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"""Pytest configuration & lightweight LLM mocking.
By default (when VOXSUM_RUN_LLM_TESTS != '1'), we *mock* heavy LLM loading
from `llama_cpp` to avoid native model initialization (which caused segfaults
in CI / constrained environments).
Set VOXSUM_RUN_LLM_TESTS=1 to run the real LLM-dependent tests.
"""
from __future__ import annotations
import os
import types
import pytest
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
# Only install mocks when user explicitly wants to skip heavy LLM tests
if os.getenv("VOXSUM_SKIP_LLM_TESTS") == "1":
# Patch src.summarization.get_llm to return a dummy object with needed interface
import src.summarization as summarization # type: ignore
class _DummyLlama:
def __init__(self):
self._calls = []
def create_chat_completion(self, messages, stream=False, **kwargs): # pragma: no cover - simple mock
# Return a deterministic short response using last user content
user_content = ""
for m in messages[::-1]:
if m.get("role") == "user":
user_content = m.get("content", "")
break
# Provide a minimal plausible answer
text = "[MOCK] " + (user_content[:80].replace('\n', ' ') if user_content else "Summary")
return {"choices": [{"message": {"content": text}}]}
def tokenize(self, data: bytes): # pragma: no cover - trivial
return list(data[:16]) # pretend small token list
def detokenize(self, tokens): # pragma: no cover - trivial
return bytes(tokens)
def _mock_get_llm(selected_gguf_model: str): # pragma: no cover - trivial
return _DummyLlama()
# Install the mock only if not already swapped
if getattr(summarization.get_llm, "__name__", "") != "_mock_get_llm":
summarization.get_llm = _mock_get_llm # type: ignore
@pytest.fixture
def dummy_llm():
"""Fixture exposing a dummy LLM (even when real tests run)."""
if os.getenv("VOXSUM_SKIP_LLM_TESTS") != "1":
import src.summarization as summarization # type: ignore
yield summarization.get_llm(list(summarization.available_gguf_llms.keys())[0]) # type: ignore
else:
# Provide a standalone dummy consistent with the mock
class _Faux:
def create_chat_completion(self, messages, stream=False, **kwargs):
return {"choices": [{"message": {"content": "[MOCK FIXTURE RESPONSE]"}}]}
yield _Faux()
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