"""
Enterprise Testing Workflow with AI Integration
Context7 MCP-powered automated testing orchestration
"""

import asyncio
import json
import logging
from datetime import datetime
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from enum import Enum

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class TestStatus(Enum):
    PENDING = "pending"
    RUNNING = "running"
    PASSED = "passed"
    FAILED = "failed"
    SKIPPED = "skipped"

@dataclass
class TestResult:
    name: str
    status: TestStatus
    execution_time: float
    assertions: int
    errors: List[str]
    browser: str
    viewport: Dict[str, int]
    timestamp: datetime

@dataclass
class TestSuite:
    name: str
    tests: List[TestResult]
    total_time: float
    pass_rate: float
    coverage_percentage: float

class EnterpriseTestOrchestrator:
    """AI-powered enterprise test orchestration with Context7 integration"""
    
    def __init__(self, context7_client=None):
        self.context7_client = context7_client
        self.test_results = []
        self.coverage_data = {}
        
    async def create_test_plan(self, application_info: Dict) -> Dict:
        """AI-powered test plan generation using Context7 patterns"""
        
        logger.info("🧠 Generating AI-powered test plan...")
        
        # Analyze application characteristics
        app_type = self._analyze_application_type(application_info)
        complexity = self._assess_complexity(application_info)
        
        # Get Context7 testing patterns
        context7_patterns = await self._get_context7_patterns(app_type)
        
        # Generate comprehensive test plan
        test_plan = {
            'application_type': app_type,
            'complexity_level': complexity,
            'test_strategies': self._generate_test_strategies(app_type, complexity, context7_patterns),
            'browser_matrix': self._create_browser_matrix(complexity),
            'coverage_targets': self._set_coverage_targets(app_type),
            'performance_benchmarks': self._define_performance_benchmarks(app_type),
            'visual_regression_config': context7_patterns.get('visual_regression', {}),
            'ai_enhancements': {
                'smart_selectors': True,
                'failure_classification': True,
                'maintenance_prediction': True,
                'performance_optimization': True
            }
        }
        
        logger.info(f"✅ Test plan generated for {app_type} application")
        return test_plan
    
    def _analyze_application_type(self, app_info: Dict) -> str:
        """AI-powered application type classification"""
        
        # Analyze characteristics
        has_forms = app_info.get('has_forms', False)
        has_authentication = app_info.get('has_authentication', False)
        is_single_page = app_info.get('is_single_page', False)
        has_apis = app_info.get('has_apis', False)
        is_e_commerce = app_info.get('is_e_commerce', False)
        
        # AI classification logic
        if is_e_commerce:
            return 'e-commerce'
        elif has_authentication and has_forms:
            return 'enterprise_portal'
        elif is_single_page and has_apis:
            return 'spa_with_apis'
        elif has_forms:
            return 'content_management'
        else:
            return 'brochure_website'
    
    def _assess_complexity(self, app_info: Dict) -> str:
        """AI-powered complexity assessment"""
        
        complexity_score = 0
        
        # Scoring factors
        if app_info.get('has_forms', False):
            complexity_score += 2
        if app_info.get('has_authentication', False):
            complexity_score += 3
        if app_info.get('has_apis', False):
            complexity_score += 2
        if app_info.get('is_multi_language', False):
            complexity_score += 2
        if app_info.get('has_file_upload', False):
            complexity_score += 1
        if app_info.get('has_real_time_features', False):
            complexity_score += 3
        
        if complexity_score >= 7:
            return 'high'
        elif complexity_score >= 4:
            return 'medium'
        else:
            return 'low'
    
    async def _get_context7_patterns(self, app_type: str) -> Dict:
        """Fetch Context7 MCP patterns for application type"""
        
        # Simulate Context7 integration
        patterns = {
            'e-commerce': {
                'critical_paths': ['product_browse', 'add_to_cart', 'checkout'],
                'visual_regression': {'ignore_regions': ['price', 'inventory_count']},
                'performance_thresholds': {'lcp': 2.0, 'fid': 100}
            },
            'enterprise_portal': {
                'critical_paths': ['login', 'dashboard', 'data_export'],
                'visual_regression': {'ignore_regions': ['user_data', 'timestamps']},
                'performance_thresholds': {'lcp': 2.5, 'fid': 150}
            },
            'spa_with_apis': {
                'critical_paths': ['initial_load', 'api_calls', 'navigation'],
                'visual_regression': {'ignore_regions': ['dynamic_content']},
                'performance_thresholds': {'lcp': 1.5, 'fid': 50}
            }
        }
        
        return patterns.get(app_type, {})
    
    def _generate_test_strategies(self, app_type: str, complexity: str, patterns: Dict) -> List[Dict]:
        """Generate AI-enhanced test strategies"""
        
        strategies = [
            {
                'name': 'functional_testing',
                'priority': 'high',
                'automation_level': 'full',
                'ai_enhancements': ['smart_selectors', 'failure_classification']
            },
            {
                'name': 'visual_regression',
                'priority': 'medium' if complexity == 'low' else 'high',
                'automation_level': 'full',
                'ai_enhancements': ['ai_comparison', 'ignore_regions']
            },
            {
                'name': 'performance_testing',
                'priority': 'medium',
                'automation_level': 'partial',
                'ai_enhancements': ['performance_prediction', 'bottleneck_detection']
            }
        ]
        
        # Add app-specific strategies
        if app_type == 'e-commerce':
            strategies.append({
                'name': 'checkout_flow_testing',
                'priority': 'critical',
                'automation_level': 'full',
                'ai_enhancements': ['payment_simulation', 'error_handling']
            })
        
        return strategies
    
    def _create_browser_matrix(self, complexity: str) -> List[Dict]:
        """AI-optimized browser testing matrix"""
        
        base_matrix = [
            {'browser': 'chromium', 'versions': ['latest'], 'viewports': ['desktop', 'mobile']},
            {'browser': 'firefox', 'versions': ['latest'], 'viewports': ['desktop']},
            {'browser': 'webkit', 'versions': ['latest'], 'viewports': ['desktop', 'mobile']}
        ]
        
        if complexity in ['high', 'medium']:
            # Add more comprehensive testing for complex applications
            base_matrix[0]['versions'].extend(['latest-1'])
            base_matrix[1]['viewports'].append('mobile')
        
        return base_matrix
    
    def _set_coverage_targets(self, app_type: str) -> Dict:
        """AI-optimized coverage targets"""
        
        targets = {
            'functional_coverage': 85,
            'visual_coverage': 80,
            'performance_coverage': 70
        }
        
        if app_type == 'e-commerce':
            targets['functional_coverage'] = 95
            targets['visual_coverage'] = 85
        elif app_type == 'enterprise_portal':
            targets['functional_coverage'] = 90
        
        return targets
    
    def _define_performance_benchmarks(self, app_type: str) -> Dict:
        """Context7-powered performance benchmarks"""
        
        benchmarks = {
            'lcp': {'target': 2.5, 'warning': 4.0},
            'fid': {'target': 100, 'warning': 300},
            'cls': {'target': 0.1, 'warning': 0.25},
            'tti': {'target': 3.8, 'warning': 7.3}
        }
        
        if app_type == 'spa_with_apis':
            benchmarks['lcp']['target'] = 1.5
            benchmarks['fid']['target'] = 50
        
        return benchmarks
    
    async def execute_test_suite(self, test_plan: Dict) -> TestSuite:
        """Execute AI-orchestrated test suite"""
        
        logger.info("🚀 Executing AI-orchestrated test suite...")
        
        test_results = []
        total_start_time = datetime.now()
        
        # Execute test strategies
        for strategy in test_plan['test_strategies']:
            strategy_results = await self._execute_strategy(strategy, test_plan)
            test_results.extend(strategy_results)
        
        total_time = (datetime.now() - total_start_time).total_seconds()
        
        # Calculate metrics
        passed_tests = [t for t in test_results if t.status == TestStatus.PASSED]
        pass_rate = len(passed_tests) / len(test_results) * 100 if test_results else 0
        
        # Calculate coverage
        coverage_percentage = self._calculate_coverage(test_results, test_plan)
        
        test_suite = TestSuite(
            name=f"Enterprise Test Suite - {test_plan['application_type']}",
            tests=test_results,
            total_time=total_time,
            pass_rate=pass_rate,
            coverage_percentage=coverage_percentage
        )
        
        logger.info(f"✅ Test suite completed: {pass_rate:.1f}% pass rate, {coverage_percentage:.1f}% coverage")
        return test_suite
    
    async def _execute_strategy(self, strategy: Dict, test_plan: Dict) -> List[TestResult]:
        """Execute individual test strategy with AI enhancements"""
        
        logger.info(f"🧪 Executing strategy: {strategy['name']}")
        
        # Mock test execution (in real implementation, this would run actual Playwright tests)
        test_results = []
        
        # Simulate test execution for each browser in matrix
        for browser_config in test_plan['browser_matrix']:
            for viewport in ['desktop', 'mobile']:
                test_name = f"{strategy['name']}_{browser_config['browser']}_{viewport}"
                
                # Simulate test execution
                execution_time = 2.5 + len(strategy['name']) * 0.1  # Simulate variable execution times
                status = TestStatus.PASSED if execution_time < 5 else TestStatus.FAILED
                
                test_result = TestResult(
                    name=test_name,
                    status=status,
                    execution_time=execution_time,
                    assertions=5 + len(strategy['ai_enhancements']),
                    errors=[] if status == TestStatus.PASSED else ["Timeout error"],
                    browser=browser_config['browser'],
                    viewport={'width': 1920 if viewport == 'desktop' else 375, 'height': 1080 if viewport == 'desktop' else 667},
                    timestamp=datetime.now()
                )
                
                test_results.append(test_result)
        
        return test_results
    
    def _calculate_coverage(self, test_results: List[TestResult], test_plan: Dict) -> float:
        """AI-powered coverage calculation"""
        
        # Simplified coverage calculation
        total_tests = len(test_results)
        passed_tests = len([t for t in test_results if t.status == TestStatus.PASSED])
        
        # Factor in different test types
        strategies_count = len(test_plan['test_strategies'])
        browser_coverage = len(test_plan['browser_matrix'])
        
        coverage = (passed_tests / total_tests * 0.6 + 
                   (strategies_count / 3) * 0.2 + 
                   (browser_coverage / 3) * 0.2) * 100
        
        return min(coverage, 100.0)
    
    async def generate_intelligence_report(self, test_suite: TestSuite) -> Dict:
        """Generate AI-powered testing intelligence report"""
        
        logger.info("📊 Generating AI intelligence report...")
        
        # Analyze test results
        failed_tests = [t for t in test_suite.tests if t.status == TestStatus.FAILED]
        performance_issues = [t for t in test_suite.tests if t.execution_time > 5.0]
        
        # AI-powered insights
        insights = {
            'summary': {
                'total_tests': len(test_suite.tests),
                'pass_rate': test_suite.pass_rate,
                'coverage': test_suite.coverage_percentage,
                'execution_time': test_suite.total_time
            },
            'quality_metrics': {
                'reliability_score': self._calculate_reliability_score(test_suite),
                'performance_score': self._calculate_performance_score(test_suite),
                'stability_score': self._calculate_stability_score(test_suite)
            },
            'ai_insights': {
                'failure_patterns': self._analyze_failure_patterns(failed_tests),
                'performance_bottlenecks': self._identify_performance_bottlenecks(performance_issues),
                'maintenance_recommendations': self._generate_maintenance_recommendations(test_suite),
                'optimization_opportunities': self._identify_optimization_opportunities(test_suite)
            },
            'context7_recommendations': {
                'latest_patterns': "Applied Context7 MCP best practices",
                'version_updates': "Playwright patterns are current",
                'community_insights': "Integrated collective testing wisdom"
            }
        }
        
        return insights
    
    def _calculate_reliability_score(self, test_suite: TestSuite) -> float:
        """AI-powered reliability scoring"""
        
        base_score = test_suite.pass_rate
        
        # Factor in coverage
        coverage_factor = test_suite.coverage_percentage / 100
        
        # Factor in consistency (standard deviation of execution times)
        execution_times = [t.execution_time for t in test_suite.tests]
        avg_time = sum(execution_times) / len(execution_times) if execution_times else 1
        variance = sum((t - avg_time) ** 2 for t in execution_times) / len(execution_times) if execution_times else 0
        consistency_factor = 1 - min(variance / avg_time**2, 1) if avg_time > 0 else 0
        
        reliability_score = base_score * 0.6 + coverage_factor * 100 * 0.2 + consistency_factor * 100 * 0.2
        return min(reliability_score, 100.0)
    
    def _calculate_performance_score(self, test_suite: TestSuite) -> float:
        """AI-powered performance scoring"""
        
        execution_times = [t.execution_time for t in test_suite.tests]
        
        if not execution_times:
            return 100.0
        
        avg_time = sum(execution_times) / len(execution_times)
        
        # Score based on average execution time (lower is better)
        if avg_time <= 2.0:
            return 100.0
        elif avg_time <= 5.0:
            return 80.0
        elif avg_time <= 10.0:
            return 60.0
        else:
            return 40.0
    
    def _calculate_stability_score(self, test_suite: TestSuite) -> float:
        """AI-powered stability scoring"""
        
        # Factor in test failure patterns
        failed_tests = [t for t in test_suite.tests if t.status == TestStatus.FAILED]
        
        if not failed_tests:
            return 100.0
        
        # Check if failures are consistent or random
        browser_failures = {}
        for test in failed_tests:
            browser_failures[test.browser] = browser_failures.get(test.browser, 0) + 1
        
        # More consistent failures across browsers indicate application issues
        # Random failures indicate test instability
        max_failures = max(browser_failures.values()) if browser_failures else 0
        total_failures = len(failed_tests)
        
        if max_failures == total_failures:
            # All failures in one browser - likely browser compatibility issue
            return 70.0
        elif max_failures > total_failures * 0.7:
            # Most failures in one browser
            return 80.0
        else:
            # Distributed failures - test instability
            return 50.0
    
    def _analyze_failure_patterns(self, failed_tests: List[TestResult]) -> List[str]:
        """AI-powered failure pattern analysis"""
        
        patterns = []
        
        if not failed_tests:
            return ["No failures detected"]
        
        # Analyze browser patterns
        browser_failures = {}
        for test in failed_tests:
            browser_failures[test.browser] = browser_failures.get(test.browser, 0) + 1
        
        if len(browser_failures) == 1:
            patterns.append(f"Browser-specific issues: {list(browser_failures.keys())[0]}")
        
        # Analyze timing patterns
        slow_tests = [t for t in failed_tests if t.execution_time > 5.0]
        if slow_tests:
            patterns.append(f"Performance-related failures: {len(slow_tests)} slow tests")
        
        return patterns
    
    def _identify_performance_bottlenecks(self, performance_issues: List[TestResult]) -> List[str]:
        """AI-powered performance bottleneck identification"""
        
        bottlenecks = []
        
        if not performance_issues:
            return bottlenecks
        
        avg_execution_time = sum(t.execution_time for t in performance_issues) / len(performance_issues)
        
        if avg_execution_time > 10.0:
            bottlenecks.append("Severe performance degradation detected")
        elif avg_execution_time > 5.0:
            bottlenecks.append("Moderate performance issues identified")
        
        return bottlenecks
    
    def _generate_maintenance_recommendations(self, test_suite: TestSuite) -> List[str]:
        """AI-powered maintenance recommendations"""
        
        recommendations = []
        
        # Coverage recommendations
        if test_suite.coverage_percentage < 80:
            recommendations.append("Increase test coverage by adding more test scenarios")
        
        # Reliability recommendations
        if test_suite.pass_rate < 90:
            recommendations.append("Investigate failing tests and improve test stability")
        
        # Performance recommendations
        avg_time = sum(t.execution_time for t in test_suite.tests) / len(test_suite.tests) if test_suite.tests else 0
        if avg_time > 5.0:
            recommendations.append("Optimize test execution time through parallelization")
        
        return recommendations
    
    def _identify_optimization_opportunities(self, test_suite: TestSuite) -> List[str]:
        """AI-powered optimization opportunity identification"""
        
        opportunities = []
        
        # Test parallelization opportunities
        if test_suite.total_time > 60:
            opportunities.append("Implement test parallelization to reduce execution time")
        
        # Smart selector opportunities
        opportunities.append("Implement AI-powered smart selectors for better test reliability")
        
        # Visual regression optimization
        opportunities.append("Optimize visual regression tests with AI-driven ignore regions")
        
        return opportunities

# Main execution example
async def main():
    """Example enterprise testing workflow"""
    
    # Initialize orchestrator
    orchestrator = EnterpriseTestOrchestrator()
    
    # Define application information
    application_info = {
        'name': 'Enterprise Web Application',
        'url': 'https://example.com',
        'has_forms': True,
        'has_authentication': True,
        'is_single_page': True,
        'has_apis': True,
        'is_e_commerce': False,
        'is_multi_language': True,
        'has_file_upload': True,
        'has_real_time_features': False
    }
    
    print("🎯 Enterprise AI-Powered Testing Workflow")
    print("=" * 50)
    
    # Step 1: Generate AI-powered test plan
    test_plan = await orchestrator.create_test_plan(application_info)
    print(f"📋 Test Plan Generated: {test_plan['application_type']} ({test_plan['complexity_level']} complexity)")
    
    # Step 2: Execute test suite
    test_suite = await orchestrator.execute_test_suite(test_plan)
    print(f"🧪 Test Suite Completed: {test_suite.pass_rate:.1f}% pass rate")
    
    # Step 3: Generate intelligence report
    intelligence_report = await orchestrator.generate_intelligence_report(test_suite)
    
    print("\n📊 AI Intelligence Report:")
    print(f"   Reliability Score: {intelligence_report['quality_metrics']['reliability_score']:.1f}/100")
    print(f"   Performance Score: {intelligence_report['quality_metrics']['performance_score']:.1f}/100")
    print(f"   Stability Score: {intelligence_report['quality_metrics']['stability_score']:.1f}/100")
    
    print("\n💡 AI Recommendations:")
    for rec in intelligence_report['ai_insights']['maintenance_recommendations']:
        print(f"   • {rec}")
    
    print("\n🚀 Optimization Opportunities:")
    for opp in intelligence_report['ai_insights']['optimization_opportunities']:
        print(f"   • {opp}")
    
    print("\n✅ Enterprise testing workflow completed successfully")

if __name__ == "__main__":
    asyncio.run(main())
