fix: cleanup tests

tests/batches_tests/test_fine_tuning_api.py

@@ -406,29 +406,6 @@ def test_convert_basic_openai_request_to_vertex_request():
     )
 
 
-@pytest.mark.asyncio()
-@pytest.mark.skip(reason="skipping - we run mock tests for vertex ai")
-async def test_create_vertex_fine_tune_jobs():
-    verbose_logger.setLevel(logging.DEBUG)
-    # load_vertex_ai_credentials()
-
-    vertex_credentials = os.getenv("GCS_PATH_SERVICE_ACCOUNT")
-    print("creating fine tuning job")
-    create_fine_tuning_response = await litellm.acreate_fine_tuning_job(
-        model="gemini-1.0-pro-002",
-        custom_llm_provider="vertex_ai",
-        training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
-        vertex_project="pathrise-convert-1606954137718",
-        vertex_location="us-central1",
-        vertex_credentials=vertex_credentials,
-    )
-    print("vertex ai create fine tuning response=", create_fine_tuning_response)
-
-    assert create_fine_tuning_response.id is not None
-    assert create_fine_tuning_response.model == "gemini-1.0-pro-002"
-    assert create_fine_tuning_response.object == "fine_tuning.job"
-
-
 @pytest.mark.asyncio
 async def test_mock_openai_create_fine_tune_job():
     """Test that create_fine_tuning_job sends correct parameters to OpenAI"""
@@ -536,7 +513,6 @@ async def test_mock_openai_retrieve_fine_tune_job():
         except Exception as e:
             print("error=", e)
 
-
         # Verify the request
         mock_retrieve.assert_called_once_with(fine_tuning_job_id="ft-123")
 
@@ -544,7 +520,9 @@ async def test_mock_openai_retrieve_fine_tune_job():
 @pytest.mark.asyncio
 async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
     """Test that Azure-specific parameters are passed through extra_body"""
-    from openai.types.fine_tuning.fine_tuning_job import Hyperparameters as OAIHyperparameters
+    from openai.types.fine_tuning.fine_tuning_job import (
+        Hyperparameters as OAIHyperparameters,
+    )
     from litellm.types.utils import LiteLLMFineTuningJob
 
     mock_response = LiteLLMFineTuningJob(
@@ -564,7 +542,9 @@ async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
     async def mock_async_create(*args, **kwargs):
         return mock_response
 
-    with patch("litellm.llms.azure.fine_tuning.handler.AzureOpenAIFineTuningAPI.create_fine_tuning_job") as mock_create:
+    with patch(
+        "litellm.llms.azure.fine_tuning.handler.AzureOpenAIFineTuningAPI.create_fine_tuning_job"
+    ) as mock_create:
         mock_create.return_value = mock_async_create()
 
         response = await litellm.acreate_fine_tuning_job(
@@ -575,10 +555,7 @@ async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
             api_key="test-key",
             api_version="2025-04-01-preview",
             trainingType=1,
-            hyperparameters={
-                "n_epochs": 3,
-                "prompt_loss_weight": 0.1
-            },
+            hyperparameters={"n_epochs": 3, "prompt_loss_weight": 0.1},
         )
 
         # Verify the request
@@ -590,7 +567,7 @@ async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
         assert create_data["model"] == "gpt-4.1-mini-2025-04-14"
         assert create_data["training_file"] == "file-123"
         assert create_data["hyperparameters"] == {"n_epochs": 3}
-        
+
         # Azure-specific parameters should be in extra_body
         assert "extra_body" in create_data
         assert create_data["extra_body"]["trainingType"] == 1

tests/batches_tests/test_openai_batches_and_files.py

@@ -54,7 +54,7 @@ def load_vertex_ai_credentials():
     print("loading vertex ai credentials")
     os.environ["GCS_FLUSH_INTERVAL"] = "1"
     filepath = os.path.dirname(os.path.abspath(__file__))
-    vertex_key_path = filepath + "/pathrise-convert-1606954137718.json"
+    vertex_key_path = filepath + "/vertex_key.json"
 
     # Read the existing content of the file or create an empty dictionary
     try:

tests/image_gen_tests/test_image_generation.py

@@ -113,7 +113,7 @@ class TestVertexImageGeneration(BaseImageGenTest):
         litellm.in_memory_llm_clients_cache = InMemoryCache()
         return {
             "model": "vertex_ai/imagen-3.0-fast-generate-001",
-            "vertex_ai_project": "pathrise-convert-1606954137718",
+            "vertex_ai_project": "litellm-ci-cd",
             "vertex_ai_location": "us-central1",
             "n": 1,
         }
@@ -129,7 +129,7 @@ class TestVertexAIGeminiImageGeneration(BaseImageGenTest):
         litellm.in_memory_llm_clients_cache = InMemoryCache()
         return {
             "model": "vertex_ai/gemini-2.5-flash-image",
-            "vertex_ai_project": "pathrise-convert-1606954137718",
+            "vertex_ai_project": "litellm-ci-cd",
             "vertex_ai_location": "us-central1",
             "n": 1,
             "size": "1024x1024",

tests/litellm_utils_tests/test_secret_manager.py

@@ -226,7 +226,7 @@ def test_google_secret_manager():
     """
     Test that we can get a secret from Google Secret Manager
     """
-    os.environ["GOOGLE_SECRET_MANAGER_PROJECT_ID"] = "pathrise-convert-1606954137718"
+    os.environ["GOOGLE_SECRET_MANAGER_PROJECT_ID"] = "litellm-ci-cd"
 
     from litellm.secret_managers.google_secret_manager import GoogleSecretManager
 
@@ -252,7 +252,7 @@ def test_google_secret_manager_read_in_memory():
     from litellm.secret_managers.google_secret_manager import GoogleSecretManager
 
     load_vertex_ai_credentials()
-    os.environ["GOOGLE_SECRET_MANAGER_PROJECT_ID"] = "pathrise-convert-1606954137718"
+    os.environ["GOOGLE_SECRET_MANAGER_PROJECT_ID"] = "litellm-ci-cd"
     secret_manager = GoogleSecretManager()
     secret_manager.cache.cache_dict["UNIQUE_KEY"] = None
     secret_manager.cache.cache_dict["UNIQUE_KEY_2"] = "lite-llm"
@@ -337,6 +337,7 @@ def test_get_secret_with_access_mode():
     litellm._key_management_settings = KeyManagementSettings()
     del os.environ[test_secret_name]
 
+
 def test_key_management_settings_defaults():
     """
     Test that KeyManagementSettings initializes with correct default values.

tests/local_testing/test_exceptions.py

@@ -24,7 +24,7 @@ from litellm import (  # AuthenticationError,; RateLimitError,; ServiceUnavailab
     embedding,
 )
 
-litellm.vertex_project = "pathrise-convert-1606954137718"
+litellm.vertex_project = "litellm-ci-cd"
 litellm.vertex_location = "us-central1"
 litellm.num_retries = 0
 

tests/old_proxy_tests/tests/test_vtx_sdk_embedding.py

@@ -37,7 +37,7 @@ class CredentialsWrapper(Credentials):
 credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
 
 vertexai.init(
-    project="pathrise-convert-1606954137718",
+    project="litellm-ci-cd",
     location="us-central1",
     api_endpoint=LITELLM_PROXY_BASE,
     credentials=credentials,

tests/proxy_unit_tests/adroit-crow-413218-bc47f303efc9.json

@@ -1,13 +0,0 @@
-{
-  "type": "service_account",
-  "project_id": "pathrise-convert-1606954137718",
-  "private_key_id": "",
-  "private_key": "",
-  "client_email": "test-adroit-crow@pathrise-convert-1606954137718.iam.gserviceaccount.com",
-  "client_id": "104886546564708740969",
-  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-  "token_uri": "https://oauth2.googleapis.com/token",
-  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/test-adroit-crow%40pathrise-convert-1606954137718.iam.gserviceaccount.com",
-  "universe_domain": "googleapis.com"
-}