Fix formatting

tests/unit/sagemaker/serve/builder/test_transformers_builder.py

@@ -16,7 +16,7 @@
 import unittest
 from sagemaker.serve.builder.model_builder import ModelBuilder
 from sagemaker.serve.mode.function_pointers import Mode
-from tests.unit.sagemaker.serve.constants import MOCK_IMAGE_CONFIG, MOCK_VPC_CONFIG
+from tests.unit.sagemaker.serve.constants import MOCK_VPC_CONFIG
 
 from sagemaker.serve.utils.predictors import TransformersLocalModePredictor
 
@@ -58,6 +58,9 @@
 mock_schema_builder = MagicMock()
 mock_schema_builder.sample_input = mock_sample_input
 mock_schema_builder.sample_output = mock_sample_output
+MOCK_IMAGE_CONFIG = \
+    "763104351884.dkr.ecr.us-west-2.amazonaws.com/" \
+    "huggingface-pytorch-inference:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04-v1.0"
 
 
 class TestTransformersBuilder(unittest.TestCase):
@@ -115,8 +118,7 @@ def test_image_uri(
             model=mock_model_id,
             schema_builder=mock_schema_builder,
             mode=Mode.LOCAL_CONTAINER,
-            vpc_config=MOCK_VPC_CONFIG,
-            image_config=MOCK_IMAGE_CONFIG,
+            image_uri=MOCK_IMAGE_CONFIG,
         )
 
         builder._prepare_for_mode = MagicMock()
@@ -128,17 +130,11 @@ def test_image_uri(
         builder.modes[str(Mode.LOCAL_CONTAINER)] = MagicMock()
         predictor = model.deploy(model_data_download_timeout=1800)
 
-        assert model.image_config == MOCK_IMAGE_CONFIG
-        assert model.vpc_config == MOCK_VPC_CONFIG
+        assert builder.image_uri == MOCK_IMAGE_CONFIG
         assert builder.env_vars["MODEL_LOADING_TIMEOUT"] == "1800"
         assert isinstance(predictor, TransformersLocalModePredictor)
 
         assert builder.nb_instance_type == "ml.g5.24xlarge"
 
-        builder._original_deploy = MagicMock()
-        builder._prepare_for_mode.return_value = (None, {})
-        predictor = model.deploy(mode=Mode.SAGEMAKER_ENDPOINT, role="mock_role_arn")
-        assert "HF_MODEL_ID" in model.env
-
         with self.assertRaises(ValueError) as _:
             model.deploy(mode=Mode.IN_PROCESS)