fix: job state name print

google/cloud/aiplatform/jobs.py

@@ -222,7 +222,7 @@ def _log_job_state(self):
             % (
                 self.__class__.__name__,
                 self._gca_resource.name,
-                self._gca_resource.state,
+                self._gca_resource.state.name,
             )
         )
 
@@ -1490,7 +1490,7 @@ def iter_outputs(
         if self.state != gca_job_state.JobState.JOB_STATE_SUCCEEDED:
             raise RuntimeError(
                 f"Cannot read outputs until BatchPredictionJob has succeeded, "
-                f"current state: {self._gca_resource.state}"
+                f"current state: {self._gca_resource.state.name}"
             )
 
         output_info = self._gca_resource.output_info

google/cloud/aiplatform/pipeline_jobs.py

@@ -786,7 +786,7 @@ def _block_until_complete(self):
                     % (
                         self.__class__.__name__,
                         self._gca_resource.name,
-                        self._gca_resource.state,
+                        self._gca_resource.state.name,
                     )
                 )
                 log_wait = min(log_wait * multiplier, max_wait)

google/cloud/aiplatform/schedules.py

@@ -223,7 +223,7 @@ def _block_until_complete(self) -> None:
                     % (
                         self.__class__.__name__,
                         self._gca_resource.name,
-                        self._gca_resource.state,
+                        self._gca_resource.state.name,
                     )
                 )
                 log_wait = min(log_wait * multiplier, max_wait)

google/cloud/aiplatform/training_jobs.py

@@ -974,7 +974,7 @@ def _block_until_complete(self):
                     % (
                         self.__class__.__name__,
                         self._gca_resource.name,
-                        self._gca_resource.state,
+                        self._gca_resource.state.name,
                     )
                 )
                 log_wait = min(log_wait * _WAIT_TIME_MULTIPLIER, _MAX_WAIT_TIME)

tests/unit/aiplatform/test_jobs.py

@@ -380,6 +380,21 @@ def test_cancel_mock_job(self, fake_job_cancel_mock):
 
         fake_job_cancel_mock.assert_called_once_with(name=_TEST_JOB_RESOURCE_NAME)
 
+    @pytest.mark.usefixtures("fake_job_getter_mock")
+    def test_log_job_state_uses_symbolic_name(self):
+        """_log_job_state must log the enum name, not the integer value (regression for Python 3.11+)."""
+        fake_job = self.FakeJob(job_name=_TEST_JOB_RESOURCE_NAME)
+        fake_job._gca_resource = mock.Mock()
+        fake_job._gca_resource.name = _TEST_JOB_RESOURCE_NAME
+        fake_job._gca_resource.state = gca_job_state_compat.JobState.JOB_STATE_RUNNING
+
+        with mock.patch.object(jobs._LOGGER, "info") as mock_info:
+            fake_job._log_job_state()
+
+        logged_msg = mock_info.call_args[0][0]
+        assert "JOB_STATE_RUNNING" in logged_msg
+        assert "current state:\n3" not in logged_msg
+
 
 @pytest.fixture
 def get_batch_prediction_job_mock():
@@ -695,6 +710,21 @@ def test_batch_prediction_iter_dirs_while_running(self):
             )
             bp.iter_outputs()
 
+    @pytest.mark.usefixtures("get_batch_prediction_job_running_bq_output_mock")
+    def test_batch_prediction_iter_dirs_while_running_error_uses_symbolic_state_name(
+        self,
+    ):
+        """RuntimeError message must use symbolic state name, not integer (regression for Python 3.11+)."""
+        with pytest.raises(RuntimeError) as exc_info:
+            bp = jobs.BatchPredictionJob(
+                batch_prediction_job_name=_TEST_BATCH_PREDICTION_JOB_NAME
+            )
+            bp.iter_outputs()
+
+        error_msg = str(exc_info.value)
+        assert "JOB_STATE_RUNNING" in error_msg
+        assert "current state: 3" not in error_msg
+
     @pytest.mark.usefixtures("get_batch_prediction_job_empty_output_mock")
     def test_batch_prediction_iter_dirs_invalid_output_info(self):
         """

tests/unit/aiplatform/test_pipeline_job_schedules.py

@@ -43,6 +43,7 @@
 )
 from google.cloud.aiplatform import (
     pipeline_job_schedules,
+    schedules as aiplatform_schedules,
 )
 from google.cloud.aiplatform.preview.pipelinejob import (
     pipeline_jobs as preview_pipeline_jobs,
@@ -434,6 +435,47 @@ def setup_method(self):
     def teardown_method(self):
         initializer.global_pool.shutdown(wait=True)
 
+    def test_block_until_complete_logs_symbolic_state_name(self):
+        """State log must use symbolic enum name, not a bare integer (regression for Python 3.11+)."""
+        state_sequence = [
+            gca_schedule.Schedule.State.ACTIVE,     # first loop check
+            gca_schedule.Schedule.State.COMPLETED,  # second check exits loop
+        ]
+        state_index = [0]
+
+        def get_state():
+            s = state_sequence[state_index[0]]
+            state_index[0] = min(state_index[0] + 1, len(state_sequence) - 1)
+            return s
+
+        mock_schedule = mock.Mock()
+        type(mock_schedule).state = mock.PropertyMock(side_effect=get_state)
+
+        active_gca = gca_schedule.Schedule(
+            name=_TEST_PIPELINE_JOB_SCHEDULE_NAME,
+            state=gca_schedule.Schedule.State.ACTIVE,
+        )
+        mock_schedule._gca_resource = active_gca
+
+        logged_messages = []
+
+        # time.time: first call sets previous_time=0; second gives 10 → triggers log (10 >= 5)
+        time_vals = iter([0.0, 10.0, 20.0])
+        with mock.patch("google.cloud.aiplatform.schedules.time.time", side_effect=time_vals), \
+             mock.patch("google.cloud.aiplatform.schedules.time.sleep"), \
+             mock.patch.object(
+                 aiplatform_schedules._LOGGER, "info",
+                 side_effect=lambda msg, *a, **kw: logged_messages.append(msg)
+             ):
+            aiplatform_schedules._Schedule._block_until_complete(mock_schedule)
+
+        state_log = next(
+            (m for m in logged_messages if "current state" in m), None
+        )
+        assert state_log is not None, "No 'current state' log message found"
+        assert "ACTIVE" in state_log
+        assert "current state:\n1" not in state_log
+
     @pytest.mark.parametrize(
         "job_spec",
         [_TEST_PIPELINE_SPEC_JSON, _TEST_PIPELINE_SPEC_YAML, _TEST_PIPELINE_JOB],

tests/unit/aiplatform/test_pipeline_jobs.py

@@ -708,6 +708,45 @@ def setup_method(self):
     def teardown_method(self):
         initializer.global_pool.shutdown(wait=True)
 
+    @mock.patch.object(pipeline_jobs, "_JOB_WAIT_TIME", 0)
+    @mock.patch.object(pipeline_jobs, "_LOG_WAIT_TIME", 0)
+    def test_block_until_complete_logs_symbolic_state_name(
+        self,
+        mock_pipeline_service_create,
+        mock_pipeline_service_get,
+        mock_pipeline_bucket_exists,
+    ):
+        """State log must use symbolic enum name, not a bare integer (regression for Python 3.11+)."""
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            staging_bucket=_TEST_GCS_BUCKET_NAME,
+            location=_TEST_LOCATION,
+            credentials=_TEST_CREDENTIALS,
+        )
+
+        logged_messages = []
+
+        with patch.object(storage.Blob, "download_as_bytes") as mock_load, \
+             mock.patch.object(
+                 pipeline_jobs._LOGGER, "info",
+                 side_effect=lambda msg, *a, **kw: logged_messages.append(msg)
+             ):
+            mock_load.return_value = _TEST_PIPELINE_SPEC_JSON.encode()
+
+            job = pipeline_jobs.PipelineJob(
+                display_name=_TEST_PIPELINE_JOB_DISPLAY_NAME,
+                template_path=_TEST_TEMPLATE_PATH,
+                job_id=_TEST_PIPELINE_JOB_ID,
+            )
+            job.run(sync=True, create_request_timeout=None)
+
+        state_log = next(
+            (m for m in logged_messages if "current state" in m), None
+        )
+        assert state_log is not None, "No 'current state' log message found"
+        assert "PIPELINE_STATE_RUNNING" in state_log
+        assert "current state:\n3" not in state_log
+
     @pytest.mark.parametrize(
         "job_spec",
         [_TEST_PIPELINE_SPEC_JSON, _TEST_PIPELINE_SPEC_YAML, _TEST_PIPELINE_JOB],

tests/unit/aiplatform/test_training_jobs.py

@@ -1288,6 +1288,59 @@ def teardown_method(self):
         pathlib.Path(self._local_script_file_name).unlink()
         initializer.global_pool.shutdown(wait=True)
 
+    def test_block_until_complete_logs_symbolic_state_name(
+        self, mock_model_service_get
+    ):
+        """State log must use symbolic enum name, not a bare integer (regression for Python 3.11+)."""
+        aiplatform.init(project=_TEST_PROJECT, staging_bucket=_TEST_BUCKET_NAME)
+
+        logged_messages = []
+
+        with mock.patch.object(
+            pipeline_service_client.PipelineServiceClient, "create_training_pipeline"
+        ) as mock_create, mock.patch.object(
+            source_utils._TrainingScriptPythonPackager, "package_and_copy_to_gcs"
+        ) as mock_pkg, mock.patch.object(
+            pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
+        ) as mock_get, mock.patch.object(
+            training_jobs, "_LOG_WAIT_TIME", 0
+        ), mock.patch.object(
+            training_jobs, "_JOB_WAIT_TIME", 0
+        ), mock.patch.object(
+            training_jobs._LOGGER, "info", side_effect=lambda msg, *a, **kw: logged_messages.append(msg)
+        ):
+            mock_pkg.return_value = _TEST_OUTPUT_PYTHON_PACKAGE_PATH
+            mock_create.return_value = gca_training_pipeline.TrainingPipeline(
+                name=_TEST_PIPELINE_RESOURCE_NAME,
+                state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
+                model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
+            )
+            _running = gca_training_pipeline.TrainingPipeline(
+                name=_TEST_PIPELINE_RESOURCE_NAME,
+                state=gca_pipeline_state.PipelineState.PIPELINE_STATE_RUNNING,
+                training_task_inputs={},
+            )
+            _succeeded = gca_training_pipeline.TrainingPipeline(
+                name=_TEST_PIPELINE_RESOURCE_NAME,
+                state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
+                training_task_inputs={},
+                model_to_upload=gca_model.Model(name=_TEST_MODEL_NAME),
+            )
+            mock_get.side_effect = [_running, _running] + [_succeeded] * 8
+            job = training_jobs.CustomTrainingJob(
+                display_name=_TEST_DISPLAY_NAME,
+                script_path=self._local_script_file_name,
+                container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
+            )
+            job.run(base_output_dir=_TEST_BASE_OUTPUT_DIR, sync=True)
+
+        state_log = next(
+            (m for m in logged_messages if "current state" in m), None
+        )
+        assert state_log is not None, "No 'current state' log message found"
+        assert "PIPELINE_STATE_RUNNING" in state_log
+        assert "current state:\n3" not in state_log
+
     @mock.patch.object(training_jobs, "_JOB_WAIT_TIME", 1)
     @mock.patch.object(training_jobs, "_LOG_WAIT_TIME", 1)
     @pytest.mark.parametrize("sync", [True, False])