#!/usr/bin/env python3
"""Test Phase 7 - Learning System (Simplified)"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def test_imports():
    logger.info("\n=== Testing Imports ===")
    try:
        from core.learning.learning_manager import LearningManager, WorkflowStats
        logger.info("✓ LearningManager imported")
        from core.learning.feedback_processor import FeedbackProcessor, FeedbackType
        logger.info("✓ FeedbackProcessor imported")
        from core.models.workflow_graph import LearningState
        logger.info("✓ LearningState imported")
        return True
    except Exception as e:
        logger.error(f"✗ Import failed: {e}")
        return False

def test_learning_manager_basic():
    logger.info("\n=== Testing LearningManager Basic ===")
    try:
        from core.learning.learning_manager import LearningManager, WorkflowStats
        from core.models.workflow_graph import LearningState
        
        manager = LearningManager()
        logger.info("✓ LearningManager created")
        
        # Create stats directly
        stats = WorkflowStats(
            workflow_id="test_wf_1",
            learning_state=LearningState.OBSERVATION
        )
        manager.workflows["test_wf_1"] = stats
        logger.info("✓ Workflow stats created")
        
        # Test observations
        for i in range(5):
            manager.record_observation("test_wf_1")
            stats.confidence_scores.append(0.92)
        
        state = manager.get_workflow_state("test_wf_1")
        logger.info(f"  After 5 observations: {state.value}")
        
        # Test executions
        for i in range(10):
            manager.record_execution("test_wf_1", success=True, confidence=0.93)
        
        state = manager.get_workflow_state("test_wf_1")
        logger.info(f"  After 10 executions: {state.value}")
        
        stats_result = manager.get_workflow_stats("test_wf_1")
        logger.info(f"  Stats: success_rate={stats_result.success_rate:.2f}")
        
        return True
    except Exception as e:
        logger.error(f"✗ Test failed: {e}", exc_info=True)
        return False

def test_feedback_processor():
    logger.info("\n=== Testing FeedbackProcessor ===")
    try:
        from core.learning.feedback_processor import FeedbackProcessor, FeedbackType
        
        processor = FeedbackProcessor()
        logger.info("✓ FeedbackProcessor created")
        
        result = processor.process_feedback(
            workflow_id="test_wf_1",
            execution_id="exec_1",
            feedback_type=FeedbackType.CORRECT,
            confidence=0.95
        )
        logger.info(f"✓ Feedback processed: {len(result['suggestions'])} suggestions")
        
        stats = processor.get_feedback_stats("test_wf_1")
        logger.info(f"✓ Stats: {stats['total']} total, accuracy={stats['accuracy']:.2f}")
        
        return True
    except Exception as e:
        logger.error(f"✗ Test failed: {e}", exc_info=True)
        return False

def test_state_transitions():
    logger.info("\n=== Testing State Transitions ===")
    try:
        from core.learning.learning_manager import LearningManager, WorkflowStats
        from core.models.workflow_graph import LearningState
        
        manager = LearningManager()
        
        # Test OBSERVATION → COACHING
        stats = WorkflowStats(workflow_id="wf_trans", learning_state=LearningState.OBSERVATION)
        manager.workflows["wf_trans"] = stats
        
        logger.info(f"  Initial: {stats.learning_state.value}")
        
        # Trigger transition
        for i in range(5):
            stats.observation_count += 1
            stats.confidence_scores.append(0.92)
        manager._check_state_transition("wf_trans")
        
        logger.info(f"  After 5 obs: {stats.learning_state.value}")
        
        # COACHING → AUTO_CANDIDATE
        for i in range(10):
            stats.execution_count += 1
            stats.success_count += 1
        manager._check_state_transition("wf_trans")
        
        logger.info(f"  After 10 exec: {stats.learning_state.value}")
        
        logger.info("✓ State transitions working")
        return True
        
    except Exception as e:
        logger.error(f"✗ Test failed: {e}", exc_info=True)
        return False

def main():
    logger.info("=" * 60)
    logger.info("Phase 7 - Learning System Tests")
    logger.info("=" * 60)
    
    tests = [
        test_imports,
        test_learning_manager_basic,
        test_feedback_processor,
        test_state_transitions
    ]
    
    results = []
    for test in tests:
        try:
            result = test()
            results.append(result)
        except Exception as e:
            logger.error(f"Test crashed: {e}", exc_info=True)
            results.append(False)
    
    passed = sum(results)
    logger.info(f"\n{'='*60}\nResults: {passed}/{len(results)} tests passed\n{'='*60}")
    return 0 if passed == len(results) else 1

if __name__ == '__main__':
    sys.exit(main())