#![allow(
    clippy::unwrap_used,
    clippy::expect_used,
    clippy::indexing_slicing,
    clippy::manual_range_contains
)]
//! TDD Tests for Ensemble Training Coordination
//!
//! These tests define the behavior we expect from the ensemble training system
//! BEFORE implementing the actual functionality.
//!
//! Test Coverage:
//! 1. Ensemble training configuration
//! 2. Multi-model coordination during training
//! 3. Ensemble weight optimization
//! 4. Checkpoint synchronization (all 4 models)
//! 5. Integration with ML Training Service

use std::collections::HashMap;

use chrono::Utc;
use ml::safety::{GradientSafetyConfig, MLSafetyConfig};
use ml::training_pipeline::{
    FinancialValidationConfig, ModelArchitectureConfig, PerformanceConfig,
    ProductionTrainingConfig, TrainingHyperparameters,
};
use ml_training_service::ensemble_training_coordinator::{
    EnsembleTrainingConfig, EnsembleTrainingCoordinator, ModelTrainingStatus,
};
use uuid::Uuid;

/// Test 1: Ensemble training configuration validation
#[tokio::test]
async fn test_ensemble_training_config_validation() {
    // Test 1.1: Valid configuration should be accepted
    let config = create_valid_ensemble_config();
    assert!(
        config.is_valid(),
        "Valid ensemble config should pass validation"
    );

    // Test 1.2: Must have all 4 models (DQN, PPO, MAMBA-2, TFT)
    let mut models = config.model_configs.keys().cloned().collect::<Vec<_>>();
    models.sort();
    assert_eq!(
        models,
        vec!["DQN", "MAMBA2", "PPO", "TFT"],
        "Must configure all 4 models"
    );

    // Test 1.3: Weights must sum to 1.0
    let weight_sum: f64 = config.model_weights.values().sum();
    assert!(
        (weight_sum - 1.0).abs() < 1e-6,
        "Model weights must sum to 1.0, got {}",
        weight_sum
    );

    // Test 1.4: Each model must have matching training and weight configuration
    for model_name in &["DQN", "PPO", "MAMBA2", "TFT"] {
        let model_key = model_name.to_string();
        assert!(
            config.model_configs.contains_key(&model_key),
            "Missing config for {}",
            model_name
        );
        assert!(
            config.model_weights.contains_key(&model_key),
            "Missing weight for {}",
            model_name
        );
    }
}

/// Test 2: Multi-model coordination during training
#[tokio::test]
async fn test_multi_model_training_coordination() {
    let config = create_valid_ensemble_config();
    let mut coordinator = create_ensemble_coordinator(config).await;

    // Test 2.1: All models should start in Pending state
    for model_name in &["DQN", "PPO", "MAMBA2", "TFT"] {
        let status = coordinator.get_model_status(model_name).await.unwrap();
        assert_eq!(
            status,
            ModelTrainingStatus::Pending,
            "{} should start Pending",
            model_name
        );
    }

    // Test 2.2: Can start training for all models
    let job_id = coordinator.start_ensemble_training().await.unwrap();
    assert_ne!(job_id, Uuid::nil(), "Should return valid job ID");

    // Test 2.3: At least one model should be training after start
    tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
    let mut any_training = false;
    for model in &["DQN", "PPO", "MAMBA2", "TFT"] {
        if matches!(
            coordinator.get_model_status(model).await,
            Ok(ModelTrainingStatus::Training)
        ) {
            any_training = true;
            break;
        }
    }
    assert!(
        any_training,
        "At least one model should be training after start"
    );
}

/// Test 3: Ensemble weight optimization
#[tokio::test]
async fn test_ensemble_weight_optimization() {
    let mut config = create_valid_ensemble_config();
    config.enable_weight_optimization = true;
    config.weight_optimization_interval_epochs = 5;

    let coordinator = create_ensemble_coordinator(config).await;

    // Test 3.1: Initial weights should match configuration
    let initial_weights = coordinator.get_current_weights().await.unwrap();
    assert_eq!(initial_weights.len(), 4, "Should have 4 model weights");

    // Test 3.2: Simulate training progress and weight updates
    coordinator.simulate_training_epochs(10).await.unwrap();

    // Test 3.3: Weights should be updated after optimization interval
    let updated_weights = coordinator.get_current_weights().await.unwrap();
    assert_ne!(
        initial_weights, updated_weights,
        "Weights should be updated after optimization"
    );

    // Test 3.4: Updated weights should still sum to 1.0
    let weight_sum: f64 = updated_weights.values().sum();
    assert!(
        (weight_sum - 1.0).abs() < 1e-6,
        "Optimized weights must sum to 1.0, got {}",
        weight_sum
    );

    // Test 3.5: Better-performing models should get higher weights
    // (This test assumes DQN performs better in simulation)
    let dqn_initial = initial_weights.get("DQN").expect("INVARIANT: Key should exist in map");
    let dqn_updated = updated_weights.get("DQN").expect("INVARIANT: Key should exist in map");
    // Weight adjustment logic will determine if this increases or decreases
    assert_ne!(
        dqn_initial, dqn_updated,
        "DQN weight should be adjusted based on performance"
    );
}

/// Test 4: Checkpoint synchronization for all models
#[tokio::test]
async fn test_checkpoint_synchronization() {
    let config = create_valid_ensemble_config();
    let mut coordinator = create_ensemble_coordinator(config).await;

    // Test 4.1: Start training and wait for first checkpoint
    let _job_id = coordinator.start_ensemble_training().await.unwrap();
    coordinator.simulate_training_epochs(1).await.unwrap();

    // Test 4.2: All models should have checkpoint paths after first epoch
    for model_name in &["DQN", "PPO", "MAMBA2", "TFT"] {
        let checkpoint = coordinator.get_latest_checkpoint(model_name).await.unwrap();
        assert!(
            checkpoint.is_some(),
            "{} should have checkpoint after epoch 1",
            model_name
        );

        let checkpoint_path = checkpoint.unwrap();
        assert!(
            checkpoint_path.contains(model_name),
            "Checkpoint path should contain model name"
        );
        assert!(
            checkpoint_path.contains("epoch_1"),
            "Checkpoint should be from epoch 1"
        );
    }

    // Test 4.3: Checkpoints should be synchronized (all from same epoch)
    let checkpoints = coordinator.get_all_checkpoints().await.unwrap();
    let epochs: Vec<_> = checkpoints
        .iter()
        .map(|(_, cp)| {
            cp.split("epoch_")
                .last()
                .unwrap()
                .split('_')
                .next()
                .unwrap()
                .parse::<u32>()
                .unwrap()
        })
        .collect();

    let first_epoch = epochs[0];
    assert!(
        epochs.iter().all(|&e| e == first_epoch),
        "All checkpoints should be from same epoch"
    );

    // Test 4.4: Can load synchronized ensemble from checkpoints
    let ensemble_restored = coordinator.load_synchronized_ensemble(first_epoch).await;
    assert!(
        ensemble_restored.is_ok(),
        "Should be able to load synchronized ensemble"
    );
}

/// Test 5: Performance-based weight adjustment
#[tokio::test]
async fn test_performance_based_weight_adjustment() {
    let mut config = create_valid_ensemble_config();
    config.enable_weight_optimization = true;

    let coordinator = create_ensemble_coordinator(config).await;

    // Test 5.1: Set different performance metrics for each model
    coordinator
        .set_model_performance("DQN", 0.85, 0.15)
        .await
        .unwrap(); // High accuracy, low loss
    coordinator
        .set_model_performance("PPO", 0.75, 0.25)
        .await
        .unwrap(); // Medium
    coordinator
        .set_model_performance("MAMBA2", 0.65, 0.35)
        .await
        .unwrap(); // Lower
    coordinator
        .set_model_performance("TFT", 0.90, 0.10)
        .await
        .unwrap(); // Highest

    // Test 5.2: Trigger weight optimization
    coordinator.optimize_weights().await.unwrap();

    // Test 5.3: TFT should have highest weight (best performance)
    let weights = coordinator.get_current_weights().await.unwrap();
    let tft_weight = weights.get("TFT").expect("INVARIANT: Key should exist in map");

    for (model, weight) in weights.iter() {
        if model != "TFT" {
            assert!(
                tft_weight >= weight,
                "TFT (best performer) should have highest or equal weight"
            );
        }
    }

    // Test 5.4: MAMBA2 should have lowest weight (worst performance)
    let mamba2_weight = weights.get("MAMBA2").expect("INVARIANT: Key should exist in map");
    for (model, weight) in weights.iter() {
        if model != "MAMBA2" {
            assert!(
                mamba2_weight <= weight,
                "MAMBA2 (worst performer) should have lowest or equal weight"
            );
        }
    }
}

/// Test 6: Training failure recovery
#[tokio::test]
async fn test_training_failure_recovery() {
    let config = create_valid_ensemble_config();
    let mut coordinator = create_ensemble_coordinator(config).await;

    // Test 6.1: Start training
    let _job_id = coordinator.start_ensemble_training().await.unwrap();

    // Test 6.2: Simulate one model failing
    coordinator.simulate_model_failure("PPO").await.unwrap();

    // Test 6.3: PPO should be in Failed state
    let ppo_status = coordinator.get_model_status("PPO").await.unwrap();
    assert_eq!(
        ppo_status,
        ModelTrainingStatus::Failed,
        "PPO should be in Failed state"
    );

    // Test 6.4: Other models should continue training
    for model_name in &["DQN", "MAMBA2", "TFT"] {
        let status = coordinator.get_model_status(model_name).await.unwrap();
        assert_ne!(
            status,
            ModelTrainingStatus::Failed,
            "{} should not fail due to PPO failure",
            model_name
        );
    }

    // Test 6.5: Can retry failed model
    let retry_result = coordinator.retry_failed_model("PPO").await;
    assert!(retry_result.is_ok(), "Should be able to retry failed model");

    // Test 6.6: PPO should return to training after retry
    tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
    let ppo_status_after_retry = coordinator.get_model_status("PPO").await.unwrap();
    assert_ne!(
        ppo_status_after_retry,
        ModelTrainingStatus::Failed,
        "PPO should not be Failed after retry"
    );
}

/// Test 7: Ensemble validation metrics
#[tokio::test]
async fn test_ensemble_validation_metrics() {
    let config = create_valid_ensemble_config();
    let mut coordinator = create_ensemble_coordinator(config).await;

    // Test 7.1: Start training
    coordinator.start_ensemble_training().await.unwrap();
    coordinator.simulate_training_epochs(5).await.unwrap();

    // Test 7.2: Should have ensemble-level metrics
    let metrics = coordinator.get_ensemble_metrics().await.unwrap();
    assert!(
        metrics.contains_key("ensemble_train_loss"),
        "Should have ensemble train loss"
    );
    assert!(
        metrics.contains_key("ensemble_val_loss"),
        "Should have ensemble val loss"
    );
    assert!(
        metrics.contains_key("ensemble_accuracy"),
        "Should have ensemble accuracy"
    );

    // Test 7.3: Ensemble metrics should be aggregated from all models
    let ensemble_loss = metrics.get("ensemble_train_loss").expect("INVARIANT: Key should exist in map");
    assert!(ensemble_loss > &0.0, "Ensemble loss should be positive");

    // Test 7.4: Should track diversity metrics
    assert!(
        metrics.contains_key("prediction_diversity"),
        "Should track prediction diversity"
    );
    let diversity = metrics.get("prediction_diversity").expect("INVARIANT: Key should exist in map");
    assert!(
        diversity >= &0.0 && diversity <= &1.0,
        "Diversity should be in [0, 1]"
    );
}

/// Test 8: Integration with ML Training Service
#[tokio::test]
async fn test_integration_with_ml_training_service() {
    // This test verifies the coordinator integrates with existing ML training infrastructure

    let config = create_valid_ensemble_config();
    let mut coordinator = create_ensemble_coordinator(config).await;

    // Test 8.1: Should use existing ProductionTrainingConfig
    for model_name in &["DQN", "PPO", "MAMBA2", "TFT"] {
        let model_config = coordinator
            .get_model_training_config(model_name)
            .await
            .unwrap();
        assert!(
            model_config.model_config.input_dim > 0,
            "Should have valid input dimension"
        );
        assert!(
            !model_config.model_config.hidden_dims.is_empty(),
            "Should have hidden layers"
        );
    }

    // Test 8.2: Should respect existing safety configurations
    let dqn_config = coordinator.get_model_training_config("DQN").await.unwrap();
    assert!(
        dqn_config.safety_config.safety_enabled,
        "Should have safety enabled"
    );
    assert!(
        dqn_config.gradient_config.max_gradient_norm > 0.0,
        "Should have gradient clipping"
    );

    // Test 8.3: Should integrate with checkpoint manager
    coordinator.start_ensemble_training().await.unwrap();
    coordinator.simulate_training_epochs(1).await.unwrap();

    let checkpoints = coordinator.get_all_checkpoints().await.unwrap();
    assert_eq!(
        checkpoints.len(),
        4,
        "Should have checkpoints for all 4 models"
    );
}

// Helper functions for tests

/// Create valid ensemble configuration
fn create_valid_ensemble_config() -> EnsembleTrainingConfig {
    let mut model_configs = HashMap::new();
    let mut model_weights = HashMap::new();

    // DQN configuration (33% weight)
    model_configs.insert(
        "DQN".to_string(),
        create_model_config("DQN", 64, vec![256, 128], 32),
    );
    model_weights.insert("DQN".to_string(), 0.33);

    // PPO configuration (33% weight)
    model_configs.insert(
        "PPO".to_string(),
        create_model_config("PPO", 64, vec![256, 128], 32),
    );
    model_weights.insert("PPO".to_string(), 0.33);

    // MAMBA-2 configuration (17% weight)
    model_configs.insert(
        "MAMBA2".to_string(),
        create_model_config("MAMBA2", 64, vec![512, 256], 32),
    );
    model_weights.insert("MAMBA2".to_string(), 0.17);

    // TFT configuration (17% weight)
    model_configs.insert(
        "TFT".to_string(),
        create_model_config("TFT", 64, vec![512, 256, 128], 32),
    );
    model_weights.insert("TFT".to_string(), 0.17);

    EnsembleTrainingConfig {
        job_id: Uuid::new_v4(),
        model_configs,
        model_weights,
        enable_weight_optimization: true,
        weight_optimization_interval_epochs: 10,
        checkpoint_interval_epochs: 1,
        max_epochs: 100,
        parallel_training: true,
        created_at: Utc::now(),
    }
}

/// Create model-specific training configuration
fn create_model_config(
    _model_type: &str,
    input_dim: usize,
    hidden_dims: Vec<usize>,
    output_dim: usize,
) -> ProductionTrainingConfig {
    ProductionTrainingConfig {
        model_config: ModelArchitectureConfig {
            input_dim,
            hidden_dims,
            output_dim,
            dropout_rate: 0.1,
            activation: "relu".to_string(),
            batch_norm: true,
            residual_connections: false,
        },
        training_params: TrainingHyperparameters {
            learning_rate: 0.001,
            batch_size: 64,
            max_epochs: 100,
            patience: 10,
            validation_split: 0.2,
            l2_regularization: 0.0001,
            lr_decay_factor: 0.5,
            lr_decay_patience: 5,
        },
        safety_config: MLSafetyConfig {
            safety_enabled: true,
            max_tensor_elements: 10_000_000,
            max_inference_timeout_ms: 5000,
            max_gpu_memory_bytes: 4_000_000_000,
            drift_sensitivity: 0.1,
            financial_precision: 8,
            nan_infinity_checks: true,
            max_prediction_value: 100.0,
            min_prediction_value: -100.0,
            bounds_checking: true,
            auto_fallback: true,
            max_retries: 3,
        },
        gradient_config: GradientSafetyConfig {
            max_gradient_norm: 1.0,
            min_gradient_norm: 1e-8,
            max_individual_gradient: 5.0,
            enable_norm_clipping: true,
            enable_value_clipping: true,
            enable_nan_detection: true,
            gradient_history_size: 100,
            explosion_threshold: 10.0,
            min_gradient_history: 10,
            enable_adaptive_scaling: false,
            lr_adjustment_factor: 0.5,
            base_learning_rate: 0.001,
        },
        financial_config: FinancialValidationConfig {
            max_prediction_multiple: 2.0,
            min_prediction_confidence: 0.6,
            validate_position_sizing: true,
            max_position_fraction: 0.2,
            min_sharpe_threshold: 0.5,
        },
        performance_config: PerformanceConfig {
            device_preference: "cpu".to_string(),
            max_memory_bytes: 4_000_000_000,
            num_workers: 2,
            gradient_accumulation_steps: 1,
        },
    }
}

/// Create ensemble coordinator instance
async fn create_ensemble_coordinator(
    config: EnsembleTrainingConfig,
) -> EnsembleTrainingCoordinator {
    EnsembleTrainingCoordinator::new(config)
        .await
        .expect("Failed to create coordinator")
}
