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use super::Device;
use crate::{shapes::*, tensor::*};

/// Computes the [softmax function](https://en.wikipedia.org/wiki/Softmax_function) across
/// `Ax`.
///
/// Equivalent to `exp(log_softmax(t))`.
///
/// **Pytorch equivalent**: `t.softmax(Axes)`
///
/// Example:
/// ```rust
/// # use dfdx::prelude::*;
/// # let dev: Cpu = Default::default();
/// let t: Tensor<Rank3<2, 3, 5>, f32, _> = dev.zeros();
/// let _ = t.softmax::<Axis<2>>();
/// ```
pub fn softmax<Ax: Axes, S: Shape, E: Dtype, D: Device<E>, T: Tape<E, D>>(
    t: Tensor<S, E, D, T>,
) -> Tensor<S, E, D, T>
where
    S: ReduceShape<Ax>,
{
    t.softmax::<Ax>()
}

impl<S: Shape, E: Dtype, D: Device<E>, T: Tape<E, D>> Tensor<S, E, D, T> {
    /// See [softmax]
    pub fn softmax<Ax: Axes>(self) -> Self
    where
        S: ReduceShape<Ax>,
    {
        self.try_softmax::<Ax>().unwrap()
    }
    /// See [softmax]
    pub fn try_softmax<Ax: Axes>(self) -> Result<Self, D::Err>
    where
        S: ReduceShape<Ax>,
    {
        self.try_log_softmax::<Ax>()?.try_exp()
    }
}

#[cfg(test)]
mod tests {
    use crate::{shapes::*, tensor::*, tensor_ops::*, tests::*};

    #[test]
    fn test_softmax_1d() {
        let dev: TestDevice = Default::default();
        let a: Tensor<_, TestDtype, _> = dev.tensor([-2.0, -1.0, 0.0, 1.0, 2.0]);
        let r = a.leaky_trace().softmax();
        assert_close(
            &r.array(),
            &[0.011656232, 0.031684924, 0.086128555, 0.23412168, 0.6364087],
        );
        let l = r * dev.tensor([0.0, 0.0, 1.0, 0.0, 0.0]);
        assert_close(&l.array(), &[0.0, 0.0, 0.086128555, 0.0, 0.0]);
        let g = l.mean().backward();
        assert_close(
            &g.get(&a).array(),
            &[
                -0.00020078686,
                -0.00054579525,
                0.015742086,
                -0.0040329117,
                -0.010962591,
            ],
        );
    }

    #[test]
    fn test_softmax_2d() {
        let dev: TestDevice = Default::default();
        let a: Tensor<_, TestDtype, _> = dev.tensor([[-2.0, -1.0, 0.0], [1.0, 4.0, 7.0]]);
        let r = a.leaky_trace().softmax::<Axis<1>>();
        assert_close(
            &r.array(),
            &[
                [0.09003058, 0.24472849, 0.66524094],
                [0.002355633, 0.047314156, 0.9503302],
            ],
        );
        let l = r * dev.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]);
        assert_close(
            &l.array(),
            &[[0.09003058, 0.0, 0.0], [0.0, 0.047314156, 0.0]],
        );
        let g = l.mean().backward();
        assert_close(
            &g.get(&a).array(),
            &[
                [0.01365418, -0.0036721744, -0.009982005],
                [-1.85758e-5, 0.0075125876, -0.0074940124],
            ],
        );
    }

    #[test]
    fn test_softmax_2d_0th_axis() {
        let dev: TestDevice = Default::default();
        let a: Tensor<_, TestDtype, _> = dev.tensor([[-2.0, -1.0, 0.0], [1.0, 4.0, 7.0]]);
        let r = a.leaky_trace().softmax::<Axis<0>>();
        assert_close(
            &r.array(),
            &[
                [0.047425874, 0.0066928514, 0.0009110514],
                [0.95257413, 0.9933072, 0.9990892],
            ],
        );
        let l = r * dev.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]);
        assert_close(
            &l.array(),
            &[[0.047425874, 0.0, 0.0], [0.0, 0.9933072, 0.0]],
        );
        let g = l.mean().backward();
        assert_close(
            &g.get(&a).array(),
            &[
                [0.0075294436, -0.0011080095, 0.0],
                [-0.0075294436, 0.0011080056, 0.0],
            ],
        );
    }

    #[test]
    fn test_softmax_3d_to_1d_12() {
        let dev: TestDevice = Default::default();
        let t: Tensor<Rank3<2, 3, 4>, TestDtype, _> = dev.sample_normal();
        let r = t.leaky_trace().softmax::<Axes2<1, 2>>();
        #[rustfmt::skip]
        assert_close(
            &r.array(),
            &[
                [[0.08535644, 0.0987266, 0.00366116, 0.04927256], [0.01169326, 0.1515922, 0.00951258, 0.07721686], [0.0776206, 0.23813945, 0.19471556, 0.00249278]],
                [[0.01881982, 0.25171953, 0.02559674, 0.03725754], [0.04064152, 0.314442, 0.02427996, 0.04708378], [0.02791536, 0.14462142, 0.02221143, 0.04541067]],
            ],
        );
    }
}