"""
Phase 4: Autonomous Decision Making Tests
Tests for autonomous systems, learning, and adaptation
"""

import pytest
import asyncio
import json
from datetime import datetime, timedelta
from unittest.mock import Mock, AsyncMock
from typing import Dict, List, Any, Optional

# Mock imports for testing
class MockAutonomousEngine:
    def __init__(self):
        self.policies = {}
        self.decisions = []
        self.learning_data = {}
        self.performance_metrics = {}
        
    async def make_autonomous_decision(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """Make autonomous decision based on context"""
        decision_id = f"auto_decision_{len(self.decisions)}"
        decision = {
            'decision_id': decision_id,
            'context': context,
            'action': self._determine_action(context),
            'reasoning': self._generate_reasoning(context),
            'confidence': self._calculate_confidence(context),
            'timestamp': datetime.utcnow().isoformat()
        }
        self.decisions.append(decision)
        return decision
    
    def _determine_action(self, context: Dict[str, Any]) -> str:
        """Determine action based on context"""
        if context.get('system_load', 0) > 0.8:
            return 'scale_resources'
        elif context.get('error_rate', 0) > 0.1:
            return 'trigger_recovery'
        elif context.get('task_queue_size', 0) > 100:
            return 'allocate_more_agents'
        else:
            return 'maintain_status'
    
    def _generate_reasoning(self, context: Dict[str, Any]) -> str:
        """Generate reasoning for decision"""
        return f"Based on system metrics: load={context.get('system_load', 0)}, errors={context.get('error_rate', 0)}"
    
    def _calculate_confidence(self, context: Dict[str, Any]) -> float:
        """Calculate confidence in decision"""
        # Simple confidence calculation based on data quality
        has_metrics = all(key in context for key in ['system_load', 'error_rate'])
        return 0.9 if has_metrics else 0.6

class MockLearningSystem:
    def __init__(self):
        self.experience_buffer = []
        self.performance_history = []
        self.adaptations = {}
        
    async def learn_from_experience(self, experience: Dict[str, Any]) -> Dict[str, Any]:
        """Learn from experience"""
        experience_id = f"exp_{len(self.experience_buffer)}"
        learning_data = {
            'experience_id': experience_id,
            'experience': experience,
            'lessons_learned': self._extract_lessons(experience),
            'performance_impact': self._calculate_impact(experience),
            'timestamp': datetime.utcnow().isoformat()
        }
        self.experience_buffer.append(learning_data)
        return learning_data
    
    def _extract_lessons(self, experience: Dict[str, Any]) -> List[str]:
        """Extract lessons from experience"""
        lessons = []
        if experience.get('success', False):
            lessons.append("Action was successful")
        if experience.get('performance_gain', 0) > 0:
            lessons.append("Performance improved")
        return lessons
    
    def _calculate_impact(self, experience: Dict[str, Any]) -> float:
        """Calculate performance impact"""
        return experience.get('performance_gain', 0.0)
    
    async def adapt_behavior(self, adaptation_data: Dict[str, Any]) -> Dict[str, Any]:
        """Adapt behavior based on learning"""
        adaptation_id = f"adapt_{len(self.adaptations)}"
        adaptation = {
            'adaptation_id': adaptation_id,
            'type': adaptation_data.get('type', 'parameter_adjustment'),
            'changes': adaptation_data.get('changes', {}),
            'expected_improvement': adaptation_data.get('expected_improvement', 0.1),
            'timestamp': datetime.utcnow().isoformat()
        }
        self.adaptations[adaptation_id] = adaptation
        return adaptation

class MockPolicyEngine:
    def __init__(self):
        self.policies = {
            'resource_management': {
                'max_cpu_usage': 0.8,
                'max_memory_usage': 0.85,
                'auto_scale_threshold': 0.7
            },
            'error_handling': {
                'max_error_rate': 0.05,
                'retry_attempts': 3,
                'recovery_timeout': 300
            },
            'task_management': {
                'max_queue_size': 1000,
                'task_timeout': 600,
                'priority_weights': {'high': 1.0, 'normal': 0.5, 'low': 0.2}
            }
        }
        
    async def evaluate_policy_compliance(self, decision: Dict[str, Any]) -> Dict[str, Any]:
        """Evaluate if decision complies with policies"""
        compliance_score = self._calculate_compliance(decision)
        violations = self._find_violations(decision)
        
        return {
            'decision_id': decision.get('decision_id'),
            'compliance_score': compliance_score,
            'violations': violations,
            'approved': compliance_score >= 0.8 and len(violations) == 0,
            'timestamp': datetime.utcnow().isoformat()
        }
    
    def _calculate_compliance(self, decision: Dict[str, Any]) -> float:
        """Calculate policy compliance score"""
        # Simplified compliance calculation
        base_score = 1.0
        if decision.get('action') == 'scale_resources':
            # Check resource management policy
            base_score -= 0.1  # Small penalty for resource scaling
        return max(0.0, base_score)
    
    def _find_violations(self, decision: Dict[str, Any]) -> List[str]:
        """Find policy violations"""
        violations = []
        context = decision.get('context', {})
        
        # Check resource limits
        if context.get('system_load', 0) > self.policies['resource_management']['max_cpu_usage']:
            violations.append("CPU usage exceeds policy limit")
        
        return violations

class TestAutonomousEngine:
    """Test autonomous decision making engine"""
    
    def setup_method(self):
        self.autonomous_engine = MockAutonomousEngine()
    
    @pytest.mark.asyncio
    async def test_autonomous_decision_making(self):
        """Test basic autonomous decision making"""
        context = {
            'system_load': 0.9,
            'error_rate': 0.02,
            'task_queue_size': 50,
            'active_agents': 5
        }
        
        decision = await self.autonomous_engine.make_autonomous_decision(context)
        
        assert decision['action'] == 'scale_resources'
        assert decision['confidence'] > 0.5
        assert 'reasoning' in decision
        assert 'timestamp' in decision
    
    @pytest.mark.asyncio
    async def test_decision_with_high_error_rate(self):
        """Test decision making with high error rate"""
        context = {
            'system_load': 0.4,
            'error_rate': 0.15,
            'task_queue_size': 30,
            'active_agents': 3
        }
        
        decision = await self.autonomous_engine.make_autonomous_decision(context)
        
        assert decision['action'] == 'trigger_recovery'
        assert 'error_rate' in decision['reasoning']
    
    @pytest.mark.asyncio
    async def test_decision_with_task_queue_pressure(self):
        """Test decision making with task queue pressure"""
        context = {
            'system_load': 0.6,
            'error_rate': 0.03,
            'task_queue_size': 150,
            'active_agents': 4
        }
        
        decision = await self.autonomous_engine.make_autonomous_decision(context)
        
        assert decision['action'] == 'allocate_more_agents'
    
    @pytest.mark.asyncio
    async def test_decision_with_normal_conditions(self):
        """Test decision making with normal conditions"""
        context = {
            'system_load': 0.5,
            'error_rate': 0.02,
            'task_queue_size': 25,
            'active_agents': 4
        }
        
        decision = await self.autonomous_engine.make_autonomous_decision(context)
        
        assert decision['action'] == 'maintain_status'
        assert decision['confidence'] > 0.8

class TestLearningSystem:
    """Test learning and adaptation system"""
    
    def setup_method(self):
        self.learning_system = MockLearningSystem()
    
    @pytest.mark.asyncio
    async def test_learning_from_successful_experience(self):
        """Test learning from successful experience"""
        experience = {
            'action': 'scale_resources',
            'success': True,
            'performance_gain': 0.15,
            'context': {'system_load': 0.9}
        }
        
        learning_result = await self.learning_system.learn_from_experience(experience)
        
        assert learning_result['experience_id'].startswith('exp_')
        assert 'lessons_learned' in learning_result
        assert learning_result['performance_impact'] == 0.15
        assert 'Action was successful' in learning_result['lessons_learned']
    
    @pytest.mark.asyncio
    async def test_learning_from_failure(self):
        """Test learning from failed experience"""
        experience = {
            'action': 'scale_resources',
            'success': False,
            'performance_gain': -0.05,
            'context': {'system_load': 0.9}
        }
        
        learning_result = await self.learning_system.learn_from_experience(experience)
        
        assert learning_result['experience_id'].startswith('exp_')
        assert learning_result['performance_impact'] == -0.05
    
    @pytest.mark.asyncio
    async def test_behavior_adaptation(self):
        """Test behavior adaptation based on learning"""
        adaptation_data = {
            'type': 'threshold_adjustment',
            'changes': {'scale_threshold': 0.75, 'error_threshold': 0.08},
            'expected_improvement': 0.1
        }
        
        adaptation = await self.learning_system.adapt_behavior(adaptation_data)
        
        assert adaptation['type'] == 'threshold_adjustment'
        assert adaptation['expected_improvement'] == 0.1
        assert 'scale_threshold' in adaptation['changes']
    
    @pytest.mark.asyncio
    async def test_experience_accumulation(self):
        """Test accumulation of experiences over time"""
        experiences = [
            {'action': 'scale_resources', 'success': True, 'performance_gain': 0.1},
            {'action': 'allocate_agents', 'success': True, 'performance_gain': 0.05},
            {'action': 'trigger_recovery', 'success': False, 'performance_gain': -0.02}
        ]
        
        for exp in experiences:
            await self.learning_system.learn_from_experience(exp)
        
        assert len(self.learning_system.experience_buffer) == 3
        assert all(exp['experience_id'].startswith('exp_') for exp in self.learning_system.experience_buffer)

class TestPolicyEngine:
    """Test policy engine for autonomous decisions"""
    
    def setup_method(self):
        self.policy_engine = MockPolicyEngine()
    
    @pytest.mark.asyncio
    async def test_policy_compliance_evaluation(self):
        """Test policy compliance evaluation"""
        decision = {
            'decision_id': 'test_decision_001',
            'action': 'scale_resources',
            'context': {
                'system_load': 0.7,
                'error_rate': 0.03,
                'task_queue_size': 50
            }
        }
        
        compliance = await self.policy_engine.evaluate_policy_compliance(decision)
        
        assert compliance['decision_id'] == 'test_decision_001'
        assert 'compliance_score' in compliance
        assert 'violations' in compliance
        assert 'approved' in compliance
        assert 'timestamp' in compliance
    
    @pytest.mark.asyncio
    async def test_policy_violation_detection(self):
        """Test detection of policy violations"""
        decision = {
            'decision_id': 'test_decision_002',
            'action': 'scale_resources',
            'context': {
                'system_load': 0.9,  # Exceeds policy limit
                'error_rate': 0.03,
                'task_queue_size': 50
            }
        }
        
        compliance = await self.policy_engine.evaluate_policy_compliance(decision)
        
        assert len(compliance['violations']) > 0
        assert any('CPU usage' in violation for violation in compliance['violations'])
    
    @pytest.mark.asyncio
    async def test_policy_approval(self):
        """Test policy approval for compliant decisions"""
        decision = {
            'decision_id': 'test_decision_003',
            'action': 'maintain_status',
            'context': {
                'system_load': 0.5,
                'error_rate': 0.02,
                'task_queue_size': 25
            }
        }
        
        compliance = await self.policy_engine.evaluate_policy_compliance(decision)
        
        assert compliance['approved'] is True
        assert compliance['compliance_score'] >= 0.8

class TestSelfCorrectionMechanism:
    """Test self-correction mechanisms"""
    
    def setup_method(self):
        self.autonomous_engine = MockAutonomousEngine()
        self.learning_system = MockLearningSystem()
        self.policy_engine = MockPolicyEngine()
    
    @pytest.mark.asyncio
    async def test_automatic_error_correction(self):
        """Test automatic error correction"""
        # Simulate error condition
        context = {
            'system_load': 0.9,
            'error_rate': 0.12,  # High error rate
            'task_queue_size': 50
        }
        
        # Make initial decision
        decision = await self.autonomous_engine.make_autonomous_decision(context)
        
        # Simulate error in execution
        error_experience = {
            'action': decision['action'],
            'success': False,
            'error_type': 'resource_exhaustion',
            'performance_gain': -0.1
        }
        
        # Learn from error
        learning_result = await self.learning_system.learn_from_experience(error_experience)
        
        # Adapt behavior
        adaptation_data = {
            'type': 'resource_threshold_adjustment',
            'changes': {'scale_threshold': 0.8},
            'expected_improvement': 0.15
        }
        
        adaptation = await self.learning_system.adapt_behavior(adaptation_data)
        
        # Verify self-correction
        assert decision['action'] == 'trigger_recovery'
        assert learning_result['experience_id'].startswith('exp_')
        assert adaptation['type'] == 'resource_threshold_adjustment'
    
    @pytest.mark.asyncio
    async def test_performance_optimization(self):
        """Test performance optimization through learning"""
        # Initial performance
        initial_context = {
            'system_load': 0.7,
            'error_rate': 0.05,
            'task_queue_size': 80
        }
        
        decision = await self.autonomous_engine.make_autonomous_decision(initial_context)
        
        # Simulate successful execution with performance gain
        success_experience = {
            'action': decision['action'],
            'success': True,
            'performance_gain': 0.2
        }
        
        learning_result = await self.learning_system.learn_from_experience(success_experience)
        
        # Adapt to optimize further
        adaptation_data = {
            'type': 'performance_optimization',
            'changes': {'aggressive_scaling': True},
            'expected_improvement': 0.1
        }
        
        adaptation = await self.learning_system.adapt_behavior(adaptation_data)
        
        # Verify optimization
        assert learning_result['performance_impact'] == 0.2
        assert adaptation['type'] == 'performance_optimization'
    
    @pytest.mark.asyncio
    async def test_goal_oriented_behavior(self):
        """Test goal-oriented autonomous behavior"""
        # Define goals
        goals = {
            'primary_goal': 'maintain_system_stability',
            'secondary_goals': ['optimize_performance', 'minimize_errors'],
            'constraints': ['resource_limits', 'policy_compliance']
        }
        
        # Simulate goal-oriented decision making
        context = {
            'system_load': 0.6,
            'error_rate': 0.04,
            'task_queue_size': 60,
            'goals': goals
        }
        
        decision = await self.autonomous_engine.make_autonomous_decision(context)
        
        # Evaluate against goals
        compliance = await self.policy_engine.evaluate_policy_compliance(decision)
        
        # Verify goal alignment
        assert decision['action'] in ['maintain_status', 'allocate_more_agents']
        assert compliance['approved'] is True  # Should be policy compliant

# Integration tests
class TestAutonomousIntegration:
    """Integration tests for autonomous systems"""
    
    @pytest.mark.asyncio
    async def test_full_autonomous_cycle(self):
        """Test complete autonomous decision cycle"""
        autonomous_engine = MockAutonomousEngine()
        learning_system = MockLearningSystem()
        policy_engine = MockPolicyEngine()
        
        # Step 1: Make autonomous decision
        context = {
            'system_load': 0.85,
            'error_rate': 0.08,
            'task_queue_size': 120
        }
        
        decision = await autonomous_engine.make_autonomous_decision(context)
        
        # Step 2: Evaluate policy compliance
        compliance = await policy_engine.evaluate_policy_compliance(decision)
        
        # Step 3: Execute and learn from result
        execution_result = {
            'action': decision['action'],
            'success': compliance['approved'],
            'performance_gain': 0.1 if compliance['approved'] else -0.05
        }
        
        learning_result = await learning_system.learn_from_experience(execution_result)
        
        # Step 4: Adapt if needed
        if not compliance['approved']:
            adaptation = await learning_system.adapt_behavior({
                'type': 'policy_compliance',
                'changes': {'more_conservative_thresholds': True}
            })
        
        # Verify complete cycle
        assert decision['decision_id'].startswith('auto_decision_')
        assert 'compliance_score' in compliance
        assert learning_result['experience_id'].startswith('exp_')
    
    @pytest.mark.asyncio
    async def test_multi_goal_optimization(self):
        """Test optimization across multiple goals"""
        goals = {
            'stability': {'weight': 0.4, 'target': 0.95},
            'performance': {'weight': 0.3, 'target': 0.8},
            'efficiency': {'weight': 0.3, 'target': 0.75}
        }
        
        contexts = [
            {'system_load': 0.7, 'error_rate': 0.05, 'goals': goals},
            {'system_load': 0.8, 'error_rate': 0.06, 'goals': goals},
            {'system_load': 0.6, 'error_rate': 0.04, 'goals': goals}
        ]
        
        autonomous_engine = MockAutonomousEngine()
        decisions = []
        
        for context in contexts:
            decision = await autonomous_engine.make_autonomous_decision(context)
            decisions.append(decision)
        
        # Verify multi-goal consideration
        assert len(decisions) == 3
        for decision in decisions:
            assert 'action' in decision
            assert 'confidence' in decision

if __name__ == '__main__':
    pytest.main([__file__])