Merge pull request #707 from mit-ll-responsible-ai/numpy-fix

Fix numpy 2.0 compat

docs/source/tutorials/using_scikit_learn.rst

@@ -417,13 +417,14 @@ stored in job directories and plot the results.
        row = i // 10
        col = i % 10
        # ax[row, col].set_axis_off()
-       ax[row, col].imshow(img)
+       _ax = ax[row, col]  # type: ignore
+       _ax.imshow(img)
 
        if row == 0:
-           ax[row, col].set_title(cname)
+           _ax.set_title(cname)
 
        if col == 0:
-           ax[row, col].set_ylabel(dname)
+           _ax.set_ylabel(dname)
 
 The resulting figure should be:
 

src/hydra_zen/structured_configs/_implementations.py

@@ -520,23 +520,27 @@ class Just:
 
 def _is_ufunc(value: Any) -> bool:
     # checks without importing numpy
-    numpy = sys.modules.get("numpy")
-    if numpy is None:  # pragma: no cover
+    if (numpy := sys.modules.get("numpy")) is None:  # pragma: no cover
         # we do actually cover this branch some runs of our CI,
         # but our coverage job installs numpy
         return False
     return isinstance(value, numpy.ufunc)
 
 
+def _is_numpy_array_func_dispatcher(value: Any) -> bool:
+    if (numpy := sys.modules.get("numpy")) is None:  # pragma: no cover
+        return False
+    return isinstance(value, type(numpy.sum))
+
+
 def _check_instance(*target_types: str, value: "Any", module: str):  # pragma: no cover
     """Checks if value is an instance of any of the target types imported
     from the specified module.
 
     Returns `False` if module/target type doesn't exists (e.g. not installed).
     This is useful for gracefully handling specialized logic for optional dependencies.
     """
-    mod = sys.modules.get(module)
-    if mod is None:
+    if (mod := sys.modules.get(module)) is None:
         return False
 
     types = []
@@ -555,10 +559,6 @@ def _check_instance(*target_types: str, value: "Any", module: str):  # pragma: n
         return any(value is t for t in types)
 
 
-_is_numpy_array_func_dispatcher = functools.partial(
-    _check_instance, "_ArrayFunctionDispatcher", module="numpy.core._multiarray_umath"
-)
-
 _is_jax_compiled_func = functools.partial(
     _check_instance, "CompiledFunction", "PjitFunction", module="jaxlib.xla_extension"
 )