Reject boolean NumPy multinomial sizes

src/pyrecest/_backend/numpy/random.py

@@ -73,7 +73,14 @@ def _validate_multinomial_pvals(pvals):
     return pvals_array
 
 
+def _validate_multinomial_size(size):
+    if size is not None and _contains_boolean_value(size):
+        raise TypeError("size must be None, an integer, or a sequence of integers")
+    return size
+
+
 def multinomial(n, pvals, size=None):
     n = _validate_multinomial_sample_count(n)
     pvals_array = _validate_multinomial_pvals(pvals)
+    size = _validate_multinomial_size(size)
     return _np.random.multinomial(n, pvals_array, size=size)

tests/backend/test_numpy_random_backend.py

@@ -101,6 +101,32 @@ def test_multinomial_accepts_integer_like_scalar_sample_counts():
     assert np.all(samples.sum(axis=1) == 2)
 
 
+@pytest.mark.parametrize(
+    "bad_size",
+    [
+        True,
+        False,
+        np.bool_(True),
+        (True,),
+        [np.bool_(False), 2],
+        np.array(True),
+        np.array([True, 2], dtype=object),
+    ],
+)
+def test_multinomial_rejects_boolean_size_arguments(bad_size):
+    with pytest.raises(TypeError, match="size"):
+        random.multinomial(2, [0.25, 0.75], size=bad_size)
+
+
+def test_multinomial_accepts_integer_like_size_argument():
+    random.seed(0)
+
+    samples = random.multinomial(2, [0.25, 0.75], size=np.array(3, dtype=np.int64))
+
+    assert samples.shape == (3, 2)
+    assert np.all(samples.sum(axis=1) == 2)
+
+
 @pytest.mark.parametrize(
     ("low", "high"),
     [