Fix dpnp.interp to return a proper result for an empty input array x

CHANGELOG.md

@@ -60,6 +60,7 @@ This release is compatible with NumPy 2.5.
 * Fixed `icx`/`icpx` warning during `conda build` by stripping the GCC-only `-fno-merge-constants` flag injected by conda-forge into `CFLAGS`/`CXXFLAGS` [#2978](https://github.com/IntelPython/dpnp/pull/2978)
 * Fixed `dpnp.asnumpy` and `dpnp.ndarray.asnumpy` ignoring the `order` keyword, which caused a non-contiguous source array to be returned with a non-contiguous layout even when `order="C"` was requested [#2980](https://github.com/IntelPython/dpnp/pull/2980)
 * Fixed `dpnp.tensor.acosh` and `dpnp.tensor.acos` returning infinity for complex numbers with large negative real parts [#2928](https://github.com/IntelPython/dpnp/pull/2928)
+* Fixed `dpnp.interp` with an empty input array `x` to return an empty array with the correct dtype [#2985](https://github.com/IntelPython/dpnp/pull/2985)
 
 ### Security
 

dpnp/dpnp_iface_mathematical.py

@@ -3011,7 +3011,7 @@ def interp(x, xp, fp, left=None, right=None, period=None):
         raise ValueError("xp and fp must be 1D arrays")
     if xp.size != fp.size:
         raise ValueError("fp and xp are not of the same length")
-    if xp.size == 0:
+    if xp.size == 0 and x.size != 0:
         raise ValueError("array of sample points is empty")
 
     usm_type, exec_q = get_usm_allocations([x, xp, fp])
@@ -3059,9 +3059,9 @@ def interp(x, xp, fp, left=None, right=None, period=None):
     right = _validate_interp_param(right, "right", exec_q, usm_type, fp.dtype)
 
     usm_type, exec_q = get_usm_allocations([x, xp, fp, period, left, right])
-    output = dpnp.empty(
-        x.shape, dtype=out_dtype, sycl_queue=exec_q, usm_type=usm_type
-    )
+    output = dpnp.empty_like(x, dtype=out_dtype, usm_type=usm_type)
+    if x.size == 0:
+        return output
 
     left_usm = left.get_array() if left is not None else None
     right_usm = right.get_array() if right is not None else None

dpnp/tests/test_mathematical.py

@@ -1140,13 +1140,14 @@ def test_complex(self, xp):
 
 
 class TestInterp:
-    @pytest.mark.parametrize(
-        "dtype_x", get_all_dtypes(no_complex=True, no_none=True)
-    )
-    @pytest.mark.parametrize(
-        "dtype_xp", get_all_dtypes(no_complex=True, no_none=True)
+    ALL_DTYPES = get_all_dtypes(no_float16=False, no_none=True)
+    ALL_DTYPES_NO_COMPLEX = get_all_dtypes(
+        no_float16=False, no_complex=True, no_none=True
     )
-    @pytest.mark.parametrize("dtype_y", get_all_dtypes(no_none=True))
+
+    @pytest.mark.parametrize("dtype_x", ALL_DTYPES_NO_COMPLEX)
+    @pytest.mark.parametrize("dtype_xp", ALL_DTYPES_NO_COMPLEX)
+    @pytest.mark.parametrize("dtype_y", ALL_DTYPES)
     def test_all_dtypes(self, dtype_x, dtype_xp, dtype_y):
         x = numpy.linspace(0.1, 9.9, 20).astype(dtype_x)
         xp = numpy.linspace(0.0, 10.0, 5).astype(dtype_xp)
@@ -1160,9 +1161,7 @@ def test_all_dtypes(self, dtype_x, dtype_xp, dtype_y):
         result = dpnp.interp(ix, ixp, ifp)
         assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "dtype_x", get_all_dtypes(no_complex=True, no_none=True)
-    )
+    @pytest.mark.parametrize("dtype_x", ALL_DTYPES_NO_COMPLEX)
     @pytest.mark.parametrize("dtype_y", get_complex_dtypes())
     def test_complex_fp(self, dtype_x, dtype_y):
         x = numpy.array([0.25, 0.75], dtype=dtype_x)
@@ -1177,9 +1176,7 @@ def test_complex_fp(self, dtype_x, dtype_y):
         result = dpnp.interp(ix, ixp, ifp)
         assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "dtype", get_all_dtypes(no_complex=True, no_none=True)
-    )
+    @pytest.mark.parametrize("dtype", ALL_DTYPES_NO_COMPLEX)
     @pytest.mark.parametrize(
         "left, right", [[-40, 40], [dpnp.array(-40), dpnp.array(40)]]
     )
@@ -1216,14 +1213,14 @@ def test_naninf(self, val):
         result = dpnp.interp(ix, ixp, ifp)
         assert_dtype_allclose(result, expected)
 
-    def test_empty_x(self):
-        x = numpy.array([])
-        xp = numpy.array([0, 1])
-        fp = numpy.array([10, 20])
-
-        ix = dpnp.array(x)
-        ixp = dpnp.array(xp)
-        ifp = dpnp.array(fp)
+    @testing.with_requires("numpy>=2.5")
+    @pytest.mark.parametrize(
+        "x, xp, fp",
+        [([], [], []), ([], [1, 2], [3, 4]), ([], [1, 2], [3 + 4j, 5 + 6j])],
+    )
+    def test_empty_x(self, x, xp, fp):
+        x, xp, fp = numpy.array(x), numpy.array(xp), numpy.array(fp)
+        ix, ixp, ifp = dpnp.array(x), dpnp.array(xp), dpnp.array(fp)
 
         expected = numpy.interp(x, xp, fp)
         result = dpnp.interp(ix, ixp, ifp)