//! Integration tests for ruvector-attention-unified-wasm
//!
//! Tests for unified attention mechanisms including:
//! - Multi-head self-attention
//! - Mamba SSM (Selective State Space Model)
//! - RWKV attention
//! - Flash attention approximation
//! - Hyperbolic attention

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

    wasm_bindgen_test_configure!(run_in_browser);

    // ========================================================================
    // Multi-Head Attention Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_multi_head_attention_basic() {
        // Setup query, keys, values
        let dim = 64;
        let num_heads = 8;
        let head_dim = dim / num_heads;
        let seq_len = 16;

        let query = random_vector(dim);
        let keys: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let values: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: When ruvector-attention-unified-wasm is implemented:
        // let attention = MultiHeadAttention::new(dim, num_heads);
        // let output = attention.forward(&query, &keys, &values);
        //
        // Assert output shape
        // assert_eq!(output.len(), dim);
        // assert_finite(&output);

        // Placeholder assertion
        assert_eq!(query.len(), dim);
        assert_eq!(keys.len(), seq_len);
    }

    #[wasm_bindgen_test]
    fn test_multi_head_attention_output_shape() {
        let dim = 128;
        let num_heads = 16;
        let seq_len = 32;

        let queries: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let keys: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let values: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: Verify output shape matches (seq_len, dim)
        // let attention = MultiHeadAttention::new(dim, num_heads);
        // let outputs = attention.forward_batch(&queries, &keys, &values);
        // assert_eq!(outputs.len(), seq_len);
        // for output in &outputs {
        //     assert_eq!(output.len(), dim);
        //     assert_finite(output);
        // }

        assert_eq!(queries.len(), seq_len);
    }

    #[wasm_bindgen_test]
    fn test_multi_head_attention_causality() {
        // Test that causal masking works correctly
        let dim = 32;
        let seq_len = 8;

        // TODO: Verify causal attention doesn't attend to future tokens
        // let attention = MultiHeadAttention::new_causal(dim, 4);
        // let weights = attention.get_attention_weights(&queries, &keys);
        //
        // For each position i, weights[i][j] should be 0 for j > i
        // for i in 0..seq_len {
        //     for j in (i+1)..seq_len {
        //         assert_eq!(weights[i][j], 0.0, "Causal violation at ({}, {})", i, j);
        //     }
        // }

        assert!(dim > 0);
    }

    // ========================================================================
    // Mamba SSM Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_mamba_ssm_basic() {
        // Test O(n) selective scan complexity
        let dim = 64;
        let seq_len = 100;

        let input: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: When Mamba SSM is implemented:
        // let mamba = MambaSSM::new(dim);
        // let output = mamba.forward(&input);
        //
        // Assert O(n) complexity by timing
        // let start = performance.now();
        // mamba.forward(&input);
        // let duration = performance.now() - start;
        //
        // Double input size should roughly double time (O(n))
        // let input_2x = (0..seq_len*2).map(|_| random_vector(dim)).collect();
        // let start_2x = performance.now();
        // mamba.forward(&input_2x);
        // let duration_2x = performance.now() - start_2x;
        //
        // assert!(duration_2x < duration * 2.5, "Should be O(n) not O(n^2)");

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

    #[wasm_bindgen_test]
    fn test_mamba_ssm_selective_scan() {
        // Test the selective scan mechanism
        let dim = 32;
        let seq_len = 50;

        let input: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: Verify selective scan produces valid outputs
        // let mamba = MambaSSM::new(dim);
        // let (output, hidden_states) = mamba.forward_with_states(&input);
        //
        // Hidden states should evolve based on input
        // for state in &hidden_states {
        //     assert_finite(state);
        // }

        assert!(dim > 0);
    }

    #[wasm_bindgen_test]
    fn test_mamba_ssm_state_propagation() {
        // Test that state is properly propagated across sequence
        let dim = 16;

        // TODO: Create a simple pattern and verify state carries information
        // let mamba = MambaSSM::new(dim);
        //
        // Input with a spike at position 0
        // let mut input = vec![vec![0.0; dim]; 20];
        // input[0] = vec![1.0; dim];
        //
        // let output = mamba.forward(&input);
        //
        // Later positions should still have some response to the spike
        // let response_at_5: f32 = output[5].iter().map(|x| x.abs()).sum();
        // assert!(response_at_5 > 0.01, "State should propagate forward");

        assert!(dim > 0);
    }

    // ========================================================================
    // RWKV Attention Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_rwkv_attention_basic() {
        let dim = 64;
        let seq_len = 100;

        let input: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: Test RWKV linear attention
        // let rwkv = RWKVAttention::new(dim);
        // let output = rwkv.forward(&input);
        // assert_eq!(output.len(), seq_len);

        assert!(input.len() == seq_len);
    }

    #[wasm_bindgen_test]
    fn test_rwkv_linear_complexity() {
        // RWKV should be O(n) in sequence length
        let dim = 32;

        // TODO: Verify linear complexity
        // let rwkv = RWKVAttention::new(dim);
        //
        // Time with 100 tokens
        // let input_100 = (0..100).map(|_| random_vector(dim)).collect();
        // let t1 = time_execution(|| rwkv.forward(&input_100));
        //
        // Time with 1000 tokens
        // let input_1000 = (0..1000).map(|_| random_vector(dim)).collect();
        // let t2 = time_execution(|| rwkv.forward(&input_1000));
        //
        // Should be roughly 10x, not 100x (O(n) vs O(n^2))
        // assert!(t2 < t1 * 20.0, "RWKV should be O(n)");

        assert!(dim > 0);
    }

    // ========================================================================
    // Flash Attention Approximation Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_flash_attention_approximation() {
        let dim = 64;
        let seq_len = 128;

        let queries: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let keys: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let values: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: Compare flash attention to standard attention
        // let standard = StandardAttention::new(dim);
        // let flash = FlashAttention::new(dim);
        //
        // let output_standard = standard.forward(&queries, &keys, &values);
        // let output_flash = flash.forward(&queries, &keys, &values);
        //
        // Should be numerically close
        // for (std_out, flash_out) in output_standard.iter().zip(output_flash.iter()) {
        //     assert_vectors_approx_eq(std_out, flash_out, 1e-4);
        // }

        assert!(queries.len() == seq_len);
    }

    #[wasm_bindgen_test]
    fn test_flash_attention_memory_efficiency() {
        // Flash attention should use less memory for long sequences
        let dim = 64;
        let seq_len = 512;

        // TODO: Verify memory usage is O(n) not O(n^2)
        // This is harder to test in WASM, but we can verify it doesn't OOM

        assert!(seq_len > 0);
    }

    // ========================================================================
    // Hyperbolic Attention Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_hyperbolic_attention_basic() {
        let dim = 32;
        let curvature = -1.0;

        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();

        // TODO: Test hyperbolic attention
        // let hyp_attn = HyperbolicAttention::new(dim, curvature);
        // let output = hyp_attn.forward(&query, &keys, &values);
        //
        // assert_eq!(output.len(), dim);
        // assert_finite(&output);

        assert!(curvature < 0.0);
    }

    #[wasm_bindgen_test]
    fn test_hyperbolic_distance_properties() {
        // Test Poincare distance metric properties
        let dim = 8;

        let u = random_vector(dim);
        let v = random_vector(dim);

        // TODO: Verify metric properties
        // let d_uv = poincare_distance(&u, &v, 1.0);
        // let d_vu = poincare_distance(&v, &u, 1.0);
        //
        // Symmetry
        // assert!((d_uv - d_vu).abs() < 1e-6);
        //
        // Non-negativity
        // assert!(d_uv >= 0.0);
        //
        // Identity
        // let d_uu = poincare_distance(&u, &u, 1.0);
        // assert!(d_uu.abs() < 1e-6);

        assert!(dim > 0);
    }

    // ========================================================================
    // Unified Interface Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_attention_mechanism_registry() {
        // Test that all mechanisms can be accessed through unified interface

        // TODO: Test mechanism registry
        // let registry = AttentionRegistry::new();
        //
        // assert!(registry.has_mechanism("multi_head"));
        // assert!(registry.has_mechanism("mamba_ssm"));
        // assert!(registry.has_mechanism("rwkv"));
        // assert!(registry.has_mechanism("flash"));
        // assert!(registry.has_mechanism("hyperbolic"));

        assert!(true);
    }

    #[wasm_bindgen_test]
    fn test_attention_factory() {
        // Test creating different attention types through factory

        // TODO: Test factory pattern
        // let factory = AttentionFactory::new();
        //
        // let config = AttentionConfig {
        //     dim: 64,
        //     num_heads: 8,
        //     mechanism: "multi_head",
        // };
        //
        // let attention = factory.create(&config);
        // assert!(attention.is_some());

        assert!(true);
    }

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

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

        // Test with large input values
        let query: Vec<f32> = (0..dim).map(|i| (i as f32) * 100.0).collect();
        let keys: Vec<Vec<f32>> = (0..10).map(|i| vec![(i as f32) * 100.0; dim]).collect();

        // TODO: Should not overflow or produce NaN
        // let attention = MultiHeadAttention::new(dim, 4);
        // let output = attention.forward(&query, &keys, &values);
        // assert_finite(&output);

        assert!(query[0].is_finite());
    }

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

        // Test with very small input values
        let query: Vec<f32> = vec![1e-10; dim];
        let keys: Vec<Vec<f32>> = (0..10).map(|_| vec![1e-10; dim]).collect();

        // TODO: Should not underflow or produce NaN
        // let attention = MultiHeadAttention::new(dim, 4);
        // let output = attention.forward(&query, &keys, &values);
        // assert_finite(&output);

        assert!(query[0].is_finite());
    }

    // ========================================================================
    // Performance Constraint Tests
    // ========================================================================

    #[wasm_bindgen_test]
    fn test_attention_latency_target() {
        // Target: <100 microseconds per mechanism at 100 tokens
        let dim = 64;
        let seq_len = 100;

        let queries: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let keys: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();
        let values: Vec<Vec<f32>> = (0..seq_len).map(|_| random_vector(dim)).collect();

        // TODO: Measure latency when implemented
        // let attention = MultiHeadAttention::new(dim, 8);
        //
        // Warm up
        // attention.forward(&queries[0], &keys, &values);
        //
        // Measure
        // let start = performance.now();
        // for _ in 0..100 {
        //     attention.forward(&queries[0], &keys, &values);
        // }
        // let avg_latency_us = (performance.now() - start) * 10.0; // 100 runs -> us
        //
        // assert!(avg_latency_us < 100.0, "Latency {} us exceeds 100 us target", avg_latency_us);

        assert!(queries.len() == seq_len);
    }
}