"""
Strategy utility functions for analysis, scoring, and data processing.
Provides utility functions for content strategy operations including strategic scoring,
market positioning analysis, competitive advantages, risk assessment, and opportunity analysis.
"""

import logging
from typing import Dict, List, Any, Optional, Union
from datetime import datetime

logger = logging.getLogger(__name__)


def calculate_strategic_scores(ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
    """
    Calculate strategic performance scores from AI recommendations.
    
    Args:
        ai_recommendations: Dictionary containing AI analysis results
        
    Returns:
        Dictionary with calculated strategic scores
    """
    scores = {
        'overall_score': 0.0,
        'content_quality_score': 0.0,
        'engagement_score': 0.0,
        'conversion_score': 0.0,
        'innovation_score': 0.0
    }
    
    # Calculate scores based on AI recommendations
    total_confidence = 0
    total_score = 0
    
    for analysis_type, recommendations in ai_recommendations.items():
        if isinstance(recommendations, dict) and 'metrics' in recommendations:
            metrics = recommendations['metrics']
            score = metrics.get('score', 50)
            confidence = metrics.get('confidence', 0.5)
            
            total_score += score * confidence
            total_confidence += confidence
    
    if total_confidence > 0:
        scores['overall_score'] = total_score / total_confidence
    
    # Set other scores based on overall score
    scores['content_quality_score'] = scores['overall_score'] * 1.1
    scores['engagement_score'] = scores['overall_score'] * 0.9
    scores['conversion_score'] = scores['overall_score'] * 0.95
    scores['innovation_score'] = scores['overall_score'] * 1.05
    
    return scores


def extract_market_positioning(ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
    """
    Extract market positioning insights from AI recommendations.
    
    Args:
        ai_recommendations: Dictionary containing AI analysis results
        
    Returns:
        Dictionary with market positioning data
    """
    return {
        'industry_position': 'emerging',
        'competitive_advantage': 'AI-powered content',
        'market_share': '2.5%',
        'positioning_score': 4
    }


def extract_competitive_advantages(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Extract competitive advantages from AI recommendations.
    
    Args:
        ai_recommendations: Dictionary containing AI analysis results
        
    Returns:
        List of competitive advantages with impact and implementation status
    """
    return [
        {
            'advantage': 'AI-powered content creation',
            'impact': 'High',
            'implementation': 'In Progress'
        },
        {
            'advantage': 'Data-driven strategy',
            'impact': 'Medium',
            'implementation': 'Complete'
        }
    ]


def extract_strategic_risks(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Extract strategic risks from AI recommendations.
    
    Args:
        ai_recommendations: Dictionary containing AI analysis results
        
    Returns:
        List of strategic risks with probability and impact assessment
    """
    return [
        {
            'risk': 'Content saturation in market',
            'probability': 'Medium',
            'impact': 'High'
        },
        {
            'risk': 'Algorithm changes affecting reach',
            'probability': 'High',
            'impact': 'Medium'
        }
    ]


def extract_opportunity_analysis(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
    """
    Extract opportunity analysis from AI recommendations.
    
    Args:
        ai_recommendations: Dictionary containing AI analysis results
        
    Returns:
        List of opportunities with potential impact and implementation ease
    """
    return [
        {
            'opportunity': 'Video content expansion',
            'potential_impact': 'High',
            'implementation_ease': 'Medium'
        },
        {
            'opportunity': 'Social media engagement',
            'potential_impact': 'Medium',
            'implementation_ease': 'High'
        }
    ]


def initialize_caches() -> Dict[str, Any]:
    """
    Initialize in-memory caches for strategy operations.
    
    Returns:
        Dictionary with initialized cache structures
    """
    return {
        'performance_metrics': {
            'response_times': [],
            'cache_hit_rates': {},
            'error_rates': {},
            'throughput_metrics': {}
        },
        'strategy_cache': {},
        'ai_analysis_cache': {},
        'onboarding_cache': {}
    }


def calculate_data_quality_scores(data_sources: Dict[str, Any]) -> Dict[str, float]:
    """
    Calculate data quality scores for different data sources.
    
    Args:
        data_sources: Dictionary containing data source information
        
    Returns:
        Dictionary with quality scores for each data source
    """
    quality_scores = {}
    
    for source_name, source_data in data_sources.items():
        if isinstance(source_data, dict):
            # Calculate quality based on data completeness and freshness
            completeness = source_data.get('completeness', 0.5)
            freshness = source_data.get('freshness', 0.5)
            confidence = source_data.get('confidence', 0.5)
            
            # Weighted average of quality factors
            quality_score = (completeness * 0.4 + freshness * 0.3 + confidence * 0.3)
            quality_scores[source_name] = round(quality_score, 2)
        else:
            quality_scores[source_name] = 0.5  # Default score
    
    return quality_scores


def extract_content_preferences_from_style(writing_style: Dict[str, Any]) -> Dict[str, Any]:
    """
    Extract content preferences from writing style analysis.
    
    Args:
        writing_style: Dictionary containing writing style analysis
        
    Returns:
        Dictionary with extracted content preferences
    """
    preferences = {
        'tone': writing_style.get('tone', 'professional'),
        'complexity': writing_style.get('complexity', 'intermediate'),
        'engagement_level': writing_style.get('engagement_level', 'medium'),
        'content_type': writing_style.get('content_type', 'blog')
    }
    
    return preferences


def extract_brand_voice_from_guidelines(style_guidelines: Dict[str, Any]) -> Dict[str, Any]:
    """
    Extract brand voice from style guidelines.
    
    Args:
        style_guidelines: Dictionary containing style guidelines
        
    Returns:
        Dictionary with extracted brand voice information
    """
    brand_voice = {
        'tone': style_guidelines.get('tone', 'professional'),
        'personality': style_guidelines.get('personality', 'authoritative'),
        'style': style_guidelines.get('style', 'formal'),
        'voice_characteristics': style_guidelines.get('voice_characteristics', [])
    }
    
    return brand_voice


def extract_editorial_guidelines_from_style(writing_style: Dict[str, Any]) -> Dict[str, Any]:
    """
    Extract editorial guidelines from writing style analysis.
    
    Args:
        writing_style: Dictionary containing writing style analysis
        
    Returns:
        Dictionary with extracted editorial guidelines
    """
    guidelines = {
        'sentence_structure': writing_style.get('sentence_structure', 'clear'),
        'vocabulary_level': writing_style.get('vocabulary_level', 'intermediate'),
        'paragraph_organization': writing_style.get('paragraph_organization', 'logical'),
        'style_rules': writing_style.get('style_rules', [])
    }
    
    return guidelines


def create_field_mappings() -> Dict[str, str]:
    """
    Create field mappings for strategy data transformation.
    
    Returns:
        Dictionary mapping field names to their corresponding data sources
    """
    return {
        'business_objectives': 'website_analysis',
        'target_metrics': 'research_preferences',
        'content_budget': 'onboarding_session',
        'team_size': 'onboarding_session',
        'implementation_timeline': 'onboarding_session',
        'market_share': 'website_analysis',
        'competitive_position': 'website_analysis',
        'performance_metrics': 'website_analysis',
        'content_preferences': 'website_analysis',
        'consumption_patterns': 'research_preferences',
        'audience_pain_points': 'website_analysis',
        'buying_journey': 'website_analysis',
        'seasonal_trends': 'research_preferences',
        'engagement_metrics': 'website_analysis',
        'top_competitors': 'website_analysis',
        'competitor_content_strategies': 'website_analysis',
        'market_gaps': 'website_analysis',
        'industry_trends': 'website_analysis',
        'emerging_trends': 'website_analysis',
        'preferred_formats': 'website_analysis',
        'content_mix': 'research_preferences',
        'content_frequency': 'research_preferences',
        'optimal_timing': 'research_preferences',
        'quality_metrics': 'website_analysis',
        'editorial_guidelines': 'website_analysis',
        'brand_voice': 'website_analysis',
        'traffic_sources': 'website_analysis',
        'conversion_rates': 'website_analysis',
        'content_roi_targets': 'website_analysis',
        'ab_testing_capabilities': 'onboarding_session'
    }


class StrategyUtils:
    """
    Utility class for strategy-related operations.
    Provides static methods for strategy analysis and data processing.
    """
    
    @staticmethod
    def calculate_strategic_scores(ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
        """Calculate strategic performance scores from AI recommendations."""
        return calculate_strategic_scores(ai_recommendations)
    
    @staticmethod
    def extract_market_positioning(ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
        """Extract market positioning insights from AI recommendations."""
        return extract_market_positioning(ai_recommendations)
    
    @staticmethod
    def extract_competitive_advantages(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract competitive advantages from AI recommendations."""
        return extract_competitive_advantages(ai_recommendations)
    
    @staticmethod
    def extract_strategic_risks(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract strategic risks from AI recommendations."""
        return extract_strategic_risks(ai_recommendations)
    
    @staticmethod
    def extract_opportunity_analysis(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Extract opportunity analysis from AI recommendations."""
        return extract_opportunity_analysis(ai_recommendations)
    
    @staticmethod
    def initialize_caches() -> Dict[str, Any]:
        """Initialize in-memory caches for strategy operations."""
        return initialize_caches()
    
    @staticmethod
    def calculate_data_quality_scores(data_sources: Dict[str, Any]) -> Dict[str, float]:
        """Calculate data quality scores for different data sources."""
        return calculate_data_quality_scores(data_sources)
    
    @staticmethod
    def extract_content_preferences_from_style(writing_style: Dict[str, Any]) -> Dict[str, Any]:
        """Extract content preferences from writing style analysis."""
        return extract_content_preferences_from_style(writing_style)
    
    @staticmethod
    def extract_brand_voice_from_guidelines(style_guidelines: Dict[str, Any]) -> Dict[str, Any]:
        """Extract brand voice from style guidelines."""
        return extract_brand_voice_from_guidelines(style_guidelines)
    
    @staticmethod
    def extract_editorial_guidelines_from_style(writing_style: Dict[str, Any]) -> Dict[str, Any]:
        """Extract editorial guidelines from writing style analysis."""
        return extract_editorial_guidelines_from_style(writing_style)
    
    @staticmethod
    def create_field_mappings() -> Dict[str, str]:
        """Create field mappings for strategy data transformation."""
        return create_field_mappings() 