tests/conftest.py

"""
Pytest configuration and fixtures for SPARKNET tests
Following FAANG best practices for test infrastructure
"""

import pytest
import asyncio
import sys
from pathlib import Path
from typing import Generator, AsyncGenerator
from unittest.mock import MagicMock, AsyncMock

# Add src to path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))


# ==============================================================================
# Async Configuration
# ==============================================================================

@pytest.fixture(scope="session")
def event_loop():
    """Create an event loop for async tests."""
    loop = asyncio.new_event_loop()
    yield loop
    loop.close()


# ==============================================================================
# Mock LLM Fixtures
# ==============================================================================

@pytest.fixture
def mock_ollama_client():
    """Mock Ollama client for unit tests."""
    client = MagicMock()
    client.generate = MagicMock(return_value="Mock LLM response")
    client.chat = MagicMock(return_value="Mock chat response")
    client.list_models = MagicMock(return_value=["llama3.2:latest", "qwen2.5:14b"])
    return client


@pytest.fixture
def mock_langchain_client():
    """Mock LangChain Ollama client for unit tests."""
    client = MagicMock()

    # Mock LLM
    mock_llm = MagicMock()
    mock_llm.invoke = MagicMock(return_value=MagicMock(content="Mock response"))
    mock_llm.ainvoke = AsyncMock(return_value=MagicMock(content="Mock async response"))

    client.get_llm = MagicMock(return_value=mock_llm)
    client.get_embeddings = MagicMock(return_value=MagicMock())

    return client


# ==============================================================================
# Mock Agent Fixtures
# ==============================================================================

@pytest.fixture
def mock_memory_agent():
    """Mock memory agent for unit tests."""
    agent = MagicMock()
    agent.retrieve_relevant_context = AsyncMock(return_value=[])
    agent.store_episode = AsyncMock(return_value=None)
    agent.search_stakeholders = AsyncMock(return_value=[])
    return agent


@pytest.fixture
def mock_planner_agent():
    """Mock planner agent for unit tests."""
    from src.agents.base_agent import Task

    agent = MagicMock()

    mock_task = Task(
        id="test_task",
        description="Test task",
        status="completed",
        result={
            "task_graph": MagicMock(
                subtasks={},
                get_execution_order=MagicMock(return_value=[])
            ),
            "execution_order": [],
            "total_subtasks": 0,
        }
    )
    agent.process_task = AsyncMock(return_value=mock_task)

    return agent


@pytest.fixture
def mock_critic_agent():
    """Mock critic agent for unit tests."""
    from src.agents.base_agent import Task

    agent = MagicMock()

    mock_validation = MagicMock(
        overall_score=0.9,
        issues=[],
        suggestions=[],
        dimension_scores={"completeness": 0.9, "clarity": 0.9}
    )

    mock_task = Task(
        id="test_task",
        description="Test task",
        status="completed",
        result=mock_validation
    )
    agent.process_task = AsyncMock(return_value=mock_task)
    agent.get_feedback_for_iteration = MagicMock(return_value="Good quality output")

    return agent


# ==============================================================================
# Test Data Fixtures
# ==============================================================================

@pytest.fixture
def sample_patent_analysis():
    """Sample patent analysis result for testing."""
    return {
        "title": "Test Patent: Novel AI System",
        "abstract": "A system for processing natural language using transformers",
        "claims": [
            "Claim 1: A method for natural language processing",
            "Claim 2: A system implementing the method of claim 1"
        ],
        "trl_level": 4,
        "innovation_domains": ["Artificial Intelligence", "Natural Language Processing"],
        "key_innovations": ["Novel attention mechanism", "Efficient inference"],
        "filing_date": "2023-01-15",
        "patent_number": "US12345678",
    }


@pytest.fixture
def sample_market_analysis():
    """Sample market analysis result for testing."""
    return {
        "opportunities": [
            {
                "name": "Enterprise NLP Market",
                "market_size": 12.5,
                "growth_rate": 0.25,
                "relevance_score": 0.85,
            },
            {
                "name": "Conversational AI",
                "market_size": 8.2,
                "growth_rate": 0.32,
                "relevance_score": 0.78,
            },
        ],
        "competitive_landscape": "Moderate competition with major players",
        "commercialization_potential": 0.8,
    }


@pytest.fixture
def sample_stakeholder_match():
    """Sample stakeholder match for testing."""
    return {
        "name": "Tech Corp Inc",
        "type": "company",
        "domain": "Enterprise Software",
        "relevance_score": 0.92,
        "contact_info": {
            "email": "licensing@techcorp.example",
            "phone": "+1-555-0123",
        },
        "match_rationale": "Strong alignment with NLP focus areas",
    }


# ==============================================================================
# Configuration Fixtures
# ==============================================================================

@pytest.fixture
def test_config():
    """Test configuration dictionary."""
    return {
        "gpu": {
            "primary": 0,
            "fallback": [1, 2, 3],
            "max_memory_per_model": "8GB",
        },
        "ollama": {
            "host": "localhost",
            "port": 11434,
            "default_model": "llama3.2:latest",
            "timeout": 300,
        },
        "memory": {
            "vector_store": "chromadb",
            "embedding_model": "nomic-embed-text:latest",
            "max_context_length": 4096,
            "persist_directory": "/tmp/sparknet_test_memory",
        },
        "workflow": {
            "max_parallel_tasks": 5,
            "task_timeout": 600,
            "retry_attempts": 3,
        },
    }


# ==============================================================================
# Cleanup Fixtures
# ==============================================================================

@pytest.fixture(autouse=True)
def cleanup_test_files():
    """Clean up any test files after each test."""
    yield

    # Clean up test output directory
    test_output_dir = Path("/tmp/sparknet_test_outputs")
    if test_output_dir.exists():
        import shutil
        shutil.rmtree(test_output_dir, ignore_errors=True)


# ==============================================================================
# Markers
# ==============================================================================

def pytest_configure(config):
    """Configure pytest markers."""
    config.addinivalue_line(
        "markers", "slow: mark test as slow (deselect with '-m \"not slow\"')"
    )
    config.addinivalue_line(
        "markers", "integration: mark test as integration test"
    )
    config.addinivalue_line(
        "markers", "gpu: mark test as requiring GPU"
    )
    config.addinivalue_line(
        "markers", "ollama: mark test as requiring Ollama server"
    )