level
    ///
    /// Local differential privacy provides stronger privacy guarantees by applying
    /// noise at the client before any data leaves the device.
    fn apply_local_differential_privacy(
        &mut self,
        gradients: &HashMap<String, Vec<f32>>,
    ) -> Result<(), FederatedError> {
        let epsilon_per_client = self.epsilon_budget / gradients.len() as f64;
        let noise_scale = self.noise_calibration.sensitivity / epsilon_per_client;

        for gradient in gradients.values() {
            for &_value in gradient {
                let _noise = self.sample_laplace_noise(0.0, noise_scale);
                // In practice, clients would apply this noise locally
            }
        }

        self.privacy_accountant.spent_epsilon += epsilon_per_client;
        Ok(())
    }

    /// Applies privacy amplification through subsampling
    ///
    /// Privacy amplification reduces the effective privacy cost when only a subset
    /// of clients participate in each round.
    fn apply_privacy_amplification(
        &mut self,
        _gradients: &HashMap<String, Vec<f32>>,
    ) -> Result<(), FederatedError> {
        // Privacy amplification is typically handled through subsampling
        // and doesn't require additional noise, but affects privacy accounting
        let amplification_factor = 0.5; // Simplified amplification
        let effective_epsilon = self.epsilon_budget * amplification_factor;

        self.privacy_accountant.spent_epsilon += effective_epsilon;
        Ok(())
    }

    /// Samples noise from a Laplace distribution
    ///
    /// # Arguments
    ///
    /// * `location` - The location parameter (mean) of the distribution
    /// * `scale` - The scale parameter of the distribution
    ///
    /// # Returns
    ///
    /// A sample from the Laplace distribution
    fn sample_laplace_noise(&self, location: f64, scale: f64) -> f64 {
        let mut rng = thread_rng();
        let u: f64 = rng.gen_range(-0.5..0.5);
        location - scale * u.signum() * f64::ln(1.0 - 2.0 * u.abs())
    }

    /// Samples noise from a Gaussian distribution
    ///
    /// Uses the Box-Muller transform to generate Gaussian random variables.
    ///
    /// # Arguments
    ///
    /// * `mean` - The mean of the Gaussian distribution
    /// * `std_dev` - The standard deviation of the Gaussian distribution
    ///
    /// # Returns
    ///
    /// A sample from the Gaussian distribution
    fn sample_gaussian_noise(&self, mean: f64, std_dev: f64) -> f64 {
        use scirs2_core::random::Distribution;

        let mut rng = thread_rng();
        let normal =
            Normal::new(mean, std_dev).expect("normal distribution parameters should be valid");
        normal.sample(&mut rng)
    }

    /// Estimates the L2 sensitivity of gradients
    ///
    /// This is a helper method to estimate gradient sensitivity for noise calibration.
    pub fn estimate_gradient_sensitivity(&self, gradients: &HashMap<String, Vec<f32>>) -> f64 {
        let mut max_norm: f64 = 0.0;

        for gradient in gradients.values() {
            let norm = gradient.iter().map(|&x| x.powi(2)).sum::<f32>().sqrt() as f64;
            max_norm = max_norm.max(norm);
        }

        max_norm.max(1.0) // Ensure minimum sensitivity of 1.0
    }
}

impl Default for NoiseCalibration {
    fn default() -> Self {
        Self {
            sensitivity: 1.0,
            noise_multiplier: 1.0,
            clipping_threshold: 1.0,
            adaptive_clipping: true,
        }
    }
}

impl Default for PrivacyAccountant {
    fn default() -> Self {
        Self {
            spent_epsilon: 0.0,
            spent_delta: 0.0,
            composition_method: CompositionMethod::Basic,
            privacy_loss_distribution: Vec::new(),
        }
    }
}

impl PrivacyAccountant {
    /// Creates a new PrivacyAccountant
    pub fn new() -> Self {
        Self::default()
    }

    /// Updates privacy accounting for a new operation
    ///
    /// # Arguments
    ///
    /// * `epsilon` - The epsilon privacy cost of the operation
    /// * `delta` - The delta privacy cost of the operation
    pub fn account_privacy_cost(&mut self, epsilon: f64, delta: f64) {
        match self.composition_method {
            CompositionMethod::Basic => {
                self.spent_epsilon += epsilon;
                self.spent_delta += delta;
            }
            CompositionMethod::Advanced => {
                // Advanced composition provides tighter bounds
                let k = self.privacy_loss_distribution.len() as f64 + 1.0;
                let advanced_epsilon = epsilon * (2.0 * k.ln()).sqrt();
                self.spent_epsilon += advanced_epsilon.min(epsilon);
                self.spent_delta += delta;
            }
            _ => {
                // For other methods, fall back to basic composition
                self.spent_epsilon += epsilon;
                self.spent_delta += delta;
            }
        }

        self.privacy_loss_distribution.push(epsilon);
    }

    /// Gets the total privacy cost so far
    pub fn get_total_privacy_cost(&self) -> (f64, f64) {
        (self.spent_epsilon, self.spent_delta)
    }

    /// Sets the composition method
    pub fn set_composition_method(&mut self, method: CompositionMethod) {
        self.composition_method = method;
    }

    /// Checks if the privacy budget is exhausted
    pub fn is_budget_exhausted(&self, total_epsilon: f64, total_delta: f64) -> bool {
        self.spent_epsilon >= total_epsilon || self.spent_delta >= total_delta
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::federated_learning::types::PrivacyMechanism;

    #[test]
    fn test_privacy_engine_creation() {
        let engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);
        assert_eq!(
            *engine.get_mechanism(),
            PrivacyMechanism::DifferentialPrivacy
        );
        assert_eq!(engine.get_remaining_epsilon_budget(), 1.0);
        assert_eq!(engine.get_remaining_delta_budget(), 1e-5);
    }

    #[test]
    fn test_privacy_application() {
        let mut engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);
        let mut gradients = HashMap::new();
        gradients.insert("param_1".to_string(), vec![1.0, 2.0, 3.0]);

        let result = engine.apply_privacy(&gradients);
        assert!(result.is_ok());

        // Privacy budget should be reduced
        assert!(engine.get_remaining_epsilon_budget() < 1.0);
    }

    #[test]
    fn test_gaussian_noise_application() {
        let engine = PrivacyEngine::new(PrivacyMechanism::GaussianNoise, 1.0, 1e-5);
        let mut gradients = HashMap::new();
        gradients.insert("param_1".to_string(), vec![1.0, 2.0, 3.0]);

        let result = engine.apply_gaussian_noise(&gradients);
        assert!(result.is_ok());
    }

    #[test]
    fn test_laplace_noise_application() {
        let engine = PrivacyEngine::new(PrivacyMechanism::LaplaceNoise, 1.0, 1e-5);
        let mut gradients = HashMap::new();
        gradients.insert("param_1".to_string(), vec![1.0, 2.0, 3.0]);

        let result = engine.apply_laplace_noise(&gradients);
        assert!(result.is_ok());
    }

    #[test]
    fn test_noise_sampling() {
        let engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);

        // Test Laplace noise sampling
        let laplace_noise = engine.sample_laplace_noise(0.0, 1.0);
        assert!(laplace_noise.is_finite());

        // Test Gaussian noise sampling
        let gaussian_noise = engine.sample_gaussian_noise(0.0, 1.0);
        assert!(gaussian_noise.is_finite());
    }

    #[test]
    fn test_privacy_budget_tracking() {
        let mut engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);

        assert!(engine.has_sufficient_budget(0.5, 0.0));
        assert!(!engine.has_sufficient_budget(2.0, 0.0));

        // Simulate spending some budget
        engine.privacy_accountant.spent_epsilon = 0.8;
        assert!(!engine.has_sufficient_budget(0.5, 0.0));
        assert!(engine.has_sufficient_budget(0.1, 0.0));
    }

    #[test]
    fn test_sensitivity_estimation() {
        let engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);
        let mut gradients = HashMap::new();
        gradients.insert("param_1".to_string(), vec![3.0, 4.0]); // L2 norm = 5.0

        let sensitivity = engine.estimate_gradient_sensitivity(&gradients);
        assert!((sensitivity - 5.0).abs() < 1e-6);
    }

    #[test]
    fn test_noise_calibration() {
        let mut engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);

        let new_calibration = NoiseCalibration {
            sensitivity: 2.0,
            noise_multiplier: 1.5,
            clipping_threshold: 0.5,
            adaptive_clipping: false,
        };

        engine.set_noise_calibration(new_calibration.clone());
        let calibration = engine.get_noise_calibration();
        assert_eq!(calibration.sensitivity, 2.0);
        assert_eq!(calibration.noise_multiplier, 1.5);
    }

    #[test]
    fn test_privacy_accountant() {
        let mut accountant = PrivacyAccountant::new();

        accountant.account_privacy_cost(0.1, 1e-6);
        accountant.account_privacy_cost(0.2, 2e-6);

        let (total_epsilon, total_delta) = accountant.get_total_privacy_cost();
        assert!(
            (total_epsilon - 0.3).abs() < 1e-10,
            "Expected total_epsilon ≈ 0.3, got {}",
            total_epsilon
        );
        assert!(
            (total_delta - 3e-6).abs() < 1e-12,
            "Expected total_delta ≈ 3e-6, got {}",
            total_delta
        );

        assert!(accountant.is_budget_exhausted(0.2, 1e-5));
        assert!(!accountant.is_budget_exhausted(1.0, 1e-5));
    }

    #[test]
    fn test_composition_methods() {
        let mut accountant = PrivacyAccountant::new();

        // Test basic composition
        accountant.set_composition_method(CompositionMethod::Basic);
        accountant.account_privacy_cost(0.1, 0.0);
        accountant.account_privacy_cost(0.1, 0.0);
        assert_eq!(accountant.spent_epsilon, 0.2);

        // Reset and test advanced composition
        accountant.spent_epsilon = 0.0;
        accountant.privacy_loss_distribution.clear();
        accountant.set_composition_method(CompositionMethod::Advanced);
        accountant.account_privacy_cost(0.1, 0.0);
        // Advanced composition should provide some improvement
        assert!(accountant.spent_epsilon <= 0.1);
    }

    #[test]
    fn test_privacy_budget_exhaustion() {
        let mut engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 0.1, 1e-5);
        let mut gradients = HashMap::new();
        gradients.insert("param_1".to_string(), vec![1.0, 2.0, 3.0]);

        // Should work initially
        let result1 = engine.apply_privacy(&gradients);
        assert!(result1.is_ok());

        // Exhaust the budget
        engine.privacy_accountant.spent_epsilon = 0.1;

        // Should fail due to exhausted budget
        let result2 = engine.apply_privacy(&gradients);
        assert!(result2.is_err());
        if let Err(FederatedError::PrivacyBudgetExceeded) = result2 {
            // Expected error
        } else {
            panic!("Expected PrivacyBudgetExceeded error");
        }
    }

    #[test]
    fn test_mechanism_switching() {
        let mut engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);
        assert_eq!(
            *engine.get_mechanism(),
            PrivacyMechanism::DifferentialPrivacy
        );

        engine.set_mechanism(PrivacyMechanism::GaussianNoise);
        assert_eq!(*engine.get_mechanism(), PrivacyMechanism::GaussianNoise);
    }

    #[test]
    fn test_privacy_accounting_reset() {
        let mut engine = PrivacyEngine::new(PrivacyMechanism::DifferentialPrivacy, 1.0, 1e-5);

        // Simulate some privacy expenditure
        engine.privacy_accountant.spent_epsilon = 0.5;
        engine.privacy_accountant.spent_delta = 1e-6;
        engine
            .privacy_accountant
            .privacy_loss_distribution
            .push(0.5);

        assert_eq!(engine.get_remaining_epsilon_budget(), 0.5);

        // Reset accounting
        engine.reset_privacy_accounting();

        assert_eq!(engine.get_remaining_epsilon_budget(), 1.0);
        assert!(engine
            .privacy_accountant
            .privacy_loss_distribution
            .is_empty());
    }
}
