"""
Regression tests for agent_performance_service.py
These tests capture current behavior before extracting shared logic.
"""

import pytest
from unittest.mock import Mock, AsyncMock, patch
from datetime import datetime, timezone
from uuid import uuid4

from app.services.agent_performance_service import MetaLearningEngine


@pytest.mark.unit
class TestMetaLearningEngine:
    """Test MetaLearningEngine class"""

    def test_initialization(self):
        """Test MetaLearningEngine initialization"""
        engine = MetaLearningEngine()
        
        assert "model_agnostic_meta_learning" in engine.meta_algorithms
        assert "reptile" in engine.meta_algorithms
        assert "meta_sgd" in engine.meta_algorithms
        assert "prototypical_networks" in engine.meta_algorithms
        
        assert "fast_adaptation" in engine.adaptation_strategies
        assert "gradual_adaptation" in engine.adaptation_strategies
        assert "transfer_adaptation" in engine.adaptation_strategies
        assert "multi_task_adaptation" in engine.adaptation_strategies
        
        assert len(engine.performance_metrics) == 4

    def test_meta_algorithms_callable(self):
        """Test that meta algorithms are callable methods"""
        engine = MetaLearningEngine()
        
        for algo_name, algo_func in engine.meta_algorithms.items():
            assert callable(algo_func), f"{algo_name} is not callable"

    def test_adaptation_strategies_callable(self):
        """Test that adaptation strategies are callable methods"""
        engine = MetaLearningEngine()
        
        for strategy_name, strategy_func in engine.adaptation_strategies.items():
            assert callable(strategy_func), f"{strategy_name} is not callable"

    @pytest.mark.asyncio
    async def test_create_meta_learning_model(self):
        """Test creating a meta-learning model"""
        mock_session = Mock()
        mock_session.add = Mock()
        mock_session.commit = Mock()
        mock_session.refresh = Mock()
        
        engine = MetaLearningEngine()
        
        with patch.object(engine, 'generate_meta_features', return_value={"feature1": "value1"}):
            with patch.object(engine, 'setup_task_distributions', return_value={"dist1": "value1"}):
                with patch('asyncio.create_task'):
                    model = await engine.create_meta_learning_model(
                        session=mock_session,
                        model_name="test_model",
                        base_algorithms=["algorithm1"],
                        meta_strategy="fast_adaptation",
                        adaptation_targets=["target1"]
                    )
        
        assert model.model_name == "test_model"
        assert model.base_algorithms == ["algorithm1"]
        assert model.status == "training"
        mock_session.add.assert_called_once()
        mock_session.commit.assert_called_once()

    @pytest.mark.asyncio
    async def test_train_meta_model_not_found(self):
        """Test training a model that doesn't exist"""
        mock_session = Mock()
        mock_session.execute = Mock(return_value=Mock(first=Mock(return_value=None)))
        
        engine = MetaLearningEngine()
        
        with pytest.raises(ValueError, match="Meta-learning model .* not found"):
            await engine.train_meta_model(mock_session, "nonexistent_model_id")

    def test_generate_meta_features(self):
        """Test meta features generation"""
        engine = MetaLearningEngine()
        
        # This is a placeholder test - the actual implementation would need to be tested
        # once we understand the full behavior
        features = engine.generate_meta_features(["target1", "target2"])
        
        assert isinstance(features, dict)

    def test_setup_task_distributions(self):
        """Test task distributions setup"""
        engine = MetaLearningEngine()
        
        # This is a placeholder test - the actual implementation would need to be tested
        # once we understand the full behavior
        distributions = engine.setup_task_distributions(["target1", "target2"])
        
        assert isinstance(distributions, dict)