//! # RTX Interpret - Model Interpretability Library
//!
//! GPU-accelerated model interpretability and explainability toolkit for RustyTorch++.
//! This crate provides PyTorch Captum-equivalent functionality for understanding
//! and interpreting neural network predictions.
//!
//! ## Features
//!
//! - **Gradient-based Attribution**: Saliency maps, Integrated Gradients, GradCAM
//! - **Perturbation-based Attribution**: Occlusion, Feature Ablation, LIME
//! - **Layer Attribution**: Layer Conductance, Internal Influence
//! - **Neuron Attribution**: Neuron activation analysis, TCAV (Testing with Concept Activation Vectors)
//! - **Attention Analysis**: Attention Rollout, Attention Flow, Head Importance
//!
//! ## Architecture
//!
//! The library is organized around core traits:
//! - `Attribution`: Methods that compute feature importance scores
//! - `Perturbation`: Methods based on input modifications
//! - `Neuron`: Methods for neuron-level interpretability including TCAV
//! - `Attention`: Methods for analyzing transformer attention mechanisms
//!
//! ## Example
//!
//! ```rust
//! use rtx_interpret::{Attribution, Saliency, SaliencyConfig};
//! use rtx_tensor::{Tensor, Device};
//!
//! # fn main() -> Result<(), Box<dyn std::error::Error>> {
//! let device = Device::cpu();
//! let input = Tensor::randn(&[1, 3, 224, 224], &device)?;
//!
//! // Define your model's forward function
//! let forward_fn = |x: &Tensor| -> rtx_tensor::Result<Tensor> {
//!     // Your model forward pass here
//!     Ok(x.clone()) // Placeholder
//! };
//!
//! // Compute saliency map
//! let config = SaliencyConfig::default();
//! let saliency = Saliency::new(config);
//! let attributions = saliency.attribute(&forward_fn, &input, Some(0))?;
//!
//! println!("Attribution shape: {:?}", attributions.attributions().shape());
//! # Ok(())
//! # }
//! ```

#![allow(clippy::module_name_repetitions)]

pub mod attention;
pub mod attribution;
pub mod error;
pub mod neuron;
pub mod perturbation;
pub mod sae;
pub mod types;

// Re-export main types
pub use attention::{
    AttentionFlow, AttentionFlowConfig, AttentionRollout, AttentionRolloutConfig, HeadFusion,
    HeadImportance, HeadImportanceConfig, HeadImportanceMethod,
};
pub use attribution::{
    Attribution, DeepLIFT, DeepLIFTConfig, GradCAM, GradCAMConfig, IntegratedGradients,
    IntegratedGradientsConfig, LRP, LRPConfig, LRPRule, Saliency, SaliencyConfig,
};
pub use error::{InterpretError, Result};
pub use neuron::{ChannelActivation, ConceptActivationVector, MaxActivation, TCAV, TCAVConfig};
pub use perturbation::{
    FeatureAblation, FeatureAblationConfig, FeatureSelection, LIME, LIMEConfig, Occlusion,
    OcclusionConfig, Perturbation, SHAP, SHAPConfig,
};
pub use sae::{
    ActivationHook, ActivationHookConfig, FeatureAnalyzer, FeatureImportance, FeatureStats,
    HookHandle, ImportanceMethod, LayerHooks, SAEConfig, SAEStats, SAETrainer, SAETrainerConfig,
    SparseAutoencoder, SparsityStats, SparsityType, TopActivation, TrainingHistory,
};
pub use types::{AttributionMetadata, AttributionOutput, Baseline, PerturbationOutput};

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

    #[test]
    fn test_error_types_accessible() {
        let err = InterpretError::tensor("test");
        assert!(err.to_string().contains("test"));
    }

    #[test]
    fn test_baseline_enum_accessible() {
        let baseline = Baseline::Zero;
        assert!(matches!(baseline, Baseline::Zero));
    }
}