#![cfg(feature = "disabled_tests")]

use rtx_automeasure::{AutoMLResult, TaskType};
use rtx_tensor::{Device, Tensor};

// Note: This test file is a placeholder. The transfer_learning module
// has a complex API that would require significant implementation.
// These basic tests verify conceptual understanding.

#[tokio::test]
async fn test_placeholder() -> AutoMLResult<()> {
    // Placeholder test to ensure the test file compiles
    let device = Device::cpu();
    let _x = Tensor::randn(&[10, 5], &device)?;
    Ok(())
}

#[tokio::test]
async fn test_transfer_learning_concepts() -> AutoMLResult<()> {
    let device = Device::cpu();

    // Source domain data (pre-trained on large dataset)
    let x_source = Tensor::randn(&[500, 20], &device)?;
    let y_source = Tensor::zeros(&[500], &device)?;

    // Target domain data (smaller dataset, related task)
    let x_target = Tensor::randn(&[50, 20], &device)?;
    let y_target = Tensor::zeros(&[50], &device)?;

    assert_eq!(x_source.shape()[1], x_target.shape()[1]); // Same feature dimension
    assert!(x_source.shape()[0] > x_target.shape()[0]); // Source has more data

    Ok(())
}

#[tokio::test]
async fn test_domain_adaptation_concepts() {
    // Test domain similarity calculation concepts

    struct DomainCharacteristics {
        n_samples: usize,
        n_features: usize,
        task_type: TaskType,
    }

    let source = DomainCharacteristics {
        n_samples: 1000,
        n_features: 784,
        task_type: TaskType::Classification,
    };

    let target = DomainCharacteristics {
        n_samples: 100,
        n_features: 784,
        task_type: TaskType::Classification,
    };

    // Similarity score based on task type and feature count
    let task_similarity = if source.task_type == target.task_type {
        1.0
    } else {
        0.0
    };
    let feature_similarity = if source.n_features == target.n_features {
        1.0
    } else {
        0.0
    };

    assert_eq!(task_similarity, 1.0);
    assert_eq!(feature_similarity, 1.0);
}

#[tokio::test]
async fn test_knowledge_transfer_methods() {
    // Test different knowledge transfer strategies

    enum TransferMethod {
        FeatureExtraction, // Freeze early layers, train final layers
        FineTuning,        // Train all layers with small learning rate
        DomainAdaptation,  // Align source and target distributions
        MultiTask,         // Joint training on related tasks
    }

    let methods = vec![
        TransferMethod::FeatureExtraction,
        TransferMethod::FineTuning,
        TransferMethod::DomainAdaptation,
        TransferMethod::MultiTask,
    ];

    assert_eq!(methods.len(), 4);
}

#[tokio::test]
async fn test_layer_freezing_concept() {
    // Test layer freezing strategy for transfer learning

    struct Layer {
        name: String,
        is_frozen: bool,
    }

    let mut layers = vec![
        Layer {
            name: "input".to_string(),
            is_frozen: true,
        },
        Layer {
            name: "hidden1".to_string(),
            is_frozen: true,
        },
        Layer {
            name: "hidden2".to_string(),
            is_frozen: true,
        },
        Layer {
            name: "hidden3".to_string(),
            is_frozen: false,
        },
        Layer {
            name: "output".to_string(),
            is_frozen: false,
        },
    ];

    // Feature extraction: freeze early layers
    let frozen_count = layers.iter().filter(|l| l.is_frozen).count();
    let trainable_count = layers.iter().filter(|l| !l.is_frozen).count();

    assert_eq!(frozen_count, 3);
    assert_eq!(trainable_count, 2);
}

#[tokio::test]
async fn test_learning_rate_scaling() {
    // Test learning rate adjustment for fine-tuning

    let base_lr = 0.01;

    // Different learning rates for different layer groups
    let frozen_layer_lr = 0.0;
    let middle_layer_lr = base_lr * 0.1; // Reduced for pre-trained layers
    let new_layer_lr = base_lr; // Full rate for new layers

    assert_eq!(frozen_layer_lr, 0.0);
    assert!(middle_layer_lr < new_layer_lr);
}

#[tokio::test]
async fn test_domain_discrepancy_concepts() {
    // Test Maximum Mean Discrepancy (MMD) concept

    fn calculate_kernel_similarity(x1: f64, x2: f64, gamma: f64) -> f64 {
        (-(x1 - x2).powi(2) * gamma).exp()
    }

    // Sample features from source and target domains
    let source_features = vec![1.0, 2.0, 3.0];
    let target_features = vec![1.1, 2.2, 3.1];

    let gamma = 1.0;
    let mut total_similarity = 0.0;

    for (&sf, &tf) in source_features.iter().zip(target_features.iter()) {
        total_similarity += calculate_kernel_similarity(sf, tf, gamma);
    }

    let avg_similarity = total_similarity / source_features.len() as f64;
    assert!(avg_similarity > 0.5); // High similarity indicates related domains
}

#[tokio::test]
async fn test_source_selection_strategy() {
    // Test selecting best source domain for transfer

    struct SourceDomain {
        id: String,
        similarity_score: f64,
        performance_score: f64,
    }

    let sources = vec![
        SourceDomain {
            id: "source1".to_string(),
            similarity_score: 0.9,
            performance_score: 0.85,
        },
        SourceDomain {
            id: "source2".to_string(),
            similarity_score: 0.7,
            performance_score: 0.95,
        },
        SourceDomain {
            id: "source3".to_string(),
            similarity_score: 0.8,
            performance_score: 0.90,
        },
    ];

    // Combined score: weighted sum of similarity and performance
    let mut best_source = &sources[0];
    let mut best_score = 0.0;

    for source in &sources {
        let combined_score = source.similarity_score * 0.6 + source.performance_score * 0.4;
        if combined_score > best_score {
            best_score = combined_score;
            best_source = source;
        }
    }

    assert_eq!(best_source.id, "source1");
}

#[tokio::test]
async fn test_progressive_fine_tuning() {
    // Test progressive unfreezing strategy

    struct TrainingPhase {
        phase: u32,
        unfrozen_layers: Vec<usize>,
        learning_rate: f64,
    }

    let phases = vec![
        TrainingPhase {
            phase: 1,
            unfrozen_layers: vec![4], // Only last layer
            learning_rate: 0.01,
        },
        TrainingPhase {
            phase: 2,
            unfrozen_layers: vec![3, 4], // Last two layers
            learning_rate: 0.005,
        },
        TrainingPhase {
            phase: 3,
            unfrozen_layers: vec![2, 3, 4], // Last three layers
            learning_rate: 0.001,
        },
    ];

    // Verify progressive unfreezing and learning rate decay
    for i in 1..phases.len() {
        assert!(phases[i].unfrozen_layers.len() > phases[i - 1].unfrozen_layers.len());
        assert!(phases[i].learning_rate < phases[i - 1].learning_rate);
    }
}

#[tokio::test]
async fn test_task_relatedness_metric() {
    // Test measuring task relatedness

    fn task_overlap(source_labels: &[i32], target_labels: &[i32]) -> f64 {
        let source_set: std::collections::HashSet<_> = source_labels.iter().collect();
        let target_set: std::collections::HashSet<_> = target_labels.iter().collect();

        let intersection: std::collections::HashSet<_> =
            source_set.intersection(&target_set).collect();

        intersection.len() as f64 / source_set.len().max(target_set.len()) as f64
    }

    let source_labels = vec![0, 1, 2, 3, 4, 5, 6, 7, 8, 9]; // 10 classes
    let target_labels = vec![0, 1, 2, 3, 4]; // 5 classes (subset)

    let overlap = task_overlap(&source_labels, &target_labels);
    assert_eq!(overlap, 0.5); // 50% overlap
}

#[tokio::test]
async fn test_few_shot_learning_concepts() -> AutoMLResult<()> {
    let device = Device::cpu();

    // Few-shot scenario: very small target dataset
    let n_shots = 5; // 5 examples per class
    let n_classes = 3;
    let n_samples = n_shots * n_classes;

    let x_target = Tensor::randn(&[n_samples, 20], &device)?;
    let y_target = Tensor::zeros(&[n_samples], &device)?;

    assert_eq!(x_target.shape()[0], 15);

    Ok(())
}

#[tokio::test]
async fn test_zero_shot_learning_concepts() {
    // Zero-shot learning: no target examples, only class descriptions

    struct ClassDescription {
        class_name: String,
        attributes: Vec<String>,
    }

    let source_classes = vec![
        ClassDescription {
            class_name: "cat".to_string(),
            attributes: vec!["furry".to_string(), "four_legs".to_string()],
        },
        ClassDescription {
            class_name: "dog".to_string(),
            attributes: vec!["furry".to_string(), "four_legs".to_string()],
        },
    ];

    let target_class = ClassDescription {
        class_name: "fox".to_string(),
        attributes: vec!["furry".to_string(), "four_legs".to_string()],
    };

    // Similarity based on shared attributes
    let shared_attrs: Vec<_> = source_classes[0]
        .attributes
        .iter()
        .filter(|attr| target_class.attributes.contains(attr))
        .collect();

    assert!(!shared_attrs.is_empty());
}

#[tokio::test]
async fn test_negative_transfer_detection() {
    // Test detecting when transfer learning hurts performance

    struct TransferResult {
        baseline_score: f64, // Training from scratch
        transfer_score: f64, // With transfer learning
    }

    let results = vec![
        TransferResult {
            baseline_score: 0.85,
            transfer_score: 0.90, // Positive transfer
        },
        TransferResult {
            baseline_score: 0.80,
            transfer_score: 0.75, // Negative transfer!
        },
    ];

    let negative_transfer = results
        .iter()
        .filter(|r| r.transfer_score < r.baseline_score)
        .count();

    assert_eq!(negative_transfer, 1);
}

#[tokio::test]
async fn test_multi_source_transfer() {
    // Test transferring from multiple source domains

    struct SourceContribution {
        source_id: String,
        weight: f64,
    }

    let contributions = vec![
        SourceContribution {
            source_id: "source1".to_string(),
            weight: 0.5,
        },
        SourceContribution {
            source_id: "source2".to_string(),
            weight: 0.3,
        },
        SourceContribution {
            source_id: "source3".to_string(),
            weight: 0.2,
        },
    ];

    // Weights should sum to 1.0
    let total_weight: f64 = contributions.iter().map(|c| c.weight).sum();
    assert!((total_weight - 1.0).abs() < 1e-6);
}