core: Implement elementwise binary operators

Including immediate variants of these operators.

nac3artiq/src/symbol_resolver.rs

@@ -5,7 +5,7 @@ use nac3core::{
     toplevel::{
         DefinitionId, 
         helper::PRIMITIVE_DEF_IDS, 
-        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelDef,
     },
     typecheck::{
@@ -654,7 +654,7 @@ impl InnerResolver {
                 }
             }
             (TypeEnum::TObj { obj_id, .. }, false) if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                let (ty, ndims) = unpack_ndarray_tvars(unifier, extracted_ty);
+                let (ty, ndims) = unpack_ndarray_var_tys(unifier, extracted_ty);
                 let len: usize = self.helper.len_fn.call1(py, (obj,))?.extract(py)?;
                 if len == 0 {
                     assert!(matches!(

nac3core/src/codegen/classes.rs

@@ -1,17 +1,17 @@
 use inkwell::{
     IntPredicate,
     types::{AnyTypeEnum, BasicTypeEnum, IntType, PointerType},
-    values::{ArrayValue, BasicValueEnum, IntValue, PointerValue},
+    values::{BasicValueEnum, IntValue, PointerValue},
 };
 use crate::codegen::{
     CodeGenContext,
     CodeGenerator,
-    irrt::{call_ndarray_calc_size, call_ndarray_flatten_index, call_ndarray_flatten_index_const},
+    irrt::{call_ndarray_calc_size, call_ndarray_flatten_index},
     llvm_intrinsics::call_int_umin,
     stmt::gen_for_callback_incrementing,
 };
 
-/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of 
+/// An LLVM value that is array-like, i.e. it contains a contiguous, sequenced collection of
 /// elements.
 pub trait ArrayLikeValue<'ctx> {
     /// Returns the element type of this array-like value.
@@ -1162,98 +1162,6 @@ impl<'ctx> ArrayLikeIndexer<'ctx> for NDArrayDataProxy<'ctx, '_> {
 impl<'ctx> UntypedArrayLikeAccessor<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
 impl<'ctx> UntypedArrayLikeMutator<'ctx, IntValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
 
-impl<'ctx> ArrayLikeIndexer<'ctx, ArrayValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {
-    unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        indices: ArrayValue<'ctx>,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        let index = call_ndarray_flatten_index_const(
-            generator,
-            ctx,
-            *self.0,
-            indices,
-        );
-
-        unsafe {
-            ctx.builder.build_in_bounds_gep(
-                self.base_ptr(ctx, generator),
-                &[index],
-                name.unwrap_or_default(),
-            )
-        }.unwrap()
-    }
-
-    fn ptr_offset<G: CodeGenerator + ?Sized>(
-        &self,
-        ctx: &mut CodeGenContext<'ctx, '_>,
-        generator: &mut G,
-        indices: ArrayValue<'ctx>,
-        name: Option<&str>,
-    ) -> PointerValue<'ctx> {
-        let llvm_usize = generator.get_size_type(ctx.ctx);
-
-        let indices_elem_ty = indices.get_type().get_element_type();
-        let Ok(indices_elem_ty) = IntType::try_from(indices_elem_ty) else {
-            panic!("Expected [int32] but got [{indices_elem_ty}]")
-        };
-        assert_eq!(indices_elem_ty.get_bit_width(), 32, "Expected [int32] but got [{indices_elem_ty}]");
-
-        let nidx_leq_ndims = ctx.builder.build_int_compare(
-            IntPredicate::SLE,
-            llvm_usize.const_int(indices.get_type().len() as u64, false),
-            self.0.load_ndims(ctx),
-            ""
-        ).unwrap();
-        ctx.make_assert(
-            generator,
-            nidx_leq_ndims,
-            "0:IndexError",
-            "invalid index to scalar variable",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        for idx in 0..indices.get_type().len() {
-            let i = llvm_usize.const_int(idx as u64, false);
-
-            let dim_idx = ctx.builder
-                .build_extract_value(indices, idx, "")
-                .map(BasicValueEnum::into_int_value)
-                .map(|v| ctx.builder.build_int_z_extend_or_bit_cast(v, llvm_usize, "").unwrap())
-                .unwrap();
-            let dim_sz = unsafe {
-                self.0.dim_sizes().get_typed_unchecked(ctx, generator, i, None)
-            };
-
-            let dim_lt = ctx.builder.build_int_compare(
-                IntPredicate::SLT,
-                dim_idx,
-                dim_sz,
-                ""
-            ).unwrap();
-
-            ctx.make_assert(
-                generator,
-                dim_lt,
-                "0:IndexError",
-                "index {0} is out of bounds for axis 0 with size {1}",
-                [Some(dim_idx), Some(dim_sz), None],
-                ctx.current_loc,
-            );
-        }
-
-        unsafe {
-            self.ptr_offset_unchecked(ctx, generator, indices, name)
-        }
-    }
-}
-
-impl<'ctx> UntypedArrayLikeAccessor<'ctx, ArrayValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
-impl<'ctx> UntypedArrayLikeMutator<'ctx, ArrayValue<'ctx>> for NDArrayDataProxy<'ctx, '_> {}
-
 impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index> for NDArrayDataProxy<'ctx, '_> {
     unsafe fn ptr_offset_unchecked<G: CodeGenerator + ?Sized>(
         &self,
@@ -1326,6 +1234,9 @@ impl<'ctx, Index: UntypedArrayLikeAccessor<'ctx>> ArrayLikeIndexer<'ctx, Index>
                         self.0.dim_sizes().get_typed_unchecked(ctx, generator, i, None),
                     )
                 };
+                let dim_idx = ctx.builder
+                    .build_int_z_extend_or_bit_cast(dim_idx, dim_sz.get_type(), "")
+                    .unwrap();
 
                 let dim_lt = ctx.builder.build_int_compare(
                     IntPredicate::SLT,

nac3core/src/codegen/expr.rs

@@ -16,6 +16,7 @@ use crate::{
         get_llvm_abi_type,
         irrt::*,
         llvm_intrinsics::{call_expect, call_float_floor, call_float_pow, call_float_powi},
+        numpy,
         stmt::{gen_raise, gen_var},
         CodeGenContext, CodeGenTask,
     },
@@ -23,7 +24,7 @@ use crate::{
     toplevel::{
         DefinitionId, 
         helper::PRIMITIVE_DEF_IDS,
-        numpy::make_ndarray_ty,
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelDef,
     },
     typecheck::{
@@ -42,6 +43,7 @@ use itertools::{chain, izip, Itertools, Either};
 use nac3parser::ast::{
     self, Boolop, Comprehension, Constant, Expr, ExprKind, Location, Operator, StrRef,
 };
+use crate::codegen::classes::ArraySliceValue;
 
 use super::{CodeGenerator, llvm_intrinsics::call_memcpy_generic, need_sret};
 
@@ -1089,34 +1091,22 @@ pub fn gen_comprehension<'ctx, G: CodeGenerator>(
     Ok(Some(list.as_ptr_value().into()))
 }
 
-/// Generates LLVM IR for a [binary operator expression][expr].
-///
-/// * `left` - The left-hand side of the binary operator.
-/// * `op` - The operator applied on the operands.
-/// * `right` - The right-hand side of the binary operator.
-/// * `loc` - The location of the full expression.
-/// * `is_aug_assign` - Whether the binary operator expression is also an assignment operator.
-pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
+/// Generates LLVM IR for a binary operator expression using the [`Type`] and 
+/// [LLVM value][`BasicValueEnum`] of the operands.
+pub fn gen_binop_expr_with_values<'ctx, G: CodeGenerator>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    left: &Expr<Option<Type>>,
+    left: (&Option<Type>, BasicValueEnum<'ctx>),
     op: &Operator,
-    right: &Expr<Option<Type>>,
+    right: (&Option<Type>, BasicValueEnum<'ctx>),
     loc: Location,
     is_aug_assign: bool,
 ) -> Result<Option<ValueEnum<'ctx>>, String> {
-    let ty1 = ctx.unifier.get_representative(left.custom.unwrap());
-    let ty2 = ctx.unifier.get_representative(right.custom.unwrap());
-    let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
-        v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
-    } else {
-        return Ok(None)
-    };
-    let right_val = if let Some(v) = generator.gen_expr(ctx, right)? {
-        v.to_basic_value_enum(ctx, generator, right.custom.unwrap())?
-    } else {
-        return Ok(None)
-    };
+    let (left_ty, left_val) = left;
+    let (right_ty, right_val) = right;
+
+    let ty1 = ctx.unifier.get_representative(left_ty.unwrap());
+    let ty2 = ctx.unifier.get_representative(right_ty.unwrap());
 
     // we can directly compare the types, because we've got their representatives
     // which would be unchanged until further unification, which we would never do
@@ -1140,8 +1130,78 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
             Some("f_pow_i")
         );
         Ok(Some(res.into()))
+    } else if ty1.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray || ty2.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray {
+        let llvm_usize = generator.get_size_type(ctx.ctx);
+
+        let is_ndarray1 = ty1.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray;
+        let is_ndarray2 = ty2.get_obj_id(&ctx.unifier) == PRIMITIVE_DEF_IDS.ndarray;
+
+        if is_ndarray1 && is_ndarray2 {
+            let (ndarray_dtype1, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
+            let (ndarray_dtype2, _) = unpack_ndarray_var_tys(&mut ctx.unifier, ty1);
+
+            assert!(ctx.unifier.unioned(ndarray_dtype1, ndarray_dtype2));
+
+            let left_val = NDArrayValue::from_ptr_val(
+                left_val.into_pointer_value(),
+                llvm_usize,
+                None
+            );
+            let res = numpy::ndarray_elementwise_binop_impl(
+                generator,
+                ctx,
+                ndarray_dtype1,
+                if is_aug_assign { Some(left_val) } else { None },
+                (left_val.as_ptr_value().into(), false),
+                (right_val.into(), false),
+                |generator, ctx, elem_ty, (lhs, rhs)| {
+                    gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(elem_ty), lhs),
+                        op,
+                        (&Some(elem_ty), rhs),
+                        ctx.current_loc,
+                        is_aug_assign,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
+                },
+            )?;
+
+            Ok(Some(res.as_ptr_value().into()))
+        } else {
+            let (ndarray_dtype, _) = unpack_ndarray_var_tys(
+                &mut ctx.unifier,
+                if is_ndarray1 { ty1 } else { ty2 },
+            );
+            let ndarray_val = NDArrayValue::from_ptr_val(
+                if is_ndarray1 { left_val } else { right_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+            let res = numpy::ndarray_elementwise_binop_impl(
+                generator,
+                ctx,
+                ndarray_dtype,
+                if is_aug_assign { Some(ndarray_val) } else { None },
+                (left_val, !is_ndarray1),
+                (right_val, !is_ndarray2),
+                |generator, ctx, elem_ty, (lhs, rhs)| {
+                    gen_binop_expr_with_values(
+                        generator,
+                        ctx,
+                        (&Some(elem_ty), lhs),
+                        op,
+                        (&Some(elem_ty), rhs),
+                        ctx.current_loc,
+                        is_aug_assign,
+                    )?.unwrap().to_basic_value_enum(ctx, generator, elem_ty)
+                },
+            )?;
+
+            Ok(Some(res.as_ptr_value().into()))
+        }
     } else {
-        let left_ty_enum = ctx.unifier.get_ty_immutable(left.custom.unwrap());
+        let left_ty_enum = ctx.unifier.get_ty_immutable(left_ty.unwrap());
         let TypeEnum::TObj { fields, obj_id, .. } = left_ty_enum.as_ref() else {
             unreachable!("must be tobj")
         };
@@ -1161,7 +1221,7 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
         let signature = if let Some(call) = ctx.calls.get(&loc.into()) {
             ctx.unifier.get_call_signature(*call).unwrap()
         } else {
-            let left_enum_ty = ctx.unifier.get_ty_immutable(left.custom.unwrap());
+            let left_enum_ty = ctx.unifier.get_ty_immutable(left_ty.unwrap());
             let TypeEnum::TObj { fields, .. } = left_enum_ty.as_ref() else {
                 unreachable!("must be tobj")
             };
@@ -1186,13 +1246,51 @@ pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
         generator
             .gen_call(
                 ctx,
-                Some((left.custom.unwrap(), left_val.into())),
+                Some((left_ty.unwrap(), left_val.into())),
                 (&signature, fun_id),
                 vec![(None, right_val.into())],
             ).map(|f| f.map(Into::into))
     }
 }
 
+/// Generates LLVM IR for a [binary operator expression][expr].
+///
+/// * `left` - The left-hand side of the binary operator.
+/// * `op` - The operator applied on the operands.
+/// * `right` - The right-hand side of the binary operator.
+/// * `loc` - The location of the full expression.
+/// * `is_aug_assign` - Whether the binary operator expression is also an assignment operator.
+pub fn gen_binop_expr<'ctx, G: CodeGenerator>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    left: &Expr<Option<Type>>,
+    op: &Operator,
+    right: &Expr<Option<Type>>,
+    loc: Location,
+    is_aug_assign: bool,
+) -> Result<Option<ValueEnum<'ctx>>, String> {
+    let left_val = if let Some(v) = generator.gen_expr(ctx, left)? {
+        v.to_basic_value_enum(ctx, generator, left.custom.unwrap())?
+    } else {
+        return Ok(None)
+    };
+    let right_val = if let Some(v) = generator.gen_expr(ctx, right)? {
+        v.to_basic_value_enum(ctx, generator, right.custom.unwrap())?
+    } else {
+        return Ok(None)
+    };
+
+    gen_binop_expr_with_values(
+        generator,
+        ctx,
+        (&left.custom, left_val),
+        op,
+        (&right.custom, right_val),
+        loc,
+        is_aug_assign,
+    )
+}
+
 /// Generates code for a subscript expression on an `ndarray`.
 ///
 /// * `ty` - The `Type` of the `NDArray` elements.
@@ -1265,12 +1363,14 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         } else {
             return Ok(None)
         };
+        let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
+        ctx.builder.build_store(index_addr, index).unwrap();
 
         Ok(Some(v.data()
             .get(
                 ctx,
                 generator,
-                ctx.ctx.i32_type().const_array(&[index]),
+                ArraySliceValue::from_ptr_val(index_addr, llvm_usize.const_int(1, false), None),
                 None,
             )
             .into()))
@@ -1286,6 +1386,8 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         } else {
             return Ok(None)
         };
+        let index_addr = generator.gen_var_alloc(ctx, index.get_type().into(), None)?;
+        ctx.builder.build_store(index_addr, index).unwrap();
 
         // Create a new array, remove the top dimension from the dimension-size-list, and copy the
         // elements over
@@ -1340,7 +1442,7 @@ fn gen_ndarray_subscript_expr<'ctx, G: CodeGenerator>(
         let v_data_src_ptr = v.data().ptr_offset(
             ctx,
             generator,
-            ctx.ctx.i32_type().const_array(&[index]),
+            ArraySliceValue::from_ptr_val(index_addr, llvm_usize.const_int(1, false), None),
             None
         );
         call_memcpy_generic(

nac3core/src/codegen/irrt/irrt.c

@@ -8,6 +8,8 @@ typedef unsigned _BitInt(64) uint64_t;
 # define MAX(a, b) (a > b ? a : b)
 # define MIN(a, b) (a > b ? b : a)
 
+# define NULL ((void *) 0)
+
 // adapted from GNU Scientific Library: https://git.savannah.gnu.org/cgit/gsl.git/tree/sys/pow_int.c
 // need to make sure `exp >= 0` before calling this function
 #define DEF_INT_EXP(T) T __nac3_int_exp_##T( \
@@ -243,13 +245,13 @@ void __nac3_ndarray_calc_nd_indices64(
     uint64_t index,
     const uint64_t* dims,
     uint64_t num_dims,
-    uint64_t* idxs
+    uint32_t* idxs
 ) {
     uint64_t stride = 1;
     for (uint64_t dim = 0; dim < num_dims; dim++) {
         uint64_t i = num_dims - dim - 1;
         __builtin_assume(dims[i] > 0);
-        idxs[i] = (index / stride) % dims[i];
+        idxs[i] = (uint32_t) ((index / stride) % dims[i]);
         stride *= dims[i];
     }
 }
@@ -293,3 +295,87 @@ uint64_t __nac3_ndarray_flatten_index64(
     }
     return idx;
 }
+
+void __nac3_ndarray_calc_broadcast(
+    const uint32_t *lhs_dims,
+    uint32_t lhs_ndims,
+    const uint32_t *rhs_dims,
+    uint32_t rhs_ndims,
+    uint32_t *out_dims
+) {
+    uint32_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
+
+    for (uint32_t i = 0; i < max_ndims; ++i) {
+        uint32_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
+        uint32_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
+        uint32_t *out_dim = &out_dims[max_ndims - i - 1];
+
+        if (lhs_dim_sz == NULL) {
+            *out_dim = *rhs_dim_sz;
+        } else if (rhs_dim_sz == NULL) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == 1) {
+            *out_dim = *rhs_dim_sz;
+        } else if (*rhs_dim_sz == 1) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == *rhs_dim_sz) {
+            *out_dim = *lhs_dim_sz;
+        } else {
+            __builtin_unreachable();
+        }
+    }
+}
+
+void __nac3_ndarray_calc_broadcast64(
+    const uint64_t *lhs_dims,
+    uint64_t lhs_ndims,
+    const uint64_t *rhs_dims,
+    uint64_t rhs_ndims,
+    uint64_t *out_dims
+) {
+    uint64_t max_ndims = lhs_ndims > rhs_ndims ? lhs_ndims : rhs_ndims;
+
+    for (uint64_t i = 0; i < max_ndims; ++i) {
+        uint64_t *lhs_dim_sz = i < lhs_ndims ? &lhs_dims[lhs_ndims - i - 1] : NULL;
+        uint64_t *rhs_dim_sz = i < rhs_ndims ? &rhs_dims[rhs_ndims - i - 1] : NULL;
+        uint64_t *out_dim = &out_dims[max_ndims - i - 1];
+
+        if (lhs_dim_sz == NULL) {
+            *out_dim = *rhs_dim_sz;
+        } else if (rhs_dim_sz == NULL) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == 1) {
+            *out_dim = *rhs_dim_sz;
+        } else if (*rhs_dim_sz == 1) {
+            *out_dim = *lhs_dim_sz;
+        } else if (*lhs_dim_sz == *rhs_dim_sz) {
+            *out_dim = *lhs_dim_sz;
+        } else {
+            __builtin_unreachable();
+        }
+    }
+}
+
+void __nac3_ndarray_calc_broadcast_idx(
+    const uint32_t *src_dims,
+    uint32_t src_ndims,
+    const uint32_t *in_idx,
+    uint32_t *out_idx
+) {
+    for (uint32_t i = 0; i < src_ndims; ++i) {
+        uint32_t src_i = src_ndims - i - 1;
+        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : in_idx[src_i];
+    }
+}
+
+void __nac3_ndarray_calc_broadcast_idx64(
+    const uint64_t *src_dims,
+    uint64_t src_ndims,
+    const uint32_t *in_idx,
+    uint32_t *out_idx
+) {
+    for (uint64_t i = 0; i < src_ndims; ++i) {
+        uint64_t src_i = src_ndims - i - 1;
+        out_idx[src_i] = src_dims[src_i] == 1 ? 0 : (uint32_t) in_idx[src_i];
+    }
+}

nac3core/src/codegen/irrt/mod.rs

@@ -1,9 +1,18 @@
 use crate::typecheck::typedef::Type;
 
 use super::{
-    classes::{ArrayLikeIndexer, ArrayLikeValue, ListValue, NDArrayValue, UntypedArrayLikeMutator},
+    classes::{
+        ArrayLikeIndexer,
+        ArrayLikeValue,
+        ArraySliceValue,
+        ListValue,
+        NDArrayValue,
+        TypedArrayLikeAdapter,
+        UntypedArrayLikeAccessor,
+    },
     CodeGenContext,
     CodeGenerator,
+    llvm_intrinsics,
 };
 use inkwell::{
     attributes::{Attribute, AttributeLoc},
@@ -11,7 +20,7 @@ use inkwell::{
     memory_buffer::MemoryBuffer,
     module::Module,
     types::{BasicTypeEnum, IntType},
-    values::{ArrayValue, BasicValueEnum, CallSiteValue, FloatValue, IntValue, PointerValue},
+    values::{BasicValueEnum, CallSiteValue, FloatValue, IntValue},
     AddressSpace, IntPredicate,
 };
 use itertools::Either;
@@ -619,7 +628,8 @@ pub fn call_ndarray_calc_size<'ctx, G, Dims>(
         .unwrap()
 }
 
-/// Generates a call to `__nac3_ndarray_calc_nd_indices`.
+/// Generates a call to `__nac3_ndarray_calc_nd_indices`. Returns a [`TypeArrayLikeAdpater`]
+/// containing `i32` indices of the flattened index.
 ///
 /// * `index` - The index to compute the multidimensional index for.
 /// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
@@ -629,10 +639,11 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
     ctx: &mut CodeGenContext<'ctx, '_>,
     index: IntValue<'ctx>,
     ndarray: NDArrayValue<'ctx>,
-) -> PointerValue<'ctx> {
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
     let llvm_void = ctx.ctx.void_type();
+    let llvm_i32 = ctx.ctx.i32_type();
     let llvm_usize = generator.get_size_type(ctx.ctx);
-
+    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
     let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
     let ndarray_calc_nd_indices_fn_name = match llvm_usize.get_bit_width() {
@@ -646,7 +657,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
                 llvm_usize.into(),
                 llvm_pusize.into(),
                 llvm_usize.into(),
-                llvm_pusize.into(),
+                llvm_pi32.into(),
             ],
             false,
         );
@@ -658,7 +669,7 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
     let ndarray_dims = ndarray.dim_sizes();
 
     let indices = ctx.builder.build_array_alloca(
-        llvm_usize,
+        llvm_i32,
         ndarray_num_dims,
         "",
     ).unwrap();
@@ -676,7 +687,11 @@ pub fn call_ndarray_calc_nd_indices<'ctx, G: CodeGenerator + ?Sized>(
         )
         .unwrap();
 
-    indices
+    TypedArrayLikeAdapter::from(
+        ArraySliceValue::from_ptr_val(indices, ndarray_num_dims, None),
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
 }
 
 fn call_ndarray_flatten_index_impl<'ctx, G, Indices>(
@@ -771,46 +786,152 @@ pub fn call_ndarray_flatten_index<'ctx, G, Index>(
         indices,
     )
 }
-/// Generates a call to `__nac3_ndarray_flatten_index`. Returns the flattened index for the
-/// multidimensional index.
-///
-/// * `ndarray` - LLVM pointer to the `NDArray`. This value must be the LLVM representation of an
-/// `NDArray`.
-/// * `indices` - The multidimensional index to compute the flattened index for.
-pub fn call_ndarray_flatten_index_const<'ctx, G: CodeGenerator + ?Sized>(
+
+/// Generates a call to `__nac3_ndarray_calc_broadcast`. Returns a tuple containing the number of
+/// dimension and size of each dimension of the resultant `ndarray`.
+pub fn call_ndarray_calc_broadcast<'ctx, G: CodeGenerator + ?Sized>(
     generator: &mut G,
     ctx: &mut CodeGenContext<'ctx, '_>,
-    ndarray: NDArrayValue<'ctx>,
-    indices: ArrayValue<'ctx>,
-) -> IntValue<'ctx> {
+    lhs: NDArrayValue<'ctx>,
+    rhs: NDArrayValue<'ctx>,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
     let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
 
-    let indices_size = indices.get_type().len();
-    let indices_alloca = generator.gen_array_var_alloc(
-        ctx,
-        indices.get_type().get_element_type(),
-        llvm_usize.const_int(indices_size as u64, false),
-        None,
-    ).unwrap();
-    for i in 0..indices_size {
-        let v = ctx.builder.build_extract_value(indices, i, "")
-            .unwrap()
-            .into_int_value();
+    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_calc_broadcast",
+        64 => "__nac3_ndarray_calc_broadcast64",
+        bw => unreachable!("Unsupported size type bit width: {}", bw)
+    };
+    let ndarray_calc_broadcast_fn = ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
+        let fn_type = llvm_usize.fn_type(
+            &[
+                llvm_pusize.into(),
+                llvm_usize.into(),
+                llvm_pusize.into(),
+                llvm_usize.into(),
+                llvm_pusize.into(),
+            ],
+            false,
+        );
 
-        unsafe {
-            indices_alloca.set_unchecked(
-                ctx,
-                generator,
-                ctx.ctx.i32_type().const_int(i as u64, false),
-                v.into(),
-            );
-        }
-    }
+        ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
+    });
 
-    call_ndarray_flatten_index_impl(
-        generator,
-        ctx,
-        ndarray,
-        &indices_alloca,
+    let lhs_ndims = lhs.load_ndims(ctx);
+    let rhs_ndims = rhs.load_ndims(ctx);
+    let max_ndims = llvm_intrinsics::call_int_umax(ctx, lhs_ndims, rhs_ndims, None);
+
+    // TODO: Generate assertion checks for whether each dimension is compatible
+    // gen_for_callback_incrementing(
+    //     generator,
+    //     ctx,
+    //     llvm_usize.const_zero(),
+    //     (max_ndims, false),
+    //     |generator, ctx, idx| {
+    //         let lhs_dim_sz =
+    //
+    //         let lhs_elem = lhs.get_dims().get(ctx, generator, idx, None);
+    //         let rhs_elem = rhs.get_dims().get(ctx, generator, idx, None);
+    //
+    //
+    //     },
+    //     llvm_usize.const_int(1, false),
+    // ).unwrap();
+
+    let lhs_dims = lhs.dim_sizes().base_ptr(ctx, generator);
+    let lhs_ndims = lhs.load_ndims(ctx);
+    let rhs_dims = rhs.dim_sizes().base_ptr(ctx, generator);
+    let rhs_ndims = rhs.load_ndims(ctx);
+    let out_dims = ctx.builder.build_array_alloca(llvm_usize, max_ndims, "").unwrap();
+    let out_dims = ArraySliceValue::from_ptr_val(out_dims, max_ndims, None);
+
+    ctx.builder
+        .build_call(
+            ndarray_calc_broadcast_fn,
+            &[
+                lhs_dims.into(),
+                lhs_ndims.into(),
+                rhs_dims.into(),
+                rhs_ndims.into(),
+                out_dims.base_ptr(ctx, generator).into(),
+            ],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        out_dims,
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
+    )
+}
+
+/// Generates a call to `__nac3_ndarray_calc_broadcast_idx`. Returns an [`ArrayAllocaValue`]
+/// containing the indices used for accessing `array` corresponding to the index of the broadcasted
+/// array `broadcast_idx`.
+pub fn call_ndarray_calc_broadcast_index<'ctx, G: CodeGenerator + ?Sized, BroadcastIdx: UntypedArrayLikeAccessor<'ctx>>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    array: NDArrayValue<'ctx>,
+    broadcast_idx: &BroadcastIdx,
+) -> TypedArrayLikeAdapter<'ctx, IntValue<'ctx>> {
+    let llvm_i32 = ctx.ctx.i32_type();
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+    let llvm_pi32 = llvm_i32.ptr_type(AddressSpace::default());
+    let llvm_pusize = llvm_usize.ptr_type(AddressSpace::default());
+
+    let ndarray_calc_broadcast_fn_name = match llvm_usize.get_bit_width() {
+        32 => "__nac3_ndarray_calc_broadcast_idx",
+        64 => "__nac3_ndarray_calc_broadcast_idx64",
+        bw => unreachable!("Unsupported size type bit width: {}", bw)
+    };
+    let ndarray_calc_broadcast_fn = ctx.module.get_function(ndarray_calc_broadcast_fn_name).unwrap_or_else(|| {
+        let fn_type = llvm_usize.fn_type(
+            &[
+                llvm_pusize.into(),
+                llvm_usize.into(),
+                llvm_pi32.into(),
+                llvm_pi32.into(),
+            ],
+            false,
+        );
+
+        ctx.module.add_function(ndarray_calc_broadcast_fn_name, fn_type, None)
+    });
+
+    // TODO: Assertions
+
+    let broadcast_size = broadcast_idx.size(ctx, generator);
+    let out_idx = ctx.builder.build_array_alloca(llvm_i32, broadcast_size, "").unwrap();
+
+    let array_dims = array.dim_sizes().base_ptr(ctx, generator);
+    let array_ndims = array.load_ndims(ctx);
+    let broadcast_idx_ptr = unsafe {
+        broadcast_idx.ptr_offset_unchecked(
+            ctx,
+            generator,
+            llvm_usize.const_zero(),
+            None
+        )
+    };
+
+    ctx.builder
+        .build_call(
+            ndarray_calc_broadcast_fn,
+            &[
+                array_dims.into(),
+                array_ndims.into(),
+                broadcast_idx_ptr.into(),
+                out_idx.into(),
+            ],
+            "",
+        )
+        .unwrap();
+
+    TypedArrayLikeAdapter::from(
+        ArraySliceValue::from_ptr_val(out_idx, broadcast_size, None),
+        Box::new(|_, v| v.into_int_value()),
+        Box::new(|_, v| v.into()),
     )
 }

nac3core/src/codegen/mod.rs

@@ -2,7 +2,7 @@ use crate::{
     symbol_resolver::{StaticValue, SymbolResolver},
     toplevel::{
         helper::PRIMITIVE_DEF_IDS, 
-        numpy::unpack_ndarray_tvars, 
+        numpy::unpack_ndarray_var_tys, 
         TopLevelContext, 
         TopLevelDef,
     },
@@ -451,7 +451,7 @@ fn get_llvm_type<'ctx, G: CodeGenerator + ?Sized>(
 
                         TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
                             let llvm_usize = generator.get_size_type(ctx);
-                            let (dtype, _) = unpack_ndarray_tvars(unifier, ty);
+                            let (dtype, _) = unpack_ndarray_var_tys(unifier, ty);
                             let element_type = get_llvm_type(
                                 ctx, 
                                 module, 

nac3core/src/codegen/numpy.rs

@@ -12,11 +12,14 @@ use crate::{
             ListValue,
             NDArrayValue,
             TypedArrayLikeAccessor,
+            TypedArrayLikeAdapter,
             UntypedArrayLikeAccessor,
         },
         CodeGenContext,
         CodeGenerator,
         irrt::{
+            call_ndarray_calc_broadcast,
+            call_ndarray_calc_broadcast_index,
             call_ndarray_calc_nd_indices,
             call_ndarray_calc_size,
         },
@@ -26,7 +29,7 @@ use crate::{
     symbol_resolver::ValueEnum,
     toplevel::{
         DefinitionId,
-        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     },
     typecheck::typedef::{FunSignature, Type},
 };
@@ -324,7 +327,7 @@ fn ndarray_fill_indexed<'ctx, G, ValueFn>(
 ) -> Result<(), String>
     where
         G: CodeGenerator + ?Sized,
-        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, PointerValue<'ctx>) -> Result<BasicValueEnum<'ctx>, String>,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, TypedArrayLikeAdapter<'ctx, IntValue<'ctx>>) -> Result<BasicValueEnum<'ctx>, String>,
 {
     ndarray_fill_flattened(
         generator,
@@ -343,6 +346,67 @@ fn ndarray_fill_indexed<'ctx, G, ValueFn>(
     )
 }
 
+/// Generates the LLVM IR for populating the entire `NDArray` from two `ndarray` or scalar value
+/// with broadcast-compatible shapes.
+fn ndarray_broadcast_fill<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: NDArrayValue<'ctx>,
+    lhs: (BasicValueEnum<'ctx>, bool),
+    rhs: (BasicValueEnum<'ctx>, bool),
+    value_fn: ValueFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator + ?Sized,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let (lhs_val, lhs_scalar) = lhs;
+    let (rhs_val, rhs_scalar) = rhs;
+
+    assert!(!(lhs_scalar && rhs_scalar),
+            "One of the operands must be a ndarray instance: `{}`, `{}`",
+            lhs_val.get_type(),
+            rhs_val.get_type());
+
+    ndarray_fill_indexed(
+        generator,
+        ctx,
+        res,
+        |generator, ctx, idx| {
+            let lhs_elem = if lhs_scalar {
+                lhs_val
+            } else {
+                let lhs = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+                let lhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, lhs, &idx);
+
+                unsafe {
+                    lhs.data().get_unchecked(ctx, generator, lhs_idx, None)
+                }
+            };
+
+            let rhs_elem = if rhs_scalar {
+                rhs_val
+            } else {
+                let rhs = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+                let rhs_idx = call_ndarray_calc_broadcast_index(generator, ctx, rhs, &idx);
+                
+                unsafe {
+                    rhs.data().get_unchecked(ctx, generator, rhs_idx, None)
+                }
+            };
+
+            debug_assert_eq!(lhs_elem.get_type(), rhs_elem.get_type());
+
+            value_fn(generator, ctx, elem_ty, (lhs_elem, rhs_elem))
+        },
+    )?;
+
+    Ok(res)
+}
+
 /// LLVM-typed implementation for generating the implementation for `ndarray.zeros`.
 ///
 /// * `elem_ty` - The element type of the `NDArray`.
@@ -470,6 +534,7 @@ fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
     ncols: IntValue<'ctx>,
     offset: IntValue<'ctx>,
 ) -> Result<NDArrayValue<'ctx>, String> {
+    let llvm_i32 = ctx.ctx.i32_type();
     let llvm_usize = generator.get_size_type(ctx.ctx);
     let llvm_usize_2 = llvm_usize.array_type(2);
 
@@ -498,21 +563,17 @@ fn call_ndarray_eye_impl<'ctx, G: CodeGenerator + ?Sized>(
         ctx,
         ndarray,
         |generator, ctx, indices| {
-            let row = ctx.build_gep_and_load(
-                indices,
-                &[llvm_usize.const_int(0, false)],
-                None,
-            ).into_int_value();
-            let col = ctx.build_gep_and_load(
-                indices,
-                &[llvm_usize.const_int(1, false)],
-                None,
-            ).into_int_value();
+            let (row, col) = unsafe {
+                (
+                    indices.get_typed_unchecked(ctx, generator, llvm_usize.const_zero(), None),
+                    indices.get_typed_unchecked(ctx, generator, llvm_usize.const_int(1, false), None),
+                )
+            };
 
             let col_with_offset = ctx.builder
                 .build_int_add(
                     col,
-                    ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_usize, "").unwrap(),
+                    ctx.builder.build_int_s_extend_or_bit_cast(offset, llvm_i32, "").unwrap(),
                     "",
                 )
                 .unwrap();
@@ -581,6 +642,108 @@ fn ndarray_copy_impl<'ctx, G: CodeGenerator + ?Sized>(
     Ok(ndarray)
 }
 
+/// LLVM-typed implementation for computing elementwise binary operations on two input operands.
+///
+/// If the operand is a `ndarray`, the broadcast index corresponding to each element in the output 
+/// is computed, the element accessed and used as an operand of the `value_fn` arguments tuple. 
+/// Otherwise, the operand is treated as a scalar value, and is used as an operand of the 
+/// `value_fn` arguments tuple for all output elements.
+///
+/// The second element of the tuple indicates whether to treat the operand value as a `ndarray`
+/// (which would be accessed by its broadcast index) or as a scalar value (which would be 
+/// broadcast to all elements).
+///
+/// * `elem_ty` - The element type of the `NDArray`.
+/// * `res` - The `ndarray` instance to write results into, or [`None`] if the result should be
+/// written to a new `ndarray`.
+/// * `value_fn` - Function mapping the two input elements into the result.
+///
+/// # Panic
+///
+/// This function will panic if neither input operands (`lhs` or `rhs`) is a `ndarray`.
+pub fn ndarray_elementwise_binop_impl<'ctx, G, ValueFn>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    elem_ty: Type,
+    res: Option<NDArrayValue<'ctx>>,
+    lhs: (BasicValueEnum<'ctx>, bool),
+    rhs: (BasicValueEnum<'ctx>, bool),
+    value_fn: ValueFn,
+) -> Result<NDArrayValue<'ctx>, String>
+    where
+        G: CodeGenerator,
+        ValueFn: Fn(&mut G, &mut CodeGenContext<'ctx, '_>, Type, (BasicValueEnum<'ctx>, BasicValueEnum<'ctx>)) -> Result<BasicValueEnum<'ctx>, String>,
+{
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    let (lhs_val, lhs_scalar) = lhs;
+    let (rhs_val, rhs_scalar) = rhs;
+
+    assert!(!(lhs_scalar && rhs_scalar),
+            "One of the operands must be a ndarray instance: `{}`, `{}`",
+            lhs_val.get_type(),
+            rhs_val.get_type());
+
+    let ndarray = res.unwrap_or_else(|| {
+        if lhs_scalar && rhs_scalar {
+            let lhs_val = NDArrayValue::from_ptr_val(lhs_val.into_pointer_value(), llvm_usize, None);
+            let rhs_val = NDArrayValue::from_ptr_val(rhs_val.into_pointer_value(), llvm_usize, None);
+
+            let ndarray_dims = call_ndarray_calc_broadcast(generator, ctx, lhs_val, rhs_val);
+
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &ndarray_dims,
+                |generator, ctx, v| {
+                    Ok(v.size(ctx, generator))
+                },
+                |generator, ctx, v, idx| {
+                    unsafe {
+                        Ok(v.get_typed_unchecked(ctx, generator, idx, None))
+                    }
+                },
+            ).unwrap()
+        } else {
+            let ndarray = NDArrayValue::from_ptr_val(
+                if lhs_scalar { rhs_val } else { lhs_val }.into_pointer_value(),
+                llvm_usize,
+                None,
+            );
+
+            create_ndarray_dyn_shape(
+                generator,
+                ctx,
+                elem_ty,
+                &ndarray,
+                |_, ctx, v| {
+                    Ok(v.load_ndims(ctx))
+                },
+                |generator, ctx, v, idx| {
+                    unsafe {
+                        Ok(v.dim_sizes().get_typed_unchecked(ctx, generator, idx, None))
+                    }
+                },
+            ).unwrap()
+        }
+    });
+
+    ndarray_broadcast_fill(
+        generator,
+        ctx,
+        elem_ty,
+        ndarray,
+        lhs,
+        rhs,
+        |generator, ctx, elem_ty, elems| {
+            value_fn(generator, ctx, elem_ty, elems)
+        },
+    )?;
+
+    Ok(ndarray)
+}
+
 /// Generates LLVM IR for `ndarray.empty`.
 pub fn gen_ndarray_empty<'ctx>(
     context: &mut CodeGenContext<'ctx, '_>,
@@ -767,7 +930,7 @@ pub fn gen_ndarray_copy<'ctx>(
     let llvm_usize = generator.get_size_type(context.ctx);
 
     let this_ty = obj.as_ref().unwrap().0;
-    let (this_elem_ty, _) = unpack_ndarray_tvars(&mut context.unifier, this_ty);
+    let (this_elem_ty, _) = unpack_ndarray_var_tys(&mut context.unifier, this_ty);
     let this_arg = obj
         .as_ref()
         .unwrap()

nac3core/src/codegen/stmt.rs

@@ -13,7 +13,7 @@ use crate::{
     toplevel::{
         DefinitionId,
         helper::PRIMITIVE_DEF_IDS,
-        numpy::unpack_ndarray_tvars,
+        numpy::unpack_ndarray_var_tys,
         TopLevelDef,
     },
     typecheck::typedef::{FunSignature, Type, TypeEnum},
@@ -251,7 +251,7 @@ pub fn gen_assign<'ctx, G: CodeGenerator>(
             let ty = match &*ctx.unifier.get_ty_immutable(target.custom.unwrap()) {
                 TypeEnum::TList { ty } => *ty,
                 TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                    unpack_ndarray_tvars(&mut ctx.unifier, target.custom.unwrap()).0
+                    unpack_ndarray_var_tys(&mut ctx.unifier, target.custom.unwrap()).0
                 }
                 _ => unreachable!(),
             };
@@ -546,7 +546,7 @@ pub fn gen_for_callback<'ctx, 'a, G, I, InitFn, CondFn, BodyFn, UpdateFn>(
 ///     body(x);
 /// }
 /// ```
-/// 
+///
 /// * `init_val` - The initial value of the loop variable. The type of this value will also be used 
 /// as the type of the loop variable.
 /// * `max_val` - A tuple containing the maximum value of the loop variable, and whether the maximum

nac3core/src/symbol_resolver.rs

@@ -170,13 +170,13 @@ impl SymbolValue {
     /// Returns the [`TypeAnnotation`] representing the data type of this value.
     pub fn get_type_annotation(&self, primitives: &PrimitiveStore, unifier: &mut Unifier) -> TypeAnnotation {
         match self {
-            SymbolValue::Bool(..) => TypeAnnotation::Primitive(primitives.bool),
-            SymbolValue::Double(..) => TypeAnnotation::Primitive(primitives.float),
-            SymbolValue::I32(..) => TypeAnnotation::Primitive(primitives.int32),
-            SymbolValue::I64(..) => TypeAnnotation::Primitive(primitives.int64),
-            SymbolValue::U32(..) => TypeAnnotation::Primitive(primitives.uint32),
-            SymbolValue::U64(..) => TypeAnnotation::Primitive(primitives.uint64),
-            SymbolValue::Str(..) => TypeAnnotation::Primitive(primitives.str),
+            SymbolValue::Bool(..)
+            | SymbolValue::Double(..)
+            | SymbolValue::I32(..)
+            | SymbolValue::I64(..)
+            | SymbolValue::U32(..)
+            | SymbolValue::U64(..)
+            | SymbolValue::Str(..) => TypeAnnotation::Primitive(self.get_type(primitives, unifier)),
             SymbolValue::Tuple(vs) => {
                 let vs_tys = vs
                     .iter()
@@ -230,6 +230,36 @@ impl Display for SymbolValue {
     }
 }
 
+impl TryFrom<SymbolValue> for u64 {
+    type Error = ();
+
+    /// TODO
+    fn try_from(value: SymbolValue) -> Result<Self, Self::Error> {
+        match value {
+            SymbolValue::I32(v) => Ok(v as u64),
+            SymbolValue::I64(v) => u64::try_from(v).map_err(|_| ()),
+            SymbolValue::U32(v) => Ok(v as u64),
+            SymbolValue::U64(v) => Ok(v),
+            _ => Err(()),
+        }
+    }
+}
+
+impl TryFrom<SymbolValue> for i128 {
+    type Error = ();
+
+    /// TODO
+    fn try_from(value: SymbolValue) -> Result<Self, Self::Error> {
+        match value {
+            SymbolValue::I32(v) => Ok(v as i128),
+            SymbolValue::I64(v) => Ok(v as i128),
+            SymbolValue::U32(v) => Ok(v as i128),
+            SymbolValue::U64(v) => Ok(v as i128),
+            _ => Err(()),
+        }
+    }
+}
+
 pub trait StaticValue {
     /// Returns a unique identifier for this value.
     fn get_unique_identifier(&self) -> u64;

nac3core/src/toplevel/builtins.rs

@@ -1,7 +1,7 @@
 use super::*;
 use crate::{
     codegen::{
-        classes::RangeValue,
+        classes::{ArrayLikeValue, NDArrayValue, RangeValue, TypedArrayLikeAccessor},
         expr::destructure_range,
         irrt::*,
         llvm_intrinsics::*,
@@ -299,6 +299,8 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
         Some("N".into()),
         None,
     );
+    let size_t = primitives.0.usize();
+
     let var_map: VarMap = vec![(num_ty.1, num_ty.0)].into_iter().collect();
     let exception_fields = vec![
         ("__name__".into(), int32, true),
@@ -345,8 +347,27 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
         .nth(1)
         .map(|(var_id, ty)| (*ty, *var_id))
         .unwrap();
+    let ndarray_usized_ndims_tvar = primitives.1.get_fresh_const_generic_var(
+        size_t, 
+        Some("ndarray_ndims".into()), 
+        None,
+    );
     let ndarray_copy_ty = *ndarray_fields.get(&"copy".into()).unwrap();
     let ndarray_fill_ty = *ndarray_fields.get(&"fill".into()).unwrap();
+    let ndarray_add_ty = *ndarray_fields.get(&"__add__".into()).unwrap();
+    let ndarray_sub_ty = *ndarray_fields.get(&"__sub__".into()).unwrap();
+    let ndarray_mul_ty = *ndarray_fields.get(&"__mul__".into()).unwrap();
+    let ndarray_truediv_ty = *ndarray_fields.get(&"__truediv__".into()).unwrap();
+    let ndarray_floordiv_ty = *ndarray_fields.get(&"__floordiv__".into()).unwrap();
+    let ndarray_mod_ty = *ndarray_fields.get(&"__mod__".into()).unwrap();
+    let ndarray_pow_ty = *ndarray_fields.get(&"__pow__".into()).unwrap();
+    let ndarray_iadd_ty = *ndarray_fields.get(&"__iadd__".into()).unwrap();
+    let ndarray_isub_ty = *ndarray_fields.get(&"__isub__".into()).unwrap();
+    let ndarray_imul_ty = *ndarray_fields.get(&"__imul__".into()).unwrap();
+    let ndarray_itruediv_ty = *ndarray_fields.get(&"__itruediv__".into()).unwrap();
+    let ndarray_ifloordiv_ty = *ndarray_fields.get(&"__ifloordiv__".into()).unwrap();
+    let ndarray_imod_ty = *ndarray_fields.get(&"__imod__".into()).unwrap();
+    let ndarray_ipow_ty = *ndarray_fields.get(&"__ipow__".into()).unwrap();
 
     let top_level_def_list = vec![
         Arc::new(RwLock::new(TopLevelComposer::make_top_level_class_def(
@@ -524,6 +545,20 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
             methods: vec![
                 ("copy".into(), ndarray_copy_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 1)),
                 ("fill".into(), ndarray_fill_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 2)),
+                ("__add__".into(), ndarray_add_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 3)),
+                ("__sub__".into(), ndarray_sub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 4)),
+                ("__mul__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 5)),
+                ("__truediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 6)),
+                ("__floordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 7)),
+                ("__mod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 8)),
+                ("__pow__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 9)),
+                ("__iadd__".into(), ndarray_iadd_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 10)),
+                ("__isub__".into(), ndarray_isub_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 11)),
+                ("__imul__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 12)),
+                ("__itruediv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 13)),
+                ("__ifloordiv__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 14)),
+                ("__imod__".into(), ndarray_mul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 15)),
+                ("__ipow__".into(), ndarray_imul_ty.0, DefinitionId(PRIMITIVE_DEF_IDS.ndarray.0 + 16)),
             ],
             ancestors: Vec::default(),
             constructor: None,
@@ -562,6 +597,216 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
             )))),
             loc: None,
         })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__add__".into(),
+            simple_name: "__add__".into(),
+            signature: ndarray_add_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__sub__".into(),
+            simple_name: "__sub__".into(),
+            signature: ndarray_sub_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__mul__".into(),
+            simple_name: "__mul__".into(),
+            signature: ndarray_mul_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__truediv__".into(),
+            simple_name: "__truediv__".into(),
+            signature: ndarray_truediv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__floordiv__".into(),
+            simple_name: "__floordiv__".into(),
+            signature: ndarray_floordiv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__mod__".into(),
+            simple_name: "__mod__".into(),
+            signature: ndarray_mod_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__pow__".into(),
+            simple_name: "__pow__".into(),
+            signature: ndarray_pow_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__iadd__".into(),
+            simple_name: "__iadd__".into(),
+            signature: ndarray_iadd_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id, ndarray_usized_ndims_tvar.1],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__isub__".into(),
+            simple_name: "__isub__".into(),
+            signature: ndarray_isub_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__imul__".into(),
+            simple_name: "__imul__".into(),
+            signature: ndarray_imul_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__itruediv__".into(),
+            simple_name: "__itruediv__".into(),
+            signature: ndarray_itruediv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__ifloordiv__".into(),
+            simple_name: "__ifloordiv__".into(),
+            signature: ndarray_ifloordiv_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__imod__".into(),
+            simple_name: "__imod__".into(),
+            signature: ndarray_imod_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
+        Arc::new(RwLock::new(TopLevelDef::Function {
+            name: "ndarray.__ipow__".into(),
+            simple_name: "__ipow__".into(),
+            signature: ndarray_ipow_ty.0,
+            var_id: vec![ndarray_dtype_var_id, ndarray_ndims_var_id],
+            instance_to_symbol: HashMap::default(),
+            instance_to_stmt: HashMap::default(),
+            resolver: None,
+            codegen_callback: Some(Arc::new(GenCall::new(Box::new(
+                |_, _, _, _, _| {
+                    unreachable!("handled in gen_expr")
+                },
+            )))),
+            loc: None,
+        })),
         Arc::new(RwLock::new(TopLevelDef::Function {
             name: "int32".into(),
             simple_name: "int32".into(),
@@ -1458,13 +1703,33 @@ pub fn get_builtins(primitives: &mut (PrimitiveStore, Unifier)) -> BuiltinInfo {
                                 }
                                 TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
                                     let llvm_i32 = ctx.ctx.i32_type();
-                                    let i32_zero = llvm_i32.const_zero();
+                                    let llvm_usize = generator.get_size_type(ctx.ctx);
 
-                                    let len = ctx.build_gep_and_load(
+                                    let arg = NDArrayValue::from_ptr_val(
                                         arg.into_pointer_value(),
-                                        &[i32_zero, i32_zero],
-                                        None,
-                                    ).into_int_value();
+                                        llvm_usize,
+                                        None
+                                    );
+
+                                    let ndims = arg.dim_sizes().size(ctx, generator);
+                                    ctx.make_assert(
+                                        generator,
+                                        ctx.builder.build_int_compare(
+                                            IntPredicate::NE,
+                                            ndims,
+                                            llvm_usize.const_zero(),
+                                            "",
+                                        ).unwrap(),
+                                        "0:TypeError",
+                                        "len() of unsized object",
+                                        [None, None, None],
+                                        ctx.current_loc,
+                                    );
+
+                                    let len = unsafe {
+                                        arg.dim_sizes()
+                                            .get_typed_unchecked(ctx, generator, llvm_usize.const_zero(), None)
+                                    };
 
                                     if len.get_type().get_bit_width() == 32 {
                                         Some(len.into())

nac3core/src/toplevel/helper.rs

@@ -1,6 +1,7 @@
 use std::convert::TryInto;
 
 use crate::symbol_resolver::SymbolValue;
+use crate::toplevel::numpy::subst_ndarray_tvars;
 use crate::typecheck::typedef::{Mapping, VarMap};
 use nac3parser::ast::{Constant, Location};
 
@@ -226,11 +227,57 @@ impl TopLevelComposer {
                 (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
             ]),
         }));
+        let ndarray_binop_fun_other_ty = unifier.get_fresh_var(None, None);
+        let ndarray_binop_fun_ret_ty = unifier.get_fresh_var(None, None);
+        let ndarray_binop_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
+            args: vec![
+                FuncArg {
+                    name: "other".into(),
+                    ty: ndarray_binop_fun_other_ty.0,
+                    default_value: None,
+                },
+            ],
+            ret: ndarray_binop_fun_ret_ty.0,
+            vars: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
+        }));
+        let ndarray_truediv_fun_other_ty = unifier.get_fresh_var(None, None);
+        let ndarray_truediv_fun_ret_ty = unifier.get_fresh_var(None, None);
+        let ndarray_truediv_fun_ty = unifier.add_ty(TypeEnum::TFunc(FunSignature {
+            args: vec![
+                FuncArg {
+                    name: "other".into(),
+                    ty: ndarray_truediv_fun_other_ty.0,
+                    default_value: None,
+                },
+            ],
+            ret: ndarray_truediv_fun_ret_ty.0,
+            vars: VarMap::from([
+                (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
+                (ndarray_ndims_tvar.1, ndarray_ndims_tvar.0),
+            ]),
+        }));
         let ndarray = unifier.add_ty(TypeEnum::TObj {
             obj_id: PRIMITIVE_DEF_IDS.ndarray,
             fields: Mapping::from([
                 ("copy".into(), (ndarray_copy_fun_ty, true)),
                 ("fill".into(), (ndarray_fill_fun_ty, true)),
+                ("__add__".into(), (ndarray_binop_fun_ty, true)),
+                ("__sub__".into(), (ndarray_binop_fun_ty, true)),
+                ("__mul__".into(), (ndarray_binop_fun_ty, true)),
+                ("__truediv__".into(), (ndarray_truediv_fun_ty, true)),
+                ("__floordiv__".into(), (ndarray_binop_fun_ty, true)),
+                ("__mod__".into(), (ndarray_binop_fun_ty, true)),
+                ("__pow__".into(), (ndarray_binop_fun_ty, true)),
+                ("__iadd__".into(), (ndarray_binop_fun_ty, true)),
+                ("__isub__".into(), (ndarray_binop_fun_ty, true)),
+                ("__imul__".into(), (ndarray_binop_fun_ty, true)),
+                ("__itruediv__".into(), (ndarray_truediv_fun_ty, true)),
+                ("__ifloordiv__".into(), (ndarray_binop_fun_ty, true)),
+                ("__imod__".into(), (ndarray_binop_fun_ty, true)),
+                ("__ipow__".into(), (ndarray_binop_fun_ty, true)),
             ]),
             params: VarMap::from([
                 (ndarray_dtype_tvar.1, ndarray_dtype_tvar.0),
@@ -238,7 +285,16 @@ impl TopLevelComposer {
             ]),
         });
 
+        let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t_ty, Some("ndarray_ndims".into()), None);
+        let ndarray_unsized = subst_ndarray_tvars(&mut unifier, ndarray, Some(ndarray_usized_ndims_tvar.0), None);
+
         unifier.unify(ndarray_copy_fun_ret_ty.0, ndarray).unwrap();
+        unifier.unify(ndarray_binop_fun_other_ty.0, ndarray_unsized).unwrap();
+        unifier.unify(ndarray_binop_fun_ret_ty.0, ndarray).unwrap();
+
+        let ndarray_float = subst_ndarray_tvars(&mut unifier, ndarray, Some(float), None);
+        unifier.unify(ndarray_truediv_fun_other_ty.0, ndarray).unwrap();
+        unifier.unify(ndarray_truediv_fun_ret_ty.0, ndarray_float).unwrap();
 
         let primitives = PrimitiveStore {
             int32,

nac3core/src/toplevel/numpy.rs

@@ -19,13 +19,30 @@ pub fn make_ndarray_ty(
     dtype: Option<Type>,
     ndims: Option<Type>,
 ) -> Type {
-    let ndarray = primitives.ndarray;
+    subst_ndarray_tvars(unifier, primitives.ndarray, dtype, ndims)
+}
 
+/// Substitutes type variables in `ndarray`.
+///
+/// * `dtype` - The element type of the `ndarray`, or [`None`] if the type variable is not
+/// specialized.
+/// * `ndims` - The number of dimensions of the `ndarray`, or [`None`] if the type variable is not
+/// specialized.
+pub fn subst_ndarray_tvars(
+    unifier: &mut Unifier,
+    ndarray: Type,
+    dtype: Option<Type>,
+    ndims: Option<Type>,
+) -> Type {
     let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
         panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
     };
     debug_assert_eq!(*obj_id, PRIMITIVE_DEF_IDS.ndarray);
 
+    if dtype.is_none() && ndims.is_none() {
+        return ndarray
+    }
+
     let tvar_ids = params.iter()
         .map(|(obj_id, _)| *obj_id)
         .collect_vec();
@@ -42,12 +59,10 @@ pub fn make_ndarray_ty(
     unifier.subst(ndarray, &tvar_subst).unwrap_or(ndarray)
 }
 
-/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
-/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
-pub fn unpack_ndarray_tvars(
+fn unpack_ndarray_tvars(
     unifier: &mut Unifier,
     ndarray: Type,
-) -> (Type, Type) {
+) -> Vec<(u32, Type)> {
     let TypeEnum::TObj { obj_id, params, .. } = &*unifier.get_ty_immutable(ndarray) else {
         panic!("Expected `ndarray` to be TObj, but got {}", unifier.stringify(ndarray))
     };
@@ -56,7 +71,33 @@ pub fn unpack_ndarray_tvars(
 
     params.iter()
         .sorted_by_key(|(obj_id, _)| *obj_id)
-        .map(|(_, ty)| *ty)
+        .map(|(var_id, ty)| (*var_id, *ty))
+        .collect_vec()
+}
+
+/// Unpacks the type variable IDs of `ndarray` into a tuple. The elements of the tuple corresponds 
+/// to `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` 
+/// respectively.
+pub fn unpack_ndarray_var_ids(
+    unifier: &mut Unifier,
+    ndarray: Type,
+) -> (u32, u32) {
+    unpack_ndarray_tvars(unifier, ndarray)
+        .into_iter()
+        .map(|v| v.0)
+        .collect_tuple()
+        .unwrap()
+}
+
+/// Unpacks the type variables of `ndarray` into a tuple. The elements of the tuple corresponds to
+/// `dtype` (the element type) and `ndims` (the number of dimensions) of the `ndarray` respectively.
+pub fn unpack_ndarray_var_tys(
+    unifier: &mut Unifier,
+    ndarray: Type,
+) -> (Type, Type) {
+    unpack_ndarray_tvars(unifier, ndarray)
+        .into_iter()
+        .map(|v| v.1)
         .collect_tuple()
         .unwrap()
 }

nac3core/src/typecheck/magic_methods.rs

@@ -1,3 +1,7 @@
+use std::cmp::max;
+use crate::symbol_resolver::SymbolValue;
+use crate::toplevel::helper::PRIMITIVE_DEF_IDS;
+use crate::toplevel::numpy::{make_ndarray_ty, unpack_ndarray_var_tys};
 use crate::typecheck::{
     type_inferencer::*,
     typedef::{FunSignature, FuncArg, Type, TypeEnum, Unifier, VarMap},
@@ -6,6 +10,7 @@ use nac3parser::ast::StrRef;
 use nac3parser::ast::{Cmpop, Operator, Unaryop};
 use std::collections::HashMap;
 use std::rc::Rc;
+use itertools::Itertools;
 
 #[must_use]
 pub fn binop_name(op: &Operator) -> &'static str {
@@ -90,7 +95,7 @@ pub fn impl_binop(
     _store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
     ops: &[Operator],
 ) {
     with_fields(unifier, ty, |unifier, fields| {
@@ -107,6 +112,8 @@ pub fn impl_binop(
             VarMap::new()
         };
 
+        let ret_ty = ret_ty.unwrap_or_else(|| unifier.get_fresh_var(None, None).0);
+
         for op in ops {
             fields.insert(binop_name(op).into(), {
                 (
@@ -193,7 +200,7 @@ pub fn impl_basic_arithmetic(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(
         unifier,
@@ -211,7 +218,7 @@ pub fn impl_pow(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Pow]);
 }
@@ -223,19 +230,25 @@ pub fn impl_bitwise_arithmetic(unifier: &mut Unifier, store: &PrimitiveStore, ty
         store,
         ty,
         &[ty],
-        ty,
+        Some(ty),
         &[Operator::BitAnd, Operator::BitOr, Operator::BitXor],
     );
 }
 
 /// `LShift`, `RShift`
 pub fn impl_bitwise_shift(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type) {
-    impl_binop(unifier, store, ty, &[store.int32, store.uint32], ty, &[Operator::LShift, Operator::RShift]);
+    impl_binop(unifier, store, ty, &[store.int32, store.uint32], Some(ty), &[Operator::LShift, Operator::RShift]);
 }
 
 /// `Div`
-pub fn impl_div(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type, other_ty: &[Type]) {
-    impl_binop(unifier, store, ty, other_ty, store.float, &[Operator::Div]);
+pub fn impl_div(
+    unifier: &mut Unifier,
+    store: &PrimitiveStore,
+    ty: Type,
+    other_ty: &[Type],
+    ret_ty: Option<Type>,
+) {
+    impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Div]);
 }
 
 /// `FloorDiv`
@@ -244,7 +257,7 @@ pub fn impl_floordiv(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::FloorDiv]);
 }
@@ -255,7 +268,7 @@ pub fn impl_mod(
     store: &PrimitiveStore,
     ty: Type,
     other_ty: &[Type],
-    ret_ty: Type,
+    ret_ty: Option<Type>,
 ) {
     impl_binop(unifier, store, ty, other_ty, ret_ty, &[Operator::Mod]);
 }
@@ -291,6 +304,137 @@ pub fn impl_eq(unifier: &mut Unifier, store: &PrimitiveStore, ty: Type) {
     impl_cmpop(unifier, store, ty, ty, &[Cmpop::Eq, Cmpop::NotEq]);
 }
 
+/// Returns the expected return type of binary operations with at least one `ndarray` operand.
+pub fn typeof_ndarray_broadcast(
+    unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
+    left: Type,
+    right: Type,
+) -> Result<Type, String> {
+    let is_left_ndarray = left.get_obj_id(unifier) == PRIMITIVE_DEF_IDS.ndarray;
+    let is_right_ndarray = right.get_obj_id(unifier) == PRIMITIVE_DEF_IDS.ndarray;
+
+    assert!(is_left_ndarray || is_right_ndarray);
+
+    if is_left_ndarray && is_right_ndarray {
+        // Perform broadcasting on two ndarray operands.
+
+        let (left_ty_dtype, left_ty_ndims) = unpack_ndarray_var_tys(unifier, left);
+        let (right_ty_dtype, right_ty_ndims) = unpack_ndarray_var_tys(unifier, right);
+
+        assert!(unifier.unioned(left_ty_dtype, right_ty_dtype));
+
+        let left_ty_ndims = match &*unifier.get_ty_immutable(left_ty_ndims) {
+            TypeEnum::TLiteral { values, .. } => values.clone(),
+            _ => unreachable!(),
+        };
+        let right_ty_ndims = match &*unifier.get_ty_immutable(right_ty_ndims) {
+            TypeEnum::TLiteral { values, .. } => values.clone(),
+            _ => unreachable!(),
+        };
+
+        let res_ndims = left_ty_ndims.into_iter()
+            .cartesian_product(right_ty_ndims.into_iter())
+            .map(|(left, right)| {
+                let left_val = u64::try_from(left).unwrap();
+                let right_val = u64::try_from(right).unwrap();
+
+                max(left_val, right_val)
+            })
+            .unique()
+            .map(|ndim| SymbolValue::U64(ndim))
+            .collect_vec();
+        let res_ndims = unifier.get_fresh_literal(res_ndims, None);
+
+        Ok(make_ndarray_ty(unifier, primitives, Some(left_ty_dtype), Some(res_ndims)))
+    } else {
+        let (ndarray_ty, scalar_ty) = if is_left_ndarray {
+            (left, right)
+        } else {
+            (right, left)
+        };
+
+        let (ndarray_ty_dtype, _) = unpack_ndarray_var_tys(unifier, ndarray_ty);
+
+        if !unifier.unioned(ndarray_ty_dtype, scalar_ty) {
+            let (expected_ty, actual_ty) = if is_left_ndarray {
+                (ndarray_ty_dtype, scalar_ty)
+            } else {
+                (scalar_ty, ndarray_ty_dtype)
+            };
+
+            Err(format!(
+                "Expected right-hand side operand to be {}, got {}",
+                unifier.stringify(expected_ty),
+                unifier.stringify(actual_ty),
+            ))
+        } else {
+            Ok(ndarray_ty)
+        }
+    }
+}
+
+/// Returns the return type given a binary operator and its primitive operands.
+pub fn typeof_binop(
+    unifier: &mut Unifier,
+    primitives: &PrimitiveStore,
+    op: &Operator,
+    lhs: Type,
+    rhs: Type,
+) -> Result<Option<Type>, String> {
+    let is_left_ndarray = lhs.get_obj_id(unifier) == PRIMITIVE_DEF_IDS.ndarray;
+    let is_right_ndarray = rhs.get_obj_id(unifier) == PRIMITIVE_DEF_IDS.ndarray;
+
+    Ok(Some(match op {
+        Operator::Add
+        | Operator::Sub
+        | Operator::Mult
+        | Operator::Mod
+        | Operator::FloorDiv => {
+            if is_left_ndarray || is_right_ndarray {
+                typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
+            } else if unifier.unioned(lhs, rhs) {
+                lhs
+            } else {
+                return Ok(None)
+            }
+        }
+
+        Operator::MatMult => typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?,
+        Operator::Div => {
+            if is_left_ndarray || is_right_ndarray {
+                typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
+            } else if unifier.unioned(lhs, rhs) {
+                primitives.float
+            } else {
+                return Ok(None)
+            }
+        }
+
+        Operator::Pow => {
+            if is_left_ndarray || is_right_ndarray {
+                typeof_ndarray_broadcast(unifier, primitives, lhs, rhs)?
+            } else if [primitives.int32, primitives.int64, primitives.uint32, primitives.uint64, primitives.float].into_iter().any(|ty| unifier.unioned(lhs, ty)) {
+                lhs
+            } else {
+                return Ok(None)
+            }
+        }
+
+        Operator::LShift
+        | Operator::RShift
+        | Operator::BitOr
+        | Operator::BitXor
+        | Operator::BitAnd => {
+            if unifier.unioned(lhs, rhs) {
+                lhs
+            } else {
+                return Ok(None)
+            }
+        }
+    }))
+}
+
 pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifier) {
     let PrimitiveStore {
         int32: int32_t,
@@ -299,18 +443,21 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
         bool: bool_t,
         uint32: uint32_t,
         uint64: uint64_t,
+        ndarray: ndarray_t,
         ..
     } = *store;
+    let size_t = store.usize();
 
     /* int ======== */
     for t in [int32_t, int64_t, uint32_t, uint64_t] {
-        impl_basic_arithmetic(unifier, store, t, &[t], t);
-        impl_pow(unifier, store, t, &[t], t);
+        let ndarray_int_t = make_ndarray_ty(unifier, store, Some(t), None);
+        impl_basic_arithmetic(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_pow(unifier, store, t, &[t, ndarray_int_t], None);
         impl_bitwise_arithmetic(unifier, store, t);
         impl_bitwise_shift(unifier, store, t);
-        impl_div(unifier, store, t, &[t]);
-        impl_floordiv(unifier, store, t, &[t], t);
-        impl_mod(unifier, store, t, &[t], t);
+        impl_div(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_floordiv(unifier, store, t, &[t, ndarray_int_t], None);
+        impl_mod(unifier, store, t, &[t, ndarray_int_t], None);
         impl_invert(unifier, store, t);
         impl_not(unifier, store, t);
         impl_comparison(unifier, store, t, t);
@@ -321,11 +468,13 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     }
 
     /* float ======== */
-    impl_basic_arithmetic(unifier, store, float_t, &[float_t], float_t);
-    impl_pow(unifier, store, float_t, &[int32_t, float_t], float_t);
-    impl_div(unifier, store, float_t, &[float_t]);
-    impl_floordiv(unifier, store, float_t, &[float_t], float_t);
-    impl_mod(unifier, store, float_t, &[float_t], float_t);
+    let ndarray_float_t = make_ndarray_ty(unifier, store, Some(float_t), None);
+    let ndarray_int32_t = make_ndarray_ty(unifier, store, Some(int32_t), None);
+    impl_basic_arithmetic(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_pow(unifier, store, float_t, &[int32_t, float_t, ndarray_int32_t, ndarray_float_t], None);
+    impl_div(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_floordiv(unifier, store, float_t, &[float_t, ndarray_float_t], None);
+    impl_mod(unifier, store, float_t, &[float_t, ndarray_float_t], None);
     impl_sign(unifier, store, float_t);
     impl_not(unifier, store, float_t);
     impl_comparison(unifier, store, float_t, float_t);
@@ -334,4 +483,15 @@ pub fn set_primitives_magic_methods(store: &PrimitiveStore, unifier: &mut Unifie
     /* bool ======== */
     impl_not(unifier, store, bool_t);
     impl_eq(unifier, store, bool_t);
+
+    /* ndarray ===== */
+    let ndarray_usized_ndims_tvar = unifier.get_fresh_const_generic_var(size_t, Some("ndarray_ndims".into()), None);
+    let ndarray_unsized_t = make_ndarray_ty(unifier, store, None, Some(ndarray_usized_ndims_tvar.0));
+    let (ndarray_dtype_t, _) = unpack_ndarray_var_tys(unifier, ndarray_t);
+    let (ndarray_unsized_dtype_t, _) = unpack_ndarray_var_tys(unifier, ndarray_unsized_t);
+    impl_basic_arithmetic(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_pow(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_div(unifier, store, ndarray_t, &[ndarray_t, ndarray_dtype_t], None);
+    impl_floordiv(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
+    impl_mod(unifier, store, ndarray_t, &[ndarray_unsized_t, ndarray_unsized_dtype_t], None);
 }

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -9,11 +9,11 @@ use crate::{
     symbol_resolver::{SymbolResolver, SymbolValue}, 
     toplevel::{
         helper::PRIMITIVE_DEF_IDS,
-        numpy::{make_ndarray_ty, unpack_ndarray_tvars},
+        numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
         TopLevelContext,
     },
 };
-use itertools::izip;
+use itertools::{Itertools, izip};
 use nac3parser::ast::{
     self,
     fold::{self, Fold},
@@ -59,6 +59,16 @@ pub struct PrimitiveStore {
 }
 
 impl PrimitiveStore {
+    /// Returns a [`Type`] representing a signed representation of `size_t`.
+    #[must_use]
+    pub fn isize(&self) -> Type {
+        match self.size_t {
+            32 => self.int32,
+            64 => self.int64,
+            _ => unreachable!(),
+        }
+    }
+
     /// Returns a [Type] representing `size_t`.
     #[must_use]
     pub fn usize(&self) -> Type {
@@ -1193,8 +1203,11 @@ impl<'a> Inferencer<'a> {
         right: &ast::Expr<Option<Type>>,
         is_aug_assign: bool,
     ) -> InferenceResult {
+        let left_ty = left.custom.unwrap();
+        let right_ty = right.custom.unwrap();
+
         let method = if let TypeEnum::TObj { fields, .. } =
-            self.unifier.get_ty_immutable(left.custom.unwrap()).as_ref()
+            self.unifier.get_ty_immutable(left_ty).as_ref()
         {
             let (binop_name, binop_assign_name) = (
                 binop_name(op).into(),
@@ -1209,12 +1222,26 @@ impl<'a> Inferencer<'a> {
         } else {
             binop_name(op).into()
         };
+
+        let ret = if is_aug_assign {
+            // The type of an augmented assignment operator should never change
+            Some(left_ty)
+        } else {
+            typeof_binop(
+                self.unifier,
+                self.primitives,
+                op,
+                left_ty,
+                right_ty,
+            ).map_err(|e| HashSet::from([format!("{} (at {})", e, location)]))?
+        };
+
         self.build_method_call(
             location,
             method,
-            left.custom.unwrap(),
-            vec![right.custom.unwrap()],
-            None,
+            left_ty,
+            vec![right_ty],
+            ret,
         )
     }
 
@@ -1334,7 +1361,7 @@ impl<'a> Inferencer<'a> {
                 let list_like_ty = match &*self.unifier.get_ty(value.custom.unwrap()) {
                     TypeEnum::TList { .. } => self.unifier.add_ty(TypeEnum::TList { ty }),
                     TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                        let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
 
                         make_ndarray_ty(self.unifier, self.primitives, Some(ty), Some(ndims))
                     }
@@ -1347,7 +1374,7 @@ impl<'a> Inferencer<'a> {
             ExprKind::Constant { value: ast::Constant::Int(val), .. } => {
                 match &*self.unifier.get_ty(value.custom.unwrap()) {
                     TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                        let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
                         self.infer_subscript_ndarray(value, ty, ndims)
                     }
                     _ => {
@@ -1379,9 +1406,18 @@ impl<'a> Inferencer<'a> {
                         Ok(ty)
                     }
                     TypeEnum::TObj { obj_id, .. } if *obj_id == PRIMITIVE_DEF_IDS.ndarray => {
-                        let (_, ndims) = unpack_ndarray_tvars(self.unifier, value.custom.unwrap());
+                        let (_, ndims) = unpack_ndarray_var_tys(self.unifier, value.custom.unwrap());
 
-                        self.constrain(slice.custom.unwrap(), self.primitives.usize(), &slice.location)?;
+                        let valid_index_tys = [
+                            self.primitives.int32,
+                            self.primitives.isize(),
+                        ].into_iter().unique().collect_vec();
+                        let valid_index_ty = self.unifier.get_fresh_var_with_range(
+                            valid_index_tys.as_slice(),
+                            None,
+                            None,
+                        ).0;
+                        self.constrain(slice.custom.unwrap(), valid_index_ty, &slice.location)?;
                         self.infer_subscript_ndarray(value, ty, ndims)
                     }
                     _ => unreachable!(),

nac3core/src/typecheck/typedef/mod.rs

@@ -765,12 +765,8 @@ impl Unifier {
 
                 // If the types don't match, try to implicitly promote integers
                 if !self.unioned(ty, value_ty) {
-                    let num_val = match *value {
-                        SymbolValue::I32(v) => v as i128,
-                        SymbolValue::I64(v) => v as i128,
-                        SymbolValue::U32(v) => v as i128,
-                        SymbolValue::U64(v) => v as i128,
-                        _ => return Self::incompatible_types(a, b),
+                    let Ok(num_val) = i128::try_from(value.clone()) else {
+                        return Self::incompatible_types(a, b)
                     };
 
                     let can_convert = if self.unioned(ty, primitives.int32) {

nac3standalone/demo/src/ndarray.py

@@ -6,6 +6,19 @@ def output_int32(x: int32):
 def output_float64(x: float):
     ...
 
+def output_ndarray_int32_1(n: ndarray[int32, Literal[1]]):
+    for i in range(len(n)):
+        output_int32(n[i])
+
+def output_ndarray_float_1(n: ndarray[float, Literal[1]]):
+    for i in range(len(n)):
+        output_float64(n[i])
+
+def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
+    for r in range(len(n)):
+        for c in range(len(n[r])):
+            output_float64(n[r][c])
+
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -19,53 +32,381 @@ def test_ndarray_empty():
 
 def test_ndarray_zeros():
     n: ndarray[float, 1] = np_zeros([1])
-    output_float64(n[0])
+    output_ndarray_float_1(n)
 
 def test_ndarray_ones():
     n: ndarray[float, 1] = np_ones([1])
-    output_float64(n[0])
+    output_ndarray_float_1(n)
 
 def test_ndarray_full():
     n_float: ndarray[float, 1] = np_full([1], 2.0)
-    output_float64(n_float[0])
+    output_ndarray_float_1(n_float)
     n_i32: ndarray[int32, 1] = np_full([1], 2)
-    output_int32(n_i32[0])
+    output_ndarray_int32_1(n_i32)
 
 def test_ndarray_eye():
     n: ndarray[float, 2] = np_eye(2)
-    n0: ndarray[float, 1] = n[0]
-    v: float = n0[0]
-    output_float64(v)
+    output_ndarray_float_2(n)
 
 def test_ndarray_identity():
     n: ndarray[float, 2] = np_identity(2)
-    output_float64(n[0][0])
-    output_float64(n[0][1])
-    output_float64(n[1][0])
-    output_float64(n[1][1])
+    output_ndarray_float_2(n)
 
 def test_ndarray_fill():
     n: ndarray[float, 2] = np_empty([2, 2])
     n.fill(1.0)
-    output_float64(n[0][0])
-    output_float64(n[0][1])
-    output_float64(n[1][0])
-    output_float64(n[1][1])
+    output_ndarray_float_2(n)
 
 def test_ndarray_copy():
     x: ndarray[float, 2] = np_identity(2)
     y = x.copy()
     x.fill(0.0)
 
-    output_float64(x[0][0])
-    output_float64(x[0][1])
-    output_float64(x[1][0])
-    output_float64(x[1][1])
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
 
-    output_float64(y[0][0])
-    output_float64(y[0][1])
-    output_float64(y[1][0])
-    output_float64(y[1][1])
+def test_ndarray_add():
+    x = np_identity(2)
+    y = x + np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x + np_ones([2])
+    y = x + np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 1.0 + x
+    y = 1.0 + x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_add_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x + 1.0
+    y = x + 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_iadd():
+    x = np_identity(2)
+    x += np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_iadd_broadcast():
+    x = np_identity(2)
+    x += np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_iadd_broadcast_scalar():
+    x = np_identity(2)
+    x += 1.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_sub():
+    x = np_ones([2, 2])
+    y = x - np_identity(2)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x - np_ones([2])
+    y = x - np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 1.0 - x
+    y = 1.0 - x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_sub_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x - 1
+    y = x - 1.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_isub():
+    x = np_ones([2, 2])
+    x -= np_identity(2)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_isub_broadcast():
+    x = np_identity(2)
+    x -= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_isub_broadcast_scalar():
+    x = np_identity(2)
+    x -= 1.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_mul():
+    x = np_ones([2, 2])
+    y = x * np_identity(2)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x * np_ones([2])
+    y = x * np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 2.0 * x
+    y = 2.0 * x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mul_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x * 2.0
+    y = x * 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imul():
+    x = np_ones([2, 2])
+    x *= np_identity(2)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imul_broadcast():
+    x = np_identity(2)
+    x *= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imul_broadcast_scalar():
+    x = np_identity(2)
+    x *= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_truediv():
+    x = np_identity(2)
+    y = x / np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x / np_ones([2])
+    y = x / np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 / x
+    y = 2.0 / x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_truediv_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x / 2.0
+    y = x / 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_itruediv():
+    x = np_identity(2)
+    x /= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_itruediv_broadcast():
+    x = np_identity(2)
+    x /= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_itruediv_broadcast_scalar():
+    x = np_identity(2)
+    x /= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_floordiv():
+    x = np_identity(2)
+    y = x // np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x // np_ones([2])
+    y = x // np_ones([2])
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 // x
+    y = 2.0 // x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_floordiv_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x // 2.0
+    y = x // 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_ifloordiv():
+    x = np_identity(2)
+    x //= np_ones([2, 2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ifloordiv_broadcast():
+    x = np_identity(2)
+    x //= np_ones([2])
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ifloordiv_broadcast_scalar():
+    x = np_identity(2)
+    x //= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_mod():
+    x = np_identity(2)
+    y = x % np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % np_ones([2])
+    y = x % np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast_lhs_scalar():
+    x = np_ones([2, 2])
+    # y: ndarray[float, 2] = 2.0 % x
+    y = 2.0 % x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_mod_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % 2.0
+    y = x % 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_imod():
+    x = np_identity(2)
+    x %= np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imod_broadcast():
+    x = np_identity(2)
+    x %= np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_imod_broadcast_scalar():
+    x = np_identity(2)
+    x %= 2.0
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_pow():
+    x = np_identity(2)
+    y = x ** np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x ** np_full([2], 2.0)
+    y = x ** np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast_lhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = 2.0 ** x
+    y = 2.0 ** x
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_pow_broadcast_rhs_scalar():
+    x = np_identity(2)
+    # y: ndarray[float, 2] = x % 2.0
+    y = x ** 2.0
+
+    output_ndarray_float_2(x)
+    output_ndarray_float_2(y)
+
+def test_ndarray_ipow():
+    x = np_identity(2)
+    x **= np_full([2, 2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ipow_broadcast():
+    x = np_identity(2)
+    x **= np_full([2], 2.0)
+
+    output_ndarray_float_2(x)
+
+def test_ndarray_ipow_broadcast_scalar():
+    x = np_identity(2)
+    x **= 2.0
+
+    output_ndarray_float_2(x)
 
 def run() -> int32:
     test_ndarray_ctor()
@@ -77,5 +418,54 @@ def run() -> int32:
     test_ndarray_identity()
     test_ndarray_fill()
     test_ndarray_copy()
+    test_ndarray_add()
+    test_ndarray_add_broadcast()
+    test_ndarray_add_broadcast_lhs_scalar()
+    test_ndarray_add_broadcast_rhs_scalar()
+    test_ndarray_iadd()
+    test_ndarray_iadd_broadcast()
+    test_ndarray_iadd_broadcast_scalar()
+    test_ndarray_sub()
+    test_ndarray_sub_broadcast()
+    test_ndarray_sub_broadcast_lhs_scalar()
+    test_ndarray_sub_broadcast_rhs_scalar()
+    test_ndarray_isub()
+    test_ndarray_isub_broadcast()
+    test_ndarray_isub_broadcast_scalar()
+    test_ndarray_mul()
+    test_ndarray_mul_broadcast()
+    test_ndarray_mul_broadcast_lhs_scalar()
+    test_ndarray_mul_broadcast_rhs_scalar()
+    test_ndarray_imul()
+    test_ndarray_imul_broadcast()
+    test_ndarray_imul_broadcast_scalar()
+    test_ndarray_truediv()
+    test_ndarray_truediv_broadcast()
+    test_ndarray_truediv_broadcast_lhs_scalar()
+    test_ndarray_truediv_broadcast_rhs_scalar()
+    test_ndarray_itruediv()
+    test_ndarray_itruediv_broadcast()
+    test_ndarray_itruediv_broadcast_scalar()
+    test_ndarray_floordiv()
+    test_ndarray_floordiv_broadcast()
+    test_ndarray_floordiv_broadcast_lhs_scalar()
+    test_ndarray_floordiv_broadcast_rhs_scalar()
+    test_ndarray_ifloordiv()
+    test_ndarray_ifloordiv_broadcast()
+    test_ndarray_ifloordiv_broadcast_scalar()
+    test_ndarray_mod()
+    test_ndarray_mod_broadcast()
+    test_ndarray_mod_broadcast_lhs_scalar()
+    test_ndarray_mod_broadcast_rhs_scalar()
+    test_ndarray_imod()
+    test_ndarray_imod_broadcast()
+    test_ndarray_imod_broadcast_scalar()
+    test_ndarray_pow()
+    test_ndarray_pow_broadcast()
+    test_ndarray_pow_broadcast_lhs_scalar()
+    test_ndarray_pow_broadcast_rhs_scalar()
+    test_ndarray_ipow()
+    test_ndarray_ipow_broadcast()
+    test_ndarray_ipow_broadcast_scalar()
 
     return 0

nix/windows/msys2_packages.nix

@@ -1,27 +1,21 @@
 { pkgs } : [
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libffi-3.4.5-1-any.pkg.tar.zst";
-  sha256 = "13br3j605wn1vmwvfd32c99x247b01dvnkpdbxp0yx7w76f0w8n5";
-  name = "mingw-w64-clang-x86_64-libffi-3.4.5-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libunwind-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "0ksz7xz1lbwsmdr9sa1444k0dlfkbd8k11pq7w08ir7r1wjy6fid";
+  name = "mingw-w64-clang-x86_64-libunwind-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libunwind-17.0.6-1-any.pkg.tar.zst";
-  sha256 = "14qpk7xixmygvli5yx66k1kgc4i31sgqv9zjwvg918bw4771cy1w";
-  name = "mingw-w64-clang-x86_64-libunwind-17.0.6-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libc++-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "0r8skyjqv4cpkqif0niakx4hdpkscil1zf6mzj34pqna0j5gdnq2";
+  name = "mingw-w64-clang-x86_64-libc++-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libc++-17.0.6-1-any.pkg.tar.zst";
-  sha256 = "1m3i8znblmzd3yanwss35wfn4v3373dvgkly1zpzxr87cwprhgw9";
-  name = "mingw-w64-clang-x86_64-libc++-17.0.6-1-any.pkg.tar.zst";
-})
-
-(pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-zlib-1.3.1-1-any.pkg.tar.zst";
-  sha256 = "06i9xjsskf4ddb2ph4h31md5c7imj9mzjhd4lc4q44j8dmpc1w5p";
-  name = "mingw-w64-clang-x86_64-zlib-1.3.1-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libffi-3.4.6-1-any.pkg.tar.zst";
+  sha256 = "1q6gms980985bp087rnnpvz2fwfakgm5266izfk3b1mbp620s1yv";
+  name = "mingw-w64-clang-x86_64-libffi-3.4.6-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -31,21 +25,27 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-gettext-runtime-0.22.4-6-any.pkg.tar.zst";
-  sha256 = "06hanbbcb3zk7b4jlw46kcfxk7xb1fdc0g5wfhm4f2i38wc0c3la";
-  name = "mingw-w64-clang-x86_64-gettext-runtime-0.22.4-6-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-gettext-runtime-0.22.5-2-any.pkg.tar.zst";
+  sha256 = "0ll6ci6d3mc7g04q0xixjc209bh8r874dqbczgns69jsad3wg6mi";
+  name = "mingw-w64-clang-x86_64-gettext-runtime-0.22.5-2-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-xz-5.4.6-2-any.pkg.tar.zst";
-  sha256 = "09fy9g84153ccfwcvb6wp8nz7zl0apbm5qwn1zqjn34287y0b71a";
-  name = "mingw-w64-clang-x86_64-xz-5.4.6-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-xz-5.6.1-1-any.pkg.tar.zst";
+  sha256 = "14p4xxaxjjy6j1ingji82xhai1mc1gls5ali6z40fbb2ylxkaggs";
+  name = "mingw-w64-clang-x86_64-xz-5.6.1-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libxml2-2.12.5-1-any.pkg.tar.zst";
-  sha256 = "0x3457cbbqadn6nl4pbji4mvc78nngc6r17js5qbzg8ir4rllj5i";
-  name = "mingw-w64-clang-x86_64-libxml2-2.12.5-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-zlib-1.3.1-1-any.pkg.tar.zst";
+  sha256 = "06i9xjsskf4ddb2ph4h31md5c7imj9mzjhd4lc4q44j8dmpc1w5p";
+  name = "mingw-w64-clang-x86_64-zlib-1.3.1-1-any.pkg.tar.zst";
+})
+
+(pkgs.fetchurl {
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libxml2-2.12.6-1-any.pkg.tar.zst";
+  sha256 = "177b3rmsknqq6hf0zqwva71s3avh20ca7vzznp2ls2z5qm8vhhlp";
+  name = "mingw-w64-clang-x86_64-libxml2-2.12.6-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -55,69 +55,69 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-libs-17.0.6-7-any.pkg.tar.zst";
-  sha256 = "073dh9s67c982f1k9jlssm0d95ikydnfl3kis70bdjyf874d41v9";
-  name = "mingw-w64-clang-x86_64-llvm-libs-17.0.6-7-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-libs-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "0ibiy01v16naik9pj32ch7a9pkbw4yrn3gyq7p0y6kcc63fkjazy";
+  name = "mingw-w64-clang-x86_64-llvm-libs-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-17.0.6-7-any.pkg.tar.zst";
-  sha256 = "17w9dzvfm0w6cxd69vy9mipng9ahhsdwabsrjxgf7dc6fhf7cg01";
-  name = "mingw-w64-clang-x86_64-llvm-17.0.6-7-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-llvm-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "1hcfz6nb6svmmcqzfrdi96az2x7mzj0cispdv2ssbgn7nkf19pi0";
+  name = "mingw-w64-clang-x86_64-llvm-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-libs-17.0.6-7-any.pkg.tar.zst";
-  sha256 = "0fb1jvvvzwnb6f2kjqqy2nagk9wb1brh7q7sx1l1blgpwzb99rgr";
-  name = "mingw-w64-clang-x86_64-clang-libs-17.0.6-7-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-libs-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "1k17d18g7rmq2ph4kq1mf84vs8133jzf52nkv6syh39ypjga67wa";
+  name = "mingw-w64-clang-x86_64-clang-libs-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-compiler-rt-17.0.6-7-any.pkg.tar.zst";
-  sha256 = "0lcllzsb4wj761kxijd9n70m50dgq6rp9ks8cqgfdk1b2hyxjhmn";
-  name = "mingw-w64-clang-x86_64-compiler-rt-17.0.6-7-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-compiler-rt-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "1w2j0vs888haz9shjr1l8dc4j957sk1p0377zzipkbqnzqwjf1z8";
+  name = "mingw-w64-clang-x86_64-compiler-rt-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-headers-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
-  sha256 = "1f3hlmrhmndqd5f6nb9kjs7z7a2dcnnjwdj6vhnq1zdnb9ng5lsz";
-  name = "mingw-w64-clang-x86_64-headers-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-headers-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
+  sha256 = "18csfwlk2h9pr4411crx1b41qjzn5jgbssm3h109nzwbdizkp62h";
+  name = "mingw-w64-clang-x86_64-headers-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-crt-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
-  sha256 = "1g13b9xr2mw88256m45gy9q6ymgbs9fxc6acz8mvai0bqns3h978";
-  name = "mingw-w64-clang-x86_64-crt-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-crt-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
+  sha256 = "03l1zkrxgxxssp430xcv2gch1d03rbnbk1c0vgiqxigcs8lljh2g";
+  name = "mingw-w64-clang-x86_64-crt-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-lld-17.0.6-7-any.pkg.tar.zst";
-  sha256 = "0v2q0770bavm5nsf57vxb5hf9iz8aip97yy34cd30g6xvx33vz95";
-  name = "mingw-w64-clang-x86_64-lld-17.0.6-7-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-lld-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "1ai4gl7ybpk9n10jmbpf3zzfa893m1krj5qhf44ajln0jabdfnbn";
+  name = "mingw-w64-clang-x86_64-lld-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libwinpthread-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
-  sha256 = "0i3ba2rwpyzai51c66kka2w8hbz7gpcc35pcmki1sskh0m9g33i6";
-  name = "mingw-w64-clang-x86_64-libwinpthread-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-libwinpthread-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
+  sha256 = "1svhjzwhvl4ldl439jhgfy47g05y2af1cjqvydgijn1dd4g8y8vq";
+  name = "mingw-w64-clang-x86_64-libwinpthread-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-winpthreads-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
-  sha256 = "0m86d2k0axdhspd3j63y8v55q463zghw5b0zq6w4f48cwaj3wvlv";
-  name = "mingw-w64-clang-x86_64-winpthreads-git-11.0.0.r631.ga4c0c1d00-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-winpthreads-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
+  sha256 = "0jxdhkl256vnr13xf1x3fyjrdf764zg70xcs3gki3rg109f0a6xk";
+  name = "mingw-w64-clang-x86_64-winpthreads-git-11.0.0.r655.gdbfdf8025-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-17.0.6-7-any.pkg.tar.zst";
-  sha256 = "0z6w4iixsl9cswc3mz9saw61dvz1wy1ssfspma2zni6s79igwdbq";
-  name = "mingw-w64-clang-x86_64-clang-17.0.6-7-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-clang-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "0ahfic7vdfv96k5v7fdkgk1agk28l833xjn2igrmbvqg96ak0w6n";
+  name = "mingw-w64-clang-x86_64-clang-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-c-ares-1.26.0-1-any.pkg.tar.zst";
-  sha256 = "18rzy1rsb25gs4rj258pa35fnlb6ri1bx54s3f52585anna75j19";
-  name = "mingw-w64-clang-x86_64-c-ares-1.26.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-c-ares-1.27.0-1-any.pkg.tar.zst";
+  sha256 = "06y3sgqv6a0gr3dsbzs36jrj8adklssgjqi2ms5clsyq6ay4f91r";
+  name = "mingw-w64-clang-x86_64-c-ares-1.27.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -157,9 +157,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-ca-certificates-20230311-1-any.pkg.tar.zst";
-  sha256 = "00hdl239695xi5bgld7a1ssp6kapkb9az02dpx80vmz7mqg6wwxx";
-  name = "mingw-w64-clang-x86_64-ca-certificates-20230311-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-ca-certificates-20240203-1-any.pkg.tar.zst";
+  sha256 = "1q5nxhsk04gidz66ai5wgd4dr04lfyakkfja9p0r5hrgg4ppqqjg";
+  name = "mingw-w64-clang-x86_64-ca-certificates-20240203-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -175,9 +175,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp2-1.59.0-1-any.pkg.tar.zst";
-  sha256 = "1id5nkz8n2d3qxvrvp0zrbycwg1z58qwv5p6msmajx4ra3gkma47";
-  name = "mingw-w64-clang-x86_64-nghttp2-1.59.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-nghttp2-1.60.0-1-any.pkg.tar.zst";
+  sha256 = "0wxw8266hf4qd2m4zpgb1wvlrnaksmcrs0kh5y9zpf2y5sy8f2bq";
+  name = "mingw-w64-clang-x86_64-nghttp2-1.60.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -199,9 +199,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-expat-2.6.0-1-any.pkg.tar.zst";
-  sha256 = "1zdrv2k04qpzqn90v5g77mcqr5fcfqm83da3i75whwkjydp5szfj";
-  name = "mingw-w64-clang-x86_64-expat-2.6.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-expat-2.6.2-1-any.pkg.tar.zst";
+  sha256 = "0kj1vzjh3qh7d2g47avlgk7a6j4nc62111hy1m63jwq0alc01k38";
+  name = "mingw-w64-clang-x86_64-expat-2.6.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -265,9 +265,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cmake-3.28.3-2-any.pkg.tar.zst";
-  sha256 = "1brv240jiw0sas8pvapyk9s5c3dhynq1cxkr9dcjr5b2rigmq3i3";
-  name = "mingw-w64-clang-x86_64-cmake-3.28.3-2-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-cmake-3.29.0-1-any.pkg.tar.zst";
+  sha256 = "0l79lf6zihn0k8hz93qnjnq259y45yq19235g9c444jc2w093si1";
+  name = "mingw-w64-clang-x86_64-cmake-3.29.0-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -301,9 +301,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-sqlite3-3.45.1-1-any.pkg.tar.zst";
-  sha256 = "04mrbn2b1ylr0vfcsmdbr22xp13y8dvyxhzc6xwnrd9yld3ylfpd";
-  name = "mingw-w64-clang-x86_64-sqlite3-3.45.1-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-sqlite3-3.45.2-1-any.pkg.tar.zst";
+  sha256 = "1icvw3f08cgi94p0177i46v72wgpsxw95p6kd0sm2w3vj0qlqbcw";
+  name = "mingw-w64-clang-x86_64-sqlite3-3.45.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -325,9 +325,9 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openmp-17.0.6-1-any.pkg.tar.zst";
-  sha256 = "0v6ha1c571glq8ghgv4dwwd6v02bk5livmh4pgyyy10awd8zsy20";
-  name = "mingw-w64-clang-x86_64-openmp-17.0.6-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-openmp-18.1.2-1-any.pkg.tar.zst";
+  sha256 = "1v9wm3ja3a7a7yna2bpqky481qf244wc98kfdl7l03k7rkvvydpl";
+  name = "mingw-w64-clang-x86_64-openmp-18.1.2-1-any.pkg.tar.zst";
 })
 
 (pkgs.fetchurl {
@@ -343,8 +343,8 @@
 })
 
 (pkgs.fetchurl {
-  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-setuptools-69.1.0-1-any.pkg.tar.zst";
-  sha256 = "16s4v18yi0xm10dkk7k5g9nk3ssgq1lplgci2fgq447x1x1cz0sy";
-  name = "mingw-w64-clang-x86_64-python-setuptools-69.1.0-1-any.pkg.tar.zst";
+  url = "https://mirror.msys2.org/mingw/clang64/mingw-w64-clang-x86_64-python-setuptools-69.1.1-1-any.pkg.tar.zst";
+  sha256 = "1mc56anasj0v92nlg84m3pa7dbqgjakxw0b4ibqlrr9cq0xzsg4b";
+  name = "mingw-w64-clang-x86_64-python-setuptools-69.1.1-1-any.pkg.tar.zst";
 })
 ]