//! STRICT TDD - Federation Manager Tests
//! These tests define the complete federated learning system behavior before implementation

use chrono::Utc;
use std::collections::HashMap;
use uuid::Uuid;

use rtx_platform::federation::{
    AggregationStrategy, FederatedTrainingJob, FederationManager, ModelParameters,
    ModelSharingRequest, ModelUpdate, ParticipantInfo, PrivacyBudget, PrivacyConfig,
};
use rtx_platform::{PlatformConfig, PlatformResult};

fn create_test_privacy_config() -> PrivacyConfig {
    PrivacyConfig {
        epsilon: 1.0, // Differential privacy parameter
        delta: 1e-5,  // Differential privacy parameter
        noise_multiplier: 1.1,
        max_grad_norm: 4.0,
        secure_aggregation: true,
        homomorphic_encryption: true,
        minimum_participants: 3,
        consent_required: true,
    }
}

fn create_test_platform_config() -> PlatformConfig {
    PlatformConfig {
        database_url: "postgresql://test:test@localhost/federation_test".to_string(),
        redis_urls: vec!["redis://localhost:6379".to_string()],
        kafka_brokers: vec!["localhost:9092".to_string()],
        metrics_endpoint: "localhost:9090".to_string(),
        regions: HashMap::new(),
        sla_targets: rtx_platform::slo::SlaTargets::default(),
    }
}

#[tokio::test]
async fn test_federation_manager_creation() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let federation_manager = FederationManager::new(&config, privacy_config).await?;

    assert!(federation_manager.is_healthy().await?);
    Ok(())
}

#[tokio::test]
async fn test_participant_registration() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let mut federation_manager = FederationManager::new(&config, privacy_config).await?;
    federation_manager.start().await?;

    let participant_info = ParticipantInfo {
        id: Uuid::new_v4(),
        tenant_id: Uuid::new_v4(),
        name: "test-participant".to_string(),
        capabilities: vec!["torch".to_string(), "tensorflow".to_string()],
        data_size: 10000,
        compute_power: 100.0,
        bandwidth: 1000.0,
        privacy_level: "high".to_string(),
    };

    federation_manager
        .register_participant(participant_info.clone())
        .await?;

    let participants = federation_manager.get_participants().await?;
    assert_eq!(participants.len(), 1);
    assert_eq!(participants[0].id, participant_info.id);

    federation_manager.shutdown().await?;
    Ok(())
}

#[tokio::test]
async fn test_federated_model_creation() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let mut federation_manager = FederationManager::new(&config, privacy_config).await?;
    federation_manager.start().await?;

    let model_params = ModelParameters {
        model_type: "neural_network".to_string(),
        architecture: "resnet50".to_string(),
        parameters: HashMap::from([
            ("layers".to_string(), "50".to_string()),
            ("input_size".to_string(), "224".to_string()),
        ]),
        weights: vec![0.1, 0.2, 0.3, 0.4, 0.5], // Simplified weights
    };

    let federated_model = federation_manager
        .create_federated_model("image_classification", model_params)
        .await?;

    assert_eq!(federated_model.name, "image_classification");
    assert_eq!(federated_model.parameters.model_type, "neural_network");

    federation_manager.shutdown().await?;
    Ok(())
}

#[tokio::test]
async fn test_secure_aggregation_with_encryption() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let federation_manager = FederationManager::new(&config, privacy_config).await?;
    let secure_aggregator = federation_manager.secure_aggregator();

    // Create model updates from multiple participants
    let updates = vec![
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![1.0, 2.0, 3.0],
            gradient_norm: 2.5,
            timestamp: Utc::now(),
            encrypted: true,
        },
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![2.0, 3.0, 4.0],
            gradient_norm: 3.2,
            timestamp: Utc::now(),
            encrypted: true,
        },
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![1.5, 2.5, 3.5],
            gradient_norm: 2.8,
            timestamp: Utc::now(),
            encrypted: true,
        },
    ];

    let aggregated_update = secure_aggregator
        .aggregate_updates(&updates, AggregationStrategy::FederatedAverage)
        .await?;

    // Verify secure aggregation (average of encrypted updates)
    assert_eq!(aggregated_update.parameters.len(), 3);
    assert!((aggregated_update.parameters[0] - 1.5).abs() < 0.1); // (1.0+2.0+1.5)/3 = 1.5
    assert!((aggregated_update.parameters[1] - 2.5).abs() < 0.1); // (2.0+3.0+2.5)/3 = 2.5
    assert!((aggregated_update.parameters[2] - 3.5).abs() < 0.1); // (3.0+4.0+3.5)/3 = 3.5

    Ok(())
}

#[tokio::test]
async fn test_differential_privacy_noise_injection() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let federation_manager = FederationManager::new(&config, privacy_config).await?;
    let dp_engine = federation_manager.differential_privacy_engine();

    let original_update = ModelUpdate {
        participant_id: Uuid::new_v4(),
        model_id: Uuid::new_v4(),
        round: 1,
        parameters: vec![1.0, 2.0, 3.0, 4.0, 5.0],
        gradient_norm: 3.0,
        timestamp: Utc::now(),
        encrypted: false,
    };

    let privacy_budget = PrivacyBudget {
        epsilon_consumed: 0.5,
        epsilon_remaining: 0.5,
        delta_consumed: 5e-6,
        delta_remaining: 5e-6,
    };

    let private_update = dp_engine
        .add_noise(original_update.clone(), privacy_budget)
        .await?;

    // Verify noise was added (parameters should be different but similar)
    assert_eq!(
        private_update.parameters.len(),
        original_update.parameters.len()
    );

    let mut differences = 0;
    for (original, private) in original_update
        .parameters
        .iter()
        .zip(private_update.parameters.iter())
    {
        if (original - private).abs() > 0.001 {
            differences += 1;
        }
    }

    // Most parameters should have noise added
    assert!(differences >= 3);

    Ok(())
}

#[tokio::test]
async fn test_homomorphic_encryption_operations() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let federation_manager = FederationManager::new(&config, privacy_config).await?;
    let he_engine = federation_manager.homomorphic_encryption();

    // Generate encryption keys
    let keypair = he_engine.generate_keypair().await?;

    // Encrypt some model parameters
    let plaintext_params = vec![1.5, 2.7, 3.9];
    let encrypted_params = he_engine
        .encrypt(&plaintext_params, &keypair.public_key)
        .await?;

    // Perform homomorphic addition (simulating secure aggregation)
    let another_encrypted = he_engine
        .encrypt(&vec![0.5, 0.3, 0.1], &keypair.public_key)
        .await?;
    let sum_encrypted = he_engine
        .add_encrypted(&encrypted_params, &another_encrypted)
        .await?;

    // Decrypt the result
    let decrypted_sum = he_engine
        .decrypt(&sum_encrypted, &keypair.private_key)
        .await?;

    // Verify homomorphic addition worked correctly
    assert_eq!(decrypted_sum.len(), 3);
    assert!((decrypted_sum[0] - 2.0).abs() < 0.1); // 1.5 + 0.5 = 2.0
    assert!((decrypted_sum[1] - 3.0).abs() < 0.1); // 2.7 + 0.3 = 3.0
    assert!((decrypted_sum[2] - 4.0).abs() < 0.1); // 3.9 + 0.1 = 4.0

    Ok(())
}

#[tokio::test]
async fn test_consent_management_system() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let mut federation_manager = FederationManager::new(&config, privacy_config).await?;
    federation_manager.start().await?;

    let consent_manager = federation_manager.consent_manager();
    let tenant_id = Uuid::new_v4();
    let model_id = Uuid::new_v4();

    // Request consent for model sharing
    let sharing_request = ModelSharingRequest {
        id: Uuid::new_v4(),
        requester_tenant_id: tenant_id,
        target_model_id: model_id,
        purpose: "collaborative_training".to_string(),
        data_usage: "aggregation_only".to_string(),
        duration_days: 30,
        privacy_guarantees: vec![
            "differential_privacy".to_string(),
            "secure_aggregation".to_string(),
        ],
    };

    consent_manager
        .request_consent(sharing_request.clone())
        .await?;

    // Verify consent request was stored
    let pending_requests = consent_manager.get_pending_requests(tenant_id).await?;
    assert_eq!(pending_requests.len(), 1);
    assert_eq!(pending_requests[0].id, sharing_request.id);

    // Grant consent
    consent_manager
        .grant_consent(sharing_request.id, tenant_id)
        .await?;

    // Verify consent was granted
    let has_consent = consent_manager.has_consent(tenant_id, model_id).await?;
    assert!(has_consent);

    federation_manager.shutdown().await?;
    Ok(())
}

#[tokio::test]
async fn test_federated_training_job_lifecycle() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let mut federation_manager = FederationManager::new(&config, privacy_config).await?;
    federation_manager.start().await?;

    // Register participants
    let participants = vec![
        ParticipantInfo {
            id: Uuid::new_v4(),
            tenant_id: Uuid::new_v4(),
            name: "participant-1".to_string(),
            capabilities: vec!["torch".to_string()],
            data_size: 5000,
            compute_power: 50.0,
            bandwidth: 500.0,
            privacy_level: "high".to_string(),
        },
        ParticipantInfo {
            id: Uuid::new_v4(),
            tenant_id: Uuid::new_v4(),
            name: "participant-2".to_string(),
            capabilities: vec!["torch".to_string()],
            data_size: 7000,
            compute_power: 70.0,
            bandwidth: 700.0,
            privacy_level: "high".to_string(),
        },
        ParticipantInfo {
            id: Uuid::new_v4(),
            tenant_id: Uuid::new_v4(),
            name: "participant-3".to_string(),
            capabilities: vec!["torch".to_string()],
            data_size: 3000,
            compute_power: 30.0,
            bandwidth: 300.0,
            privacy_level: "high".to_string(),
        },
    ];

    for participant in &participants {
        federation_manager
            .register_participant(participant.clone())
            .await?;
    }

    // Create federated training job
    let job = FederatedTrainingJob {
        id: Uuid::new_v4(),
        name: "image_classification_job".to_string(),
        model_id: Uuid::new_v4(),
        participants: participants.iter().map(|p| p.id).collect(),
        target_rounds: 5,
        current_round: 0,
        convergence_threshold: 0.001,
        privacy_budget: PrivacyBudget {
            epsilon_consumed: 0.0,
            epsilon_remaining: 1.0,
            delta_consumed: 0.0,
            delta_remaining: 1e-5,
        },
        status: rtx_platform::federation::TrainingStatus::Initializing,
        created_at: Utc::now(),
        updated_at: Utc::now(),
    };

    let started_job = federation_manager.start_training_job(job).await?;

    assert_eq!(
        started_job.status,
        rtx_platform::federation::TrainingStatus::Running
    );
    assert_eq!(started_job.participants.len(), 3);

    // Simulate training round completion
    federation_manager
        .advance_training_round(started_job.id)
        .await?;

    let updated_job = federation_manager.get_training_job(started_job.id).await?;
    assert_eq!(updated_job.current_round, 1);

    federation_manager.shutdown().await?;
    Ok(())
}

#[tokio::test]
async fn test_privacy_budget_tracking() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let mut privacy_config = create_test_privacy_config();
    privacy_config.epsilon = 2.0; // Higher budget for testing

    let federation_manager = FederationManager::new(&config, privacy_config).await?;
    let dp_engine = federation_manager.differential_privacy_engine();

    let tenant_id = Uuid::new_v4();

    // Initial privacy budget should be full
    let initial_budget = dp_engine.get_privacy_budget(tenant_id).await?;
    assert_eq!(initial_budget.epsilon_remaining, 2.0);
    assert_eq!(initial_budget.epsilon_consumed, 0.0);

    // Consume some privacy budget
    let update = ModelUpdate {
        participant_id: Uuid::new_v4(),
        model_id: Uuid::new_v4(),
        round: 1,
        parameters: vec![1.0, 2.0, 3.0],
        gradient_norm: 2.0,
        timestamp: Utc::now(),
        encrypted: false,
    };

    let consumed_budget = PrivacyBudget {
        epsilon_consumed: 0.5,
        epsilon_remaining: 1.5,
        delta_consumed: 2e-6,
        delta_remaining: 8e-6,
    };

    dp_engine.add_noise(update, consumed_budget.clone()).await?;

    // Update privacy budget tracking
    dp_engine
        .update_privacy_budget(tenant_id, consumed_budget.clone())
        .await?;

    // Check updated budget
    let updated_budget = dp_engine.get_privacy_budget(tenant_id).await?;
    assert_eq!(updated_budget.epsilon_consumed, 0.5);
    assert_eq!(updated_budget.epsilon_remaining, 1.5);

    Ok(())
}

#[tokio::test]
async fn test_cross_tenant_model_sharing() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let mut federation_manager = FederationManager::new(&config, privacy_config).await?;
    federation_manager.start().await?;

    let tenant_a = Uuid::new_v4();
    let tenant_b = Uuid::new_v4();

    // Create a model for tenant A
    let model_params = ModelParameters {
        model_type: "neural_network".to_string(),
        architecture: "transformer".to_string(),
        parameters: HashMap::from([
            ("layers".to_string(), "12".to_string()),
            ("hidden_size".to_string(), "768".to_string()),
        ]),
        weights: vec![0.1, 0.2, 0.3],
    };

    let model = federation_manager
        .create_federated_model("nlp_model", model_params)
        .await?;

    // Request sharing consent between tenants
    let sharing_request = ModelSharingRequest {
        id: Uuid::new_v4(),
        requester_tenant_id: tenant_b,
        target_model_id: model.id,
        purpose: "transfer_learning".to_string(),
        data_usage: "parameter_sharing".to_string(),
        duration_days: 14,
        privacy_guarantees: vec![
            "differential_privacy".to_string(),
            "encrypted_transfer".to_string(),
        ],
    };

    federation_manager
        .request_model_sharing(sharing_request.clone())
        .await?;

    // Grant consent (simulating tenant A approval)
    federation_manager
        .approve_model_sharing(sharing_request.id, tenant_a)
        .await?;

    // Verify tenant B can now access the model
    let shared_model = federation_manager
        .get_shared_model(model.id, tenant_b)
        .await?;

    assert_eq!(shared_model.id, model.id);
    assert_eq!(shared_model.name, "nlp_model");

    federation_manager.shutdown().await?;
    Ok(())
}

#[tokio::test]
async fn test_byzantine_fault_tolerance() -> PlatformResult<()> {
    let config = create_test_platform_config();
    let privacy_config = create_test_privacy_config();

    let federation_manager = FederationManager::new(&config, privacy_config).await?;
    let secure_aggregator = federation_manager.secure_aggregator();

    // Create mix of honest and malicious updates
    let updates = vec![
        // Honest updates
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![1.0, 2.0, 3.0],
            gradient_norm: 2.5,
            timestamp: Utc::now(),
            encrypted: true,
        },
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![1.1, 2.1, 3.1],
            gradient_norm: 2.6,
            timestamp: Utc::now(),
            encrypted: true,
        },
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![0.9, 1.9, 2.9],
            gradient_norm: 2.4,
            timestamp: Utc::now(),
            encrypted: true,
        },
        // Malicious update (extreme values)
        ModelUpdate {
            participant_id: Uuid::new_v4(),
            model_id: Uuid::new_v4(),
            round: 1,
            parameters: vec![100.0, 200.0, 300.0], // Outlier values
            gradient_norm: 50.0,
            timestamp: Utc::now(),
            encrypted: true,
        },
    ];

    let aggregated_update = secure_aggregator
        .aggregate_updates(&updates, AggregationStrategy::ByzantineRobust)
        .await?;

    // Verify Byzantine-robust aggregation filtered out outliers
    assert_eq!(aggregated_update.parameters.len(), 3);
    // Should be close to average of honest updates (~1.0, ~2.0, ~3.0)
    assert!(aggregated_update.parameters[0] < 10.0); // Much less than outlier
    assert!(aggregated_update.parameters[1] < 20.0);
    assert!(aggregated_update.parameters[2] < 30.0);

    Ok(())
}