Fix upcasting with python builtin numbers and numpy 2

xarray/core/duck_array_ops.py

@@ -30,7 +30,12 @@
     zeros_like,  # noqa
 )
 from numpy import concatenate as _concatenate
-from numpy.core.multiarray import normalize_axis_index  # type: ignore[attr-defined]
+try:
+    # numpy 2.x
+    from numpy.lib.array_utils import normalize_axis_index  # type: ignore[attr-defined]
+except ImportError:
+    # numpy 1.x
+    from numpy.core.multiarray import normalize_axis_index  # type: ignore[attr-defined]
 from numpy.lib.stride_tricks import sliding_window_view  # noqa
 
 from xarray.core import dask_array_ops, dtypes, nputils
@@ -202,13 +207,15 @@ def as_shared_dtype(scalars_or_arrays, xp=np):
 
         arrays = [asarray(x, xp=cp) for x in scalars_or_arrays]
     else:
-        arrays = [asarray(x, xp=xp) for x in scalars_or_arrays]
+        #arrays = [asarray(x, xp=xp) for x in scalars_or_arrays]
+        arrays = [x if isinstance(x, (int, float, complex)) else asarray(x, xp=xp) for x in scalars_or_arrays]
     # Pass arrays directly instead of dtypes to result_type so scalars
     # get handled properly.
     # Note that result_type() safely gets the dtype from dask arrays without
     # evaluating them.
     out_type = dtypes.result_type(*arrays)
-    return [astype(x, out_type, copy=False) for x in arrays]
+    #return [astype(x, out_type, copy=False) for x in arrays]
+    return [astype(x, out_type, copy=False) if hasattr(x, "dtype") else x for x in arrays]
 
 
 def broadcast_to(array, shape):