Add explicit checks to disallow size with any boolean item

Author: antonwolfy

dpnp/dpnp_utils/dpnp_algo_utils.pyx

@@ -390,17 +390,26 @@ cpdef inline tuple _object_to_tuple(object obj):
     if obj is None:
         return ()
 
-    if cpython.PySequence_Check(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

@@ -1125,3 +1125,17 @@ def test_size_castable_to_integer(self):
         for size in sizes:
             result = RandomState().uniform(size=size)
             assert result.shape == size
+
+    @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))