Follow up the fix implemented in gh-2530 to fix dpnp.unique with axis=0 and 1d input (#2587)

The PR improves the fix implemented in `dpnp.unique` for axis=0 and 1d
input.
Also it adds an explicit validation in case of floating axis passed.

Author: antonwolfy

CHANGELOG.md

@@ -58,7 +58,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Updated `pre-commit` GitHub workflow to pass `no-commit-to-branch` check [#2501](https://github.com/IntelPython/dpnp/pull/2501)
 * Updated the math formulas in summary of `dpnp.matvec` and `dpnp.vecmat` to correct a typo [#2503](https://github.com/IntelPython/dpnp/pull/2503)
 * Avoided negating unsigned integers in ceil division used in `dpnp.resize` implementation [#2508](https://github.com/IntelPython/dpnp/pull/2508)
-* Fixed `dpnp.unique` with 1d input array and `axis=0`, `equal_nan=True` keywords passed where the produced result doesn't collapse the NaNs [#2530](https://github.com/IntelPython/dpnp/pull/2530)
+* Fixed `dpnp.unique` with 1d input array and `axis=0`, `equal_nan=True` keywords passed where the produced result doesn't collapse the NaNs [#2530](https://github.com/IntelPython/dpnp/pull/2530), [#2587](https://github.com/IntelPython/dpnp/pull/2587)
 * Resolved issue when `dpnp.ndarray` constructor is called with `dpnp.ndarray.data` as `buffer` keyword [#2533](https://github.com/IntelPython/dpnp/pull/2533)
 * Fixed `dpnp.linalg.cond` to always return a real dtype [#2547](https://github.com/IntelPython/dpnp/pull/2547)
 * Resolved the issue in `dpnp.random` functions to allow any value of `size` where each element is castable to `Py_ssize_t` type [#2578](https://github.com/IntelPython/dpnp/pull/2578)

dpnp/dpnp_iface_manipulation.py

@@ -4270,7 +4270,13 @@ def unique(
 
     """
 
-    if axis is None or (axis == 0 and ar.ndim == 1):
+    dpnp.check_supported_arrays_type(ar)
+    nd = ar.ndim
+
+    if axis is None or nd == 1:
+        if axis is not None:
+            normalize_axis_index(axis, nd)
+
         return _unique_1d(
             ar, return_index, return_inverse, return_counts, equal_nan
         )
@@ -4280,7 +4286,7 @@ def unique(
         ar = dpnp.moveaxis(ar, axis, 0)
     except AxisError:
         # this removes the "axis1" or "axis2" prefix from the error message
-        raise AxisError(axis, ar.ndim) from None
+        raise AxisError(axis, nd) from None
 
     # reshape input array into a contiguous 2D array
     orig_sh = ar.shape

dpnp/tests/test_manipulation.py

@@ -1852,17 +1852,28 @@ def test_equal_nan(self, eq_nan_kwd):
 
     # TODO: uncomment once numpy 2.4.0 release is published
     # @testing.with_requires("numpy>=2.4.0")
-    def test_1d_equal_nan_axis0(self):
+    @pytest.mark.parametrize("axis", [0, -1])
+    def test_1d_equal_nan_axis(self, axis):
         a = numpy.array([numpy.nan, 0, 0, numpy.nan])
         ia = dpnp.array(a)
 
-        result = dpnp.unique(ia, axis=0, equal_nan=True)
-        expected = numpy.unique(a, axis=0, equal_nan=True)
+        result = dpnp.unique(ia, axis=axis, equal_nan=True)
+        expected = numpy.unique(a, axis=axis, equal_nan=True)
         # TODO: remove when numpy#29372 is released
         if numpy_version() < "2.4.0":
             expected = numpy.array([0.0, numpy.nan])
         assert_array_equal(result, expected)
 
+    # TODO: uncomment once numpy 2.4.0 release is published
+    # @testing.with_requires("numpy>=2.4.0")
+    @pytest.mark.parametrize("equal_nan", [True, False])
+    # @pytest.mark.parametrize("xp", [numpy, dpnp])
+    @pytest.mark.parametrize("xp", [dpnp])
+    def test_1d_axis_float_raises_typeerror(self, xp, equal_nan):
+        a = xp.array([xp.nan, 0, 0, xp.nan])
+        with pytest.raises(TypeError, match="integer argument expected"):
+            xp.unique(a, axis=0.0, equal_nan=equal_nan)
+
     @testing.with_requires("numpy>=2.0.1")
     @pytest.mark.parametrize("dt", get_float_complex_dtypes())
     @pytest.mark.parametrize(