"""
GCP Cloud Functions Discovery Mixin

Discovery functionality for Google Cloud Functions.
Provides methods to discover and analyze existing functions.
"""

from typing import Dict, Any, List


class CloudFunctionsDiscoveryMixin:
    """
    Mixin for Cloud Functions discovery operations.
    
    This mixin provides:
    - Discovery of existing functions
    - Function analysis and comparison
    - Related resource detection
    - Performance and cost analysis
    """
    
    def discover_functions(self, region: str = None, pattern: str = None) -> List[Dict[str, Any]]:
        """Discover existing Cloud Functions
        
        Args:
            region: Specific region to search (searches all regions if None)
            pattern: Optional pattern to filter function names
            
        Returns:
            List of function information dictionaries
        """
        self._ensure_authenticated()
        
        try:
            discovered_functions = []
            
            # Mock discovery for now - in real implementation this would use GCP SDK
            if hasattr(self, 'functions_manager') and self.functions_manager:
                # In real implementation, this would call:
                # functions = self.functions_manager.list_functions(region)
                # and filter based on pattern
                pass
            
            print(f"🔍 Discovered {len(discovered_functions)} Cloud Functions")
            if region:
                print(f"   📍 Region: {region}")
            if pattern:
                print(f"   🔍 Filtered by pattern: {pattern}")
            
            return discovered_functions
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to discover functions: {str(e)}")
            return []
    
    def analyze_function(self, function_name: str = None, region: str = None) -> Dict[str, Any]:
        """Analyze a function's configuration and performance
        
        Args:
            function_name: Function to analyze (defaults to current function)
            region: Region to search in (defaults to current region)
            
        Returns:
            Dictionary with analysis results
        """
        self._ensure_authenticated()
        
        target_function = function_name or self.function_name
        target_region = region or self.region
        
        try:
            # Mock analysis for now - in real implementation this would use GCP SDK
            analysis = {
                'function_name': target_function,
                'exists': True,
                'region': target_region,
                'runtime': 'python39',
                'status': 'ACTIVE',
                'memory': '256MB',
                'timeout': '60s',
                'last_update': '2024-01-01 12:00:00',
                'invocation_count_30d': 0,
                'error_rate_30d': 0.0,
                'avg_duration_ms': 0,
                'cost_analysis': {
                    'current_monthly_cost': '$0.00',
                    'potential_savings': '$0.00',
                    'optimization_suggestions': []
                },
                'performance_analysis': {
                    'cold_start_rate': 'Unknown',
                    'avg_cold_start_ms': 'Unknown',
                    'avg_execution_ms': 'Unknown',
                    'performance_score': 75
                },
                'security_analysis': {
                    'ingress_settings': 'ALLOW_ALL',
                    'iam_policies': 'Default',
                    'vpc_connector': 'None',
                    'security_score': 70
                }
            }
            
            print(f"📊 Function Analysis: {target_function}")
            print(f"   📍 Region: {analysis['region']}")
            print(f"   🏃 Runtime: {analysis['runtime']}")
            print(f"   📊 Status: {analysis['status']}")
            print(f"   💾 Memory: {analysis['memory']}")
            print(f"   ⏱️  Timeout: {analysis['timeout']}")
            print(f"   📈 Invocations (30d): {analysis['invocation_count_30d']}")
            print(f"   ❌ Error Rate: {analysis['error_rate_30d']:.1%}")
            print(f"   🔒 Security Score: {analysis['security_analysis']['security_score']}/100")
            print(f"   ⚡ Performance Score: {analysis['performance_analysis']['performance_score']}/100")
            
            return analysis
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to analyze function {target_function}: {str(e)}")
            return {'function_name': target_function, 'error': str(e)}
    
    def compare_configurations(self, other_function_name: str, other_region: str = None) -> Dict[str, Any]:
        """Compare current function configuration with another function
        
        Args:
            other_function_name: Name of function to compare with
            other_region: Region of other function (defaults to current region)
            
        Returns:
            Dictionary with comparison results
        """
        self._ensure_authenticated()
        
        try:
            # Get configurations for both functions
            current_config = self.get_function_info()
            other_analysis = self.analyze_function(other_function_name, other_region)
            
            comparison = {
                'current_function': self.function_name,
                'other_function': other_function_name,
                'differences': [],
                'similarities': [],
                'recommendations': []
            }
            
            # Compare key attributes
            attributes_to_compare = [
                'runtime', 'memory', 'timeout', 'trigger_type',
                'ingress_settings', 'region'
            ]
            
            for attr in attributes_to_compare:
                current_val = current_config.get(attr, 'Unknown')
                other_val = other_analysis.get(attr, 'Unknown')
                
                if current_val != other_val:
                    comparison['differences'].append({
                        'attribute': attr,
                        'current_value': current_val,
                        'other_value': other_val
                    })
                else:
                    comparison['similarities'].append({
                        'attribute': attr,
                        'value': current_val
                    })
            
            print(f"📊 Configuration Comparison")
            print(f"   📋 Current: {self.function_name}")
            print(f"   📋 Other: {other_function_name}")
            print(f"   🔄 Differences: {len(comparison['differences'])}")
            print(f"   ✅ Similarities: {len(comparison['similarities'])}")
            
            return comparison
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to compare configurations: {str(e)}")
            return {'error': str(e)}
    
    def find_related_resources(self) -> Dict[str, List[str]]:
        """Find resources related to this function
        
        Returns:
            Dictionary with lists of related resource names
        """
        self._ensure_authenticated()
        
        try:
            related_resources = {
                'storage_buckets': [],
                'pubsub_topics': [],
                'firestore_collections': [],
                'cloud_sql_instances': [],
                'vpc_connectors': [],
                'load_balancers': [],
                'api_gateways': [],
                'schedulers': []
            }
            
            # Mock discovery for now - in real implementation this would:
            # 1. Search for storage buckets with function triggers
            # 2. Find Pub/Sub topics that trigger this function
            # 3. Locate Firestore collections with function triggers
            # 4. Find Cloud SQL instances accessed by this function
            # 5. Discover VPC connectors used by this function
            # 6. Find load balancers routing to this function
            # 7. Locate API Gateway configs using this function
            # 8. Find Cloud Scheduler jobs triggering this function
            
            total_related = sum(len(resources) for resources in related_resources.values())
            
            print(f"🔍 Related Resources for {self.function_name}")
            print(f"   📊 Total related resources: {total_related}")
            for resource_type, resources in related_resources.items():
                if resources:
                    print(f"   📋 {resource_type}: {len(resources)}")
            
            return related_resources
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to find related resources: {str(e)}")
            return {}
    
    def get_performance_metrics(self, days: int = 30) -> Dict[str, Any]:
        """Analyze function performance metrics over time
        
        Args:
            days: Number of days to analyze
            
        Returns:
            Dictionary with performance metrics
        """
        self._ensure_authenticated()
        
        try:
            # Mock metrics analysis for now - in real implementation this would:
            # 1. Query Cloud Monitoring API for function metrics
            # 2. Analyze invocation patterns, error rates, and execution times
            # 3. Identify performance bottlenecks and optimization opportunities
            
            metrics = {
                'analysis_period_days': days,
                'total_invocations': 0,
                'avg_daily_invocations': 0,
                'peak_invocation_hour': 'Unknown',
                'error_count': 0,
                'error_rate': 0.0,
                'avg_execution_time_ms': 0,
                'max_execution_time_ms': 0,
                'avg_cold_start_time_ms': 0,
                'cold_start_rate': 0.0,
                'memory_utilization_avg': 0.0,
                'memory_utilization_max': 0.0,
                'recommendations': []
            }
            
            print(f"📈 Performance Metrics for {self.function_name} ({days} days)")
            print(f"   📊 Total Invocations: {metrics['total_invocations']}")
            print(f"   📊 Daily Average: {metrics['avg_daily_invocations']} invocations")
            print(f"   ⏰ Peak Hour: {metrics['peak_invocation_hour']}")
            print(f"   ❌ Error Rate: {metrics['error_rate']:.1%}")
            print(f"   ⚡ Avg Execution: {metrics['avg_execution_time_ms']}ms")
            print(f"   🧊 Cold Start Rate: {metrics['cold_start_rate']:.1%}")
            print(f"   💾 Memory Usage: {metrics['memory_utilization_avg']:.1%} avg")
            
            return metrics
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to analyze performance metrics: {str(e)}")
            return {'error': str(e)}
    
    def get_cost_breakdown(self, days: int = 30) -> Dict[str, Any]:
        """Get detailed cost breakdown for the function
        
        Args:
            days: Number of days to analyze
            
        Returns:
            Dictionary with cost breakdown
        """
        self._ensure_authenticated()
        
        try:
            # Mock cost analysis for now - in real implementation this would:
            # 1. Query Cloud Billing API for function costs
            # 2. Break down costs by invocations, compute time, and networking
            # 3. Provide optimization recommendations
            
            cost_breakdown = {
                'analysis_period_days': days,
                'total_cost': 0.0,
                'invocation_cost': 0.0,
                'compute_cost': 0.0,
                'network_cost': 0.0,
                'always_on_cost': 0.0,
                'cost_per_invocation': 0.0,
                'projected_monthly_cost': 0.0,
                'optimization_opportunities': []
            }
            
            print(f"💰 Cost Breakdown for {self.function_name} ({days} days)")
            print(f"   💵 Total Cost: ${cost_breakdown['total_cost']:.2f}")
            print(f"   📞 Invocation Costs: ${cost_breakdown['invocation_cost']:.2f}")
            print(f"   ⚙️  Compute Costs: ${cost_breakdown['compute_cost']:.2f}")
            print(f"   🌐 Network Costs: ${cost_breakdown['network_cost']:.2f}")
            if cost_breakdown['always_on_cost'] > 0:
                print(f"   🔥 Always-on Costs: ${cost_breakdown['always_on_cost']:.2f}")
            print(f"   📊 Cost per Invocation: ${cost_breakdown['cost_per_invocation']:.6f}")
            print(f"   📅 Projected Monthly: ${cost_breakdown['projected_monthly_cost']:.2f}")
            
            return cost_breakdown
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to analyze cost breakdown: {str(e)}")
            return {'error': str(e)}