//! Comprehensive RTX Losses - Complete Loss Function Library
//!
//! This provides working implementations of critical loss functions for:
//! - **Metric Learning**: TripletLoss with distance metrics and mining strategies
//! - **Contrastive Learning**: InfoNCE, SwAV, SimCLR for self-supervised learning
//! - **Robust Regression**: HuberLoss, QuantileLoss, LogCoshLoss for outlier-resistant training
//!
//! All implementations use a minimal tensor backend to avoid ecosystem compilation issues
//! while providing full functionality and comprehensive test coverage.

pub mod bce_with_logits_loss;
pub mod center_loss;
pub mod combined_loss;
pub mod error_minimal;
pub mod hinge_loss;
pub mod huber_loss;
pub mod info_nce_minimal;
pub mod iou_loss;
pub mod kl_divergence_loss;
pub mod logcosh_loss;
pub mod mae_loss;
pub mod minimal_tensor;
pub mod quantile_loss;
pub mod reduction_minimal;
pub mod simclr_minimal;
pub mod swav_minimal;
pub mod triplet_loss;
pub mod wasserstein_loss;

#[cfg(test)]
mod integration_test;
#[cfg(test)]
mod triplet_test_standalone;

// Re-export main types
pub use error_minimal::{LossError, Result};
pub use minimal_tensor::{MinimalDevice, MinimalTensor};
pub use reduction_minimal::Reduction;

// Self-supervised and contrastive learning losses
pub use info_nce_minimal::InfoNCEMinimal;
pub use simclr_minimal::SimCLRMinimal;
pub use swav_minimal::SwAVMinimal;

// Metric learning losses
pub use triplet_loss::{DistanceMetric, TripletLoss};

// Robust regression losses
pub use huber_loss::HuberLoss;
pub use logcosh_loss::LogCoshLoss;
pub use quantile_loss::QuantileLoss;

// SVM-based classification losses
pub use hinge_loss::{HingeLoss, HingeLossVariant};

// Wasserstein distance and optimal transport losses
pub use wasserstein_loss::{GroundMetric, WassersteinDistance, WassersteinLoss};

// Face recognition and metric learning losses
pub use center_loss::CenterLoss;

// Basic regression losses
pub use mae_loss::MAELoss;

// Binary classification losses
pub use bce_with_logits_loss::BCEWithLogitsLoss;

// Distribution distance losses
pub use kl_divergence_loss::KLDivLoss;

// Computer vision losses
pub use iou_loss::{IoULoss, IoUVariant};

// Loss combination utilities
pub use combined_loss::{CombinedLoss, CombinedLossBuilder, LossComponent};

// Simplified Loss trait for minimal implementation
pub trait MinimalLoss {
    /// Reduction mode for this loss
    fn reduction(&self) -> Reduction;
}