Tweak RandomGenerator tests

pytensor/tensor/random/op.py

@@ -447,6 +447,8 @@ class AbstractRNGConstructor(Op):
     def make_node(self, seed=None):
         if seed is None:
             seed = NoneConst
+        elif isinstance(seed, Variable) and isinstance(seed.type, NoneTypeT):
+            pass
         else:
             seed = as_tensor_variable(seed)
         inputs = [seed]

tests/tensor/random/test_op.py

@@ -3,7 +3,9 @@ import pytest
 
 import pytensor.tensor as pt
 from pytensor import config, function
+from pytensor.compile import get_default_mode
 from pytensor.graph.replace import vectorize_graph
+from pytensor.link.numba import NumbaLinker
 from pytensor.raise_op import Assert
 from pytensor.tensor.math import eq
 from pytensor.tensor.random import normal
@@ -157,26 +159,30 @@ def test_RandomVariable_floatX(strict_test_value_flags):
         assert test_rv_op(0, 1).dtype == new_floatX
 
 
-@pytest.mark.parametrize(
-    "seed, maker_op, numpy_res",
-    [
-        (3, default_rng, np.random.default_rng(3)),
-    ],
-)
-def test_random_maker_op(strict_test_value_flags, seed, maker_op, numpy_res):
-    seed = pt.as_tensor_variable(seed)
-    z = function(inputs=[], outputs=[maker_op(seed)])()
-    aes_res = z[0]
-    assert maker_op.random_type.values_eq(aes_res, numpy_res)
+def test_default_rng_op():
+    seed = pt.scalar(dtype="int64")
+    res = function(inputs=[seed], outputs=default_rng(seed))(3)
+    expected_res = np.random.default_rng(3)
+    assert default_rng.random_type.values_eq(res, expected_res)
 
 
-def test_random_maker_ops_no_seed(strict_test_value_flags):
+def test_random_maker_ops_none_seed():
     # Testing the initialization when seed=None
     # Since internal states randomly generated,
     # we just check the output classes
-    z = function(inputs=[], outputs=[default_rng()])()
-    aes_res = z[0]
-    assert isinstance(aes_res, np.random.Generator)
+    seed = none_type_t()
+    res = function(inputs=[seed], outputs=default_rng(seed))(None)
+    assert isinstance(res, np.random.Generator)
+
+
+@pytest.mark.xfail(
+    condition=isinstance(get_default_mode().linker, NumbaLinker),
+    reason="Numba cannot lower default_rng as a literal",
+)
+def test_constant_rng_op():
+    res = function(inputs=[], outputs=default_rng(3))()
+    expected_res = np.random.default_rng(3)
+    assert default_rng.random_type.values_eq(res, expected_res)
 
 
 def test_RandomVariable_incompatible_size(strict_test_value_flags):