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2 Commits
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188208b959
Author | SHA1 | Date |
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David Nadlinger | 188208b959 | |
David Nadlinger | 164edd266e |
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@ -1181,14 +1181,30 @@ impl<'a> Inferencer<'a> {
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slice.location,
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),
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TypeEnum::TNDArray { ty: elem_ty, num_dims } => {
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let num_idxs = if let TypeEnum::TTuple { ty: idx_tys } =
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&*self.unifier.get_ty(slice.custom.unwrap())
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{
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let num_idxs = match &*self.unifier.get_ty(slice.custom.unwrap()) {
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TypeEnum::TTuple { ty: idx_tys } => {
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for idx_ty in idx_tys.iter() {
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self.constrain(*idx_ty, self.primitives.int32, &slice.location)?;
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// xxx: NumPy supports a tuple of tuples for "advanced indexing"
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// of multidimensional arrays (sequence index -> subset). We
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// don't support this, but could give a better error message.
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self.constrain(
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*idx_ty,
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self.primitives.int32,
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&slice.location,
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)?;
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}
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idx_tys.len()
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} else {
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}
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TypeEnum::TList { .. } | TypeEnum::TNDArray { .. } => {
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return report_error(
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concat!(
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"ndarray index is list/array, but NumPy advanced (subset)",
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"indexing is not supported yet"
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),
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slice.location,
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);
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}
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_ => {
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// xxx: Could lead to suboptimal error message, as higher-dimensional indexing is not mentioned?!
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self.constrain(
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slice.custom.unwrap(),
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@ -1196,6 +1212,7 @@ impl<'a> Inferencer<'a> {
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&slice.location,
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)?;
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1
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}
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};
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if *num_dims < num_idxs {
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@ -518,12 +518,13 @@ impl TestEnvironment {
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a = array([1, 2])
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a0 = a[0]
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b = array([[1, 2], [3, 4]])
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# b0 = b[0]
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b0 = b[0]
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b00 = b[0, 0]
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c = 1
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ac = a[c]
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"},
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[("a", "ndarray[int32, 1]"), ("b", "ndarray[int32, 2]"), ("a0", "int32"), ("b00", "int32"), ("ac", "int32")].iter().cloned().collect(),
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[("a", "ndarray[int32, 1]"), ("a0", "int32"), ("b", "ndarray[int32, 2]"),
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("b0", "ndarray[int32, 1]"), ("b00", "int32"), ("ac", "int32")].iter().cloned().collect(),
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&[]
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; "array test")]
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#[test_case(indoc! {"
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@ -662,13 +662,17 @@ impl Unifier {
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for (k, v) in fields.iter() {
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match *k {
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RecordKey::Int(_) => {
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if *num_dims > 1 {
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unreachable!("xxx implement unification for scalar indexing of multidimensional array");
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}
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self.unify_impl(v.ty, *ty, false).map_err(|e| e.at(v.loc))?
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// .<n> is generated during generic scalar indexing lowering.
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let indexed_ty = if *num_dims == 1 {
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*ty
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} else {
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self.add_ty(TNDArray { ty: *ty, num_dims: *num_dims - 1 })
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};
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self.unify_impl(v.ty, indexed_ty, false).map_err(|e| e.at(v.loc))?
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}
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RecordKey::Str(_) => {
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return Err(TypeError::new(TypeErrorKind::NoSuchField(*k, b), v.loc))
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// xxx: Implement .shape here?
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return Err(TypeError::new(TypeErrorKind::NoSuchField(*k, b), v.loc));
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}
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}
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}
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