"""
GCP Cloud Storage Discovery Mixin

Discovery functionality for Google Cloud Storage buckets.
Provides methods to discover and analyze existing buckets.
"""

from typing import Dict, Any, List


class StorageDiscoveryMixin:
    """
    Mixin for Cloud Storage bucket discovery operations.
    
    This mixin provides:
    - Discovery of existing buckets
    - Bucket analysis and comparison
    - Related resource detection
    """
    
    def discover_buckets(self, pattern: str = None) -> List[Dict[str, Any]]:
        """Discover existing Cloud Storage buckets
        
        Args:
            pattern: Optional pattern to filter bucket names
            
        Returns:
            List of bucket information dictionaries
        """
        self._ensure_authenticated()
        
        try:
            discovered_buckets = []
            
            # Mock discovery for now - in real implementation this would use GCP SDK
            if hasattr(self, 'bucket_manager') and self.bucket_manager:
                # In real implementation, this would call:
                # buckets = self.bucket_manager.storage_client.list_buckets()
                # and filter based on pattern
                pass
            
            print(f"🔍 Discovered {len(discovered_buckets)} Cloud Storage buckets")
            if pattern:
                print(f"   🔍 Filtered by pattern: {pattern}")
            
            return discovered_buckets
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to discover buckets: {str(e)}")
            return []
    
    def analyze_bucket(self, bucket_name: str = None) -> Dict[str, Any]:
        """Analyze a bucket's configuration and usage
        
        Args:
            bucket_name: Bucket to analyze (defaults to current bucket)
            
        Returns:
            Dictionary with analysis results
        """
        self._ensure_authenticated()
        
        target_bucket = bucket_name or self.bucket_name
        
        try:
            # Mock analysis for now - in real implementation this would use GCP SDK
            analysis = {
                'bucket_name': target_bucket,
                'exists': True,
                'location': 'US',
                'storage_class': 'STANDARD',
                'size_gb': 0,
                'object_count': 0,
                'cost_analysis': {
                    'current_monthly_cost': '$0.00',
                    'potential_savings': '$0.00',
                    'optimization_suggestions': []
                },
                'security_analysis': {
                    'public_access': 'Private',
                    'versioning': 'Disabled',
                    'encryption': 'Google-managed',
                    'security_score': 85
                },
                'performance_analysis': {
                    'access_patterns': 'Unknown',
                    'request_rate': 'Low',
                    'performance_score': 75
                }
            }
            
            print(f"📊 Bucket Analysis: {target_bucket}")
            print(f"   📍 Location: {analysis['location']}")
            print(f"   🏷️  Storage Class: {analysis['storage_class']}")
            print(f"   📦 Size: {analysis['size_gb']} GB")
            print(f"   📄 Objects: {analysis['object_count']}")
            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 bucket {target_bucket}: {str(e)}")
            return {'bucket_name': target_bucket, 'error': str(e)}
    
    def compare_configurations(self, other_bucket_name: str) -> Dict[str, Any]:
        """Compare current bucket configuration with another bucket
        
        Args:
            other_bucket_name: Name of bucket to compare with
            
        Returns:
            Dictionary with comparison results
        """
        self._ensure_authenticated()
        
        try:
            # Get configurations for both buckets
            current_config = self.get_bucket_info()
            other_analysis = self.analyze_bucket(other_bucket_name)
            
            comparison = {
                'current_bucket': self.bucket_name,
                'other_bucket': other_bucket_name,
                'differences': [],
                'similarities': [],
                'recommendations': []
            }
            
            # Compare key attributes
            attributes_to_compare = [
                'location', 'storage_class', 'versioning_enabled',
                'public_access_prevention'
            ]
            
            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.bucket_name}")
            print(f"   📋 Other: {other_bucket_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 bucket
        
        Returns:
            Dictionary with lists of related resource names
        """
        self._ensure_authenticated()
        
        try:
            related_resources = {
                'compute_instances': [],
                'cloud_functions': [],
                'app_engine_apps': [],
                'gke_clusters': [],
                'load_balancers': [],
                'cdn_distributions': []
            }
            
            # Mock discovery for now - in real implementation this would:
            # 1. Search for compute instances with this bucket in startup scripts
            # 2. Find Cloud Functions with bucket triggers
            # 3. Locate App Engine apps using this bucket
            # 4. Find GKE workloads mounting this bucket
            # 5. Discover load balancers serving from this bucket
            # 6. Find CDN distributions with this bucket as origin
            
            total_related = sum(len(resources) for resources in related_resources.values())
            
            print(f"🔍 Related Resources for {self.bucket_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_usage_patterns(self, days: int = 30) -> Dict[str, Any]:
        """Analyze bucket usage patterns over time
        
        Args:
            days: Number of days to analyze
            
        Returns:
            Dictionary with usage pattern analysis
        """
        self._ensure_authenticated()
        
        try:
            # Mock usage analysis for now - in real implementation this would:
            # 1. Query Cloud Monitoring API for bucket metrics
            # 2. Analyze request patterns, bandwidth usage, and access frequency
            # 3. Identify peak usage times and access patterns
            
            usage_patterns = {
                'analysis_period_days': days,
                'total_requests': 0,
                'avg_daily_requests': 0,
                'peak_request_hour': 'Unknown',
                'bandwidth_gb': 0,
                'avg_daily_bandwidth_gb': 0,
                'most_accessed_objects': [],
                'access_pattern': 'Unknown',  # Regular, Sporadic, Heavy, Light
                'recommendations': []
            }
            
            print(f"📈 Usage Patterns for {self.bucket_name} ({days} days)")
            print(f"   📊 Total Requests: {usage_patterns['total_requests']}")
            print(f"   📊 Daily Average: {usage_patterns['avg_daily_requests']} requests")
            print(f"   🌐 Bandwidth: {usage_patterns['bandwidth_gb']} GB")
            print(f"   ⏰ Peak Hour: {usage_patterns['peak_request_hour']}")
            print(f"   📋 Pattern: {usage_patterns['access_pattern']}")
            
            return usage_patterns
            
        except Exception as e:
            print(f"⚠️  Warning: Failed to analyze usage patterns: {str(e)}")
            return {'error': str(e)}