//! Integration tests for ruvector-nervous-system-wasm
//!
//! Tests for bio-inspired neural components:
//! - HDC (Hyperdimensional Computing)
//! - BTSP (Behavioral Time-Scale Plasticity)
//! - Spiking Neural Networks
//! - Neuromorphic processing primitives

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

    wasm_bindgen_test_configure!(run_in_browser);

    // ========================================================================
    // HDC (Hyperdimensional Computing) Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_hdc_vector_encoding() {
        // Test hypervector encoding
        let dim = 10000; // HDC typically uses very high dimensions

        // TODO: When HDC is implemented:
        // let encoder = HDCEncoder::new(dim);
        //
        // // Encode a symbol
        // let hv_a = encoder.encode_symbol("A");
        // let hv_b = encoder.encode_symbol("B");
        //
        // // Should be orthogonal (low similarity)
        // let similarity = cosine_similarity(&hv_a, &hv_b);
        // assert!(similarity.abs() < 0.1, "Random HVs should be near-orthogonal");
        //
        // // Same symbol should produce same vector
        // let hv_a2 = encoder.encode_symbol("A");
        // assert_vectors_approx_eq(&hv_a, &hv_a2, 1e-6);

        assert!(dim >= 1000);
    }

    #[wasm_bindgen_test]
    fn test_hdc_bundling() {
        // Test bundling (element-wise addition) operation
        let dim = 10000;

        // TODO: Test bundling
        // let encoder = HDCEncoder::new(dim);
        //
        // let hv_a = encoder.encode_symbol("A");
        // let hv_b = encoder.encode_symbol("B");
        // let hv_c = encoder.encode_symbol("C");
        //
        // // Bundle A, B, C
        // let bundled = HDC::bundle(&[&hv_a, &hv_b, &hv_c]);
        //
        // // Bundled vector should be similar to all components
        // assert!(cosine_similarity(&bundled, &hv_a) > 0.3);
        // assert!(cosine_similarity(&bundled, &hv_b) > 0.3);
        // assert!(cosine_similarity(&bundled, &hv_c) > 0.3);

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_hdc_binding() {
        // Test binding (element-wise XOR or multiplication) operation
        let dim = 10000;

        // TODO: Test binding
        // let encoder = HDCEncoder::new(dim);
        //
        // let hv_a = encoder.encode_symbol("A");
        // let hv_b = encoder.encode_symbol("B");
        //
        // // Bind A with B
        // let bound = HDC::bind(&hv_a, &hv_b);
        //
        // // Bound vector should be orthogonal to both components
        // assert!(cosine_similarity(&bound, &hv_a).abs() < 0.1);
        // assert!(cosine_similarity(&bound, &hv_b).abs() < 0.1);
        //
        // // Unbinding should recover original
        // let recovered = HDC::bind(&bound, &hv_b); // bind is its own inverse
        // assert!(cosine_similarity(&recovered, &hv_a) > 0.9);

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_hdc_permutation() {
        // Test permutation for sequence encoding
        let dim = 10000;

        // TODO: Test permutation
        // let encoder = HDCEncoder::new(dim);
        //
        // let hv_a = encoder.encode_symbol("A");
        //
        // // Permute by position 1, 2, 3
        // let hv_a_pos1 = HDC::permute(&hv_a, 1);
        // let hv_a_pos2 = HDC::permute(&hv_a, 2);
        //
        // // Permuted vectors should be orthogonal to original
        // assert!(cosine_similarity(&hv_a, &hv_a_pos1).abs() < 0.1);
        //
        // // Inverse permutation should recover original
        // let recovered = HDC::permute_inverse(&hv_a_pos1, 1);
        // assert_vectors_approx_eq(&hv_a, &recovered, 1e-6);

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_hdc_associative_memory() {
        // Test HDC as associative memory
        let dim = 10000;

        // TODO: Test associative memory
        // let mut memory = HDCAssociativeMemory::new(dim);
        //
        // // Store key-value pairs
        // let key1 = random_vector(dim);
        // let value1 = random_vector(dim);
        // memory.store(&key1, &value1);
        //
        // let key2 = random_vector(dim);
        // let value2 = random_vector(dim);
        // memory.store(&key2, &value2);
        //
        // // Retrieve by key
        // let retrieved1 = memory.retrieve(&key1);
        // assert!(cosine_similarity(&retrieved1, &value1) > 0.8);
        //
        // // Noisy key should still retrieve correct value
        // let noisy_key1: Vec<f32> = key1.iter()
        //     .map(|x| x + (rand::random::<f32>() - 0.5) * 0.1)
        //     .collect();
        // let retrieved_noisy = memory.retrieve(&noisy_key1);
        // assert!(cosine_similarity(&retrieved_noisy, &value1) > 0.6);

        assert!(dim > 0);
    }

    // ========================================================================
    // BTSP (Behavioral Time-Scale Plasticity) Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_btsp_basic() {
        // Test BTSP learning rule
        let num_inputs = 100;
        let num_outputs = 10;

        // TODO: When BTSP is implemented:
        // let mut btsp = BTSPNetwork::new(num_inputs, num_outputs);
        //
        // // Present input pattern
        // let input = random_vector(num_inputs);
        // let output = btsp.forward(&input);
        //
        // // Apply eligibility trace
        // btsp.update_eligibility(&input);
        //
        // // Apply behavioral signal (reward/plateau potential)
        // btsp.apply_behavioral_signal(1.0);
        //
        // // Weights should be modified
        // let output_after = btsp.forward(&input);
        //
        // // Output should change due to learning
        // let diff: f32 = output.iter().zip(output_after.iter())
        //     .map(|(a, b)| (a - b).abs())
        //     .sum();
        // assert!(diff > 0.01, "BTSP should modify network");

        assert!(num_inputs > 0);
    }

    #[wasm_bindgen_test]
    fn test_btsp_eligibility_trace() {
        // Test eligibility trace dynamics
        let num_inputs = 50;

        // TODO: Test eligibility trace
        // let mut btsp = BTSPNetwork::new(num_inputs, 10);
        //
        // // Present input
        // let input = random_vector(num_inputs);
        // btsp.update_eligibility(&input);
        //
        // let trace_t0 = btsp.get_eligibility_trace();
        //
        // // Trace should decay over time
        // btsp.step_time(10);
        // let trace_t10 = btsp.get_eligibility_trace();
        //
        // let trace_t0_norm: f32 = trace_t0.iter().map(|x| x * x).sum();
        // let trace_t10_norm: f32 = trace_t10.iter().map(|x| x * x).sum();
        //
        // assert!(trace_t10_norm < trace_t0_norm, "Eligibility should decay");

        assert!(num_inputs > 0);
    }

    #[wasm_bindgen_test]
    fn test_btsp_one_shot_learning() {
        // BTSP should enable one-shot learning with plateau potential
        let num_inputs = 100;
        let num_outputs = 10;

        // TODO: Test one-shot learning
        // let mut btsp = BTSPNetwork::new(num_inputs, num_outputs);
        //
        // // Input pattern
        // let input = random_vector(num_inputs);
        //
        // // Target activation
        // let target_output = 5; // Activate neuron 5
        //
        // // One-shot learning: present input + apply plateau to target
        // btsp.forward(&input);
        // btsp.update_eligibility(&input);
        // btsp.apply_plateau_potential(target_output, 1.0);
        //
        // // Clear state
        // btsp.reset_state();
        //
        // // Re-present input
        // let output = btsp.forward(&input);
        //
        // // Target neuron should be more active
        // let target_activity = output[target_output];
        // let other_max = output.iter()
        //     .enumerate()
        //     .filter(|(i, _)| *i != target_output)
        //     .map(|(_, v)| *v)
        //     .fold(f32::NEG_INFINITY, f32::max);
        //
        // assert!(target_activity > other_max, "Target should be most active after one-shot learning");

        assert!(num_outputs > 0);
    }

    // ========================================================================
    // Spiking Neural Network Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_spiking_neuron_lif() {
        // Test Leaky Integrate-and-Fire neuron
        let threshold = 1.0;
        let tau_m = 10.0; // Membrane time constant

        // TODO: When SNN is implemented:
        // let mut lif = LIFNeuron::new(threshold, tau_m);
        //
        // // Sub-threshold input should not spike
        // lif.inject_current(0.5);
        // for _ in 0..10 {
        //     let spike = lif.step(1.0);
        //     assert!(!spike, "Should not spike below threshold");
        // }
        //
        // // Super-threshold input should spike
        // lif.reset();
        // lif.inject_current(2.0);
        // let mut spiked = false;
        // for _ in 0..20 {
        //     if lif.step(1.0) {
        //         spiked = true;
        //         break;
        //     }
        // }
        // assert!(spiked, "Should spike above threshold");

        assert!(threshold > 0.0);
    }

    #[wasm_bindgen_test]
    fn test_spiking_network_propagation() {
        // Test spike propagation through network
        let num_layers = 3;
        let neurons_per_layer = 10;

        // TODO: Test spike propagation
        // let mut network = SpikingNetwork::new(&[
        //     neurons_per_layer,
        //     neurons_per_layer,
        //     neurons_per_layer,
        // ]);
        //
        // // Inject strong current into first layer
        // network.inject_current(0, vec![2.0; neurons_per_layer]);
        //
        // // Run for several timesteps
        // let mut layer_spikes = vec![vec![]; num_layers];
        // for t in 0..50 {
        //     let spikes = network.step(1.0);
        //     for (layer, layer_spikes_t) in spikes.iter().enumerate() {
        //         if layer_spikes_t.iter().any(|&s| s) {
        //             layer_spikes[layer].push(t);
        //         }
        //     }
        // }
        //
        // // Spikes should propagate through layers
        // assert!(!layer_spikes[0].is_empty(), "First layer should spike");
        // assert!(!layer_spikes[2].is_empty(), "Output layer should receive spikes");
        //
        // // Output layer should spike after input layer
        // if !layer_spikes[2].is_empty() {
        //     assert!(layer_spikes[2][0] > layer_spikes[0][0],
        //         "Causality: output should spike after input");
        // }

        assert!(num_layers > 0);
    }

    #[wasm_bindgen_test]
    fn test_stdp_learning() {
        // Test Spike-Timing-Dependent Plasticity
        let a_plus = 0.01;  // Potentiation coefficient
        let a_minus = 0.01; // Depression coefficient
        let tau = 20.0;     // Time constant

        // TODO: Test STDP
        // let mut stdp = STDPRule::new(a_plus, a_minus, tau);
        //
        // let initial_weight = 0.5;
        //
        // // Pre before post (potentiation)
        // let pre_spike_time = 0.0;
        // let post_spike_time = 10.0;
        // let delta_w = stdp.compute_weight_change(pre_spike_time, post_spike_time);
        // assert!(delta_w > 0.0, "Pre-before-post should potentiate");
        //
        // // Post before pre (depression)
        // let pre_spike_time = 10.0;
        // let post_spike_time = 0.0;
        // let delta_w = stdp.compute_weight_change(pre_spike_time, post_spike_time);
        // assert!(delta_w < 0.0, "Post-before-pre should depress");

        assert!(tau > 0.0);
    }

    #[wasm_bindgen_test]
    fn test_spiking_temporal_coding() {
        // Test rate vs temporal coding
        let num_neurons = 10;

        // TODO: Test temporal coding
        // let mut network = SpikingNetwork::temporal_coding(num_neurons);
        //
        // // Encode value as spike time (earlier = higher value)
        // let values: Vec<f32> = (0..num_neurons).map(|i| (i as f32) / (num_neurons as f32)).collect();
        // network.encode_temporal(&values);
        //
        // // Run and record spike times
        // let mut spike_times = vec![f32::INFINITY; num_neurons];
        // for t in 0..100 {
        //     let spikes = network.step(1.0);
        //     for (i, &spiked) in spikes.iter().enumerate() {
        //         if spiked && spike_times[i] == f32::INFINITY {
        //             spike_times[i] = t as f32;
        //         }
        //     }
        // }
        //
        // // Higher values should spike earlier
        // for i in 1..num_neurons {
        //     if spike_times[i] < f32::INFINITY && spike_times[i-1] < f32::INFINITY {
        //         assert!(spike_times[i] < spike_times[i-1],
        //             "Higher value should spike earlier");
        //     }
        // }

        assert!(num_neurons > 0);
    }

    // ========================================================================
    // Neuromorphic Processing Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_neuromorphic_attention() {
        // Test neuromorphic attention mechanism
        let dim = 64;
        let num_heads = 4;

        // TODO: Test neuromorphic attention
        // let attention = NeuromorphicAttention::new(dim, num_heads);
        //
        // let query = random_vector(dim);
        // let keys: Vec<Vec<f32>> = (0..10).map(|_| random_vector(dim)).collect();
        // let values: Vec<Vec<f32>> = (0..10).map(|_| random_vector(dim)).collect();
        //
        // let output = attention.forward(&query, &keys, &values);
        //
        // assert_eq!(output.len(), dim);
        // assert_finite(&output);

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_reservoir_computing() {
        // Test Echo State Network / Reservoir Computing
        let input_dim = 10;
        let reservoir_size = 100;
        let output_dim = 5;

        // TODO: Test reservoir
        // let reservoir = ReservoirComputer::new(input_dim, reservoir_size, output_dim);
        //
        // // Run sequence through reservoir
        // let sequence: Vec<Vec<f32>> = (0..50).map(|_| random_vector(input_dim)).collect();
        //
        // for input in &sequence {
        //     reservoir.step(input);
        // }
        //
        // // Get reservoir state
        // let state = reservoir.get_state();
        // assert_eq!(state.len(), reservoir_size);
        // assert_finite(&state);
        //
        // // Train readout
        // let targets: Vec<Vec<f32>> = (0..50).map(|_| random_vector(output_dim)).collect();
        // reservoir.train_readout(&targets);
        //
        // // Get output
        // let output = reservoir.predict();
        // assert_eq!(output.len(), output_dim);

        assert!(reservoir_size > 0);
    }

    // ========================================================================
    // Integration Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_hdc_snn_integration() {
        // Test using HDC with SNN for efficient inference
        let hd_dim = 1000;
        let num_classes = 10;

        // TODO: Test HDC + SNN integration
        // let encoder = HDCEncoder::new(hd_dim);
        // let classifier = HDCClassifier::new(hd_dim, num_classes);
        //
        // // Convert to spiking
        // let snn = classifier.to_spiking();
        //
        // // Encode and classify with SNN
        // let input = random_vector(hd_dim);
        // let encoded = encoder.encode(&input);
        //
        // let output = snn.forward(&encoded);
        // assert_eq!(output.len(), num_classes);

        assert!(num_classes > 0);
    }

    #[wasm_bindgen_test]
    fn test_energy_efficiency() {
        // Neuromorphic should be more energy efficient (fewer operations)
        let dim = 64;
        let seq_len = 100;

        // TODO: Compare operation counts
        // let standard_attention = StandardAttention::new(dim);
        // let neuromorphic_attention = NeuromorphicAttention::new(dim, 4);
        //
        // let queries = (0..seq_len).map(|_| random_vector(dim)).collect();
        // let keys = (0..seq_len).map(|_| random_vector(dim)).collect();
        //
        // let standard_ops = standard_attention.count_operations(&queries, &keys);
        // let neuro_ops = neuromorphic_attention.count_operations(&queries, &keys);
        //
        // // Neuromorphic should use fewer ops (event-driven)
        // assert!(neuro_ops < standard_ops,
        //     "Neuromorphic should be more efficient: {} vs {}", neuro_ops, standard_ops);

        assert!(seq_len > 0);
    }

    // ========================================================================
    // WASM-Specific Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_nervous_system_wasm_initialization() {
        // Test WASM module initialization
        // TODO: Verify init
        // ruvector_nervous_system_wasm::init();
        // assert!(ruvector_nervous_system_wasm::version().len() > 0);

        assert!(true);
    }

    #[wasm_bindgen_test]
    fn test_nervous_system_serialization() {
        // Test serialization for WASM interop
        let num_neurons = 10;

        // TODO: Test serialization
        // let network = SpikingNetwork::new(&[num_neurons, num_neurons]);
        //
        // // Serialize to JSON
        // let json = network.to_json();
        // assert!(json.len() > 0);
        //
        // // Deserialize
        // let restored = SpikingNetwork::from_json(&json);
        //
        // // Should produce same output
        // let input = random_vector(num_neurons);
        // let output1 = network.forward(&input);
        // let output2 = restored.forward(&input);
        // assert_vectors_approx_eq(&output1, &output2, 1e-6);

        assert!(num_neurons > 0);
    }
}