core/typecheck: Inferencer allow heterogenerous assignemnt

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -389,7 +389,7 @@ impl<'a> Fold<()> for Inferencer<'a> {
             }
             ast::StmtKind::Assign { targets, value, .. } => {
                 for target in targets {
-                    self.unify(target.custom.unwrap(), value.custom.unwrap(), &target.location)?;
+                    self.fold_assign(target, value)?;
                 }
             }
             ast::StmtKind::Raise { exc, cause, .. } => {
@@ -2159,4 +2159,58 @@ impl<'a> Inferencer<'a> {
         self.constrain(body.custom.unwrap(), orelse.custom.unwrap(), &body.location)?;
         Ok(body.custom.unwrap())
     }
+
+    fn fold_assign(
+        &mut self,
+        target: &ast::Expr<Option<Type>>,
+        value: &ast::Expr<Option<Type>>,
+    ) -> Result<(), HashSet<String>> {
+        let target_ty = target.custom.unwrap();
+        let value_ty = value.custom.unwrap();
+
+        match (&target.node, &*self.unifier.get_ty(target_ty)) {
+            (ExprKind::Subscript { .. }, TypeEnum::TObj { obj_id: target_obj_id, .. })
+                if *target_obj_id == self.primitives.ndarray.obj_id(self.unifier).unwrap() =>
+            {
+                // Pattern match expressions like `my_ndarray[slices] = value`.
+                // TODO: `(my_ndarray[slices1], my_ndarray[slices2]) = (value1, value2)` are not supported for now.
+
+                // Suppose `my_ndarray` has type `ndarray[target_dtype, ndims]`
+                // value's type could be one of the following:
+                //   Case 1. `target_dtype`
+                //   Case 2. `ndarray[target_dtype, ?]`
+                //   Case 3. list, tuple, iterables (TODO: NOT IMPLEMENTED)
+
+                let (target_dtype, _) = unpack_ndarray_var_tys(self.unifier, target_ty);
+
+                // Typecheck `value_ty`
+                match &*self.unifier.get_ty(value_ty) {
+                    TypeEnum::TObj { obj_id: value_obj_id, .. }
+                        if *value_obj_id
+                            == self.primitives.ndarray.obj_id(self.unifier).unwrap() =>
+                    {
+                        // Case 2
+                        // - `dtype` of `target_ty` and `value_ty` must unify.
+                        // - `ndims` of `value_ty` is ignored.
+                        let (value_dtype, _) = unpack_ndarray_var_tys(self.unifier, value_ty);
+
+                        self.unify(target_dtype, value_dtype, &target.location)?;
+                    }
+                    _ => {
+                        // If `value_ty` is not an ndarray, simply typecheck as through it has to be Case 1.
+                        self.unify(target_dtype, value_ty, &target.location)?;
+                    }
+                }
+            }
+            _ => {
+                // To handle
+                //   - variable assignments `target = value`
+                //   - and attribute assignments `target.my_attr = value`
+                //
+                // For these cases in nac3core, types of LHS and RHS must unify
+                self.unify(target_ty, value_ty, &target.location)?;
+            }
+        }
+        Ok(())
+    }
 }