diff --git a/docs/changelog.md b/docs/changelog.md
index 389fc07..2df9c76 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -2,6 +2,33 @@
 
 ## 1.*
 
+### 1.2.3 - 24-07-31 - Vendor `nptyping`
+
+`nptyping` vendored into `numpydantic.vendor.nptyping` - 
+`nptyping` is no longer maintained, and pins `numpy<2`.
+It also has many obnoxious warnings and we have to monkeypatch it
+so it performs halfway decently. Since we are en-route to deprecating
+usage of `nptyping` anyway, in the meantime we have just vendored it in
+(it is MIT licensed, included) so that we can make those changes ourselves
+and have to patch less of it. Currently the whole package is vendored with 
+modifications, but will be whittled away until we have replaced it with
+updated type specification system :)
+
+Bugfix:
+- [#2](https://github.com/p2p-ld/numpydantic/issues/2) - Support `numpy>=2`
+- Remove deprecated numpy dtypes
+
+CI:
+- Add windows and mac tests
+- Add testing with numpy>=2 and <2
+
+DevOps:
+- Make a tox file for local testing, not used in CI.
+
+Tidying:
+- Remove `monkeypatch` module! we don't need it anymore!
+  everything has either been upstreamed or vendored.
+
 ### 1.2.2 - 24-07-31
 
 Add `datetime` map to numpy's :class:`numpy.datetime64` type
diff --git a/pyproject.toml b/pyproject.toml
index 12a1b50..3da4cd6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "numpydantic"
-version = "1.2.2"
+version = "1.2.3"
 description = "Type and shape validation and serialization for numpy arrays in pydantic models"
 authors = [
     {name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},
diff --git a/src/numpydantic/schema.py b/src/numpydantic/schema.py
index bf3fd90..9636190 100644
--- a/src/numpydantic/schema.py
+++ b/src/numpydantic/schema.py
@@ -18,7 +18,7 @@ from numpydantic.maps import np_to_python
 from numpydantic.types import DtypeType, NDArrayType, ShapeType
 from numpydantic.vendor.nptyping.structure import StructureMeta
 
-if TYPE_CHECKING:
+if TYPE_CHECKING:  # pragma: no cover
     from numpydantic import Shape
 
 _handler_type = Callable[[Any], core_schema.CoreSchema]
@@ -143,7 +143,7 @@ def list_of_lists_schema(shape: "Shape", array_type: CoreSchema) -> ListSchema:
                     arg = int(arg)
                     arg_min = arg
                     arg_max = arg
-                except ValueError as e:
+                except ValueError as e:  # pragma: no cover
 
                     raise ValueError(
                         "Array shapes must be integers, wildcards, ellipses, or "
diff --git a/tests/test_ndarray.py b/tests/test_ndarray.py
index 62ae367..203a4f2 100644
--- a/tests/test_ndarray.py
+++ b/tests/test_ndarray.py
@@ -40,6 +40,56 @@ def test_ndarray_type():
     instance = Model(array=np.zeros((2, 3)), array_any=np.ones((3, 4, 5)))
 
 
+def test_schema_unsupported_type():
+    """
+    Complex numbers should just be made with an `any` schema
+    """
+
+    class Model(BaseModel):
+        array: NDArray[Shape["2 x, * y"], complex]
+
+    schema = Model.model_json_schema()
+    assert schema["properties"]["array"]["items"] == {
+        "items": {},
+        "type": "array",
+    }
+
+
+def test_schema_tuple():
+    """
+    Types specified as tupled should have their schemas as a union
+    """
+
+    class Model(BaseModel):
+        array: NDArray[Shape["2 x, * y"], (np.uint8, np.uint16)]
+
+    schema = Model.model_json_schema()
+    assert schema["properties"]["array"]["items"] == {
+        "items": {
+            "anyOf": [
+                {"maximum": 255, "minimum": 0, "type": "integer"},
+                {"maximum": 65535, "minimum": 0, "type": "integer"},
+            ]
+        },
+        "type": "array",
+    }
+
+
+def test_schema_number():
+    """
+    np.numeric should just be the float schema
+    """
+
+    class Model(BaseModel):
+        array: NDArray[Shape["2 x, * y"], np.number]
+
+    schema = Model.model_json_schema()
+    assert schema["properties"]["array"]["items"] == {
+        "items": {"type": "number"},
+        "type": "array",
+    }
+
+
 def test_ndarray_union():
     class Model(BaseModel):
         array: Optional[