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use crate as burn;

use crate::config::Config;
use crate::module::Module;
use crate::tensor::backend::Backend;
use crate::tensor::{Distribution, Tensor};

/// Configuration to create a [Dropout](Dropout) layer.
#[derive(Config)]
pub struct DropoutConfig {
    /// The probability of randomly zeroes some elements of the input tensor during training.
    pub prob: f64,
}

/// Set at random some elements of the input tensor to zero during training.
///
/// This is an effective regularization technique as describe in the paper
/// [Improving neural networks by preventing co-adaptation of feature detectors](https://arxiv.org/abs/1207.0580).
///
/// The input is also scaled during training to `1 / (1 - prob_keep)`.
#[derive(Module, Clone, Debug)]
pub struct Dropout {
    prob: f64,
}

impl DropoutConfig {
    /// Initialize a new [dropout](Dropout) module.
    pub fn init(&self) -> Dropout {
        Dropout { prob: self.prob }
    }
}

impl Dropout {
    /// Applies the forward pass on the input tensor.
    ///
    /// # Shapes
    ///
    /// - input: `[..., any]`
    /// - output: `[..., any]`
    pub fn forward<B: Backend, const D: usize>(&self, input: Tensor<B, D>) -> Tensor<B, D> {
        if !B::ad_enabled() || self.prob == 0.0 {
            return input;
        }

        let prob_keep = 1.0 - self.prob;
        let random = input.random_like(Distribution::Bernoulli(prob_keep));
        let x = input * random;

        x * (1.0 / prob_keep)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::tensor::Shape;

    #[cfg(feature = "std")]
    use crate::{TestADBackend, TestBackend};

    #[cfg(not(feature = "std"))]
    use crate::TestBackend;

    #[cfg(feature = "std")]
    #[test]
    fn with_ad_backend_should_mark_input() {
        let tensor = Tensor::<TestADBackend, 2>::ones(Shape::new([100, 100]));
        let dropout = DropoutConfig::new(0.5).init();

        let output = dropout.forward(tensor.clone());

        assert_ne!(tensor.to_data(), output.to_data());
    }

    #[test]
    fn without_ad_backend_should_not_change_input() {
        let tensor = Tensor::<TestBackend, 2>::ones(Shape::new([100, 100]));
        let dropout = DropoutConfig::new(0.5).init();

        let output = dropout.forward(tensor.clone());

        assert_eq!(tensor.to_data(), output.to_data());
    }
}