//! Unit tests for exo-manifold learned manifold engine

#[cfg(test)]
mod manifold_retrieval_tests {
    use super::*;
    // use exo_manifold::*;
    // use burn::backend::NdArray;

    #[test]
    fn test_manifold_retrieve_basic() {
        // Test basic retrieval operation
        // let backend = NdArray::<f32>::default();
        // let config = ManifoldConfig::default();
        // let engine = ManifoldEngine::<NdArray<f32>>::new(config);
        //
        // let query = Tensor::from_floats([0.1, 0.2, 0.3, 0.4]);
        // let results = engine.retrieve(query, 5);
        //
        // assert_eq!(results.len(), 5);
    }

    #[test]
    fn test_manifold_retrieve_convergence() {
        // Test that gradient descent converges
        // let engine = setup_test_engine();
        // let query = random_query();
        //
        // let results = engine.retrieve(query.clone(), 10);
        //
        // // Verify convergence (gradient norm below threshold)
        // assert!(engine.last_gradient_norm() < 1e-4);
    }

    #[test]
    fn test_manifold_retrieve_different_k() {
        // Test retrieval with different k values
        // for k in [1, 5, 10, 50, 100] {
        //     let results = engine.retrieve(query.clone(), k);
        //     assert_eq!(results.len(), k);
        // }
    }

    #[test]
    fn test_manifold_retrieve_empty() {
        // Test retrieval from empty manifold
        // let engine = ManifoldEngine::new(config);
        // let results = engine.retrieve(query, 10);
        // assert!(results.is_empty());
    }
}

#[cfg(test)]
mod manifold_deformation_tests {
    use super::*;

    #[test]
    fn test_manifold_deform_basic() {
        // Test basic deformation operation
        // let mut engine = setup_test_engine();
        // let pattern = sample_pattern();
        //
        // engine.deform(pattern, 0.8);
        //
        // // Verify manifold was updated
        // assert!(engine.has_been_deformed());
    }

    #[test]
    fn test_manifold_deform_salience() {
        // Test deformation with different salience values
        // let mut engine = setup_test_engine();
        //
        // let high_salience = sample_pattern();
        // engine.deform(high_salience, 0.9);
        //
        // let low_salience = sample_pattern();
        // engine.deform(low_salience, 0.1);
        //
        // // Verify high salience has stronger influence
    }

    #[test]
    fn test_manifold_deform_gradient_update() {
        // Test that deformation updates network weights
        // let mut engine = setup_test_engine();
        // let initial_params = engine.network_parameters().clone();
        //
        // engine.deform(sample_pattern(), 0.5);
        //
        // let updated_params = engine.network_parameters();
        // assert_ne!(initial_params, updated_params);
    }

    #[test]
    fn test_manifold_deform_smoothness_regularization() {
        // Test that smoothness loss is applied
        // Verify manifold doesn't overfit to single patterns
    }
}

#[cfg(test)]
mod strategic_forgetting_tests {
    use super::*;

    #[test]
    fn test_forget_low_salience_regions() {
        // Test forgetting mechanism
        // let mut engine = setup_test_engine();
        //
        // // Populate with low-salience patterns
        // for i in 0..10 {
        //     engine.deform(low_salience_pattern(i), 0.1);
        // }
        //
        // // Apply forgetting
        // let region = engine.identify_low_salience_regions(0.2);
        // engine.forget(&region, 0.5);
        //
        // // Verify patterns are less retrievable
    }

    #[test]
    fn test_forget_preserves_high_salience() {
        // Test that forgetting doesn't affect high-salience regions
        // let mut engine = setup_test_engine();
        //
        // engine.deform(high_salience_pattern(), 0.9);
        // let before = engine.retrieve(query, 1);
        //
        // engine.forget(&low_salience_region, 0.5);
        //
        // let after = engine.retrieve(query, 1);
        // assert_similar(before, after);
    }

    #[test]
    fn test_forget_kernel_application() {
        // Test Gaussian smoothing kernel
    }
}

#[cfg(test)]
mod siren_network_tests {
    use super::*;

    #[test]
    fn test_siren_forward_pass() {
        // Test SIREN network forward propagation
        // let network = LearnedManifold::new(config);
        // let input = Tensor::from_floats([0.5, 0.5]);
        // let output = network.forward(input);
        //
        // assert!(output.dims()[0] > 0);
    }

    #[test]
    fn test_siren_backward_pass() {
        // Test gradient computation through SIREN layers
    }

    #[test]
    fn test_siren_sinusoidal_activation() {
        // Test that SIREN uses sinusoidal activations correctly
    }
}

#[cfg(test)]
mod fourier_features_tests {
    use super::*;

    #[test]
    fn test_fourier_encoding() {
        // Test Fourier feature transformation
        // let encoding = FourierEncoding::new(config);
        // let input = Tensor::from_floats([0.1, 0.2]);
        // let features = encoding.encode(input);
        //
        // // Verify feature dimensionality
        // assert_eq!(features.dims()[1], config.num_fourier_features);
    }

    #[test]
    fn test_fourier_frequency_spectrum() {
        // Test frequency spectrum configuration
    }
}

#[cfg(test)]
mod tensor_train_tests {
    use super::*;

    #[test]
    #[cfg(feature = "tensor-train")]
    fn test_tensor_train_decomposition() {
        // Test Tensor Train compression
        // let engine = setup_engine_with_tt();
        //
        // // Verify compression ratio
        // let original_size = engine.uncompressed_size();
        // let compressed_size = engine.compressed_size();
        //
        // assert!(compressed_size < original_size / 10);  // >10x compression
    }

    #[test]
    #[cfg(feature = "tensor-train")]
    fn test_tensor_train_accuracy() {
        // Test that TT preserves accuracy
    }
}

#[cfg(test)]
mod edge_cases_tests {
    use super::*;

    #[test]
    fn test_nan_handling() {
        // Test handling of NaN values in embeddings
        // let mut engine = setup_test_engine();
        // let pattern_with_nan = Pattern {
        //     embedding: vec![f32::NAN, 0.2, 0.3],
        //     ..Default::default()
        // };
        //
        // let result = engine.deform(pattern_with_nan, 0.5);
        // assert!(result.is_err());
    }

    #[test]
    fn test_infinity_handling() {
        // Test handling of infinity values
    }

    #[test]
    fn test_zero_dimension_embedding() {
        // Test empty embedding vector
        // let pattern = Pattern {
        //     embedding: vec![],
        //     ..Default::default()
        // };
        //
        // assert!(engine.deform(pattern, 0.5).is_err());
    }

    #[test]
    fn test_max_iterations_reached() {
        // Test gradient descent timeout
    }
}