//! F081-F100: Generalization & Robustness (20 points)

use trueno::tuner::{BrickTuner, QuantType, ThroughputRegressor, TunerFeatures};

/// F081: Handles extreme small model
#[test]
fn f081_extreme_small_model() {
    let regressor = ThroughputRegressor::new();
    let features = TunerFeatures::builder()
        .model_params_b(0.01) // 10M params
        .batch_size(1)
        .build();

    let pred = regressor.predict(&features);
    assert!(
        pred.predicted_tps.is_finite() && pred.predicted_tps > 0.0,
        "F081 FALSIFIED: extreme small model prediction invalid: {}",
        pred.predicted_tps
    );
}

/// F082: Handles extreme large model
#[test]
fn f082_extreme_large_model() {
    let regressor = ThroughputRegressor::new();
    let features = TunerFeatures::builder()
        .model_params_b(100.0) // 100B params
        .batch_size(1)
        .build();

    let pred = regressor.predict(&features);
    assert!(
        pred.predicted_tps.is_finite() && pred.predicted_tps > 0.0,
        "F082 FALSIFIED: extreme large model prediction invalid: {}",
        pred.predicted_tps
    );
}

/// F083: Handles extreme batch size
#[test]
fn f083_extreme_batch_size() {
    let regressor = ThroughputRegressor::new();
    let features = TunerFeatures::builder().model_params_b(1.5).batch_size(64).build();

    let pred = regressor.predict(&features);
    assert!(
        pred.predicted_tps.is_finite(),
        "F083 FALSIFIED: extreme batch size prediction invalid"
    );
}

/// F084: Handles zero batch size gracefully
#[test]
fn f084_zero_batch_size() {
    let features = TunerFeatures::builder()
        .model_params_b(1.5)
        .batch_size(0) // Invalid
        .build();

    // Should either error or treat as 1
    let result = features.validate();
    if result.is_ok() {
        let regressor = ThroughputRegressor::new();
        let pred = regressor.predict(&features);
        assert!(pred.predicted_tps.is_finite(), "F084 FALSIFIED: zero batch handled badly");
    }
}

/// F085: Handles extreme memory bandwidth
#[test]
fn f085_extreme_bandwidth() {
    let regressor = ThroughputRegressor::new();

    // Extremely slow GPU
    let slow = TunerFeatures::builder()
        .model_params_b(1.5)
        .gpu_mem_bw_gbs(10.0) // Very slow
        .build();

    // Extremely fast GPU
    let fast = TunerFeatures::builder()
        .model_params_b(1.5)
        .gpu_mem_bw_gbs(10000.0) // Very fast
        .build();

    let slow_pred = regressor.predict(&slow);
    let fast_pred = regressor.predict(&fast);

    assert!(slow_pred.predicted_tps.is_finite());
    assert!(fast_pred.predicted_tps.is_finite());
    assert!(fast_pred.predicted_tps >= slow_pred.predicted_tps);
}

/// F086: All quant types produce valid predictions
#[test]
fn f086_all_quant_types() {
    let regressor = ThroughputRegressor::new();

    for qt in [
        QuantType::Q4_0,
        QuantType::Q4_1,
        QuantType::Q4K,
        QuantType::Q5K,
        QuantType::Q6K,
        QuantType::Q8_0,
        QuantType::F16,
        QuantType::F32,
    ] {
        let features = TunerFeatures::builder().model_params_b(1.5).quant_type(qt).build();

        let pred = regressor.predict(&features);
        assert!(
            pred.predicted_tps.is_finite() && pred.predicted_tps > 0.0,
            "F086 FALSIFIED: {:?} produces invalid prediction: {}",
            qt,
            pred.predicted_tps
        );
    }
}

/// F087: Concept drift detection (placeholder)
#[test]
fn f087_concept_drift_placeholder() {
    // Will be implemented with T-TUNER-005
    // For now, verify prediction stability
    let regressor = ThroughputRegressor::new();
    let features = TunerFeatures::builder().model_params_b(1.5).build();

    let predictions: Vec<_> = (0..10).map(|_| regressor.predict(&features).predicted_tps).collect();

    let variance: f32 =
        predictions.iter().map(|p| (p - predictions[0]).powi(2)).sum::<f32>() / 10.0;

    assert!(variance < 0.001, "F087 FALSIFIED: prediction variance {} too high", variance);
}

/// F088: Retraining improves accuracy (placeholder)
#[test]
fn f088_retraining_placeholder() {
    // Will be implemented with T-TUNER-005
}

/// F089: Handles missing hardware info
#[test]
fn f089_missing_hardware() {
    let features = TunerFeatures::builder()
        .model_params_b(1.5)
        // No GPU info
        .build();

    let regressor = ThroughputRegressor::new();
    let pred = regressor.predict(&features);

    assert!(
        pred.predicted_tps.is_finite(),
        "F089 FALSIFIED: missing hardware causes invalid prediction"
    );
}

/// F090: Handles all zeros gracefully
#[test]
fn f090_all_zeros() {
    let features = TunerFeatures::default();
    let vec = features.to_vector();

    // Should not panic or produce NaN
    for (i, &v) in vec.iter().enumerate() {
        assert!(v.is_finite(), "F090 FALSIFIED: default feature[{}] is not finite", i);
    }
}

/// F091: Stress test - many predictions
#[test]
fn f091_stress_many_predictions() {
    let regressor = ThroughputRegressor::new();
    let features = TunerFeatures::builder().model_params_b(1.5).build();

    for _ in 0..10000 {
        let pred = regressor.predict(&features);
        assert!(pred.predicted_tps.is_finite());
    }
}

/// F092: Stress test - varied features
#[test]
fn f092_stress_varied_features() {
    let regressor = ThroughputRegressor::new();

    for i in 0..1000 {
        let features = TunerFeatures::builder()
            .model_params_b((i % 100) as f32 * 0.1 + 0.1)
            .batch_size((i % 8 + 1) as u32)
            .build();

        let pred = regressor.predict(&features);
        assert!(
            pred.predicted_tps.is_finite(),
            "F092 FALSIFIED: iteration {} produced invalid prediction",
            i
        );
    }
}

/// F093: Memory stability
#[test]
fn f093_memory_stability() {
    // Create and drop many instances
    for _ in 0..100 {
        let regressor = ThroughputRegressor::new();
        let features = TunerFeatures::builder().model_params_b(1.5).build();
        let _ = regressor.predict(&features);
        // regressor dropped here
    }
}

/// F094: Feature importance consistency
#[test]
fn f094_feature_importance_consistency() {
    let regressor = ThroughputRegressor::new();
    let features = TunerFeatures::builder().model_params_b(1.5).batch_size(4).build();

    let pred1 = regressor.predict(&features);
    let pred2 = regressor.predict(&features);

    assert_eq!(
        pred1.top_features.len(),
        pred2.top_features.len(),
        "F094 FALSIFIED: feature importance count varies"
    );
}

/// F095: Recommendations are actionable
#[test]
fn f095_recommendations_actionable() {
    let tuner = BrickTuner::new();
    let features = TunerFeatures::builder().model_params_b(1.5).batch_size(1).build();

    let rec = tuner.recommend(&features);

    // Should have at least one suggestion
    assert!(
        !rec.suggested_experiments.is_empty(),
        "F095 FALSIFIED: no suggestions provided for M=1"
    );
}

/// F096: Bottleneck classification is deterministic
#[test]
fn f096_bottleneck_deterministic() {
    let tuner = BrickTuner::new();
    let features = TunerFeatures::builder().model_params_b(7.0).batch_size(1).build();

    let rec1 = tuner.recommend(&features);
    let rec2 = tuner.recommend(&features);

    // Same features should produce same bottleneck classification
    assert_eq!(
        rec1.bottleneck.class, rec2.bottleneck.class,
        "F096 FALSIFIED: bottleneck classification not deterministic: {:?} vs {:?}",
        rec1.bottleneck.class, rec2.bottleneck.class
    );
}

/// F097: Version string format
#[test]
fn f097_version_format() {
    let tuner = BrickTuner::new();
    let features = TunerFeatures::builder().model_params_b(1.5).build();
    let rec = tuner.recommend(&features);

    // Version should match semver pattern
    let parts: Vec<&str> = rec.model_version.split('.').collect();
    assert!(parts.len() >= 2, "F097 FALSIFIED: version '{}' not semver", rec.model_version);
}

/// F098: Confidence overall in valid range
#[test]
fn f098_confidence_range() {
    let tuner = BrickTuner::new();
    let features = TunerFeatures::builder().model_params_b(1.5).build();
    let rec = tuner.recommend(&features);

    assert!(
        rec.confidence_overall >= 0.0 && rec.confidence_overall <= 1.0,
        "F098 FALSIFIED: confidence_overall {} out of range",
        rec.confidence_overall
    );
}

/// F099: Multiple recommendations don't interfere
#[test]
fn f099_no_interference() {
    let tuner = BrickTuner::new();

    let f1 = TunerFeatures::builder().model_params_b(1.5).build();
    let f2 = TunerFeatures::builder().model_params_b(7.0).build();

    let r1 = tuner.recommend(&f1);
    let r2 = tuner.recommend(&f2);
    let r1_again = tuner.recommend(&f1);

    assert_eq!(
        r1.throughput.predicted_tps, r1_again.throughput.predicted_tps,
        "F099 FALSIFIED: recommendations interfere"
    );

    // Different features should give different predictions
    assert_ne!(
        r1.throughput.predicted_tps, r2.throughput.predicted_tps,
        "F099 FALSIFIED: different inputs produce same output"
    );
}

/// F100: Final sanity check - complete workflow
#[test]
fn f100_complete_workflow() {
    // Build features
    let features = TunerFeatures::builder()
        .model_params_b(1.5)
        .hidden_dim(1536)
        .num_layers(28)
        .num_heads(12)
        .batch_size(4)
        .seq_len(512)
        .quant_type(QuantType::Q4K)
        .gpu_mem_bw_gbs(1000.0)
        .gpu_sm_count(128)
        .cuda_graphs(true)
        .build();

    // Validate
    assert!(features.validate().is_ok());

    // Get recommendations
    let tuner = BrickTuner::new();
    let rec = tuner.recommend(&features);

    // Verify complete output
    assert!(rec.throughput.predicted_tps > 0.0);
    assert!(rec.throughput.confidence > 0.0);
    assert!(!rec.suggested_experiments.is_empty());
    assert!(rec.confidence_overall >= 0.0);

    println!("F100 PASSED: Complete workflow successful");
    println!("  Predicted throughput: {:.1} tok/s", rec.throughput.predicted_tps);
    println!("  Recommended kernel: {:?}", rec.kernel.top_kernel);
    println!("  Bottleneck: {:?}", rec.bottleneck);
}
