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fix(test): mock vertexai module in GPT-OSS tests to prevent authentication

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The test_vertex_ai_gpt_oss_simple_request and test_vertex_ai_gpt_oss_reasoning_effort
tests were failing in CI with 401 authentication errors. This was because the
vertexai module import was triggering authentication attempts even though the
_ensure_access_token method was mocked.

Added patch.dict('sys.modules', ...) to mock the vertexai module entirely,
preventing it from trying to authenticate when imported. This ensures tests
are fully isolated and don't attempt real API calls regardless of environment
variables or test execution order.

This follows the same pattern used in other Vertex AI tests and works in
combination with the autouse fixture that clears environment variables.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Julio Quinteros Pro 2026-02-15 19:44:23 -03:00
parent 03215c6495
commit 03d67d7801
1 changed files with 24 additions and 12 deletions
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36
tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/gpt_oss/test_vertex_ai_gpt_oss_transformation.py
View file

@ -73,7 +73,7 @@ class TestVertexAIGPTOSSTransformation:
@pytest.mark.asyncio
async def test_vertex_ai_gpt_oss_simple_request():
"""
Test that a simple request to vertex_ai/openai/gpt-oss-20b-maas lands at the correct URL
Test that a simple request to vertex_ai/openai/gpt-oss-20b-maas lands at the correct URL
with the correct request body.
"""
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
@ -106,14 +106,20 @@ async def test_vertex_ai_gpt_oss_simple_request():
"total_tokens": 70
}
}
client = AsyncHTTPHandler()
async def mock_post_func(*args, **kwargs):
return mock_response
# Mock vertexai module to prevent import from triggering authentication
mock_vertexai = MagicMock()
mock_vertexai.preview = MagicMock()
mock_vertexai.preview.language_models = MagicMock()
with patch.object(client, "post", side_effect=mock_post_func) as mock_post, \
patch.object(VertexLLM, "_ensure_access_token", return_value=("fake-token", "pathrise-convert-1606954137718")):
patch.object(VertexLLM, "_ensure_access_token", return_value=("fake-token", "pathrise-convert-1606954137718")), \
patch.dict('sys.modules', {'vertexai': mock_vertexai, 'vertexai.preview': mock_vertexai.preview}):
response = await litellm.acompletion(
model="vertex_ai/openai/gpt-oss-20b-maas",
messages=[
@ -122,7 +128,7 @@ async def test_vertex_ai_gpt_oss_simple_request():
"content": "Your name is Litellm Bot, you are a helpful assistant"
},
{
"role": "user",
"role": "user",
"content": "Hello, what is your name and can you tell me the weather?"
}
],
@ -170,7 +176,7 @@ async def test_vertex_ai_gpt_oss_simple_request():
@pytest.mark.asyncio
async def test_vertex_ai_gpt_oss_reasoning_effort():
"""
Test that reasoning_effort parameter is correctly passed in the request body
Test that reasoning_effort parameter is correctly passed in the request body
for GPT-OSS models.
"""
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
@ -184,7 +190,7 @@ async def test_vertex_ai_gpt_oss_reasoning_effort():
mock_response.headers = {}
mock_response.json.return_value = {
"id": "chatcmpl-test456",
"object": "chat.completion",
"object": "chat.completion",
"created": 1234567890,
"model": "openai/gpt-oss-20b-maas",
"choices": [
@ -203,14 +209,20 @@ async def test_vertex_ai_gpt_oss_reasoning_effort():
"total_tokens": 67
}
}
client = AsyncHTTPHandler()
async def mock_post_func(*args, **kwargs):
return mock_response
# Mock vertexai module to prevent import from triggering authentication
mock_vertexai = MagicMock()
mock_vertexai.preview = MagicMock()
mock_vertexai.preview.language_models = MagicMock()
with patch.object(client, "post", side_effect=mock_post_func) as mock_post, \
patch.object(VertexLLM, "_ensure_access_token", return_value=("fake-token", "pathrise-convert-1606954137718")):
patch.object(VertexLLM, "_ensure_access_token", return_value=("fake-token", "pathrise-convert-1606954137718")), \
patch.dict('sys.modules', {'vertexai': mock_vertexai, 'vertexai.preview': mock_vertexai.preview}):
response = await litellm.acompletion(
model="vertex_ai/openai/gpt-oss-20b-maas",
messages=[