cleanup of deprecated test methods

PiperOrigin-RevId: 716500241
Change-Id: I75de675b87746e49d250dc312aacf718707245d7

tf_agents/policies/categorical_q_policy_test.py

@@ -112,7 +112,7 @@ def testBuild(self):
     self.assertLen(policy.variables(), 2)
 
   def testMultipleActionsRaiseError(self):
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         TypeError, '.*action_spec must be a BoundedTensorSpec.*'
     ):
       # Replace the action_spec for this test.

tf_agents/policies/py_tf_policy_test.py

@@ -134,7 +134,7 @@ def testRaiseValueErrorWithoutSession(self):
     if tf.executing_eagerly():
       self.skipTest('b/123770140: Handling sessions with eager mode is buggy')
     policy = py_tf_policy.PyTFPolicy(self._tf_policy)
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         AttributeError,
         "No TensorFlow session-like object was set on this 'PyTFPolicy'.*",
     ):
@@ -376,7 +376,7 @@ def testSaveWrappedPolicyRestoreOuterCheckAssertConsumed(self, batch_size=5):
       )
       # 3). Restoring the actor policy while checking that all variables in
       # the checkpoint were found in the graph should fail.
-      with self.assertRaisesRegexp(
+      with self.assertRaisesRegex(
           AssertionError, 'not bound to checkpointed values*'
       ):
         policy_restored.restore(

tf_agents/policies/q_policy_test.py

@@ -81,7 +81,7 @@ def testBuild(self):
 
   def testMultipleActionsRaiseError(self):
     action_spec = [tensor_spec.BoundedTensorSpec([], tf.int32, 0, 1)] * 2
-    with self.assertRaisesRegexp(ValueError, 'Only scalar actions'):
+    with self.assertRaisesRegex(ValueError, 'Only scalar actions'):
       q_policy.QPolicy(self._time_step_spec, action_spec, q_network=DummyNet())
 
   def testAction(self):
@@ -186,7 +186,7 @@ def testActionSpecsIncompatible(self):
     network_action_spec = tensor_spec.BoundedTensorSpec([2], tf.int32, 0, 1)
     q_net = DummyNetWithActionSpec(network_action_spec)
 
-    with self.assertRaisesRegexp(
+    with self.assertRaisesRegex(
         ValueError, 'action_spec must be compatible with q_network.action_spec'
     ):
       q_policy.QPolicy(self._time_step_spec, self._action_spec, q_net)

tf_agents/policies/scripted_py_policy_test.py

@@ -218,7 +218,7 @@ def testEpisodeLength(self):
     self.assertEqual(action_script[1][1], action_step.action)
     action_step = policy.action(self._time_step, action_step.state)
     self.assertEqual(action_script[1][1], action_step.action)
-    with self.assertRaisesRegexp(ValueError, '.*Episode is longer than.*'):
+    with self.assertRaisesRegex(ValueError, '.*Episode is longer than.*'):
       policy.action(self._time_step, action_step.state)
 
   def testPolicyStateSpecIsEmpty(self):

tf_agents/policies/tf_policy_test.py

@@ -254,7 +254,7 @@ def testValidateArgsDisabled(self):
     self.assertAllEqual([[1], [1], [1], [1]], action)
 
   def testMismatchedDtypes(self):
-    with self.assertRaisesRegexp(TypeError, ".*dtype that doesn't match.*"):
+    with self.assertRaisesRegex(TypeError, ".*dtype that doesn't match.*"):
       policy = TFPolicyMismatchedDtypes()
       observation = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
       time_step = ts.restart(observation)
@@ -271,7 +271,7 @@ def testMatchedDtypes(self):
     policy.action(time_step)
 
   def testMismatchedDtypesListAction(self):
-    with self.assertRaisesRegexp(TypeError, ".*dtype that doesn't match.*"):
+    with self.assertRaisesRegex(TypeError, ".*dtype that doesn't match.*"):
       policy = TFPolicyMismatchedDtypesListAction()
       observation = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
       time_step = ts.restart(observation)