Merge master into skip_cholesky_tests_dg2

Author: vlad-perevezentsev

CHANGELOG.md

@@ -57,6 +57,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * 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)
 * 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)
 
 ### Security
 

dpnp/dpnp_utils/dpnp_algo_utils.pyx

@@ -390,10 +390,26 @@ cpdef inline tuple _object_to_tuple(object obj):
     if obj is None:
         return ()
 
-    if cpython.PySequence_Check(obj):
-        return tuple(obj)
+    # dpnp.ndarray unconditionally succeeds in PySequence_Check as it implements __getitem__
+    if cpython.PySequence_Check(obj) and not dpnp.is_supported_array_type(obj):
+        if isinstance(obj, numpy.ndarray):
+            obj = numpy.atleast_1d(obj)
+
+        nd = len(obj)
+        shape = []
+
+        for i in range(0, nd):
+            if cpython.PyBool_Check(obj[i]):
+                raise TypeError("DPNP object_to_tuple(): no item in size can be bool")
+
+            # Assumes each item is castable to Py_ssize_t,
+            # otherwise TypeError will be raised
+            shape.append(<Py_ssize_t> obj[i])
+        return tuple(shape)
 
     if dpnp.isscalar(obj):
+        if cpython.PyBool_Check(obj):
+            raise TypeError("DPNP object_to_tuple(): 'obj' can't be bool")
         return (obj, )
 
     raise ValueError("DPNP object_to_tuple(): 'obj' should be 'None', collections.abc.Sequence, or 'int'")

dpnp/tests/test_random_state.py

@@ -21,6 +21,7 @@
     is_cpu_device,
     is_gpu_device,
 )
+from .third_party.cupy import testing
 
 # aspects of default device:
 _def_device = dpctl.SyclQueue().sycl_device
@@ -1115,3 +1116,31 @@ def test_invalid_dtype(self, dtype):
     def test_invalid_usm_type(self, usm_type):
         # dtype must be float32 or float64
         assert_raises(ValueError, RandomState().uniform, usm_type=usm_type)
+
+    def test_size_castable_to_integer(self):
+        M = numpy.int64(31)
+        N = numpy.int64(31)
+        K = 63  # plain Python int
+
+        sizes = [(M, K), (M, N), (K, N)]
+        for size in sizes:
+            result = RandomState().uniform(size=size)
+            assert result.shape == size
+
+    @testing.with_requires("numpy>=2.3.2")
+    @pytest.mark.parametrize("xp", [numpy, dpnp])
+    @pytest.mark.parametrize(
+        "size",
+        [True, [True], dpnp.bool(True), numpy.array(True), numpy.array([True])],
+    )
+    def test_bool_size(self, xp, size):
+        rs = xp.random.RandomState()
+        assert_raises(TypeError, rs.uniform, size=size)
+
+    @pytest.mark.parametrize("size", [numpy.array(1), numpy.array([2])])
+    def test_numpy_ndarray_size(self, size):
+        result = RandomState().uniform(size=size)
+        assert result.shape == size
+
+    def test_dpnp_ndarray_size(self):
+        assert_raises(ValueError, RandomState().uniform, size=dpnp.array(1))