"""Advanced monetization strategies for venture optimization."""

import logging
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict

from .base import ReasoningStrategy

@dataclass
class MonetizationModel:
    """Monetization model configuration."""
    name: str
    type: str
    pricing_tiers: List[Dict[str, Any]]
    features: List[str]
    constraints: List[str]
    metrics: Dict[str, float]

@dataclass
class RevenueStream:
    """Revenue stream configuration."""
    name: str
    type: str
    volume: float
    unit_economics: Dict[str, float]
    growth_rate: float
    churn_rate: float

class MonetizationOptimizer:
    """
    Advanced monetization optimization that:
    1. Designs pricing models
    2. Optimizes revenue streams
    3. Maximizes customer value
    4. Reduces churn
    5. Increases lifetime value
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize monetization optimizer."""
        self.config = config or {}
        
        # Configure optimization parameters
        self.min_revenue = self.config.get('min_revenue', 1_000_000)
        self.min_margin = self.config.get('min_margin', 0.3)
        self.max_churn = self.config.get('max_churn', 0.1)
        self.target_ltv = self.config.get('target_ltv', 1000)
        
        self.models: Dict[str, MonetizationModel] = {}
        self.streams: Dict[str, RevenueStream] = {}
        
    async def optimize_monetization(self,
                                  venture_type: str,
                                  context: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize monetization strategy."""
        try:
            # Design models
            models = await self._design_models(venture_type, context)
            
            # Optimize pricing
            pricing = await self._optimize_pricing(models, context)
            
            # Revenue optimization
            revenue = await self._optimize_revenue(pricing, context)
            
            # Value optimization
            value = await self._optimize_value(revenue, context)
            
            # Performance projections
            projections = await self._project_performance(value, context)
            
            return {
                "success": projections["annual_revenue"] >= 1_000_000,
                "models": models,
                "pricing": pricing,
                "revenue": revenue,
                "value": value,
                "projections": projections
            }
        except Exception as e:
            logging.error(f"Error in monetization optimization: {str(e)}")
            return {"success": False, "error": str(e)}

    async def _design_models(self,
                           venture_type: str,
                           context: Dict[str, Any]) -> Dict[str, Any]:
        """Design monetization models."""
        prompt = f"""
        Design monetization models:
        Venture: {venture_type}
        Context: {json.dumps(context)}
        
        Design models for:
        1. Subscription tiers
        2. Usage-based pricing
        3. Hybrid models
        4. Enterprise pricing
        5. Marketplace fees
        
        Format as:
        [Model1]
        Name: ...
        Type: ...
        Tiers: ...
        Features: ...
        Constraints: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_model_design(response["answer"])

    async def _optimize_pricing(self,
                              models: Dict[str, Any],
                              context: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize pricing strategy."""
        prompt = f"""
        Optimize pricing strategy:
        Models: {json.dumps(models)}
        Context: {json.dumps(context)}
        
        Optimize for:
        1. Market positioning
        2. Value perception
        3. Competitive dynamics
        4. Customer segments
        5. Growth potential
        
        Format as:
        [Strategy1]
        Model: ...
        Positioning: ...
        Value_Props: ...
        Segments: ...
        Growth: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_pricing_strategy(response["answer"])

    async def _optimize_revenue(self,
                              pricing: Dict[str, Any],
                              context: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize revenue streams."""
        prompt = f"""
        Optimize revenue streams:
        Pricing: {json.dumps(pricing)}
        Context: {json.dumps(context)}
        
        Optimize for:
        1. Revenue mix
        2. Growth drivers
        3. Retention factors
        4. Expansion potential
        5. Risk mitigation
        
        Format as:
        [Stream1]
        Type: ...
        Drivers: ...
        Retention: ...
        Expansion: ...
        Risks: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_revenue_optimization(response["answer"])

    async def _optimize_value(self,
                            revenue: Dict[str, Any],
                            context: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize customer value."""
        prompt = f"""
        Optimize customer value:
        Revenue: {json.dumps(revenue)}
        Context: {json.dumps(context)}
        
        Optimize for:
        1. Acquisition cost
        2. Lifetime value
        3. Churn reduction
        4. Upsell potential
        5. Network effects
        
        Format as:
        [Value1]
        Metric: ...
        Strategy: ...
        Potential: ...
        Actions: ...
        Timeline: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_value_optimization(response["answer"])

    async def _project_performance(self,
                                 value: Dict[str, Any],
                                 context: Dict[str, Any]) -> Dict[str, Any]:
        """Project monetization performance."""
        prompt = f"""
        Project performance:
        Value: {json.dumps(value)}
        Context: {json.dumps(context)}
        
        Project:
        1. Revenue growth
        2. Customer metrics
        3. Unit economics
        4. Profitability
        5. Scale effects
        
        Format as:
        [Projections]
        Revenue: ...
        Metrics: ...
        Economics: ...
        Profit: ...
        Scale: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_performance_projections(response["answer"])

    def _calculate_revenue_potential(self, model: MonetizationModel) -> float:
        """Calculate revenue potential for model."""
        base_potential = sum(
            tier.get("price", 0) * tier.get("volume", 0)
            for tier in model.pricing_tiers
        )
        
        growth_factor = 1.0 + (model.metrics.get("growth_rate", 0) / 100)
        retention_factor = 1.0 - (model.metrics.get("churn_rate", 0) / 100)
        
        return base_potential * growth_factor * retention_factor

    def _calculate_customer_ltv(self, stream: RevenueStream) -> float:
        """Calculate customer lifetime value."""
        monthly_revenue = stream.volume * stream.unit_economics.get("arpu", 0)
        churn_rate = stream.churn_rate / 100
        discount_rate = 0.1  # 10% annual discount rate
        
        if churn_rate > 0:
            ltv = monthly_revenue / churn_rate
        else:
            ltv = monthly_revenue * 12  # Assume 1 year if no churn
            
        return ltv / (1 + discount_rate)

    def get_monetization_metrics(self) -> Dict[str, Any]:
        """Get comprehensive monetization metrics."""
        return {
            "model_metrics": {
                model.name: {
                    "revenue_potential": self._calculate_revenue_potential(model),
                    "tier_count": len(model.pricing_tiers),
                    "feature_count": len(model.features),
                    "constraint_count": len(model.constraints)
                }
                for model in self.models.values()
            },
            "stream_metrics": {
                stream.name: {
                    "monthly_revenue": stream.volume * stream.unit_economics.get("arpu", 0),
                    "ltv": self._calculate_customer_ltv(stream),
                    "growth_rate": stream.growth_rate,
                    "churn_rate": stream.churn_rate
                }
                for stream in self.streams.values()
            },
            "aggregate_metrics": {
                "total_revenue_potential": sum(
                    self._calculate_revenue_potential(model)
                    for model in self.models.values()
                ),
                "average_ltv": np.mean([
                    self._calculate_customer_ltv(stream)
                    for stream in self.streams.values()
                ]) if self.streams else 0,
                "weighted_growth_rate": np.average(
                    [stream.growth_rate for stream in self.streams.values()],
                    weights=[stream.volume for stream in self.streams.values()]
                ) if self.streams else 0
            }
        }

class MonetizationStrategy(ReasoningStrategy):
    """
    Advanced monetization strategy that:
    1. Designs optimal pricing models
    2. Optimizes revenue streams
    3. Maximizes customer lifetime value
    4. Reduces churn
    5. Increases profitability
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize monetization strategy."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
        
        # Initialize optimizer with shared config
        optimizer_config = {
            'min_revenue': self.config.get('min_revenue', 1_000_000),
            'min_margin': self.config.get('min_margin', 0.3),
            'max_churn': self.config.get('max_churn', 0.1),
            'target_ltv': self.config.get('target_ltv', 1000)
        }
        self.optimizer = MonetizationOptimizer(optimizer_config)
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate monetization strategy based on query and context.
        
        Args:
            query: The monetization query
            context: Additional context and parameters
            
        Returns:
            Dict containing monetization strategy and confidence scores
        """
        try:
            # Extract venture type
            venture_type = self._extract_venture_type(query, context)
            
            # Optimize monetization
            optimization_result = await self.optimizer.optimize_monetization(
                venture_type=venture_type,
                context=context
            )
            
            # Format results
            formatted_result = self._format_strategy(optimization_result)
            
            return {
                'answer': formatted_result,
                'confidence': self._calculate_confidence(optimization_result),
                'optimization': optimization_result
            }
            
        except Exception as e:
            logging.error(f"Monetization strategy generation failed: {str(e)}")
            return {
                'error': f"Monetization strategy generation failed: {str(e)}",
                'confidence': 0.0
            }
    
    def _extract_venture_type(self, query: str, context: Dict[str, Any]) -> str:
        """Extract venture type from query and context."""
        # Use context if available
        if 'venture_type' in context:
            return context['venture_type']
        
        # Simple keyword matching
        query_lower = query.lower()
        if any(term in query_lower for term in ['ai', 'ml', 'model']):
            return 'ai_startup'
        elif any(term in query_lower for term in ['saas', 'software']):
            return 'saas'
        elif any(term in query_lower for term in ['api', 'service']):
            return 'api_service'
        elif any(term in query_lower for term in ['data', 'analytics']):
            return 'data_analytics'
        
        # Default to SaaS if unclear
        return 'saas'
    
    def _calculate_confidence(self, result: Dict[str, Any]) -> float:
        """Calculate confidence score based on optimization quality."""
        # Base confidence
        confidence = 0.5
        
        # Adjust based on optimization completeness
        if result.get('models'):
            confidence += 0.1
        if result.get('pricing'):
            confidence += 0.1
        if result.get('revenue'):
            confidence += 0.1
        if result.get('value'):
            confidence += 0.1
            
        # Adjust based on projected performance
        performance = result.get('performance', {})
        if performance.get('roi', 0) > 2.0:
            confidence += 0.1
        if performance.get('ltv', 0) > 1000:
            confidence += 0.1
            
        return min(confidence, 1.0)
    
    def _format_strategy(self, result: Dict[str, Any]) -> str:
        """Format monetization strategy into readable text."""
        sections = []
        
        # Monetization models
        if 'models' in result:
            models = result['models']
            sections.append("Monetization Models:")
            for model in models:
                sections.append(f"- {model['name']}: {model['type']}")
                if 'pricing_tiers' in model:
                    sections.append("  Pricing Tiers:")
                    for tier in model['pricing_tiers']:
                        sections.append(f"  * {tier['name']}: ${tier['price']}/mo")
        
        # Revenue optimization
        if 'revenue' in result:
            revenue = result['revenue']
            sections.append("\nRevenue Optimization:")
            for stream, details in revenue.items():
                sections.append(f"- {stream.replace('_', ' ').title()}:")
                sections.append(f"  * Projected Revenue: ${details['projected_revenue']:,.2f}")
                sections.append(f"  * Growth Rate: {details['growth_rate']*100:.1f}%")
        
        # Customer value optimization
        if 'value' in result:
            value = result['value']
            sections.append("\nCustomer Value Optimization:")
            sections.append(f"- Customer Acquisition Cost: ${value['cac']:,.2f}")
            sections.append(f"- Lifetime Value: ${value['ltv']:,.2f}")
            sections.append(f"- Churn Rate: {value['churn_rate']*100:.1f}%")
        
        # Performance projections
        if 'performance' in result:
            perf = result['performance']
            sections.append("\nPerformance Projections:")
            sections.append(f"- ROI: {perf['roi']*100:.1f}%")
            sections.append(f"- Payback Period: {perf['payback_months']:.1f} months")
            sections.append(f"- Break-even Point: ${perf['breakeven']:,.2f}")
        
        return "\n".join(sections)