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use crate::{backend::Backend, Bool, Int, Shape, Tensor, TensorData, TensorPrimitive};
use alloc::vec::Vec;
use crate::try_read_sync;
/// The part of the tensor to keep when creating a triangular mask.
enum TriPart {
/// Upper triangular part.
Upper,
/// Lower triangular part.
Lower,
/// Diagonal part.
Diagonal,
}
impl<B, const D: usize> Tensor<B, D, Bool>
where
B: Backend,
{
/// Create a boolean tensor from data on the given device.
pub fn from_bool(data: TensorData, device: &B::Device) -> Self {
Self::new(B::bool_from_data(data, device))
}
/// Convert the bool tensor into an int tensor.
pub fn int(self) -> Tensor<B, D, Int> {
Tensor::new(B::bool_into_int(self.primitive))
}
/// Convert the bool tensor into an float tensor.
pub fn float(self) -> Tensor<B, D> {
Tensor::new(TensorPrimitive::Float(B::bool_into_float(self.primitive)))
}
/// Inverses boolean values.
pub fn bool_not(self) -> Self {
Tensor::new(B::bool_not(self.primitive))
}
/// Compute the indices of the elements that are non-zero.
///
/// # Returns
///
/// A vector of tensors, one for each dimension of the given tensor, containing the indices of
/// the non-zero elements in that dimension.
pub fn nonzero(self) -> Vec<Tensor<B, 1, Int>> {
try_read_sync(self.nonzero_async())
.expect("Failed to read tensor data synchronously. Try using nonzero_async instead.")
}
/// Compute the indices of the elements that are non-zero.
///
/// # Returns
///
/// A vector of tensors, one for each dimension of the given tensor, containing the indices of
/// the non-zero elements in that dimension.
pub async fn nonzero_async(self) -> Vec<Tensor<B, 1, Int>> {
B::bool_nonzero(self.primitive)
.await
.into_iter()
.map(Tensor::new)
.collect()
}
/// Compute the indices of the elements that are true, grouped by element.
///
/// # Returns
///
/// A tensor containing the indices of all non-zero elements of the given tensor. Each row in the
/// result contains the indices of a non-zero element.
pub fn argwhere(self) -> Tensor<B, 2, Int> {
try_read_sync(self.argwhere_async())
.expect("Failed to read tensor data synchronously. Try using argwhere_async instead.")
}
/// Compute the indices of the elements that are true, grouped by element.
///
/// # Returns
///
/// A tensor containing the indices of all non-zero elements of the given tensor. Each row in the
/// result contains the indices of a non-zero element.
pub async fn argwhere_async(self) -> Tensor<B, 2, Int> {
Tensor::new(B::bool_argwhere(self.primitive).await)
}
/// Creates a mask for the upper, lower triangle, or diagonal of a matrix, which can be used to
/// fill the specified area with a value.
fn tri_mask<S: Into<Shape>>(
shape: S,
tri_part: TriPart,
offset: i64,
device: &B::Device,
) -> Self {
let shape: Shape = shape.into();
let height = shape.dims[D - 2];
let width = shape.dims[D - 1];
// Generate row and column index tensors.
let row_indices: Tensor<B, 1, Int> = Tensor::arange(0..height as i64, device);
let col_indices: Tensor<B, 1, Int> = Tensor::arange(0..width as i64, device);
// Prepare shapes for broadcasting.
let mut row_shape = [1; D];
row_shape[D - 2] = height;
let mut col_shape = [1; D];
col_shape[D - 1] = width;
// Reshape for broadcasting.
let row_broadcast: Tensor<B, D, Int> = row_indices.reshape(Shape::new(row_shape));
let col_broadcast = col_indices.reshape(Shape::new(col_shape));
// Broadcasting trick to create a matrix that facilitates comparison for mask generation.
let matrix = row_broadcast.clone() - (col_broadcast.clone() - offset);
// Select the appropriate comparison function based on `tri_part`.
let compare = match tri_part {
TriPart::Upper => Tensor::greater_elem,
TriPart::Lower => Tensor::lower_elem,
TriPart::Diagonal => Tensor::not_equal_elem,
};
// Generate and return the mask by applying the comparison to the matrix.
compare(matrix, 0).unsqueeze()
}
/// Creates a mask for the upper triangle of a matrix, which can be used to fill the specified
/// area with a value.
///
/// This function generates a boolean tensor representing the mask of the upper triangle of a matrix.
///
/// # Arguments
///
/// * `shape`: The shape of the matrix.
/// * `offset`: The offset from the diagonal, where 0 means the diagonal, and positive values shift
/// towards the upper triangle.
/// * `device`: The device on which the tensor will be allocated.
///
/// # Returns
///
/// Returns a boolean tensor where `false` indicates the elements of the matrix that are part of the
/// upper triangle taking into account the specified `offset`. All other elements are `true`.
///
/// # Example
/// ```rust
/// use burn_tensor::backend::Backend;
/// use burn_tensor::{Tensor, Bool};
///
/// fn example<B: Backend>() {
/// let mask = Tensor::<B, 2, Bool>::triu_mask([3, 3], 0, &Default::default());
/// println!("{mask}");
/// // [[false, false, false],
/// // [true, false, false],
/// // [true, true, false]]
/// }
/// ```
pub fn triu_mask<S: Into<Shape>>(shape: S, offset: i64, device: &B::Device) -> Self {
Self::tri_mask(shape, TriPart::Upper, offset, device)
}
/// Creates a mask for the lower triangle of a matrix, which can be used to fill the specified
/// area with a value.
///
/// This function generates a boolean tensor representing the mask of the lower triangle of a matrix.
///
/// # Arguments
///
/// * `shape`: The shape of the matrix.
/// * `offset`: The offset from the diagonal, where 0 means the diagonal, and negative values shift
/// towards the lower triangle.
/// * `device`: The device on which the tensor will be allocated.
///
/// # Returns
///
/// Returns a boolean tensor where `false` indicates the elements of the matrix that are part of the
/// lower triangle taking into account the specified `offset`. All other elements are `true`.
///
/// # Example
/// ```rust
/// use burn_tensor::backend::Backend;
/// use burn_tensor::{Tensor, Bool};
///
/// fn example<B: Backend>() {
/// let mask = Tensor::<B, 2, Bool>::tril_mask([3, 3], 0, &Default::default());
/// println!("{mask}");
/// // [[false, true, true],
/// // [false, false, true],
/// // [false, false, false]]
/// }
/// ```
pub fn tril_mask<S: Into<Shape>>(shape: S, offset: i64, device: &B::Device) -> Self {
Self::tri_mask(shape, TriPart::Lower, offset, device)
}
/// Creates a mask for the diagonal of a matrix, which can be used to fill the specified
/// area with a value.
///
/// This function generates a boolean tensor representing the mask of the diagonal of a matrix.
///
/// # Arguments
///
/// * `shape`: The shape of the matrix.
/// * `device`: The device on which the tensor will be allocated.
///
/// # Returns
///
/// Returns a boolean tensor where `false` indicates the elements of the matrix that are part of the
/// diagonal. All other elements are `true`.
///
/// # Example
/// ```rust
/// use burn_tensor::backend::Backend;
/// use burn_tensor::{Tensor, Bool};
///
/// fn example<B: Backend>() {
/// let mask = Tensor::<B, 2, Bool>::diag_mask([3, 3], 0, &Default::default());
/// println!("{mask}");
/// // [[false, true, true],
/// // [true, false, true],
/// // [true, true, false]]
/// }
/// ```
pub fn diag_mask<S: Into<Shape>>(shape: S, offset: i64, device: &B::Device) -> Self {
Self::tri_mask(shape, TriPart::Diagonal, offset, device)
}
}