Add proper exception handling for nested lists in dpnp.repeat

CHANGELOG.md

@@ -83,7 +83,7 @@ This release is compatible with NumPy 2.5.
 * Fixed comparison functions (`dpnp.equal`, `dpnp.not_equal`, `dpnp.less`, `dpnp.less_equal`, `dpnp.greater`, `dpnp.greater_equal`) and `dpnp.divide` raising `OverflowError` when comparing an integer array against a Python integer scalar outside the array dtype's range [#3017](https://github.com/IntelPython/dpnp/pull/3017)
 * Fixed a crash in boolean-mask advanced indexing (`dpnp.ndarray` get/set item) when the selection is empty (e.g. a scalar `False` index that injects a length-0 axis) [#3019](https://github.com/IntelPython/dpnp/pull/3019)
 * Released the GIL before the remaining blocking OneMKL BLAS and LAPACK calls to prevent host tasks contention, completing the work started in [#2850](https://github.com/IntelPython/dpnp/pull/2850) [#3027](https://github.com/IntelPython/dpnp/pull/3027)
-
+* Fixed `dpnp.repeat` raising an unclear `TypeError` for a nested sequence of `repeats` [#3024](https://github.com/IntelPython/dpnp/pull/3024)
 
 ### Security
 

dpnp/dpnp_iface_manipulation.py

@@ -2800,14 +2800,15 @@ def repeat(a, repeats, axis=None):
 
     Parameters
     ----------
-    x : {dpnp.ndarray, usm_ndarray}
+    a : {dpnp.ndarray, usm_ndarray}
         Input array.
     repeats : {int, tuple, list, range, dpnp.ndarray, usm_ndarray}
         The number of repetitions for each element. `repeats` is broadcasted to
         fit the shape of the given axis.
         If `repeats` is an array, it must have an integer data type.
         Otherwise, `repeats` must be a Python integer or sequence of Python
-        integers (i.e., a tuple, list, or range).
+        integers (i.e., a tuple, list, or range). A sequence must be 0- or
+        1-dimensional.
     axis : {None, int}, optional
         The axis along which to repeat values. By default, use the flattened
         input array, and return a flat output array.

dpnp/tensor/_manipulation_functions.py

@@ -576,7 +576,8 @@ def repeat(x, repeats, /, *, axis=None):
 
             If `repeats` is an array, it must have an integer data type.
             Otherwise, `repeats` must be a Python integer or sequence of
-            Python integers (i.e., a tuple, list, or range).
+            Python integers (i.e., a tuple, list, or range). A sequence must
+            be 0- or 1-dimensional.
 
         axis (Optional[int]):
             The axis along which to repeat values. If `axis` is `None`, the
@@ -663,14 +664,20 @@ def repeat(x, repeats, /, *, axis=None):
         usm_type = x.usm_type
         exec_q = x.sycl_queue
 
-        len_reps = len(repeats)
-        if len_reps == 1:
-            repeats = repeats[0]
+        # inspect the sequence on the host to preserve the scalar fast path
+        repeats = np.asarray(repeats)
+        if repeats.ndim > 1:
+            raise ValueError(
+                "`repeats` sequence must be 0- or 1-dimensional, got "
+                f"{repeats.ndim} dimensions"
+            )
+        if repeats.size == 1:
+            scalar = True
+            repeats = int(repeats[0])
             if repeats < 0:
                 raise ValueError("`repeats` elements must be positive")
-            scalar = True
         else:
-            if len_reps != axis_size:
+            if repeats.size != axis_size:
                 raise ValueError(
                     "`repeats` sequence must have the same length as the "
                     "repeated axis"

dpnp/tests/tensor/test_usm_ndarray_manipulation.py

@@ -1464,6 +1464,14 @@ def test_repeat_arg_validation():
     with pytest.raises(ValueError):
         dpt.repeat(x, dpt.ones((1, 1), dtype="i8"))
 
+    # repeats nested sequence must be 0d or 1d
+    with pytest.raises(ValueError, match="0- or 1-dimensional"):
+        dpt.repeat(x, [[4]])
+    with pytest.raises(ValueError, match="0- or 1-dimensional"):
+        dpt.repeat(x, [[1, 2, 3, 4, 5]])
+    with pytest.raises(ValueError, match="0- or 1-dimensional"):
+        dpt.repeat(x, [[1], [2], [3], [4], [5]])
+
     # repeats must be castable to i8
     with pytest.raises(TypeError):
         dpt.repeat(x, dpt.asarray(2.0, dtype="f4"))

dpnp/tests/third_party/cupy/manipulation_tests/test_tiling.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import unittest
 
 import numpy
@@ -29,11 +31,7 @@ def test_array_repeat(self, xp):
     {"repeats": [2], "axis": None},
     {"repeats": [2], "axis": 1},
 )
-class TestRepeatListBroadcast(unittest.TestCase):
-    """Test for `repeats` argument using single element list.
-
-    This feature is only supported in NumPy 1.10 or later.
-    """
+class TestRepeatListBroadcast:
 
     @testing.numpy_cupy_array_equal()
     def test_array_repeat(self, xp):
@@ -48,7 +46,7 @@ def test_array_repeat(self, xp):
     {"repeats": [1, 2, 3, 4], "axis": None},
     {"repeats": [1, 2, 3, 4], "axis": 0},
 )
-class TestRepeat1D(unittest.TestCase):
+class TestRepeat1D:
 
     @testing.numpy_cupy_array_equal()
     def test_array_repeat(self, xp):
@@ -60,8 +58,7 @@ def test_array_repeat(self, xp):
     {"repeats": [2], "axis": None},
     {"repeats": [2], "axis": 0},
 )
-class TestRepeat1DListBroadcast(unittest.TestCase):
-    """See comment in TestRepeatListBroadcast class."""
+class TestRepeat1DListBroadcast:
 
     @testing.numpy_cupy_array_equal()
     def test_array_repeat(self, xp):
@@ -77,7 +74,7 @@ def test_array_repeat(self, xp):
     {"repeats": 2, "axis": -4},
     {"repeats": 2, "axis": 3},
 )
-class TestRepeatFailure(unittest.TestCase):
+class TestRepeatFailure:
 
     def test_repeat_failure(self):
         for xp in (numpy, cupy):
@@ -191,38 +188,6 @@ def test_reversed(self, xp):
         return xp.repeat(x, xp.array([0, 1, 2, 1, 0]))
 
 
-class TestRepeatNdarrayDtypeEdges:
-
-    @testing.numpy_cupy_array_equal()
-    def test_bool_perelement(self, xp):
-        return xp.repeat(xp.arange(3), xp.array([True, False, True]))
-
-    @testing.numpy_cupy_array_equal()
-    def test_bool_broadcast(self, xp):
-        return xp.repeat(
-            testing.shaped_arange((3, 4), xp), xp.array([True]), axis=0
-        )
-
-    @testing.numpy_cupy_array_equal()
-    def test_uint32_accepted(self, xp):
-        return xp.repeat(
-            xp.arange(4), xp.array([1, 2, 3, 4], dtype=numpy.uint32)
-        )
-
-
-class TestRepeatNdarrayLarge:
-
-    @testing.numpy_cupy_array_equal()
-    def test_large_single(self, xp):
-        return xp.repeat(
-            testing.shaped_arange((3,), xp), xp.array([0, 100000, 0])
-        )
-
-    @testing.numpy_cupy_array_equal()
-    def test_large_broadcast(self, xp):
-        return xp.repeat(testing.shaped_arange((3,), xp), xp.array([50000]))
-
-
 class TestRepeatScalarEquivalence:
     """All scalar-like repeats inputs produce identical results."""
 
@@ -293,9 +258,8 @@ def test_ndim_gt1_matches_numpy(self):
             with pytest.raises(ValueError):
                 xp.repeat(xp.arange(6), xp.array([[1, 2, 3, 4, 5, 6]]))
 
-    @pytest.mark.skip("different message for nested lists")
     def test_ndim_gt1_list_rejected(self):
-        with pytest.raises(ValueError, match=r"too deep"):
+        with pytest.raises(ValueError, match=r"0- or 1-dimensional"):
             cupy.repeat(cupy.arange(6), [[1, 2, 3, 4, 5, 6]])
 
     def test_bad_axis(self):
@@ -310,6 +274,38 @@ def test_method_interface(self):
         testing.assert_array_equal(a.repeat(reps), cupy.repeat(a, reps))
 
 
+class TestRepeatNdarrayDtypeEdges:
+
+    @testing.numpy_cupy_array_equal()
+    def test_bool_perelement(self, xp):
+        return xp.repeat(xp.arange(3), xp.array([True, False, True]))
+
+    @testing.numpy_cupy_array_equal()
+    def test_bool_broadcast(self, xp):
+        return xp.repeat(
+            testing.shaped_arange((3, 4), xp), xp.array([True]), axis=0
+        )
+
+    @testing.numpy_cupy_array_equal()
+    def test_uint32_accepted(self, xp):
+        return xp.repeat(
+            xp.arange(4), xp.array([1, 2, 3, 4], dtype=numpy.uint32)
+        )
+
+
+class TestRepeatNdarrayLarge:
+
+    @testing.numpy_cupy_array_equal()
+    def test_large_single(self, xp):
+        return xp.repeat(
+            testing.shaped_arange((3,), xp), xp.array([0, 100000, 0])
+        )
+
+    @testing.numpy_cupy_array_equal()
+    def test_large_broadcast(self, xp):
+        return xp.repeat(testing.shaped_arange((3,), xp), xp.array([50000]))
+
+
 @testing.parameterize(
     {"reps": 0},
     {"reps": 1},