// Main federated privacy coordinator implementation

use super::super::noise_mechanisms::{
    GaussianMechanism, LaplaceMechanism, NoiseMechanism as NoiseMechanismTrait,
};
use super::super::{DifferentialPrivacyConfig, NoiseMechanism};
use super::super::{MomentsAccountant, PrivacyBudget};
use super::components::*;
use super::config::*;
use crate::error::{OptimError, Result};
use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::Float;
use scirs2_core::random::{thread_rng, Rng};
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

/// Federated differential privacy coordinator
pub struct FederatedPrivacyCoordinator<T: Float + Debug + Send + Sync + 'static> {
    /// Global privacy configuration
    config: FederatedPrivacyConfig,

    /// Per-client privacy accountants
    client_accountants: HashMap<String, MomentsAccountant>,

    /// Global privacy accountant
    global_accountant: MomentsAccountant,

    /// Secure aggregation protocol
    secure_aggregator: SecureAggregator<T>,

    /// Privacy amplification analyzer
    amplification_analyzer: PrivacyAmplificationAnalyzer,

    /// Cross-device privacy manager
    cross_device_manager: CrossDevicePrivacyManager<T>,

    /// Composition analyzer for multi-round privacy
    composition_analyzer: FederatedCompositionAnalyzer,

    /// Byzantine-robust aggregation engine
    byzantine_aggregator: ByzantineRobustAggregator<T>,

    /// Personalized federated learning manager
    personalization_manager: PersonalizationManager<T>,

    /// Adaptive privacy budget manager
    adaptive_budget_manager: AdaptiveBudgetManager<T>,

    /// Communication efficiency optimizer
    communication_optimizer: CommunicationOptimizer<T>,

    /// Continual learning coordinator
    continual_learning_coordinator: ContinualLearningCoordinator<T>,

    /// Current round number
    current_round: usize,

    /// Client participation history
    participation_history: VecDeque<ParticipationRound>,
}

/// Federated round plan with privacy guarantees
#[derive(Debug, Clone)]
pub struct FederatedRoundPlan {
    pub round_number: usize,
    pub selectedclients: Vec<String>,
    pub sampling_probability: f64,
    pub amplificationfactor: f64,
    pub client_privacy_allocations: HashMap<String, ClientPrivacyAllocation>,
    pub aggregation_plan: Option<SecureAggregationPlan>,
    pub privacy_analysis: RoundPrivacyAnalysis,
}

/// Client privacy allocation for a round
#[derive(Debug, Clone)]
pub struct ClientPrivacyAllocation {
    pub epsilon: f64,
    pub delta: f64,
    pub noise_multiplier: f64,
    pub clipping_threshold: f64,
    pub amplificationfactor: f64,
}

/// Secure aggregation plan
#[derive(Debug, Clone)]
pub struct SecureAggregationPlan {
    pub masking_seeds: HashMap<String, u64>,
    pub aggregation_threshold: usize,
    pub dropout_tolerance: usize,
}

/// Privacy analysis for a round
#[derive(Debug, Clone)]
pub struct RoundPrivacyAnalysis {
    pub round_epsilon: f64,
    pub round_delta: f64,
    pub cumulative_epsilon: f64,
    pub cumulative_delta: f64,
    pub amplification_benefit: f64,
    pub composition_tightness: f64,
}

/// Advanced aggregation result with comprehensive metrics
#[derive(Debug)]
pub struct AdvancedAggregationResult<T: Float + Debug + Send + Sync + 'static> {
    pub aggregated_update: Array1<T>,
    pub outlier_detection_results: Vec<OutlierDetectionResult>,
    pub adaptive_privacy_allocations: HashMap<String, AdaptivePrivacyAllocation>,
    pub personalization_metrics: PersonalizationMetrics,
    pub communication_efficiency: CommunicationEfficiencyStats,
    pub continual_learning_status: ContinualLearningStatus,
    pub privacy_guarantees: AdvancedPrivacyGuarantees,
    pub fairness_metrics: FairnessMetrics,
}

/// Adaptive privacy allocation
#[derive(Debug, Clone)]
pub struct AdaptivePrivacyAllocation {
    pub epsilon: f64,
    pub delta: f64,
    pub importance_weight: f64,
    pub context_factors: HashMap<String, f64>,
}

/// Personalization metrics
#[derive(Debug, Clone)]
pub struct PersonalizationMetrics {
    pub cluster_assignments: HashMap<String, usize>,
    pub adaptation_effectiveness: f64,
    pub model_diversity: f64,
    pub personalization_overhead: f64,
}

/// Communication efficiency statistics
#[derive(Debug, Clone)]
pub struct CommunicationEfficiencyStats {
    pub compression_ratio: f64,
    pub bandwidth_utilization: f64,
    pub transmission_latency: f64,
    pub quality_of_service_score: f64,
}

/// Continual learning status
#[derive(Debug, Clone)]
pub struct ContinualLearningStatus {
    pub task_changes_detected: usize,
    pub forgetting_prevention_active: bool,
    pub memory_utilization: f64,
    pub knowledge_transfer_score: f64,
}

/// Advanced privacy guarantees
#[derive(Debug, Clone)]
pub struct AdvancedPrivacyGuarantees {
    pub basic_guarantees: PrivacyBudget,
    pub amplification_benefit: f64,
    pub byzantine_robustness_factor: f64,
    pub personalization_privacy_cost: f64,
    pub continual_learning_overhead: f64,
    pub multi_level_protection: bool,
    pub adaptive_budgeting_enabled: bool,
    pub communication_privacy_enabled: bool,
}

/// Enhanced sampling result
#[derive(Debug)]
pub struct EnhancedSamplingResult {
    pub selectedclients: Vec<String>,
    pub sampling_weights: HashMap<String, f64>,
    pub reputation_scores: HashMap<String, f64>,
    pub fairness_weights: HashMap<String, f64>,
    pub communication_scores: HashMap<String, f64>,
    pub diversity_metrics: DiversityMetrics,
}

/// Diversity metrics for client selection
#[derive(Debug, Clone)]
pub struct DiversityMetrics {
    pub geographic_diversity: f64,
    pub device_type_diversity: f64,
    pub data_distribution_diversity: f64,
    pub participation_frequency_diversity: f64,
}

/// Personalized round result
#[derive(Debug)]
pub struct PersonalizedRoundResult<T: Float + Debug + Send + Sync + 'static> {
    pub cluster_assignments: HashMap<usize, Vec<String>>,
    pub cluster_aggregates: HashMap<usize, Array1<T>>,
    pub meta_gradients: HashMap<String, Array1<T>>,
    pub personalized_models: HashMap<String, PersonalizedModel<T>>,
    pub effectiveness_metrics: PersonalizationMetrics,
    pub privacy_cost: f64,
}

impl<
        T: Float
            + Default
            + Clone
            + Send
            + Sync
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand
            + std::fmt::Debug,
    > FederatedPrivacyCoordinator<T>
{
    /// Create a new federated privacy coordinator
    pub fn new(config: FederatedPrivacyConfig) -> Result<Self> {
        let global_accountant = MomentsAccountant::new(
            config.base_config.noise_multiplier,
            config.base_config.target_delta,
            config.clients_per_round,
            config.total_clients,
        );

        let secure_aggregator = SecureAggregator::new(config.secure_aggregation.clone())?;
        let amplification_analyzer =
            PrivacyAmplificationAnalyzer::new(config.amplification_config.clone());
        let cross_device_manager =
            CrossDevicePrivacyManager::new(config.cross_device_config.clone());
        let composition_analyzer = FederatedCompositionAnalyzer::new(config.composition_method);

        Ok(Self {
            config,
            client_accountants: HashMap::new(),
            global_accountant,
            secure_aggregator,
            amplification_analyzer,
            cross_device_manager,
            composition_analyzer,
            byzantine_aggregator: ByzantineRobustAggregator::new()?,
            personalization_manager: PersonalizationManager::new()?,
            adaptive_budget_manager: AdaptiveBudgetManager::new()?,
            communication_optimizer: CommunicationOptimizer::new()?,
            continual_learning_coordinator: ContinualLearningCoordinator::new()?,
            current_round: 0,
            participation_history: VecDeque::with_capacity(1000),
        })
    }

    /// Start a new federated round with privacy guarantees
    pub fn start_federated_round(
        &mut self,
        availableclients: &[String],
    ) -> Result<FederatedRoundPlan> {
        self.current_round += 1;

        // Sample clients for this round
        let selectedclients = self.sample_clients(availableclients)?;

        // Check global privacy budget
        let global_budget = self.get_global_privacy_budget()?;
        if !self.has_sufficient_privacy_budget(&global_budget)? {
            return Err(OptimError::PrivacyBudgetExhausted {
                consumed_epsilon: global_budget.epsilon_consumed,
                target_epsilon: self.config.base_config.target_epsilon,
            });
        }

        // Compute sampling probability for amplification
        let sampling_probability = selectedclients.len() as f64 / availableclients.len() as f64;

        // Analyze privacy amplification
        let amplificationfactor = if self.config.amplification_config.enabled {
            self.amplification_analyzer
                .compute_amplification_factor(sampling_probability, self.current_round)?
        } else {
            1.0
        };

        // Prepare secure aggregation if enabled
        let aggregation_plan = if self.config.secure_aggregation.enabled {
            Some(self.prepare_secure_aggregation(&selectedclients)?)
        } else {
            None
        };

        // Compute per-client privacy allocations
        let client_privacy_allocations =
            self.compute_client_privacy_allocations(&selectedclients, amplificationfactor)?;

        // Create round plan
        let roundplan = FederatedRoundPlan {
            round_number: self.current_round,
            selectedclients: selectedclients.clone(),
            sampling_probability,
            amplificationfactor,
            client_privacy_allocations,
            aggregation_plan,
            privacy_analysis: self.analyze_round_privacy(&selectedclients, amplificationfactor)?,
        };

        // Record participation
        self.record_participation_round(
            &selectedclients,
            sampling_probability,
            amplificationfactor,
        );

        Ok(roundplan)
    }

    /// Perform secure aggregation of client updates
    pub fn secure_aggregate_updates(
        &mut self,
        clientupdates: &HashMap<String, Array1<T>>,
        _roundplan: &FederatedRoundPlan,
    ) -> Result<Array1<T>> {
        if self.config.secure_aggregation.enabled {
            // Placeholder for secure aggregation
            self.simple_aggregate(clientupdates)
        } else {
            // Simple averaging
            self.simple_aggregate(clientupdates)
        }
    }

    /// Simple aggregation (averaging) of client updates
    fn simple_aggregate(&self, clientupdates: &HashMap<String, Array1<T>>) -> Result<Array1<T>> {
        if clientupdates.is_empty() {
            return Err(OptimError::InvalidParameter(
                "No client updates provided".to_string(),
            ));
        }

        let mut first_update: Option<Array1<T>> = None;
        let mut count = 0;

        for update in clientupdates.values() {
            if let Some(ref mut aggregate) = first_update {
                for (i, &value) in update.iter().enumerate() {
                    if i < aggregate.len() {
                        aggregate[i] = aggregate[i] + value;
                    }
                }
            } else {
                first_update = Some(update.clone());
            }
            count += 1;
        }

        if let Some(mut aggregate) = first_update {
            let count_t = T::from(count).unwrap_or_else(|| T::zero());
            for value in aggregate.iter_mut() {
                *value = *value / count_t;
            }
            Ok(aggregate)
        } else {
            Err(OptimError::InvalidParameter(
                "Failed to aggregate updates".to_string(),
            ))
        }
    }

    /// Sample clients for federated round
    fn sample_clients(&self, availableclients: &[String]) -> Result<Vec<String>> {
        use scirs2_core::random::Rng;
        let mut rng = thread_rng();
        let target_count = self.config.clients_per_round.min(availableclients.len());

        match self.config.sampling_strategy {
            ClientSamplingStrategy::UniformRandom => {
                // Simple random selection
                let mut selected = Vec::new();
                let mut remaining = availableclients.to_vec();
                for _ in 0..target_count.min(remaining.len()) {
                    let index = rng.gen_range(0..remaining.len());
                    selected.push(remaining.swap_remove(index));
                }
                Ok(selected)
            }
            _ => {
                // Fallback to uniform random for other strategies
                let mut selected = Vec::new();
                let mut remaining = availableclients.to_vec();
                for _ in 0..target_count.min(remaining.len()) {
                    let index = rng.gen_range(0..remaining.len());
                    selected.push(remaining.swap_remove(index));
                }
                Ok(selected)
            }
        }
    }

    /// Get global privacy budget
    fn get_global_privacy_budget(&self) -> Result<PrivacyBudget> {
        use super::super::AccountingMethod;
        // Placeholder implementation
        Ok(PrivacyBudget {
            epsilon_consumed: 0.1,
            delta_consumed: 1e-5,
            epsilon_remaining: self.config.base_config.target_epsilon - 0.1,
            delta_remaining: self.config.base_config.target_delta - 1e-5,
            steps_taken: self.current_round,
            accounting_method: AccountingMethod::MomentsAccountant,
            estimated_steps_remaining: 100,
        })
    }

    /// Check if sufficient privacy budget is available
    fn has_sufficient_privacy_budget(&self, budget: &PrivacyBudget) -> Result<bool> {
        Ok(budget.epsilon_remaining > 0.0 && budget.delta_remaining > 0.0)
    }

    /// Prepare secure aggregation plan
    fn prepare_secure_aggregation(
        &self,
        selectedclients: &[String],
    ) -> Result<SecureAggregationPlan> {
        use scirs2_core::random::Rng;
        let mut rng = thread_rng();

        let mut masking_seeds = HashMap::new();
        for client in selectedclients {
            masking_seeds.insert(client.clone(), rng.random::<u64>());
        }

        Ok(SecureAggregationPlan {
            masking_seeds,
            aggregation_threshold: self.config.secure_aggregation.min_clients,
            dropout_tolerance: self.config.secure_aggregation.max_dropouts,
        })
    }

    /// Compute privacy allocations for each client
    fn compute_client_privacy_allocations(
        &self,
        selectedclients: &[String],
        amplificationfactor: f64,
    ) -> Result<HashMap<String, ClientPrivacyAllocation>> {
        let mut allocations = HashMap::new();

        let base_epsilon = self.config.base_config.target_epsilon / amplificationfactor;
        let base_delta = self.config.base_config.target_delta;

        for clientid in selectedclients {
            allocations.insert(
                clientid.clone(),
                ClientPrivacyAllocation {
                    epsilon: base_epsilon,
                    delta: base_delta,
                    noise_multiplier: self.config.base_config.noise_multiplier,
                    clipping_threshold: self.config.base_config.l2_norm_clip,
                    amplificationfactor,
                },
            );
        }

        Ok(allocations)
    }

    /// Analyze privacy for the current round
    fn analyze_round_privacy(
        &self,
        selectedclients: &[String],
        amplificationfactor: f64,
    ) -> Result<RoundPrivacyAnalysis> {
        let round_epsilon = self.config.base_config.target_epsilon / amplificationfactor;
        let round_delta = self.config.base_config.target_delta;

        let cumulative_epsilon = round_epsilon * self.current_round as f64;
        let cumulative_delta = round_delta * self.current_round as f64;

        Ok(RoundPrivacyAnalysis {
            round_epsilon,
            round_delta,
            cumulative_epsilon,
            cumulative_delta,
            amplification_benefit: amplificationfactor - 1.0,
            composition_tightness: 0.95, // Placeholder
        })
    }

    /// Record participation for this round
    fn record_participation_round(
        &mut self,
        selectedclients: &[String],
        sampling_probability: f64,
        amplificationfactor: f64,
    ) {
        let participation = ParticipationRound {
            round: self.current_round,
            participating_clients: selectedclients.to_vec(),
            sampling_probability,
            privacy_cost: PrivacyCost {
                epsilon: self.config.base_config.target_epsilon / amplificationfactor,
                delta: self.config.base_config.target_delta,
                client_contribution: 1.0 / selectedclients.len() as f64,
                amplificationfactor,
                composition_cost: 0.1, // Placeholder
            },
            aggregation_noise: self.config.base_config.noise_multiplier,
        };

        self.participation_history.push_back(participation);

        // Keep only last 1000 rounds
        if self.participation_history.len() > 1000 {
            self.participation_history.pop_front();
        }
    }

    /// Get current privacy guarantees
    pub fn get_privacy_guarantees(&self) -> PrivacyBudget {
        use super::super::AccountingMethod;
        // Placeholder implementation
        PrivacyBudget {
            epsilon_consumed: 0.1 * self.current_round as f64,
            delta_consumed: 1e-5 * self.current_round as f64,
            epsilon_remaining: self.config.base_config.target_epsilon
                - (0.1 * self.current_round as f64),
            delta_remaining: self.config.base_config.target_delta
                - (1e-5 * self.current_round as f64),
            steps_taken: self.current_round,
            accounting_method: AccountingMethod::MomentsAccountant,
            estimated_steps_remaining: 100,
        }
    }

    /// Get current round number
    pub fn current_round(&self) -> usize {
        self.current_round
    }

    /// Get configuration
    pub fn config(&self) -> &FederatedPrivacyConfig {
        &self.config
    }
}

// Placeholder implementations for missing methods in components
impl<T: Float + Debug + Send + Sync + 'static> ByzantineRobustAggregator<T> {
    pub fn compute_robustness_factor(&self) -> Result<f64> {
        Ok(0.9) // Placeholder
    }

    pub fn get_client_reputations(&self, _clients: &[String]) -> HashMap<String, f64> {
        HashMap::new() // Placeholder
    }

    pub fn detect_byzantine_clients(
        &self,
        _updates: &HashMap<String, Array1<T>>,
        _round: usize,
    ) -> Result<Vec<OutlierDetectionResult>> {
        Ok(Vec::new()) // Placeholder
    }

    pub fn robust_aggregate(
        &self,
        updates: &HashMap<String, Array1<T>>,
        _allocations: &HashMap<String, AdaptivePrivacyAllocation>,
    ) -> Result<Array1<T>> {
        // Simple average as placeholder
        if updates.is_empty() {
            return Err(OptimError::InvalidParameter(
                "No updates to aggregate".to_string(),
            ));
        }

        let mut result = updates.values().next().unwrap().clone();
        let mut count = 1;

        for update in updates.values().skip(1) {
            for (i, &value) in update.iter().enumerate() {
                if i < result.len() {
                    result[i] = result[i] + value;
                }
            }
            count += 1;
        }

        for value in result.iter_mut() {
            *value = *value / T::from(count).unwrap_or_else(|| T::zero());
        }

        Ok(result)
    }
}

impl<T: Float + Debug + Send + Sync + 'static> AdaptiveBudgetManager<T> {
    pub fn compute_adaptive_allocations(
        &self,
        _updates: &HashMap<String, Array1<T>>,
        _plan: &FederatedRoundPlan,
    ) -> Result<HashMap<String, AdaptivePrivacyAllocation>> {
        Ok(HashMap::new()) // Placeholder
    }
}

impl<T: Float + Debug + Send + Sync + 'static> PersonalizationManager<T> {
    pub fn personalize_client_updates(
        &self,
        updates: &HashMap<String, Array1<T>>,
        _plan: &FederatedRoundPlan,
    ) -> Result<HashMap<String, Array1<T>>> {
        Ok(updates.clone()) // Placeholder - just return original updates
    }

    pub fn get_metrics(&self) -> PersonalizationMetrics {
        PersonalizationMetrics {
            cluster_assignments: HashMap::new(),
            adaptation_effectiveness: 0.8,
            model_diversity: 0.6,
            personalization_overhead: 0.1,
        }
    }

    pub fn compute_privacy_cost(&self) -> Result<f64> {
        Ok(0.1) // Placeholder
    }

    pub fn update_global_model(&self, aggregate: &Array1<T>) -> Result<Array1<T>> {
        Ok(aggregate.clone()) // Placeholder
    }

    pub fn cluster_clients(
        &self,
        _updates: &HashMap<String, Array1<T>>,
    ) -> Result<HashMap<usize, Vec<String>>> {
        Ok(HashMap::new()) // Placeholder
    }

    pub fn generate_personalized_models(
        &self,
        _clusters: &HashMap<usize, Vec<String>>,
        _gradients: &HashMap<String, Array1<T>>,
    ) -> Result<HashMap<String, PersonalizedModel<T>>> {
        Ok(HashMap::new()) // Placeholder
    }

    pub fn compute_effectiveness_metrics(
        &self,
        _models: &HashMap<String, PersonalizedModel<T>>,
    ) -> Result<PersonalizationMetrics> {
        Ok(PersonalizationMetrics {
            cluster_assignments: HashMap::new(),
            adaptation_effectiveness: 0.8,
            model_diversity: 0.6,
            personalization_overhead: 0.1,
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static> CommunicationOptimizer<T> {
    pub fn compress_and_schedule(
        &self,
        updates: &HashMap<String, Array1<T>>,
        _plan: &FederatedRoundPlan,
    ) -> Result<HashMap<String, Array1<T>>> {
        Ok(updates.clone()) // Placeholder
    }

    pub fn get_efficiency_stats(&self) -> CommunicationEfficiencyStats {
        CommunicationEfficiencyStats {
            compression_ratio: 0.5,
            bandwidth_utilization: 0.8,
            transmission_latency: 100.0,
            quality_of_service_score: 0.9,
        }
    }

    pub fn compute_efficiency_scores(&self, _clients: &[String]) -> Result<HashMap<String, f64>> {
        Ok(HashMap::new()) // Placeholder
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ContinualLearningCoordinator<T> {
    pub fn adapt_to_new_task(
        &mut self,
        _updates: &HashMap<String, Array1<T>>,
        _round: usize,
    ) -> Result<()> {
        Ok(()) // Placeholder
    }

    pub fn get_status(&self) -> ContinualLearningStatus {
        ContinualLearningStatus {
            task_changes_detected: 0,
            forgetting_prevention_active: false,
            memory_utilization: 0.5,
            knowledge_transfer_score: 0.7,
        }
    }

    pub fn compute_privacy_overhead(&self) -> Result<f64> {
        Ok(0.05) // Placeholder
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default> FederatedMetaLearner<T> {
    pub fn compute_meta_gradients(
        &self,
        _aggregates: &HashMap<usize, Array1<T>>,
    ) -> Result<Array1<T>> {
        Ok(Array1::default(0)) // Placeholder
    }
}

impl<T: Float + Debug + Send + Sync + 'static> SecureAggregator<T> {
    pub fn prepare_round(&self, _clients: &[String]) -> Result<SecureAggregationPlan> {
        Ok(SecureAggregationPlan {
            masking_seeds: HashMap::new(),
            aggregation_threshold: 10,
            dropout_tolerance: 2,
        })
    }

    pub fn aggregate_with_masks(
        &self,
        updates: &HashMap<String, Array1<T>>,
        _plan: &SecureAggregationPlan,
    ) -> Result<Array1<T>> {
        // Placeholder - just do simple aggregation
        if updates.is_empty() {
            return Err(OptimError::InvalidParameter(
                "No updates to aggregate".to_string(),
            ));
        }

        let mut result = updates.values().next().unwrap().clone();
        let mut count = 1;

        for update in updates.values().skip(1) {
            for (i, &value) in update.iter().enumerate() {
                if i < result.len() {
                    result[i] = result[i] + value;
                }
            }
            count += 1;
        }

        for value in result.iter_mut() {
            *value = *value / T::from(count).unwrap_or_else(|| T::zero());
        }

        Ok(result)
    }
}
