//! Integration tests for ruvector-learning-wasm
//!
//! Tests for adaptive learning mechanisms:
//! - MicroLoRA: Lightweight Low-Rank Adaptation
//! - SONA: Self-Organizing Neural Architecture
//! - Online learning / continual learning
//! - Meta-learning primitives

#[cfg(test)]
mod tests {
    use wasm_bindgen_test::*;
    use super::super::common::*;

    wasm_bindgen_test_configure!(run_in_browser);

    // ========================================================================
    // MicroLoRA Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_micro_lora_initialization() {
        // Test MicroLoRA adapter initialization
        let base_dim = 64;
        let rank = 4; // Low rank for efficiency

        // TODO: When MicroLoRA is implemented:
        // let lora = MicroLoRA::new(base_dim, rank);
        //
        // Verify A and B matrices are initialized
        // assert_eq!(lora.get_rank(), rank);
        // assert_eq!(lora.get_dim(), base_dim);
        //
        // Initial delta should be near zero
        // let delta = lora.compute_delta();
        // let norm: f32 = delta.iter().map(|x| x * x).sum::<f32>().sqrt();
        // assert!(norm < 1e-6, "Initial LoRA delta should be near zero");

        assert!(rank < base_dim);
    }

    #[wasm_bindgen_test]
    fn test_micro_lora_forward_pass() {
        let base_dim = 64;
        let rank = 8;
        let input = random_vector(base_dim);

        // TODO: Test forward pass through LoRA adapter
        // let lora = MicroLoRA::new(base_dim, rank);
        // let output = lora.forward(&input);
        //
        // assert_eq!(output.len(), base_dim);
        // assert_finite(&output);
        //
        // Initially should be close to input (small adaptation)
        // let diff: f32 = input.iter().zip(output.iter())
        //     .map(|(a, b)| (a - b).abs())
        //     .sum::<f32>();
        // assert!(diff < 1.0, "Initial LoRA should have minimal effect");

        assert_eq!(input.len(), base_dim);
    }

    #[wasm_bindgen_test]
    fn test_micro_lora_rank_constraint() {
        // Verify low-rank constraint is maintained
        let base_dim = 128;
        let rank = 16;

        // TODO: Test rank constraint
        // let lora = MicroLoRA::new(base_dim, rank);
        //
        // Perform some updates
        // let gradients = random_vector(base_dim);
        // lora.update(&gradients, 0.01);
        //
        // Verify delta matrix still has effective rank <= rank
        // let delta = lora.get_delta_matrix();
        // let effective_rank = compute_effective_rank(&delta);
        // assert!(effective_rank <= rank as f32 + 0.5);

        assert!(rank < base_dim);
    }

    #[wasm_bindgen_test]
    fn test_micro_lora_parameter_efficiency() {
        // LoRA should use much fewer parameters than full fine-tuning
        let base_dim = 256;
        let rank = 8;

        // Full matrix: base_dim * base_dim = 65536 parameters
        // LoRA: base_dim * rank * 2 = 4096 parameters (16x fewer)

        // TODO: Verify parameter count
        // let lora = MicroLoRA::new(base_dim, rank);
        // let num_params = lora.num_parameters();
        //
        // let full_params = base_dim * base_dim;
        // assert!(num_params < full_params / 10,
        //     "LoRA should use 10x fewer params: {} vs {}", num_params, full_params);

        let lora_params = base_dim * rank * 2;
        let full_params = base_dim * base_dim;
        assert!(lora_params < full_params / 10);
    }

    #[wasm_bindgen_test]
    fn test_micro_lora_gradient_update() {
        let base_dim = 64;
        let rank = 4;
        let learning_rate = 0.01;

        // TODO: Test gradient-based update
        // let mut lora = MicroLoRA::new(base_dim, rank);
        //
        // let input = random_vector(base_dim);
        // let target = random_vector(base_dim);
        //
        // // Forward and compute loss
        // let output = lora.forward(&input);
        // let loss_before = mse_loss(&output, &target);
        //
        // // Backward and update
        // let gradients = compute_gradients(&output, &target);
        // lora.update(&gradients, learning_rate);
        //
        // // Loss should decrease
        // let output_after = lora.forward(&input);
        // let loss_after = mse_loss(&output_after, &target);
        // assert!(loss_after < loss_before, "Loss should decrease after update");

        assert!(learning_rate > 0.0);
    }

    // ========================================================================
    // SONA (Self-Organizing Neural Architecture) Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_sona_initialization() {
        let input_dim = 64;
        let hidden_dim = 128;
        let output_dim = 32;

        // TODO: Test SONA initialization
        // let sona = SONA::new(input_dim, hidden_dim, output_dim);
        //
        // assert_eq!(sona.input_dim(), input_dim);
        // assert_eq!(sona.output_dim(), output_dim);
        //
        // Initial architecture should be valid
        // assert!(sona.validate_architecture());

        assert!(hidden_dim > input_dim);
    }

    #[wasm_bindgen_test]
    fn test_sona_forward_pass() {
        let input_dim = 64;
        let output_dim = 32;

        let input = random_vector(input_dim);

        // TODO: Test SONA forward pass
        // let sona = SONA::new(input_dim, 128, output_dim);
        // let output = sona.forward(&input);
        //
        // assert_eq!(output.len(), output_dim);
        // assert_finite(&output);

        assert_eq!(input.len(), input_dim);
    }

    #[wasm_bindgen_test]
    fn test_sona_architecture_adaptation() {
        // SONA should adapt its architecture based on data
        let input_dim = 32;
        let output_dim = 16;

        // TODO: Test architecture adaptation
        // let mut sona = SONA::new(input_dim, 64, output_dim);
        //
        // let initial_params = sona.num_parameters();
        //
        // // Train on simple data (should simplify architecture)
        // let simple_data: Vec<(Vec<f32>, Vec<f32>)> = (0..100)
        //     .map(|_| (random_vector(input_dim), random_vector(output_dim)))
        //     .collect();
        //
        // sona.train(&simple_data, 10);
        // sona.adapt_architecture();
        //
        // Architecture might change
        // let new_params = sona.num_parameters();
        //
        // At least verify it still works
        // let output = sona.forward(&simple_data[0].0);
        // assert_eq!(output.len(), output_dim);

        assert!(output_dim < input_dim);
    }

    #[wasm_bindgen_test]
    fn test_sona_neuron_pruning() {
        // Test that SONA can prune unnecessary neurons
        let input_dim = 64;
        let hidden_dim = 256; // Larger than needed
        let output_dim = 32;

        // TODO: Test neuron pruning
        // let mut sona = SONA::new(input_dim, hidden_dim, output_dim);
        //
        // // Train with low-complexity target
        // let data: Vec<_> = (0..100)
        //     .map(|i| {
        //         let input = random_vector(input_dim);
        //         // Simple linear target
        //         let output: Vec<f32> = input[..output_dim].to_vec();
        //         (input, output)
        //     })
        //     .collect();
        //
        // sona.train(&data, 20);
        //
        // let active_neurons_before = sona.count_active_neurons();
        // sona.prune_inactive_neurons(0.01); // Prune neurons with low activity
        // let active_neurons_after = sona.count_active_neurons();
        //
        // // Should have pruned some neurons
        // assert!(active_neurons_after < active_neurons_before);

        assert!(hidden_dim > output_dim);
    }

    #[wasm_bindgen_test]
    fn test_sona_connection_growth() {
        // Test that SONA can grow new connections when needed
        let input_dim = 32;
        let output_dim = 16;

        // TODO: Test connection growth
        // let mut sona = SONA::new_sparse(input_dim, 64, output_dim, 0.1); // Start sparse
        //
        // let initial_connections = sona.count_connections();
        //
        // // Train with complex data requiring more connections
        // let complex_data = generate_complex_dataset(100, input_dim, output_dim);
        // sona.train(&complex_data, 50);
        //
        // let final_connections = sona.count_connections();
        //
        // // Should have grown connections
        // assert!(final_connections > initial_connections);

        assert!(output_dim < input_dim);
    }

    // ========================================================================
    // Online / Continual Learning Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_online_learning_single_sample() {
        let dim = 32;

        let input = random_vector(dim);
        let target = random_vector(dim);

        // TODO: Test single-sample update
        // let mut learner = OnlineLearner::new(dim);
        //
        // let loss_before = learner.predict(&input)
        //     .iter().zip(target.iter())
        //     .map(|(p, t)| (p - t).powi(2))
        //     .sum::<f32>();
        //
        // learner.learn_sample(&input, &target);
        //
        // let loss_after = learner.predict(&input)
        //     .iter().zip(target.iter())
        //     .map(|(p, t)| (p - t).powi(2))
        //     .sum::<f32>();
        //
        // assert!(loss_after < loss_before);

        assert_eq!(input.len(), target.len());
    }

    #[wasm_bindgen_test]
    fn test_continual_learning_no_catastrophic_forgetting() {
        // Test that learning new tasks doesn't completely forget old ones
        let dim = 32;

        // TODO: Test catastrophic forgetting mitigation
        // let mut learner = ContinualLearner::new(dim);
        //
        // // Task 1: Learn identity mapping
        // let task1_data: Vec<_> = (0..50)
        //     .map(|_| {
        //         let x = random_vector(dim);
        //         (x.clone(), x)
        //     })
        //     .collect();
        //
        // learner.train_task(&task1_data, 10);
        // let task1_perf = learner.evaluate(&task1_data);
        //
        // // Task 2: Learn negation
        // let task2_data: Vec<_> = (0..50)
        //     .map(|_| {
        //         let x = random_vector(dim);
        //         let y: Vec<f32> = x.iter().map(|v| -v).collect();
        //         (x, y)
        //     })
        //     .collect();
        //
        // learner.train_task(&task2_data, 10);
        // let task1_perf_after = learner.evaluate(&task1_data);
        //
        // // Should retain some performance on task 1
        // assert!(task1_perf_after > task1_perf * 0.5,
        //     "Should retain at least 50% of task 1 performance");

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_experience_replay() {
        // Test experience replay buffer
        let dim = 32;
        let buffer_size = 100;

        // TODO: Test replay buffer
        // let mut buffer = ExperienceReplayBuffer::new(buffer_size);
        //
        // // Fill buffer
        // for _ in 0..150 {
        //     let experience = Experience {
        //         input: random_vector(dim),
        //         target: random_vector(dim),
        //         priority: 1.0,
        //     };
        //     buffer.add(experience);
        // }
        //
        // // Buffer should maintain max size
        // assert_eq!(buffer.len(), buffer_size);
        //
        // // Should be able to sample
        // let batch = buffer.sample(10);
        // assert_eq!(batch.len(), 10);

        assert!(buffer_size > 0);
    }

    // ========================================================================
    // Meta-Learning Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_meta_learning_fast_adaptation() {
        // Test that meta-learned model can adapt quickly to new tasks
        let dim = 32;

        // TODO: Test fast adaptation
        // let meta_learner = MetaLearner::new(dim);
        //
        // // Pre-train on distribution of tasks
        // let task_distribution = generate_task_distribution(20, dim);
        // meta_learner.meta_train(&task_distribution, 100);
        //
        // // New task (not seen during training)
        // let new_task = generate_random_task(dim);
        //
        // // Should adapt with very few samples
        // let few_shot_samples = new_task.sample(5);
        // meta_learner.adapt(&few_shot_samples);
        //
        // // Evaluate on held-out samples from new task
        // let test_samples = new_task.sample(20);
        // let accuracy = meta_learner.evaluate(&test_samples);
        //
        // assert!(accuracy > 0.6, "Should achieve >60% with 5-shot learning");

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_learning_to_learn() {
        // Test that learning rate itself is learned/adapted
        let dim = 32;

        // TODO: Test learned learning rate
        // let mut learner = AdaptiveLearner::new(dim);
        //
        // // Initial learning rate
        // let initial_lr = learner.get_learning_rate();
        //
        // // Train on varied data
        // let data = generate_varied_dataset(100, dim);
        // learner.train_with_adaptation(&data, 50);
        //
        // // Learning rate should have been adapted
        // let final_lr = learner.get_learning_rate();
        //
        // // Not necessarily larger or smaller, just different
        // assert!((initial_lr - final_lr).abs() > 1e-6,
        //     "Learning rate should adapt during training");

        assert!(dim > 0);
    }

    // ========================================================================
    // Memory and Efficiency Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_micro_lora_memory_footprint() {
        // Verify MicroLoRA uses minimal memory
        let base_dim = 512;
        let rank = 16;

        // TODO: Check memory footprint
        // let lora = MicroLoRA::new(base_dim, rank);
        //
        // // A: base_dim x rank, B: rank x base_dim
        // // Total: 2 * base_dim * rank * 4 bytes (f32)
        // let expected_bytes = 2 * base_dim * rank * 4;
        //
        // let actual_bytes = lora.memory_footprint();
        //
        // // Allow some overhead
        // assert!(actual_bytes < expected_bytes * 2,
        //     "Memory footprint {} exceeds expected {}", actual_bytes, expected_bytes);

        let expected_params = 2 * base_dim * rank;
        assert!(expected_params < base_dim * base_dim / 10);
    }

    #[wasm_bindgen_test]
    fn test_learning_wasm_bundle_size() {
        // Learning WASM should be <50KB gzipped
        // This is verified at build time, but we can check module is loadable

        // TODO: Verify module loads correctly
        // assert!(ruvector_learning_wasm::version().len() > 0);

        assert!(true);
    }

    // ========================================================================
    // Numerical Stability Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_gradient_clipping() {
        // Test that gradients are properly clipped to prevent explosion
        let dim = 32;

        // TODO: Test gradient clipping
        // let mut lora = MicroLoRA::new(dim, 4);
        //
        // // Huge gradients
        // let huge_gradients: Vec<f32> = vec![1e10; dim];
        // lora.update(&huge_gradients, 0.01);
        //
        // // Parameters should still be reasonable
        // let params = lora.get_parameters();
        // assert!(params.iter().all(|p| p.abs() < 1e6),
        //     "Parameters should be clipped");

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_numerical_stability_long_training() {
        // Test stability over many updates
        let dim = 32;
        let num_updates = 1000;

        // TODO: Test long training stability
        // let mut lora = MicroLoRA::new(dim, 4);
        //
        // for _ in 0..num_updates {
        //     let gradients = random_vector(dim);
        //     lora.update(&gradients, 0.001);
        // }
        //
        // // Should still produce finite outputs
        // let input = random_vector(dim);
        // let output = lora.forward(&input);
        // assert_finite(&output);

        assert!(num_updates > 0);
    }
}