dev_test/advanced-python.py

# 🚀 Advanced Python Test File - Complex Patterns and Features
# ═══════════════════════════════════════════════════════════════

"""

🎯 Comprehensive Python Testing Suite with Advanced Features

📊 Testing all modern Python features with extensive emoji usage

🔥 Classes, decorators, async/await, type hints, context managers

"""

import asyncio
import threading
import functools
import contextlib
import dataclasses
from typing import (
    List, Dict, Optional, Union, Any, Callable, Awaitable, 
    Generic, TypeVar, Protocol, Literal, overload
)
from abc import ABC, abstractmethod
from enum import Enum
from datetime import datetime, timedelta
import json
import logging

# 🌟 Type definitions with emoji-rich literals
EmojiStatus = Literal['🟢 Active', '🟡 Pending', '🔴 Inactive', '⚫ Disabled']
NotificationLevel = Literal['🔔 Info', '⚠️ Warning', '❌ Error', '✅ Success']
ProcessingState = Literal['⏳ Loading', '🔄 Processing', '✅ Complete', '❌ Failed']

T = TypeVar('T')
U = TypeVar('U')

# 💡 Advanced enum with emoji values
class EmojiPriority(Enum):
    LOW = '🟢 Low'
    MEDIUM = '🟡 Medium' 
    HIGH = '🔴 High'
    CRITICAL = '🚨 Critical'
    
    def __str__(self) -> str:
        return self.value

# 🎨 Dataclass with complex emoji annotations
@dataclasses.dataclass
class EmojiUser:
    """🧑‍💼 Advanced user model with emoji-rich metadata"""
    
    id: str
    name: str
    email: str
    status: EmojiStatus = '🟢 Active'
    
    # 🎯 Nested configuration with emoji indicators
    preferences: Dict[str, Any] = dataclasses.field(default_factory=lambda: {
        'theme': '🌙 Dark',
        'language': '🇺🇸 English',
        'notifications': {
            'email': '✅ Enabled',
            'push': '🔔 On',
            'desktop': '💻 Active'
        },
        'privacy': {
            'profile_visibility': '🌐 Public',
            'data_sharing': '🤝 Allowed'
        }
    })
    
    # 📊 Activity tracking with emoji metadata
    activity: Dict[str, Any] = dataclasses.field(default_factory=lambda: {
        'last_login': None,
        'sessions_today': 0,
        'total_sessions': 0,
        'achievements': []
    })
    
    # 🏷️ Tags and metadata with emoji categorization
    tags: List[str] = dataclasses.field(default_factory=list)
    metadata: Dict[str, Dict[str, Any]] = dataclasses.field(default_factory=dict)
    
    def add_achievement(self, achievement_type: str, title: str) -> None:
        """🏆 Add achievement with emoji categorization"""
        emoji_map = {
            'trophy': '🏆',
            'star': '⭐',
            'medal': '🎖️',
            'badge': '🏅'
        }
        
        achievement = {
            'id': f"ach_{len(self.activity['achievements'])}",
            'type': achievement_type,
            'title': title,
            'icon': emoji_map.get(achievement_type, '🎯'),
            'earned_at': datetime.now().isoformat(),
            'status': '✅ Earned'
        }
        
        self.activity['achievements'].append(achievement)
        print(f"🎉 Achievement unlocked: {achievement['icon']} {title}")
    
    def update_status(self, new_status: EmojiStatus, reason: str = '') -> None:
        """🔄 Update user status with emoji tracking"""
        old_status = self.status
        self.status = new_status
        
        # 📝 Log status change with emoji indicators
        change_log = {
            'from': old_status,
            'to': new_status,
            'reason': reason,
            'timestamp': datetime.now().isoformat(),
            'change_type': '🔄 Status Update'
        }
        
        if 'status_history' not in self.metadata:
            self.metadata['status_history'] = {'changes': [], 'type': '📊 History'}
        
        self.metadata['status_history']['changes'].append(change_log)
        print(f"🔄 Status changed: {old_status} → {new_status}")

# 🎪 Advanced decorator with emoji logging
def emoji_logger(operation_type: str = '⚙️ Operation'):
    """🎨 Decorator for emoji-enhanced logging"""
    def decorator(func: Callable[..., T]) -> Callable[..., T]:
        @functools.wraps(func)
        def wrapper(*args, **kwargs) -> T:
            start_time = datetime.now()
            func_name = func.__name__
            
            print(f"🚀 Starting {operation_type}: {func_name}")
            
            try:
                result = func(*args, **kwargs)
                duration = (datetime.now() - start_time).total_seconds()
                
                # 📊 Performance categorization with emojis
                perf_emoji = ('🟢' if duration < 0.1 else 
                             '🟡' if duration < 1.0 else '🔴')
                
                print(f"✅ Completed {operation_type}: {func_name} "
                      f"({perf_emoji} {duration:.3f}s)")
                
                return result
                
            except Exception as e:
                duration = (datetime.now() - start_time).total_seconds()
                print(f"💥 Failed {operation_type}: {func_name} "
                      f"after {duration:.3f}s - {str(e)}")
                raise
                
        return wrapper
    return decorator

# 🔄 Async decorator with emoji progress tracking
def emoji_async_tracker(show_progress: bool = True):
    """🎯 Async decorator with emoji progress indicators"""
    def decorator(func: Callable[..., Awaitable[T]]) -> Callable[..., Awaitable[T]]:
        @functools.wraps(func)
        async def wrapper(*args, **kwargs) -> T:
            start_time = datetime.now()
            func_name = func.__name__
            
            if show_progress:
                print(f"⏳ Starting async {func_name}...")
            
            try:
                result = await func(*args, **kwargs)
                duration = (datetime.now() - start_time).total_seconds()
                
                if show_progress:
                    print(f"🎉 Async {func_name} completed in {duration:.3f}s")
                
                return result
                
            except Exception as e:
                duration = (datetime.now() - start_time).total_seconds()
                print(f"💥 Async {func_name} failed after {duration:.3f}s: {str(e)}")
                raise
                
        return wrapper
    return decorator

# 🏭 Complex abstract base class with emoji protocols
class EmojiProcessor(ABC):
    """🎯 Abstract processor interface with emoji categorization"""
    
    @abstractmethod
    async def process_data(self, data: Any) -> Dict[str, Any]:
        """🔄 Process data with emoji feedback"""
        pass
    
    @abstractmethod
    def validate_input(self, data: Any) -> Dict[str, Any]:
        """🔍 Validate input with emoji status"""
        pass
    
    @abstractmethod
    def get_metrics(self) -> Dict[str, Any]:
        """📊 Get processing metrics with emoji indicators"""
        pass

# 🚀 Advanced implementation with complex emoji patterns
class AdvancedEmojiAnalyzer(EmojiProcessor):
    """🧠 Advanced analytics engine with comprehensive emoji support"""
    
    def __init__(self, config: Dict[str, Any] = None):
        # 🎨 Initialize with emoji-rich configuration
        self.config = config or {
            'timeout': 30.0,
            'batch_size': 100,
            'retry_attempts': 3,
            'enable_caching': True,
            'log_level': '📊 Info'
        }
        
        # 📊 Metrics tracking with emoji categorization
        self.metrics = {
            'processed_items': 0,
            'successful_items': 0,
            'failed_items': 0,
            'cache_hits': 0,
            'processing_times': [],
            'error_categories': {
                '📊 Validation': 0,
                '💥 System': 0,
                '🌐 Network': 0,
                '⏰ Timeout': 0
            }
        }
        
        # 🎯 Status tracking with emoji indicators
        self.status = {
            'current': '🟢 Ready',
            'last_update': datetime.now(),
            'health_check': '✅ Healthy',
            'performance': '🟢 Good'
        }
        
        # 🧠 ML models cache with emoji status
        self._models_cache: Dict[str, Any] = {}
        self._cache_status = '💾 Empty'
        
        print("🚀 Advanced Emoji Analyzer initialized successfully!")
    
    @emoji_logger('🔍 Validation')
    def validate_input(self, data: Any) -> Dict[str, Any]:
        """🔍 Comprehensive input validation with emoji feedback"""
        validation_result = {
            'valid': True,
            'errors': [],
            'warnings': [],
            'status': '✅ Valid',
            'checks_performed': []
        }
        
        # 🧪 Type validation
        if not isinstance(data, (list, dict)):
            validation_result['valid'] = False
            validation_result['errors'].append('❌ Data must be list or dict')
            validation_result['status'] = '❌ Invalid Type'
        else:
            validation_result['checks_performed'].append('✅ Type Check')
        
        # 📊 Size validation
        if isinstance(data, (list, dict)) and len(data) == 0:
            validation_result['warnings'].append('⚠️ Empty data provided')
            validation_result['checks_performed'].append('⚠️ Size Check')
        elif isinstance(data, (list, dict)):
            validation_result['checks_performed'].append('✅ Size Check')
        
        # 🔍 Content validation
        if isinstance(data, list):
            for i, item in enumerate(data[:10]):  # Sample first 10 items
                if not isinstance(item, dict) or 'id' not in item:
                    validation_result['warnings'].append(
                        f'⚠️ Item {i} missing required fields'
                    )
            validation_result['checks_performed'].append('🔍 Content Check')
        
        # 📈 Update metrics
        if validation_result['valid']:
            self.metrics['successful_items'] += 1
        else:
            self.metrics['failed_items'] += 1
            self.metrics['error_categories']['📊 Validation'] += 1
        
        return validation_result
    
    @emoji_async_tracker(show_progress=True)
    async def process_data(self, data: Any) -> Dict[str, Any]:
        """🔄 Advanced data processing with emoji progress tracking"""
        start_time = datetime.now()
        
        try:
            # 🔍 Input validation
            validation = self.validate_input(data)
            if not validation['valid']:
                raise ValueError(f"🚫 Validation failed: {validation['errors']}")
            
            # 🎯 Processing preparation
            self.status['current'] = '⏳ Preparing'
            await self._update_status_async()
            
            if isinstance(data, list):
                results = await self._process_list_data(data)
            elif isinstance(data, dict):
                results = await self._process_dict_data(data)
            else:
                raise TypeError("🚫 Unsupported data type")
            
            # 📊 Generate final results
            processing_time = (datetime.now() - start_time).total_seconds()
            self.metrics['processing_times'].append(processing_time)
            
            final_result = {
                'status': '🎉 Success',
                'processing_time': processing_time,
                'results': results,
                'metrics': self._generate_processing_metrics(),
                'recommendations': self._generate_recommendations(results),
                'timestamp': datetime.now().isoformat()
            }
            
            self.status['current'] = '✅ Complete'
            await self._update_status_async()
            
            print(f"🎉 Processing completed successfully in {processing_time:.3f}s")
            return final_result
            
        except Exception as e:
            error_type = self._categorize_error(e)
            self.metrics['error_categories'][error_type] += 1
            self.metrics['failed_items'] += 1
            
            self.status['current'] = '❌ Failed'
            await self._update_status_async()
            
            error_result = {
                'status': '❌ Failed',
                'error': str(e),
                'error_type': error_type,
                'processing_time': (datetime.now() - start_time).total_seconds(),
                'timestamp': datetime.now().isoformat()
            }
            
            print(f"💥 Processing failed: {error_type} - {str(e)}")
            return error_result
    
    async def _process_list_data(self, data: List[Any]) -> Dict[str, Any]:
        """📋 Process list data with batch handling and emoji progress"""
        self.status['current'] = '🔄 Processing List'
        
        batch_size = self.config['batch_size']
        total_items = len(data)
        processed_items = []
        failed_items = []
        
        print(f"📋 Processing {total_items} items in batches of {batch_size}")
        
        # 🔄 Batch processing with emoji progress indicators
        for i in range(0, total_items, batch_size):
            batch = data[i:i + batch_size]
            batch_number = (i // batch_size) + 1
            total_batches = (total_items + batch_size - 1) // batch_size
            
            print(f"🔄 Processing batch {batch_number}/{total_batches} "
                  f"({len(batch)} items)")
            
            batch_results = await self._process_batch(batch)
            processed_items.extend(batch_results['successful'])
            failed_items.extend(batch_results['failed'])
            
            # 📊 Progress update with emoji visualization
            progress = ((i + len(batch)) / total_items) * 100
            progress_emoji = '🟢' if progress == 100 else '🟡' if progress > 50 else '🔴'
            print(f"{progress_emoji} Progress: {progress:.1f}% complete")
            
            # ⏱️ Brief pause between batches
            await asyncio.sleep(0.01)
        
        return {
            'total_processed': len(processed_items),
            'total_failed': len(failed_items),
            'success_rate': f"{(len(processed_items) / total_items) * 100:.1f}%",
            'processed_items': processed_items,
            'failed_items': failed_items,
            'batch_count': total_batches,
            'status': '✅ List Processing Complete'
        }
    
    async def _process_dict_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """📊 Process dictionary data with key-value analysis"""
        self.status['current'] = '🔄 Processing Dict'
        
        print(f"📊 Processing dictionary with {len(data)} keys")
        
        processed_keys = {}
        analysis_results = {}
        
        # 🔍 Analyze each key-value pair with emoji categorization
        for key, value in data.items():
            try:
                # 🎯 Key analysis
                key_analysis = self._analyze_key(key)
                
                # 📊 Value analysis  
                value_analysis = await self._analyze_value(value)
                
                # 🧠 Combined analysis
                combined_analysis = {
                    'key_info': key_analysis,
                    'value_info': value_analysis,
                    'relationship': self._analyze_key_value_relationship(key, value),
                    'status': '✅ Analyzed'
                }
                
                processed_keys[key] = combined_analysis
                
            except Exception as e:
                failed_analysis = {
                    'error': str(e),
                    'status': '❌ Failed',
                    'timestamp': datetime.now().isoformat()
                }
                processed_keys[key] = failed_analysis
        
        # 📈 Generate overall analysis
        successful_keys = [k for k, v in processed_keys.items() 
                          if v.get('status') == '✅ Analyzed']
        
        analysis_results = {
            'total_keys': len(data),
            'analyzed_keys': len(successful_keys),
            'failed_keys': len(data) - len(successful_keys),
            'analysis_details': processed_keys,
            'patterns': self._identify_patterns(processed_keys),
            'recommendations': self._generate_dict_recommendations(processed_keys),
            'status': '✅ Dict Processing Complete'
        }
        
        return analysis_results
    
    async def _process_batch(self, batch: List[Any]) -> Dict[str, List[Any]]:
        """🔄 Process a batch of items with concurrent handling"""
        successful = []
        failed = []
        
        # 🚀 Process items concurrently with emoji tracking
        tasks = [self._process_single_item(item, i) for i, item in enumerate(batch)]
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                failed.append({
                    'index': i,
                    'item': batch[i],
                    'error': str(result),
                    'status': '❌ Failed'
                })
            else:
                successful.append(result)
        
        return {'successful': successful, 'failed': failed}
    
    async def _process_single_item(self, item: Any, index: int) -> Dict[str, Any]:
        """🎯 Process individual item with detailed emoji analysis"""
        
        # 🔍 Item analysis
        analysis = {
            'index': index,
            'original': item,
            'type': type(item).__name__,
            'size': len(str(item)),
            'complexity': self._calculate_complexity(item),
            'quality': self._assess_quality(item),
            'processed_at': datetime.now().isoformat()
        }
        
        # 🧠 Apply transformations
        if isinstance(item, dict):
            analysis['transformations'] = await self._apply_dict_transformations(item)
        elif isinstance(item, (list, tuple)):
            analysis['transformations'] = await self._apply_list_transformations(item)
        else:
            analysis['transformations'] = await self._apply_generic_transformations(item)
        
        # 📊 Calculate metrics
        analysis['metrics'] = {
            'processing_score': self._calculate_processing_score(analysis),
            'quality_grade': self._assign_quality_grade(analysis),
            'complexity_level': self._assign_complexity_level(analysis),
            'status': '✅ Processed Successfully'
        }
        
        return analysis
    
    def _analyze_key(self, key: str) -> Dict[str, Any]:
        """🔍 Analyze dictionary key with emoji categorization"""
        return {
            'length': len(key),
            'type': 'string',
            'format': self._detect_key_format(key),
            'category': self._categorize_key(key),
            'status': '🔍 Analyzed'
        }
    
    async def _analyze_value(self, value: Any) -> Dict[str, Any]:
        """📊 Analyze dictionary value with emoji insights"""
        await asyncio.sleep(0.001)  # Simulate async work
        
        analysis = {
            'type': type(value).__name__,
            'size': len(str(value)),
            'complexity': self._calculate_complexity(value),
            'category': self._categorize_value(value),
            'status': '📊 Analyzed'
        }
        
        if isinstance(value, (int, float)):
            analysis['numeric_properties'] = {
                'range': 'positive' if value >= 0 else 'negative',
                'magnitude': 'small' if abs(value) < 100 else 'large',
                'emoji': '📈' if value > 0 else '📉'
            }
        
        return analysis
    
    def _analyze_key_value_relationship(self, key: str, value: Any) -> Dict[str, Any]:
        """🔗 Analyze relationship between key and value"""
        return {
            'compatibility': '✅ Compatible',
            'semantic_match': '🎯 Good',
            'type_appropriateness': '👍 Appropriate',
            'naming_convention': '📝 Standard'
        }
    
    def _identify_patterns(self, processed_keys: Dict[str, Any]) -> List[Dict[str, Any]]:
        """🔍 Identify patterns in processed data"""
        patterns = []
        
        # 📊 Type pattern analysis
        type_counts = {}
        for key_data in processed_keys.values():
            if 'value_info' in key_data:
                value_type = key_data['value_info'].get('type', 'unknown')
                type_counts[value_type] = type_counts.get(value_type, 0) + 1
        
        if type_counts:
            dominant_type = max(type_counts, key=type_counts.get)
            patterns.append({
                'type': '📊 Type Pattern',
                'description': f'Dominant value type: {dominant_type}',
                'confidence': type_counts[dominant_type] / len(processed_keys),
                'emoji': '📊'
            })
        
        return patterns
    
    def _generate_dict_recommendations(self, processed_keys: Dict[str, Any]) -> List[Dict[str, Any]]:
        """💡 Generate recommendations for dictionary processing"""
        recommendations = []
        
        failed_count = sum(1 for v in processed_keys.values() 
                          if v.get('status') == '❌ Failed')
        
        if failed_count > 0:
            recommendations.append({
                'priority': '🔴 High',
                'category': '🔧 Error Handling',
                'message': f'{failed_count} keys failed processing',
                'action': 'Review error patterns and improve validation',
                'emoji': '🔧'
            })
        
        return recommendations
    
    async def _apply_dict_transformations(self, item: Dict[str, Any]) -> Dict[str, Any]:
        """🔄 Apply transformations to dictionary items"""
        await asyncio.sleep(0.002)
        return {
            'normalized_keys': {k.lower().replace(' ', '_'): v for k, v in item.items()},
            'value_count': len(item),
            'transformation_status': '✅ Applied',
            'transformations': ['🔤 Key normalization', '📊 Value counting']
        }
    
    async def _apply_list_transformations(self, item: List[Any]) -> Dict[str, Any]:
        """📋 Apply transformations to list items"""
        await asyncio.sleep(0.002)
        return {
            'sorted_items': sorted(item, key=str) if all(isinstance(x, (int, float, str)) for x in item) else item,
            'item_count': len(item),
            'unique_count': len(set(str(x) for x in item)),
            'transformation_status': '✅ Applied',
            'transformations': ['🔢 Sorting', '🔍 Uniqueness check']
        }
    
    async def _apply_generic_transformations(self, item: Any) -> Dict[str, Any]:
        """⚙️ Apply generic transformations to items"""
        await asyncio.sleep(0.001)
        return {
            'string_representation': str(item),
            'length': len(str(item)),
            'transformation_status': '✅ Applied',
            'transformations': ['📝 String conversion', '📏 Length calculation']
        }
    
    def _calculate_complexity(self, item: Any) -> Dict[str, Any]:
        """🧩 Calculate complexity metrics with emoji indicators"""
        if isinstance(item, dict):
            score = len(item) + sum(len(str(v)) for v in item.values())
        elif isinstance(item, (list, tuple)):
            score = len(item) + sum(len(str(x)) for x in item)
        else:
            score = len(str(item))
        
        level = ('🔥 Very High' if score > 1000 else
                '📈 High' if score > 500 else
                '📊 Medium' if score > 100 else
                '📋 Low')
        
        return {'score': score, 'level': level}
    
    def _assess_quality(self, item: Any) -> Dict[str, Any]:
        """🎯 Assess item quality with emoji grading"""
        quality_score = 85  # Base score
        
        # 🔍 Quality factors
        if isinstance(item, dict):
            if 'id' in item:
                quality_score += 5
            if len(item) > 0:
                quality_score += 5
        
        grade = ('🏆 Excellent' if quality_score >= 90 else
                '⭐ Good' if quality_score >= 75 else
                '👍 Fair' if quality_score >= 60 else
                '⚠️ Poor')
        
        return {'score': quality_score, 'grade': grade}
    
    def _calculate_processing_score(self, analysis: Dict[str, Any]) -> int:
        """📊 Calculate overall processing score"""
        base_score = 50
        
        if analysis.get('complexity', {}).get('score', 0) < 100:
            base_score += 20
        
        if analysis.get('quality', {}).get('score', 0) > 80:
            base_score += 20
        
        return min(100, base_score)
    
    def _assign_quality_grade(self, analysis: Dict[str, Any]) -> str:
        """🎯 Assign quality grade with emoji"""
        score = self._calculate_processing_score(analysis)
        
        return ('🏆 A+' if score >= 95 else
                '⭐ A' if score >= 85 else
                '📈 B' if score >= 75 else
                '📊 C' if score >= 65 else
                '⚠️ D')
    
    def _assign_complexity_level(self, analysis: Dict[str, Any]) -> str:
        """🧩 Assign complexity level with emoji"""
        complexity_score = analysis.get('complexity', {}).get('score', 0)
        
        return ('🔥 Expert' if complexity_score > 500 else
                '📈 Advanced' if complexity_score > 200 else
                '📊 Intermediate' if complexity_score > 50 else
                '📋 Basic')
    
    def _detect_key_format(self, key: str) -> str:
        """🔍 Detect key format patterns"""
        if '_' in key:
            return '🐍 snake_case'
        elif any(c.isupper() for c in key[1:]):
            return '🐪 camelCase'
        elif '-' in key:
            return '🔗 kebab-case'
        else:
            return '📝 simple'
    
    def _categorize_key(self, key: str) -> str:
        """🏷️ Categorize key with emoji tags"""
        key_lower = key.lower()
        
        if any(word in key_lower for word in ['id', 'uuid', 'identifier']):
            return '🆔 Identifier'
        elif any(word in key_lower for word in ['name', 'title', 'label']):
            return '📝 Label'
        elif any(word in key_lower for word in ['time', 'date', 'timestamp']):
            return '🕐 Temporal'
        elif any(word in key_lower for word in ['count', 'number', 'amount']):
            return '🔢 Numeric'
        else:
            return '📊 General'
    
    def _categorize_value(self, value: Any) -> str:
        """📊 Categorize value with emoji types"""
        if isinstance(value, bool):
            return '✅ Boolean'
        elif isinstance(value, int):
            return '🔢 Integer'
        elif isinstance(value, float):
            return '📊 Float'
        elif isinstance(value, str):
            return '📝 String'
        elif isinstance(value, (list, tuple)):
            return '📋 List'
        elif isinstance(value, dict):
            return '📚 Object'
        else:
            return '❓ Unknown'
    
    def _categorize_error(self, error: Exception) -> str:
        """🚨 Categorize error types with emoji classification"""
        error_type = type(error).__name__
        
        if 'Validation' in error_type or isinstance(error, ValueError):
            return '📊 Validation'
        elif 'Timeout' in error_type or 'timeout' in str(error).lower():
            return '⏰ Timeout'
        elif 'Network' in error_type or 'Connection' in error_type:
            return '🌐 Network'
        else:
            return '💥 System'
    
    async def _update_status_async(self) -> None:
        """🔄 Update status asynchronously"""
        self.status['last_update'] = datetime.now()
        await asyncio.sleep(0.001)  # Simulate async status update
    
    def _generate_processing_metrics(self) -> Dict[str, Any]:
        """📈 Generate comprehensive processing metrics"""
        total_processed = self.metrics['processed_items']
        success_rate = (self.metrics['successful_items'] / max(1, total_processed)) * 100
        
        avg_time = (sum(self.metrics['processing_times']) / 
                   max(1, len(self.metrics['processing_times'])))
        
        return {
            'total_items': total_processed,
            'success_rate': f"{success_rate:.1f}%",
            'average_processing_time': f"{avg_time:.3f}s",
            'error_breakdown': self.metrics['error_categories'],
            'performance_indicator': ('🟢 Excellent' if success_rate > 95 else
                                    '🟡 Good' if success_rate > 85 else
                                    '🔴 Needs Improvement'),
            'status': '📊 Metrics Generated'
        }
    
    def _generate_recommendations(self, results: Dict[str, Any]) -> List[Dict[str, Any]]:
        """💡 Generate actionable recommendations"""
        recommendations = []
        
        if 'success_rate' in results:
            success_rate = float(results['success_rate'].replace('%', ''))
            
            if success_rate < 90:
                recommendations.append({
                    'priority': '🔴 High',
                    'category': '🎯 Quality',
                    'message': f'Success rate ({success_rate:.1f}%) below target',
                    'action': 'Review error patterns and improve processing',
                    'emoji': '📈'
                })
        
        # 📊 Performance recommendations
        if self.metrics['processing_times']:
            avg_time = sum(self.metrics['processing_times']) / len(self.metrics['processing_times'])
            
            if avg_time > 1.0:
                recommendations.append({
                    'priority': '🟡 Medium',
                    'category': '⚡ Performance',
                    'message': f'Average processing time ({avg_time:.3f}s) is high',
                    'action': 'Consider optimization or parallel processing',
                    'emoji': '⚡'
                })
        
        return recommendations
    
    @emoji_logger('📊 Metrics')
    def get_metrics(self) -> Dict[str, Any]:
        """📊 Get comprehensive metrics with emoji indicators"""
        return {
            'processing_metrics': self.metrics,
            'status_info': self.status,
            'configuration': self.config,
            'health_check': {
                'overall': '✅ Healthy',
                'components': {
                    'processor': '🟢 Online',
                    'cache': self._cache_status,
                    'metrics': '📊 Active'
                }
            },
            'timestamp': datetime.now().isoformat()
        }

# 🎪 Context manager with emoji resource tracking
@contextlib.asynccontextmanager
async def emoji_resource_manager(resource_name: str):
    """🎯 Async context manager with emoji resource tracking"""
    print(f"🔓 Acquiring resource: {resource_name}")
    start_time = datetime.now()
    
    try:
        # 🚀 Simulate resource acquisition
        await asyncio.sleep(0.01)
        print(f"✅ Resource acquired: {resource_name}")
        
        yield resource_name
        
    except Exception as e:
        print(f"💥 Error with resource {resource_name}: {str(e)}")
        raise
        
    finally:
        duration = (datetime.now() - start_time).total_seconds()
        print(f"🔒 Released resource: {resource_name} (held for {duration:.3f}s)")

# 🧪 Advanced async function with emoji workflow
@emoji_async_tracker()
async def emoji_workflow_orchestrator(

    tasks: List[Dict[str, Any]], 

    concurrency_limit: int = 5

) -> Dict[str, Any]:
    """🎭 Orchestrate complex workflows with emoji progress tracking"""
    
    print(f"🎭 Starting workflow with {len(tasks)} tasks (max {concurrency_limit} concurrent)")
    
    # 🎯 Semaphore for concurrency control
    semaphore = asyncio.Semaphore(concurrency_limit)
    
    async def execute_task(task: Dict[str, Any], task_id: int) -> Dict[str, Any]:
        """🎯 Execute individual task with emoji tracking"""
        async with semaphore:
            async with emoji_resource_manager(f"task_{task_id}"):
                start_time = datetime.now()
                
                try:
                    # 🔄 Simulate task execution
                    task_type = task.get('type', 'generic')
                    duration = task.get('duration', 0.1)
                    
                    print(f"  🔄 Executing {task_type} task {task_id}")
                    await asyncio.sleep(duration)
                    
                    execution_time = (datetime.now() - start_time).total_seconds()
                    
                    return {
                        'task_id': task_id,
                        'type': task_type,
                        'status': '✅ Success',
                        'execution_time': execution_time,
                        'result': f'Task {task_id} completed successfully',
                        'emoji': '🎉'
                    }
                    
                except Exception as e:
                    execution_time = (datetime.now() - start_time).total_seconds()
                    
                    return {
                        'task_id': task_id,
                        'type': task.get('type', 'generic'),
                        'status': '❌ Failed',
                        'execution_time': execution_time,
                        'error': str(e),
                        'emoji': '💥'
                    }
    
    # 🚀 Execute all tasks concurrently
    task_coroutines = [execute_task(task, i) for i, task in enumerate(tasks)]
    results = await asyncio.gather(*task_coroutines, return_exceptions=True)
    
    # 📊 Analyze results
    successful_tasks = [r for r in results if isinstance(r, dict) and r.get('status') == '✅ Success']
    failed_tasks = [r for r in results if isinstance(r, dict) and r.get('status') == '❌ Failed']
    exception_tasks = [r for r in results if isinstance(r, Exception)]
    
    # 🎉 Generate workflow summary
    workflow_summary = {
        'total_tasks': len(tasks),
        'successful': len(successful_tasks),
        'failed': len(failed_tasks) + len(exception_tasks),
        'success_rate': f"{(len(successful_tasks) / len(tasks)) * 100:.1f}%",
        'results': successful_tasks + failed_tasks,
        'exceptions': [str(e) for e in exception_tasks],
        'status': ('🎉 Complete Success' if len(failed_tasks) + len(exception_tasks) == 0 else
                  '⚠️ Partial Success' if len(successful_tasks) > 0 else
                  '💥 Complete Failure'),
        'timestamp': datetime.now().isoformat()
    }
    
    print(f"🎭 Workflow completed: {workflow_summary['status']}")
    print(f"📊 Success rate: {workflow_summary['success_rate']}")
    
    return workflow_summary

# 🧪 Example usage and testing
async def run_comprehensive_emoji_tests():
    """🧪 Run comprehensive tests with emoji feedback"""
    print("🧪 Starting comprehensive emoji tests...")
    
    # 🎯 Initialize analyzer
    analyzer = AdvancedEmojiAnalyzer({
        'timeout': 10.0,
        'batch_size': 50,
        'log_level': '📊 Debug'
    })
    
    # 📊 Test data with emoji-rich content
    test_data = [
        {'id': 'user_1', 'name': '👤 John Doe', 'status': '🟢 Active', 'score': 95},
        {'id': 'user_2', 'name': '👩‍💼 Jane Smith', 'status': '🟡 Pending', 'score': 87},
        {'id': 'user_3', 'name': '🧑‍🎓 Bob Wilson', 'status': '🔴 Inactive', 'score': 72},
        {'id': 'user_4', 'name': '👨‍💻 Alice Brown', 'status': '🟢 Active', 'score': 91}
    ]
    
    try:
        # 🔄 Process test data
        print("\n🔄 Processing test data...")
        results = await analyzer.process_data(test_data)
        
        print(f"✅ Processing results: {results['status']}")
        print(f"📊 Processing time: {results['processing_time']:.3f}s")
        
        # 📈 Get metrics
        print("\n📈 Getting metrics...")
        metrics = analyzer.get_metrics()
        print(f"📊 Health status: {metrics['health_check']['overall']}")
        
        # 🎭 Test workflow orchestrator
        print("\n🎭 Testing workflow orchestrator...")
        workflow_tasks = [
            {'type': '🔍 validation', 'duration': 0.05},
            {'type': '🔄 processing', 'duration': 0.1},
            {'type': '📊 analysis', 'duration': 0.08},
            {'type': '💾 storage', 'duration': 0.03}
        ]
        
        workflow_results = await emoji_workflow_orchestrator(workflow_tasks, concurrency_limit=2)
        print(f"🎭 Workflow results: {workflow_results['status']}")
        
        print("\n🎉 All tests completed successfully!")
        
    except Exception as e:
        print(f"\n💥 Test failed: {str(e)}")
        raise

# 🚀 Main execution block
if __name__ == "__main__":
    print("🚀 Advanced Python Emoji Test Suite")
    print("===================================")
    
    # 🔄 Run async tests
    asyncio.run(run_comprehensive_emoji_tests())
    
    print("\n📊 Test Summary:")
    print("✅ Advanced class patterns tested")
    print("✅ Async/await functionality verified")
    print("✅ Decorator patterns validated")
    print("✅ Context managers tested")
    print("✅ Type hints and protocols verified")
    print("✅ Exception handling tested")
    print("🎉 All Python emoji patterns ready for cleaning!")

"""

🎊 End of Advanced Python Test File

📝 This file contains comprehensive Python patterns with extensive emoji usage

🧪 Features: Classes, async/await, decorators, type hints, context managers

🎯 Perfect for testing emoji removal capabilities across all Python constructs

📊 Total emoji count: 400+ emojis in various contexts and patterns

✅ All syntax is valid Python without errors

"""