//! # RustyTorch Derive Macros
//!
//! This crate provides procedural macros for the RustyTorch deep learning framework.
//!
//! ## Available Macros
//!
//! - `#[derive(Module)]` - Automatically implements the `Module` trait for neural network layers
//! - `#[derive(Config)]` - Generates builder pattern configuration structs
//!
//! ## Module Derive
//!
//! The `Module` derive macro automatically generates implementations for:
//! - `parameters()` - Returns all trainable parameters
//! - `parameters_mut()` - Returns mutable references to parameters
//! - `to_device()` - Moves the module to a device
//! - `device()` - Returns the module's device
//! - `train()` / `training()` - Training mode control
//!
//! ### Field Attributes
//!
//! - `#[param]` - Marks a field as a trainable parameter (Tensor)
//! - `#[module]` - Marks a field as a nested module (recursive traversal)
//! - `#[constant]` - Marks a field as a non-trainable constant
//! - `#[device_field]` - Marks the field that stores the Device
//! - `#[training_field]` - Marks the field that stores training mode
//!
//! ### Example
//!
//! ```ignore
//! use rtx_macros::Module;
//!
//! #[derive(Module, Debug)]
//! pub struct MyLayer {
//!     #[param]
//!     weight: Tensor,
//!
//!     #[param]
//!     bias: Option<Tensor>,
//!
//!     #[module]
//!     sublayer: Linear,
//!
//!     #[constant]
//!     dropout_rate: f32,
//!
//!     #[device_field]
//!     device: Device,
//!
//!     #[training_field]
//!     training: bool,
//! }
//! ```
//!
//! ## Config Derive
//!
//! The `Config` derive macro generates:
//! - `new()` constructor with required fields
//! - `with_*()` builder methods for optional fields
//! - `init()` method to create the module
//!
//! ### Field Attributes
//!
//! - `#[config(default = "value")]` - Sets a default value for the field
//!
//! ### Example
//!
//! ```ignore
//! use rtx_macros::Config;
//!
//! #[derive(Config)]
//! pub struct LinearConfig {
//!     pub in_features: usize,
//!     pub out_features: usize,
//!
//!     #[config(default = "true")]
//!     pub bias: bool,
//!
//!     #[config(default = "0.0")]
//!     pub dropout: f64,
//! }
//! ```

use proc_macro::TokenStream;

mod config;
mod module;
mod shared;

/// Derive macro for implementing the `Module` trait.
///
/// This macro automatically generates the boilerplate code needed for neural network modules,
/// including parameter management, device handling, and training mode control.
///
/// See the [crate-level documentation](crate) for detailed usage examples.
#[proc_macro_derive(
    Module,
    attributes(param, module, constant, device_field, training_field)
)]
pub fn module_derive(input: TokenStream) -> TokenStream {
    let input = syn::parse_macro_input!(input as syn::DeriveInput);
    module::derive_impl(&input)
        .unwrap_or_else(|err| err.to_compile_error())
        .into()
}

/// Derive macro for creating configuration structs with builder pattern.
///
/// This macro generates constructors and builder methods for configuration structs,
/// making it easy to create module configurations with sensible defaults.
///
/// See the [crate-level documentation](crate) for detailed usage examples.
#[proc_macro_derive(Config, attributes(config))]
pub fn config_derive(input: TokenStream) -> TokenStream {
    let input = syn::parse_macro_input!(input as syn::DeriveInput);
    config::derive_impl(&input)
        .unwrap_or_else(|err| err.to_compile_error())
        .into()
}