core: support tuple and int32 input for np_empty, np_ones, and more

nac3core/src/codegen/numpy.rs

@@ -163,10 +163,11 @@ fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
 ) -> Result<NDArrayValue<'ctx>, String> {
     let llvm_usize = generator.get_size_type(ctx.ctx);
 
-    for shape_dim in shape {
+    for &shape_dim in shape {
+        let shape_dim = ctx.builder.build_int_z_extend(shape_dim, llvm_usize, "").unwrap();
         let shape_dim_gez = ctx
             .builder
-            .build_int_compare(IntPredicate::SGE, *shape_dim, llvm_usize.const_zero(), "")
+            .build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
             .unwrap();
 
         ctx.make_assert(
@@ -189,7 +190,8 @@ fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
     let ndarray_num_dims = ndarray.load_ndims(ctx);
     ndarray.create_dim_sizes(ctx, llvm_usize, ndarray_num_dims);
 
-    for (i, shape_dim) in shape.iter().enumerate() {
+    for (i, &shape_dim) in shape.iter().enumerate() {
+        let shape_dim = ctx.builder.build_int_z_extend(shape_dim, llvm_usize, "").unwrap();
         let ndarray_dim = unsafe {
             ndarray.dim_sizes().ptr_offset_unchecked(
                 ctx,
@@ -199,7 +201,7 @@ fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
             )
         };
 
-        ctx.builder.build_store(ndarray_dim, *shape_dim).unwrap();
+        ctx.builder.build_store(ndarray_dim, shape_dim).unwrap();
     }
 
     let ndarray = ndarray_init_data(generator, ctx, elem_ty, ndarray);
@@ -286,22 +288,68 @@ fn ndarray_one_value<'ctx, G: CodeGenerator + ?Sized>(
 ///
 /// * `elem_ty` - The element type of the `NDArray`.
 /// * `shape` - The `shape` parameter used to construct the `NDArray`.
+///
+/// ### Notes on `shape`
+///
+/// Just like numpy, the `shape` argument can be:
+///   1. A list of `int32`;   e.g., `np.empty([600, 800, 3])`
+///   2. A tuple of `int32`;  e.g., `np.empty((600, 800, 3))`
+///   3. A scalar `int32`;     e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+///
+/// See also [`typecheck::type_inferencer::fold_numpy_function_call_shape_argument`] to
+/// learn how `shape` gets from being a Python user expression to here.
 fn call_ndarray_empty_impl<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     elem_ty: Type,
-    shape: ListValue<'ctx>,
+    shape: BasicValueEnum<'ctx>,
 ) -> Result<NDArrayValue<'ctx>, String> {
-    create_ndarray_dyn_shape(
+    let llvm_usize = generator.get_size_type(ctx.ctx);
-        generator,
+
-        ctx,
+    match shape {
-        elem_ty,
+        BasicValueEnum::PointerValue(shape_list_ptr)
-        &shape,
+            if ListValue::is_instance(shape_list_ptr, llvm_usize).is_ok() =>
-        |_, ctx, shape| Ok(shape.load_size(ctx, None)),
+        {
-        |generator, ctx, shape, idx| {
+            // 1. A list of ints; e.g., `np.empty([600, 800, 3])`
-            Ok(shape.data().get(ctx, generator, &idx, None).into_int_value())
+
-        },
+            let shape_list = ListValue::from_ptr_val(shape_list_ptr, llvm_usize, None);
-    )
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &shape_list,
+                |_, ctx, shape_list| Ok(shape_list.load_size(ctx, None)),
+                |generator, ctx, shape_list, idx| {
+                    Ok(shape_list.data().get(ctx, generator, &idx, None).into_int_value())
+                },
+            )
+        }
+        BasicValueEnum::StructValue(shape_tuple) => {
+            // 2. A tuple of ints; e.g., `np.empty((600, 800, 3))`
+            // Read [`codegen::expr::gen_expr`] to see how `nac3core` translates a Python tuple into LLVM.
+
+            // Get the length/size of the tuple, which also happens to be the value of `ndims`.
+            let ndims = shape_tuple.get_type().count_fields();
+
+            let mut shape = Vec::with_capacity(ndims as usize);
+            for dim_i in 0..ndims {
+                let dim = ctx
+                    .builder
+                    .build_extract_value(shape_tuple, dim_i, format!("dim{dim_i}").as_str())
+                    .unwrap()
+                    .into_int_value();
+
+                shape.push(dim);
+            }
+            create_ndarray_const_shape(generator, ctx, elem_ty, shape.as_slice())
+        }
+        BasicValueEnum::IntValue(shape_int) => {
+            // 3. A scalar int; e.g., `np.empty(3)`, this is functionally equivalent to `np.empty([3])`
+
+            create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
+        }
+        _ => unreachable!(),
+    }
 }
 
 /// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
@@ -486,7 +534,7 @@ fn call_ndarray_zeros_impl<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     elem_ty: Type,
-    shape: ListValue<'ctx>,
+    shape: BasicValueEnum<'ctx>,
 ) -> Result<NDArrayValue<'ctx>, String> {
     let supported_types = [
         ctx.primitives.int32,
@@ -517,7 +565,7 @@ fn call_ndarray_ones_impl<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     elem_ty: Type,
-    shape: ListValue<'ctx>,
+    shape: BasicValueEnum<'ctx>,
 ) -> Result<NDArrayValue<'ctx>, String> {
     let supported_types = [
         ctx.primitives.int32,
@@ -548,7 +596,7 @@ fn call_ndarray_full_impl<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
     elem_ty: Type,
-    shape: ListValue<'ctx>,
+    shape: BasicValueEnum<'ctx>,
     fill_value: BasicValueEnum<'ctx>,
 ) -> Result<NDArrayValue<'ctx>, String> {
     let ndarray = call_ndarray_empty_impl(generator, ctx, elem_ty, shape)?;
@@ -1674,17 +1722,11 @@ pub fn gen_ndarray_empty<'ctx>(
     assert!(obj.is_none());
     assert_eq!(args.len(), 1);
 
-    let llvm_usize = generator.get_size_type(context.ctx);
     let shape_ty = fun.0.args[0].ty;
     let shape_arg = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
 
-    call_ndarray_empty_impl(
+    call_ndarray_empty_impl(generator, context, context.primitives.float, shape_arg)
-        generator,
+        .map(NDArrayValue::into)
-        context,
-        context.primitives.float,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-    )
-    .map(NDArrayValue::into)
 }
 
 /// Generates LLVM IR for `ndarray.zeros`.
@@ -1698,17 +1740,11 @@ pub fn gen_ndarray_zeros<'ctx>(
     assert!(obj.is_none());
     assert_eq!(args.len(), 1);
 
-    let llvm_usize = generator.get_size_type(context.ctx);
     let shape_ty = fun.0.args[0].ty;
     let shape_arg = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
 
-    call_ndarray_zeros_impl(
+    call_ndarray_zeros_impl(generator, context, context.primitives.float, shape_arg)
-        generator,
+        .map(NDArrayValue::into)
-        context,
-        context.primitives.float,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-    )
-    .map(NDArrayValue::into)
 }
 
 /// Generates LLVM IR for `ndarray.ones`.
@@ -1722,17 +1758,11 @@ pub fn gen_ndarray_ones<'ctx>(
     assert!(obj.is_none());
     assert_eq!(args.len(), 1);
 
-    let llvm_usize = generator.get_size_type(context.ctx);
     let shape_ty = fun.0.args[0].ty;
     let shape_arg = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
 
-    call_ndarray_ones_impl(
+    call_ndarray_ones_impl(generator, context, context.primitives.float, shape_arg)
-        generator,
+        .map(NDArrayValue::into)
-        context,
-        context.primitives.float,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-    )
-    .map(NDArrayValue::into)
 }
 
 /// Generates LLVM IR for `ndarray.full`.
@@ -1746,21 +1776,14 @@ pub fn gen_ndarray_full<'ctx>(
     assert!(obj.is_none());
     assert_eq!(args.len(), 2);
 
-    let llvm_usize = generator.get_size_type(context.ctx);
     let shape_ty = fun.0.args[0].ty;
     let shape_arg = args[0].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
     let fill_value_ty = fun.0.args[1].ty;
     let fill_value_arg =
         args[1].1.clone().to_basic_value_enum(context, generator, fill_value_ty)?;
 
-    call_ndarray_full_impl(
+    call_ndarray_full_impl(generator, context, fill_value_ty, shape_arg, fill_value_arg)
-        generator,
+        .map(NDArrayValue::into)
-        context,
-        fill_value_ty,
-        ListValue::from_ptr_val(shape_arg.into_pointer_value(), llvm_usize, None),
-        fill_value_arg,
-    )
-    .map(NDArrayValue::into)
 }
 
 pub fn gen_ndarray_array<'ctx>(

nac3core/src/toplevel/builtins.rs

@@ -324,6 +324,9 @@ struct BuiltinBuilder<'a> {
 
     num_or_ndarray_ty: TypeVar,
     num_or_ndarray_var_map: VarMap,
+
+    /// See [`BuiltinBuilder::build_ndarray_from_shape_factory_function`]
+    ndarray_factory_fn_shape_arg_tvar: TypeVar,
 }
 
 impl<'a> BuiltinBuilder<'a> {
@@ -394,6 +397,8 @@ impl<'a> BuiltinBuilder<'a> {
 
         let list_int32 = unifier.add_ty(TypeEnum::TList { ty: int32 });
 
+        let ndarray_factory_fn_shape_arg_tvar = unifier.get_fresh_var(Some("Shape".into()), None);
+
         BuiltinBuilder {
             unifier,
             primitives,
@@ -421,6 +426,8 @@ impl<'a> BuiltinBuilder<'a> {
 
             num_or_ndarray_ty,
             num_or_ndarray_var_map,
+
+            ndarray_factory_fn_shape_arg_tvar,
         }
     }
 
@@ -959,21 +966,46 @@ impl<'a> BuiltinBuilder<'a> {
         )
     }
 
-    /// Build ndarray factory functions that only take in an argument `shape` of type `list[int32]` and return an ndarray.
+    /// Build ndarray factory functions that only take in an argument `shape`.
+    ///
+    /// `shape` can be a tuple of int32s, a list of int32s, or a scalar int32.
     fn build_ndarray_from_shape_factory_function(&mut self, prim: PrimDef) -> TopLevelDef {
         debug_assert_prim_is_allowed(
             prim,
             &[PrimDef::FunNpNDArray, PrimDef::FunNpEmpty, PrimDef::FunNpZeros, PrimDef::FunNpOnes],
         );
 
+        // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
+        // the `param_ty` for `create_fn_by_codegen`.
+        //
+        // Ideally, we should have created a [`TypeVar`] to define all possible input
+        // types for the parameter "shape" like so:
+        // ```rust
+        // self.unifier.get_fresh_var_with_range(
+        //   &[int32, list_int32, /* and more... */],
+        //   Some("T".into()), None)
+        // )
+        // ```
+        //
+        // However, there is (currently) no way to type a tuple of arbitrary length in `nac3core`.
+        //
+        // And this is the best we could do:
+        // ```rust
+        // &[ int32, list_int32, tuple_1_int32, tuple_2_int32, tuple_3_int32, ... ],
+        // ```
+        //
+        // But this is not ideal.
+        //
+        // Instead, we delegate the responsibility of typechecking
+        // to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
+        // and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
+
         create_fn_by_codegen(
             self.unifier,
             &VarMap::new(),
             prim.name(),
             self.ndarray_float,
-            // We are using List[int32] here, as I don't know a way to specify an n-tuple bound on a
+            &[(self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
-            // type variable
-            &[(self.list_int32, "shape")],
             Box::new(move |ctx, obj, fun, args, generator| {
                 let func = match prim {
                     PrimDef::FunNpNDArray | PrimDef::FunNpEmpty => gen_ndarray_empty,

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(239)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(240)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar228]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar228\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar229]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar229\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(241)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(242)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(246)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(247)]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar227, typevar228]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar227\", \"typevar228\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar228, typevar229]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar228\", \"typevar229\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(247)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(248)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(255)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(256)]\n}\n",
 ]

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -814,6 +814,150 @@ impl<'a> Inferencer<'a> {
         })
     }
 
+    /// Fold an ndarray `shape` argument. This function aims to fold `shape` arguments like that of
+    /// <https://numpy.org/doc/stable/reference/generated/numpy.zeros.html> (for `np_zeros`).
+    ///
+    /// Arguments:
+    ///   * `id` - The name of the function of the function call this `shape` argument is in. Used for error reporting.
+    ///   * `arg_index` - The position (0-based) of this argument in the function call. Used for error reporting.
+    ///   * `shape_expr` - [`Located<ExprKind>`] of the input argument.
+    ///
+    /// On success, it returns a tuple of
+    ///   1) the `ndims` value inferred from the input `shape`,
+    ///   2) and the elaborated expression. Like what other fold functions of [`Inferencer`] would normally return.
+    fn fold_numpy_function_call_shape_argument(
+        &mut self,
+        id: StrRef,
+        arg_index: usize,
+        shape_expr: Located<ExprKind>,
+    ) -> Result<(u64, ast::Expr<Option<Type>>), HashSet<String>> {
+        /*
+            ### Further explanation
+
+            As said, this function aims to fold `shape` arguments, but this is *not* trivial.
+            The root of the issue is that `nac3core` has to deduce the `ndims`
+            of the created (for in the case of `np_zeros`) ndarray statically - i.e., during inference time.
+
+            There are three types of valid input to `shape`:
+              1. A python `List` (all `int32s`);   e.g., `np_zeros([600, 800, 3])`
+              2. A python `Tuple` (all `int32s`);  e.g., `np_zeros((600, 800, 3))`
+              3. An `int32`; e.g., `np_zeros(256)` - this is functionally equivalent to `np_zeros([256])`
+
+            For 2. and 3., `ndims` can be deduce immediately from the inferred type of the input:
+              - For 2. `ndims` is simply the number of elements found in [`TypeEnum::TTuple`] after typechecking the `shape` argument.
+              - For 3. `ndims` is simply 1.
+
+            For 1., `ndims` is supposedly the length of the input list. However, the length of the input list
+            is a runtime property. Therefore (as a hack) we resort to analyzing the argument expression [`ExprKind::List`]
+            itself to extract the input list length statically.
+
+            This implies that the user could only write:
+
+            ```python
+            my_rgba_image = np_zeros([600, 800, 4])
+            # the shape argument is directly written as a list literal.
+            # and `nac3core` could therefore tell that ndims is `3` by
+            # looking at the raw AST expression itself.
+            ```
+
+            But not:
+
+            ```python
+            my_image_dimension = [600, 800, 4]
+            mystery_function_that_mutates_my_list(my_image_dimension)
+            my_image = np_zeros(my_image_dimension)
+            # what is the length now? what is `ndims`?
+
+            # it is *basically impossible* to generally determine the
+            # length of `my_image_dimension` statically for `ndims`!!
+            ```
+        */
+
+        // Fold `shape`
+        let shape = self.fold_expr(shape_expr)?;
+        let shape_ty = shape.custom.unwrap(); // The inferred type of `shape`
+
+        // Check `shape_ty` to see if its a list of int32s, a tuple of int32s, or just int32.
+        // Otherwise throw an error as that would mean the user wrote an ill-typed `shape_expr`.
+        //
+        // Here, we also take the opportunity to deduce `ndims` statically for 2. and 3.
+        let shape_ty_enum = &*self.unifier.get_ty(shape_ty);
+        let ndims = match shape_ty_enum {
+            TypeEnum::TList { ty } => {
+                // Handle 1. A list of int32s
+
+                // Typecheck
+                self.unifier.unify(*ty, self.primitives.int32).map_err(|err| {
+                    HashSet::from([err
+                        .at(Some(shape.location))
+                        .to_display(self.unifier)
+                        .to_string()])
+                })?;
+
+                // Special handling for (1. A python `List` (all `int32s`)).
+                // Read the doc above this function to see what is going on here.
+                if let ExprKind::List { elts, .. } = &shape.node {
+                    // The user wrote a List literal as the input argument
+                    elts.len() as u64
+                } else {
+                    // This means the user is passing an expression of type `List`,
+                    // but it is done so indirectly (like putting a variable referencing a `List`)
+                    // rather than writing a List literal. We need to report an error.
+                    return Err(HashSet::from([
+                        format!(
+                            "Expected list literal, tuple, or int32 for argument {arg_num} of {id} at {location}. Input argument is of type list but not a list literal.",
+                            arg_num = arg_index + 1,
+                            location = shape.location
+                        )
+                    ]));
+                }
+            }
+            TypeEnum::TTuple { ty: tuple_element_types } => {
+                // Handle 2. A tuple of int32s
+
+                // Typecheck
+                // The expected type is just the tuple but with all its elements being int32.
+                let expected_ty = self.unifier.add_ty(TypeEnum::TTuple {
+                    ty: tuple_element_types.iter().map(|_| self.primitives.int32).collect_vec(),
+                });
+                self.unifier.unify(shape_ty, expected_ty).map_err(|err| {
+                    HashSet::from([err
+                        .at(Some(shape.location))
+                        .to_display(self.unifier)
+                        .to_string()])
+                })?;
+
+                // `ndims` can be deduced statically from the inferred Tuple type.
+                tuple_element_types.len() as u64
+            }
+            TypeEnum::TObj { .. } => {
+                // Handle 3. An integer (generalized as [`TypeEnum::TObj`])
+
+                // Typecheck
+                self.unify(self.primitives.int32, shape_ty, &shape.location)?;
+
+                // Deduce `ndims`
+                1
+            }
+            _ => {
+                // The user wrote an ill-typed `shape_expr`,
+                // so throw an error.
+                let shape_ty_str = self.unifier.stringify(shape_ty);
+                return report_error(
+                    format!(
+                        "Expected list literal, tuple, or int32 for argument {arg_num} of {id}, got {shape_expr_name} of type {shape_ty_str}",
+                        arg_num = arg_index + 1,
+                        shape_expr_name = shape.node.name(),
+                    )
+                    .as_str(),
+                    shape.location,
+                );
+            }
+        };
+
+        Ok((ndims, shape))
+    }
+
     /// Tries to fold a special call. Returns [`Some`] if the call expression `func` is a special call, otherwise
     /// returns [`None`].
     fn try_fold_special_call(
@@ -1141,25 +1285,15 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
-        // 1-argument ndarray n-dimensional creation functions
+        // 1-argument ndarray n-dimensional factory functions
         if ["np_ndarray".into(), "np_empty".into(), "np_zeros".into(), "np_ones".into()]
             .contains(id)
             && args.len() == 1
         {
-            let ExprKind::List { elts, .. } = &args[0].node else {
+            let shape_expr = args.remove(0);
-                return report_error(
+            let (ndims, shape) =
-                    format!(
+                self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?; // Special handling the `shape`
-                        "Expected List literal for first argument of {id}, got {}",
-                        args[0].node.name()
-                    )
-                    .as_str(),
-                    args[0].location,
-                );
-            };
 
-            let ndims = elts.len() as u64;
-
-            let arg0 = self.fold_expr(args.remove(0))?;
             let ndims = self.unifier.get_fresh_literal(vec![SymbolValue::U64(ndims)], None);
             let ret = make_ndarray_ty(
                 self.unifier,
@@ -1170,7 +1304,7 @@ impl<'a> Inferencer<'a> {
             let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
                 args: vec![FuncArg {
                     name: "shape".into(),
-                    ty: arg0.custom.unwrap(),
+                    ty: shape.custom.unwrap(),
                     default_value: None,
                 }],
                 ret,
@@ -1186,7 +1320,7 @@ impl<'a> Inferencer<'a> {
                         location: func.location,
                         node: ExprKind::Name { id: *id, ctx: *ctx },
                     }),
-                    args: vec![arg0],
+                    args: vec![shape],
                     keywords: vec![],
                 },
             }));

nac3standalone/demo/src/ndarray.py

@@ -71,17 +71,44 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
+def consume_ndarray_2(n: ndarray[float, Literal[2]]):
+    pass
+
 def test_ndarray_ctor():
     n: ndarray[float, Literal[1]] = np_ndarray([1])
     consume_ndarray_1(n)
 
 def test_ndarray_empty():
-    n: ndarray[float, 1] = np_empty([1])
+    n1: ndarray[float, 1] = np_empty([1])
-    consume_ndarray_1(n)
+    consume_ndarray_1(n1)
+
+    n2: ndarray[float, 1] = np_empty(10)
+    consume_ndarray_1(n2)
+
+    n3: ndarray[float, 1] = np_empty((2,))
+    consume_ndarray_1(n3)
+
+    n4: ndarray[float, 2] = np_empty((4, 4))
+    consume_ndarray_2(n4)
+
+    dim4 = (5, 2)
+    n5: ndarray[float, 2] = np_empty(dim4)
+    consume_ndarray_2(n5)
 
 def test_ndarray_zeros():
-    n: ndarray[float, 1] = np_zeros([1])
+    n1: ndarray[float, 1] = np_zeros([1])
-    output_ndarray_float_1(n)
+    output_ndarray_float_1(n1)
+
+    k = 3 + int32(n1[0]) # to test variable shape inputs
+    n2: ndarray[float, 1] = np_zeros(k * k)
+    output_ndarray_float_1(n2)
+
+    n3: ndarray[float, 1] = np_zeros((k * 2,))
+    output_ndarray_float_1(n3)
+
+    dim4 = (3, 2 * k)
+    n4: ndarray[float, 2] = np_zeros(dim4)
+    output_ndarray_float_2(n4)
 
 def test_ndarray_ones():
     n: ndarray[float, 1] = np_ones([1])