faer 0.24.0

use crate::internal_prelude_sp::*;
use crate::{assert, debug_assert};
/// sorts `row_indices` and `values` simultaneously so that `row_indices` is
/// nonincreasing.
pub fn sort_indices<I: Index, T>(
	col_ptr: &[I],
	col_nnz: Option<&[I]>,
	row_idx: &mut [I],
	val: &mut [T],
) {
	assert!(col_ptr.len() > 0);
	let n = col_ptr.len() - 1;
	for j in 0..n {
		let start = col_ptr[j].zx();
		let end = col_nnz
			.map(|nnz| start + nnz[j].zx())
			.unwrap_or(col_ptr[j + 1].zx());
		unsafe {
			crate::sort::sort_indices(
				&mut row_idx[start..end],
				&mut val[start..end],
			)
		};
	}
}
/// sorts and deduplicates `row_indices` and `values` simultaneously so that
/// `row_indices` is nonincreasing and contains no duplicate indices.
pub fn sort_dedup_indices<I: Index, T: ComplexField>(
	col_ptr: &[I],
	col_nnz: &mut [I],
	row_idx: &mut [I],
	val: &mut [T],
) {
	assert!(col_ptr.len() > 0);
	let n = col_ptr.len() - 1;
	for j in 0..n {
		let start = col_ptr[j].zx();
		let end = start + col_nnz[j].zx();
		unsafe {
			crate::sort::sort_indices(
				&mut row_idx[start..end],
				&mut val[start..end],
			)
		};
		let mut prev = I::truncate(usize::MAX);
		let mut writer = start;
		let mut reader = start;
		while reader < end {
			let cur = row_idx[reader];
			if cur == prev {
				writer -= 1;
				val[writer] = (&val[writer]) + (&val[reader]);
			} else {
				val[writer] = val[reader].copy();
			}
			prev = cur;
			reader += 1;
			writer += 1;
		}
		col_nnz[j] = I::truncate(writer - start);
	}
}
/// computes the workspace layout required to apply a two sided permutation to a
/// self-adjoint sparse matrix
pub fn permute_self_adjoint_scratch<I: Index>(dim: usize) -> StackReq {
	StackReq::new::<I>(dim)
}
/// computes the workspace layout required to apply a two sided permutation to a
/// self-adjoint sparse matrix and deduplicate its elements
pub fn permute_dedup_self_adjoint_scratch<I: Index>(dim: usize) -> StackReq {
	StackReq::new::<I>(dim)
}
/// computes the self-adjoint permutation $P A P^\top$ of the matrix $A$
///
/// the result is stored in `new_col_ptrs`, `new_row_indices`
///
/// # note
/// allows unsorted matrices, producing a sorted output. duplicate entries are
/// kept, however
pub fn permute_self_adjoint<
	'out,
	N: Shape,
	I: Index,
	T: ComplexField,
	C: Conjugate<Canonical = T>,
>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, C, N, N>,
	perm: PermRef<'_, I, N>,
	in_side: Side,
	out_side: Side,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, N, N> {
	let n = A.nrows();
	with_dim!(N, n.unbound());
	permute_self_adjoint_imp(
		new_val,
		new_col_ptr,
		new_row_idx,
		A.as_shape(N, N).canonical(),
		Conj::get::<C>(),
		perm.as_shape(N),
		in_side,
		out_side,
		true,
		stack,
	)
	.as_shape_mut(n, n)
}
/// computes the self-adjoint permutation $P A P^\top$ of the matrix $A$ without
/// sorting the row indices, and returns a view over it
///
/// the result is stored in `new_col_ptrs`, `new_row_indices`
///
/// # note
/// allows unsorted matrices, producing an sorted output
pub fn permute_self_adjoint_to_unsorted<
	'out,
	N: Shape,
	I: Index,
	T: ComplexField,
	C: Conjugate<Canonical = T>,
>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, C, N, N>,
	perm: PermRef<'_, I, N>,
	in_side: Side,
	out_side: Side,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, N, N> {
	let n = A.nrows();
	with_dim!(N, n.unbound());
	permute_self_adjoint_imp(
		new_val,
		new_col_ptr,
		new_row_idx,
		A.as_shape(N, N).canonical(),
		Conj::get::<C>(),
		perm.as_shape(N),
		in_side,
		out_side,
		false,
		stack,
	)
	.as_shape_mut(n, n)
}
/// computes the self-adjoint permutation $P A P^\top$ of the matrix $A$ and
/// deduplicate the elements of the output matrix
///
/// the result is stored in `new_col_ptrs`, `new_row_indices`
///
/// # note
/// allows unsorted matrices, producing a sorted output. duplicate entries are
/// merged
pub fn permute_dedup_self_adjoint<
	'out,
	N: Shape,
	I: Index,
	T: ComplexField,
	C: Conjugate<Canonical = T>,
>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, C, N, N>,
	perm: PermRef<'_, I, N>,
	in_side: Side,
	out_side: Side,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, N, N> {
	let n = A.nrows();
	with_dim!(N, n.unbound());
	permute_dedup_self_adjoint_imp(
		new_val,
		new_col_ptr,
		new_row_idx,
		A.as_shape(N, N).canonical(),
		Conj::get::<C>(),
		perm.as_shape(N),
		in_side,
		out_side,
		stack,
	)
	.as_shape_mut(n, n)
}
fn permute_self_adjoint_imp<'N, 'out, I: Index, T: ComplexField>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, T, Dim<'N>, Dim<'N>>,
	conj_A: Conj,
	perm: PermRef<'_, I, Dim<'N>>,
	in_side: Side,
	out_side: Side,
	sort: bool,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Dim<'N>, Dim<'N>> {
	let src_to_cmp = {
		let mask = match in_side {
			Side::Lower => 0,
			Side::Upper => usize::MAX,
		};
		move |i: usize| mask ^ i
	};
	let dst_to_cmp = {
		let mask = match out_side {
			Side::Lower => 0,
			Side::Upper => usize::MAX,
		};
		move |i: usize| mask ^ i
	};
	let conj_A = conj_A.is_conj();
	let N = A.ncols();
	let n = *N;
	assert!(new_col_ptr.len() == n + 1);
	let (_, perm_inv) = perm.bound_arrays();
	let (mut cur_row_pos, _) = stack.collect(repeat_n!(I::truncate(0), n));
	let cur_row_pos = Array::from_mut(&mut cur_row_pos, N);
	let col_counts = &mut *cur_row_pos;
	for old_j in N.indices() {
		let new_j = perm_inv[old_j].zx();
		let old_j_cmp = src_to_cmp(*old_j);
		let new_j_cmp = dst_to_cmp(*new_j);
		for old_i in A.row_idx_of_col(old_j) {
			let new_i = perm_inv[old_i].zx();
			let old_i_cmp = src_to_cmp(*old_i);
			let new_i_cmp = dst_to_cmp(*new_i);
			if old_i_cmp >= old_j_cmp {
				let lower = new_i_cmp >= new_j_cmp;
				let new_j = if lower { new_j } else { new_i };
				col_counts[new_j] += I::truncate(1);
			}
		}
	}
	new_col_ptr[0] = I::truncate(0);
	for (count, [ci0, ci1]) in iter::zip(
		col_counts.as_mut(),
		windows2(Cell::as_slice_of_cells(Cell::from_mut(&mut *new_col_ptr))),
	) {
		let ci0 = ci0.get();
		ci1.set(ci0 + *count);
		*count = ci0;
	}
	let nnz = new_col_ptr[n].zx();
	let new_row_idx = &mut new_row_idx[..nnz];
	let new_val = &mut new_val[..nnz];
	{
		with_dim!(NNZ, nnz);
		let new_val = Array::from_mut(new_val, NNZ);
		let new_row_idx = Array::from_mut(new_row_idx, NNZ);
		let conj_if = |cond: bool, x: &T| -> T {
			if const { T::IS_REAL } {
				x.copy()
			} else {
				if cond != conj_A { x.conj() } else { x.copy() }
			}
		};
		for old_j in N.indices() {
			let new_j = perm_inv[old_j].zx();
			let old_j_cmp = src_to_cmp(*old_j);
			let new_j_cmp = dst_to_cmp(*new_j);
			for (old_i, val) in
				iter::zip(A.row_idx_of_col(old_j), A.val_of_col(old_j))
			{
				let new_i = perm_inv[old_i].zx();
				let old_i_cmp = src_to_cmp(*old_i);
				let new_i_cmp = dst_to_cmp(*new_i);
				if old_i_cmp >= old_j_cmp {
					let lower = new_i_cmp >= new_j_cmp;
					let (new_j, new_i) = if lower {
						(new_j, new_i)
					} else {
						(new_i, new_j)
					};
					let cur_row_pos = &mut cur_row_pos[new_j];
					let row_pos =
						unsafe { Idx::new_unchecked(cur_row_pos.zx(), NNZ) };
					*cur_row_pos += I::truncate(1);
					new_val[row_pos] = conj_if(!lower, val);
					new_row_idx[row_pos] = I::truncate(*new_i);
				}
			}
		}
	}
	if sort {
		sort_indices(new_col_ptr, None, new_row_idx, new_val);
	}
	unsafe {
		SparseColMatMut::new(
			SymbolicSparseColMatRef::new_unchecked(
				N,
				N,
				new_col_ptr,
				None,
				new_row_idx,
			),
			new_val,
		)
	}
}
fn permute_dedup_self_adjoint_imp<'N, 'out, I: Index, T: ComplexField>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, T, Dim<'N>, Dim<'N>>,
	conj_A: Conj,
	perm: PermRef<'_, I, Dim<'N>>,
	in_side: Side,
	out_side: Side,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Dim<'N>, Dim<'N>> {
	let N = A.nrows();
	permute_self_adjoint_imp(
		new_val,
		new_col_ptr,
		new_row_idx,
		A,
		conj_A,
		perm,
		in_side,
		out_side,
		false,
		stack,
	);
	{
		let new_col_ptr = Cell::as_slice_of_cells(Cell::from_mut(new_col_ptr));
		let start = Array::from_ref(&new_col_ptr[..*N], N);
		let end = Array::from_ref(&new_col_ptr[1..], N);
		let mut writer = 0usize;
		for j in N.indices() {
			let start = start[j].replace(I::truncate(writer)).zx();
			let end = end[j].get().zx();
			unsafe {
				crate::sort::sort_indices(
					&mut new_row_idx[start..end],
					&mut new_val[start..end],
				);
			}
			let mut prev = I::truncate(usize::MAX);
			let mut reader = start;
			while reader < end {
				let cur = new_row_idx[reader];
				if cur == prev {
					writer -= 1;
					new_val[writer] = (&new_val[writer]) + (&new_val[reader]);
				} else {
					new_row_idx[writer] = new_row_idx[reader];
					new_val[writer] = new_val[reader].copy();
				}
				prev = cur;
				reader += 1;
				writer += 1;
			}
		}
		new_col_ptr[*N].set(I::truncate(writer));
	}
	unsafe {
		SparseColMatMut::new(
			SymbolicSparseColMatRef::new_unchecked(
				N,
				N,
				new_col_ptr,
				None,
				new_row_idx,
			),
			new_val,
		)
	}
}
/// computes the workspace layout required to transpose a matrix
pub fn transpose_scratch<I: Index>(nrows: usize, ncols: usize) -> StackReq {
	_ = ncols;
	StackReq::new::<usize>(nrows)
}
/// computes the workspace layout required to transpose a matrix and deduplicate
/// the output elements
pub fn transpose_dedup_scratch<I: Index>(
	nrows: usize,
	ncols: usize,
) -> StackReq {
	_ = ncols;
	StackReq::new::<usize>(nrows).array(2)
}
/// computes the transpose of the matrix $A$ and returns a view over it.
///
/// the result is stored in `new_col_ptrs`, `new_row_indices` and `new_values`.
///
/// # note
/// allows unsorted matrices, producing a sorted output. duplicate entries are
/// kept, however
pub fn transpose<'out, Rows: Shape, Cols: Shape, I: Index, T: Clone>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, T, Rows, Cols>,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Cols, Rows> {
	let (m, n) = A.shape();
	with_dim!(M, m.unbound());
	with_dim!(N, n.unbound());
	transpose_imp(
		T::clone,
		new_val,
		new_col_ptr,
		new_row_idx,
		A.as_shape(M, N),
		stack,
	)
	.as_shape_mut(n, m)
}
/// computes the adjoint of the matrix $A$ and returns a view over it.
///
/// the result is stored in `new_col_ptrs`, `new_row_indices` and `new_values`.
///
/// # note
/// allows unsorted matrices, producing a sorted output. duplicate entries are
/// kept, however
pub fn adjoint<'out, Rows: Shape, Cols: Shape, I: Index, T: ComplexField>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, T, Rows, Cols>,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Cols, Rows> {
	let (m, n) = A.shape();
	with_dim!(M, m.unbound());
	with_dim!(N, n.unbound());
	transpose_imp(
		conj::<T>,
		new_val,
		new_col_ptr,
		new_row_idx,
		A.as_shape(M, N),
		stack,
	)
	.as_shape_mut(n, m)
}
/// computes the transpose of the matrix $A$ and returns a view over it.
///
/// the result is stored in `new_col_ptrs`, `new_row_indices` and `new_values`.
///
/// # note
/// allows unsorted matrices, producing a sorted output. duplicate entries are
/// merged
pub fn transpose_dedup<
	'out,
	Rows: Shape,
	Cols: Shape,
	I: Index,
	T: ComplexField,
	C: Conjugate<Canonical = T>,
>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, C, Rows, Cols>,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Cols, Rows> {
	let (m, n) = A.shape();
	with_dim!(M, m.unbound());
	with_dim!(N, n.unbound());
	transpose_dedup_imp(
		new_val,
		new_col_ptr,
		new_row_idx,
		A.as_shape(M, N),
		stack,
	)
	.as_shape_mut(n, m)
}
fn transpose_imp<'ROWS, 'COLS, 'out, I: Index, T>(
	clone: impl Fn(&T) -> T,
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, T, Dim<'ROWS>, Dim<'COLS>>,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Dim<'COLS>, Dim<'ROWS>> {
	let (M, N) = A.shape();
	assert!(new_col_ptr.len() == *M + 1);
	let (mut col_count, _) = stack.collect(repeat_n!(I::truncate(0), *M));
	let col_count = Array::from_mut(&mut col_count, M);
	for j in N.indices() {
		for i in A.row_idx_of_col(j) {
			col_count[i] += I::truncate(1);
		}
	}
	new_col_ptr[0] = I::truncate(0);
	for (j, [pj0, pj1]) in iter::zip(
		M.indices(),
		windows2(Cell::as_slice_of_cells(Cell::from_mut(new_col_ptr))),
	) {
		let cj = &mut col_count[j];
		let pj = pj0.get();
		pj1.set(pj + *cj);
		*cj = pj;
	}
	let new_row_idx = &mut new_row_idx[..new_col_ptr[*M].zx()];
	let new_val = &mut new_val[..new_col_ptr[*M].zx()];
	let cur_row_pos = col_count;
	for j in N.indices() {
		for (i, val) in iter::zip(A.row_idx_of_col(j), A.val_of_col(j)) {
			let ci = &mut cur_row_pos[i];
			unsafe {
				let ci = ci.zx();
				*new_row_idx.get_unchecked_mut(ci) = I::truncate(*j);
				*new_val.get_unchecked_mut(ci) = clone(val);
			}
			*ci += I::truncate(1);
		}
	}
	debug_assert!(cur_row_pos.as_ref() == &new_col_ptr[1..]);
	unsafe {
		SparseColMatMut::new(
			SymbolicSparseColMatRef::new_unchecked(
				N,
				M,
				new_col_ptr,
				None,
				new_row_idx,
			),
			new_val,
		)
	}
}
fn transpose_dedup_imp<
	'ROWS,
	'COLS,
	'out,
	I: Index,
	T: ComplexField,
	C: Conjugate<Canonical = T>,
>(
	new_val: &'out mut [T],
	new_col_ptr: &'out mut [I],
	new_row_idx: &'out mut [I],
	A: SparseColMatRef<'_, I, C, Dim<'ROWS>, Dim<'COLS>>,
	stack: &mut MemStack,
) -> SparseColMatMut<'out, I, T, Dim<'COLS>, Dim<'ROWS>> {
	let (M, N) = A.shape();
	assert!(new_col_ptr.len() == *M + 1);
	let A = A.canonical();
	let sentinel = I::truncate(usize::MAX);
	let (mut col_count, stack) = stack.collect(repeat_n!(I::truncate(0), *M));
	let (mut last_seen, _) = stack.collect(repeat_n!(sentinel, *M));
	let col_count = Array::from_mut(&mut col_count, M);
	let last_seen = Array::from_mut(&mut last_seen, M);
	for j in N.indices() {
		for i in A.row_idx_of_col(j) {
			let j = I::truncate(*j);
			if last_seen[i] == j {
				continue;
			}
			last_seen[i] = j;
			col_count[i] += I::truncate(1);
		}
	}
	new_col_ptr[0] = I::truncate(0);
	for (j, [pj0, pj1]) in iter::zip(
		M.indices(),
		windows2(Cell::as_slice_of_cells(Cell::from_mut(new_col_ptr))),
	) {
		let cj = &mut col_count[j];
		let pj = pj0.get();
		pj1.set(pj + *cj);
		*cj = pj;
	}
	last_seen.as_mut().fill(sentinel);
	let new_row_idx = &mut new_row_idx[..new_col_ptr[*M].zx()];
	let new_val = &mut new_val[..new_col_ptr[*M].zx()];
	let cur_row_pos = col_count;
	for j in N.indices() {
		for (i, val) in iter::zip(A.row_idx_of_col(j), A.val_of_col(j)) {
			let ci = &mut cur_row_pos[i];
			let val = if Conj::get::<C>().is_conj() {
				val.conj()
			} else {
				val.copy()
			};
			let j = I::truncate(*j);
			unsafe {
				if last_seen[i] == j {
					let ci = ci.zx() - 1;
					*new_val.get_unchecked_mut(ci) =
						new_val.get_unchecked(ci) + (&val);
				} else {
					last_seen[i] = j;
					*ci += I::truncate(1);
					let ci = ci.zx() - 1;
					{
						*new_row_idx.get_unchecked_mut(ci) = j;
						*new_val.get_unchecked_mut(ci) = val;
					}
				}
			}
		}
	}
	debug_assert!(cur_row_pos.as_ref() == &new_col_ptr[1..]);
	unsafe {
		SparseColMatMut::new(
			SymbolicSparseColMatRef::new_unchecked(
				N,
				M,
				new_col_ptr,
				None,
				new_row_idx,
			),
			new_val,
		)
	}
}
#[cfg(test)]
mod tests {
	use super::*;
	use crate::assert;
	use crate::stats::prelude::*;
	use dyn_stack::MemBuffer;
	#[test]
	fn test_transpose() {
		let nrows = 5;
		let ncols = 3;
		let A = SparseColMatRef::new(
			SymbolicSparseColMatRef::new_unsorted_checked(
				nrows,
				ncols,
				&[0usize, 4, 8, 11],
				None,
				&[0, 0, 2, 4, 2, 1, 1, 0, 0, 1, 3],
			),
			&[1.0, 2.0, 3.0, 4.0, 11.0, 12.0, 13.0, 14.0, 21.0, 22.0, 23.0],
		);
		let nnz = A.compute_nnz();
		let new_col_ptr = &mut *vec![0usize; nrows + 1];
		let new_row_idx = &mut *vec![0usize; nnz];
		let new_val = &mut *vec![0.0; nnz];
		{
			let out = transpose(
				new_val,
				new_col_ptr,
				new_row_idx,
				A,
				MemStack::new(&mut MemBuffer::new(transpose_scratch::<usize>(
					nrows, ncols,
				))),
			)
			.into_const();
			let target = SparseColMatRef::new(
				SymbolicSparseColMatRef::new_unsorted_checked(
					ncols,
					nrows,
					&[0usize, 4, 7, 9, 10, 11],
					None,
					&[0, 0, 1, 2, 1, 1, 2, 0, 1, 2, 0],
				),
				&[1.0, 2.0, 14.0, 21.0, 12.0, 13.0, 22.0, 3.0, 11.0, 23.0, 4.0],
			);
			assert!(all(
				out.col_ptr() == target.col_ptr(),
				out.row_idx() == target.row_idx(),
				out.val() == target.val()
			));
		}
		{
			let out = transpose_dedup(
				new_val,
				new_col_ptr,
				new_row_idx,
				A,
				MemStack::new(&mut MemBuffer::new(transpose_dedup_scratch::<
					usize,
				>(nrows, ncols))),
			)
			.into_const();
			let target = SparseColMatRef::new(
				SymbolicSparseColMatRef::new_unsorted_checked(
					ncols,
					nrows,
					&[0usize, 3, 5, 7, 8, 9],
					None,
					&[0, 1, 2, 1, 2, 0, 1, 2, 0],
				),
				&[3.0, 14.0, 21.0, 25.0, 22.0, 3.0, 11.0, 23.0, 4.0],
			);
			assert!(all(
				out.col_ptr() == target.col_ptr(),
				out.row_idx() == target.row_idx(),
				out.val() == target.val()
			));
		}
	}
	#[test]
	fn test_permute_self_adjoint() {
		let n = 5;
		let rng = &mut StdRng::seed_from_u64(0);
		let diag_rng = &mut StdRng::seed_from_u64(1);
		let mut rand = || {
			ComplexDistribution::new(StandardNormal, StandardNormal)
				.rand::<c64>(rng)
		};
		let mut rand_diag = || c64::new(StandardNormal.rand(diag_rng), 0.0);
		let val = &[
			rand_diag(),
			rand_diag(),
			rand(),
			rand(),
			rand(),
			rand_diag(),
			rand_diag(),
			rand(),
			rand(),
			rand(),
			rand(),
			rand(),
			rand_diag(),
			rand(),
			rand_diag(),
			rand(),
			rand(),
		];
		let A = SparseColMatRef::new(
			SymbolicSparseColMatRef::new_unsorted_checked(
				n,
				n,
				&[0usize, 4, 8, 11, 14, 17],
				None,
				&[0, 0, 2, 4, 2, 1, 1, 0, 0, 1, 3, 2, 3, 4, 4, 3, 2],
			),
			val,
		);
		let nnz = A.compute_nnz();
		let perm_fwd = &mut *vec![0, 4, 1, 3, 2usize];
		let perm_bwd = &mut *vec![0; 5];
		for i in 0..n {
			perm_bwd[perm_fwd[i]] = i;
		}
		let perm = PermRef::new_checked(perm_fwd, perm_bwd, n);
		let new_col_ptr = &mut *vec![0usize; n + 1];
		let new_row_idx = &mut *vec![0usize; nnz];
		let new_val = &mut *vec![c64::ZERO; nnz];
		for f in [
			permute_self_adjoint_to_unsorted,
			permute_self_adjoint,
			permute_dedup_self_adjoint,
		] {
			for (in_side, out_side) in [
				(Side::Lower, Side::Lower),
				(Side::Lower, Side::Upper),
				(Side::Upper, Side::Lower),
				(Side::Upper, Side::Upper),
			] {
				let mut out = f(
					new_val,
					new_col_ptr,
					new_row_idx,
					A,
					perm,
					in_side,
					out_side,
					MemStack::new(&mut MemBuffer::new(
						permute_self_adjoint_scratch::<usize>(n),
					)),
				)
				.to_dense();
				let mut A = A.to_dense();
				match in_side {
					Side::Lower => {
						z!(&mut A).for_each_triangular_upper(
							linalg::zip::Diag::Skip,
							|uz!(x)| *x = c64::ZERO,
						);
						for j in 0..n {
							for i in 0..j {
								A[(i, j)] = A[(j, i)].conj();
							}
						}
					},
					Side::Upper => {
						z!(&mut A).for_each_triangular_lower(
							linalg::zip::Diag::Skip,
							|uz!(x)| *x = c64::ZERO,
						);
						for j in 0..n {
							for i in j + 1..n {
								A[(i, j)] = A[(j, i)].conj();
							}
						}
					},
				}
				match out_side {
					Side::Lower => {
						for j in 0..n {
							for i in 0..j {
								out[(i, j)] = out[(j, i)].conj();
							}
						}
					},
					Side::Upper => {
						for j in 0..n {
							for i in j + 1..n {
								out[(i, j)] = out[(j, i)].conj();
							}
						}
					},
				}
				assert!(out == perm * &A * perm.inverse());
			}
		}
	}
}