//! ONNX Runtime integration for RustyTorch++
//!
//! This crate provides high-performance ONNX model inference using ONNX Runtime,
//! with support for multiple execution providers (CPU, CUDA, CoreML, TensorRT).
//!
//! # Features
//!
//! - **CPU execution**: Always available, no additional dependencies
//! - **CUDA execution**: GPU acceleration on NVIDIA hardware (requires `cuda` feature)
//! - **CoreML execution**: Apple Neural Engine acceleration (requires `coreml` feature)
//! - **TensorRT execution**: Optimized inference on NVIDIA GPUs (requires `tensorrt` feature)
//!
//! # Example
//!
//! ```ignore
//! use rtx_onnx::{OnnxSession, OnnxSessionConfig};
//! use rtx_tensor::{Tensor, Device};
//! use std::collections::HashMap;
//!
//! // Load model with default config (auto-detect best execution provider)
//! let config = OnnxSessionConfig::default();
//! let session = OnnxSession::from_file("model.onnx", config)?;
//!
//! // Create input tensor
//! let input = Tensor::randn(vec![1, 3, 224, 224], &Device::CPU)?;
//!
//! // Run inference
//! let inputs = HashMap::from([("input".to_string(), &input)]);
//! let outputs = session.run(inputs)?;
//!
//! // Get output
//! let output = outputs.get("output").unwrap();
//! println!("Output shape: {:?}", output.shape());
//! ```

pub mod error;
pub mod execution_provider;
pub mod session;
pub mod tensor_bridge;

// Re-export main types
pub use error::{OnnxError, Result};
pub use execution_provider::{CpuOptions, ExecutionProviderType, detect_best_provider};
pub use session::{OnnxSession, OnnxSessionConfig, OptimizationLevel};
pub use tensor_bridge::{ort_to_rtx, rtx_to_ort};

// Re-export feature-gated execution provider options
#[cfg(feature = "cuda")]
pub use execution_provider::{ArenaExtendStrategy, CudaOptions, CudnnConvAlgoSearch};

#[cfg(feature = "coreml")]
pub use execution_provider::CoreMLOptions;

#[cfg(feature = "tensorrt")]
pub use execution_provider::TensorRTOptions;

#[cfg(feature = "directml")]
pub use execution_provider::DirectMLOptions;