//! Ergonomics & safety focused deep learning in Rust. Main features include:
//! 1. Tensor library, complete with const generic shapes, activation functions, and more.
//! 2. Safe & Easy to use neural network building blocks.
//! 3. Standard deep learning optimizers such as Sgd and Adam.
//! 4. Reverse mode auto differentiation implementation.
//!
//! # A quick tutorial
//!
//! 1. [crate::tensor::Tensor]s can be created with normal rust arrays. See [crate::tensor].
//! ```rust
//! # use dfdx::prelude::*;
//! let x = Tensor2D::new([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]);
//! let y: Tensor2D<2, 3> = Tensor2D::ones();
//! ```
//!
//! 2. Neural networks are built with types. Tuples are sequential models. See [crate::nn].
//! ```rust
//! # use dfdx::prelude::*;
//! type Mlp = (
//!     Linear<5, 3>,
//!     ReLU,
//!     Linear<3, 2>,
//! );
//! ```
//!
//! 3. Instantiate models with [Default], and randomize with [crate::nn::ResetParams]
//! ```rust
//! # use dfdx::prelude::*;
//! # let mut rng = rand::thread_rng();
//! let mut mlp: Linear<5, 2> = Default::default();
//! mlp.reset_params(&mut rng);
//! ```
//!
//! 4. Pass data through networks with [crate::nn::Module]
//! ```rust
//! # use dfdx::prelude::*;
//! let mut mlp: Linear<5, 2> = Default::default();
//! let x: Tensor1D<5> = Tensor1D::zeros();
//! let y = mlp.forward(x); // rust will auto figure out that `y` is `Tensor1D<2>`!
//! ```
//!
//! 5. Trace gradients using [crate::tensor::trace()]
//! ```rust
//! # use dfdx::prelude::*;
//! # let mut rng = rand::thread_rng();
//! # let model: Linear<10, 5> = Default::default();
//! # let y_true: Tensor1D<5> = Tensor1D::randn(&mut rng).softmax();
//! // tensors default to not having a tape
//! let x: Tensor1D<10, NoneTape> = Tensor1D::zeros();
//!
//! // `.trace()` clones `x` and inserts a gradient tape.
//! let x_t: Tensor1D<10, OwnedTape> = x.trace();
//!
//! // The tape is moved through the model during `.forward()`, and ends up in `y`.
//! let y: Tensor1D<5, OwnedTape> = model.forward(x_t);
//! ```
//!
//! 6. Compute gradients with [crate::tensor_ops::backward()]
//! ```rust
//! # use dfdx::prelude::*;
//! # let mut rng = rand::thread_rng();
//! # let model: Linear<10, 5> = Default::default();
//! # let y_true: Tensor1D<5> = Tensor1D::randn(&mut rng).softmax();
//! # let y: Tensor1D<5, OwnedTape> = model.forward(Tensor1D::zeros().trace());
//! // compute cross entropy loss
//! let loss: Tensor0D<OwnedTape> = cross_entropy_with_logits_loss(y, &y_true);
//!
//! // call `backward()` to compute gradients. The tensor *must* have `OwnedTape`!
//! let gradients: Gradients = loss.backward();
//! ```
//! 7. Use an optimizer from [crate::optim] to optimize your network!
//! ```rust
//! # use dfdx::prelude::*;
//! # let mut rng = rand::thread_rng();
//! # let mut model: Linear<10, 5> = Default::default();
//! # let x: Tensor1D<10> = Tensor1D::zeros();
//! # let y_true: Tensor1D<5> = Tensor1D::randn(&mut rng).softmax();
//! # let y: Tensor1D<5, OwnedTape> = model.forward(x.trace());
//! # let loss = cross_entropy_with_logits_loss(y, &y_true);
//! # let gradients: Gradients = loss.backward();
//! // Use stochastic gradient descent (Sgd), with a learning rate of 1e-2, and 0.9 momentum.
//! let mut opt = Sgd::new(1e-2, Some(Momentum::Classic(0.9)));
//!
//! // pass the gradients & the model into the optimizer's update method
//! opt.update(&mut model, gradients);
//! ```

pub mod arrays;
pub mod data;
pub mod devices;
pub mod gradients;
pub mod losses;
pub mod nn;
pub mod numpy;
pub mod optim;
pub mod tensor;
pub mod tensor_ops;
pub mod unique_id;

/// Contains all public exports.
pub mod prelude {
    pub use crate::arrays::*;
    pub use crate::data::*;
    pub use crate::devices::*;
    pub use crate::gradients::*;
    pub use crate::losses::*;
    pub use crate::nn::*;
    pub use crate::optim::*;
    pub use crate::tensor::*;
    pub use crate::tensor_ops::*;
    pub use crate::unique_id::*;
}

/// Sets a CPU `sse` flag to flush denormal floating point numbers to zero. The opposite of this is [keep_denormals()].
///
/// Some resources:
/// 1. [Effects of Flush-To-Zero mode](https://developer.arm.com/documentation/dui0473/c/neon-and-vfp-programming/the-effects-of-using-flush-to-zero-mode?lang=en)
/// 2. [When to use Flush-To-Zero mode](https://developer.arm.com/documentation/dui0473/c/neon-and-vfp-programming/when-to-use-flush-to-zero-mode?lang=en)
pub fn flush_denormals_to_zero() {
    #[cfg(all(target_arch = "x86", target_feature = "sse"))]
    {
        use std::arch::x86::{_MM_FLUSH_ZERO_ON, _MM_SET_FLUSH_ZERO_MODE};
        unsafe { _MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON) }
    }

    #[cfg(all(target_arch = "x86_64", target_feature = "sse"))]
    {
        use std::arch::x86_64::{_MM_FLUSH_ZERO_ON, _MM_SET_FLUSH_ZERO_MODE};
        unsafe { _MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_ON) }
    }
}

/// Sets a CPU flag to keep denormal floating point numbers. The opposite of this is [flush_denormals_to_zero()].
///
/// Some resources:
/// 1. [Effects of Flush-To-Zero mode](https://developer.arm.com/documentation/dui0473/c/neon-and-vfp-programming/the-effects-of-using-flush-to-zero-mode?lang=en)
/// 2. [When to use Flush-To-Zero mode](https://developer.arm.com/documentation/dui0473/c/neon-and-vfp-programming/when-to-use-flush-to-zero-mode?lang=en)
pub fn keep_denormals() {
    #[cfg(all(target_arch = "x86", target_feature = "sse"))]
    {
        use std::arch::x86::{_MM_FLUSH_ZERO_OFF, _MM_SET_FLUSH_ZERO_MODE};
        unsafe { _MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_OFF) }
    }

    #[cfg(all(target_arch = "x86_64", target_feature = "sse"))]
    {
        use std::arch::x86_64::{_MM_FLUSH_ZERO_OFF, _MM_SET_FLUSH_ZERO_MODE};
        unsafe { _MM_SET_FLUSH_ZERO_MODE(_MM_FLUSH_ZERO_OFF) }
    }
}