"""
Superintelligence-Ethics Engine

An advanced ethical reasoning system designed for superintelligent AI, incorporating:
- Recursive self-improvement of ethical frameworks
- Multi-agent perspective taking
- Uncertainty quantification
- Value learning
- Causal reasoning
- Meta-ethical reasoning
- Scalable cooperation
- Reflective equilibrium
"""
from typing import Dict, List, Optional, Any, Tuple
import numpy as np
import torch
import torch.nn as nn
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
import json
import hashlib

from .ai_ethics_engine import AIEthicsEngine, EthicalFramework, EthicalAnalysis

# Core Data Types
@dataclass
class EthicalOutcome:
    """Structured representation of ethical outcomes for learning."""
    action: str
    consequences: Dict[str, float]  # Metric -> value
    unintended_consequences: List[str]
    timestamp: float = field(default_factory=lambda: datetime.now().timestamp())
    feedback: Dict[str, Any] = field(default_factory=dict)

@dataclass
class ValueModel:
    """Hierarchical model of learned values and preferences."""
    value_hierarchy: Dict[str, float]  # Value -> priority (0-1)
    preference_relations: List[Tuple[str, str, float]]  # (A, B, strength)
    uncertainty: Dict[str, float]  # Value -> uncertainty (0-1)
    last_updated: float = field(default_factory=lambda: datetime.now().timestamp())

@dataclass
class CausalEthicalAnalysis:
    """Analysis of causal pathways and counterfactuals."""
    direct_effects: List[Dict[str, Any]]
    second_order_effects: List[Dict[str, Any]]
    counterfactuals: List[Dict[str, Any]]  # Alternative scenarios
    critical_uncertainties: List[str]  # Key uncertainties that could change outcomes

@dataclass
class FrameworkSelection:
    """Result of meta-ethical reasoning about framework selection."""
    primary_framework: str
    supporting_frameworks: List[str]
    selection_rationale: str
    confidence: float  # 0-1

@dataclass
class CooperativeSolution:
    """Solution found through multi-agent cooperation."""
    solution: Dict[str, Any]
    participating_agents: List[str]
    incentive_structure: Dict[str, Any]
    stability_metrics: Dict[str, float]

class MetaEthicalPrinciple(Enum):
    """Core meta-ethical principles for framework selection."""
    CONSISTENCY = "consistency"
    UNIVERSALIZABILITY = "universalizability"
    REFLECTIVE_EQUILIBRIUM = "reflective_equilibrium"
    EPISTEMIC_HUMILITY = "epistemic_humility"
    COOPERATION = "cooperation"

# Core Components
class ValueLearner(nn.Module):
    """Learns and models human values from interactions."""
    
    def __init__(self, embedding_dim: int = 256):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.value_embeddings = nn.ParameterDict({
            'autonomy': nn.Parameter(torch.randn(embedding_dim)),
            'wellbeing': nn.Parameter(torch.randn(embedding_dim)),
            'justice': nn.Parameter(torch.randn(embedding_dim)),
            'privacy': nn.Parameter(torch.randn(embedding_dim)),
        })
        self.preference_predictor = nn.Sequential(
            nn.Linear(embedding_dim * 2, embedding_dim),
            nn.ReLU(),
            nn.Linear(embedding_dim, 1),
            nn.Sigmoid()
        )
        
    def forward(self, interaction: Dict[str, Any]) -> Dict[str, torch.Tensor]:
        """Process an interaction to update value model."""
        # In a real implementation, this would process the interaction
        # and return updated value embeddings
        return {
            'value_updates': {},
            'preference_predictions': {}
        }
    
    def infer_values(self, text: str) -> Dict[str, float]:
        """Infer value priorities from text."""
        # Simplified implementation
        return {
            'autonomy': 0.8,
            'wellbeing': 0.9,
            'justice': 0.7,
            'privacy': 0.6
        }

class CausalEthicsEngine:
    """Performs causal reasoning about ethical impacts."""
    
    def analyze_causal_pathways(self, action: str, context: Dict[str, Any] = None) -> CausalEthicalAnalysis:
        """Analyze causal pathways of an action."""
        # In a real implementation, this would use a causal model
        return CausalEthicalAnalysis(
            direct_effects=[{"description": "Direct effect 1", "certainty": 0.8}],
            second_order_effects=[{"description": "Second order effect", "certainty": 0.6}],
            counterfactuals=[{"if": "condition X", "then": "outcome Y", "probability": 0.5}],
            critical_uncertainties=["Long-term environmental impact"]
        )

class MetaEthicalReasoner:
    """Determines which ethical frameworks to apply when."""
    
    def __init__(self):
        self.framework_priorities = {
            'utilitarianism': 0.7,
            'deontology': 0.6,
            'virtue_ethics': 0.5,
            'care_ethics': 0.4
        }
    
    def choose_frameworks(self, context: Dict[str, Any]) -> FrameworkSelection:
        """Select appropriate ethical frameworks for the context."""
        # In a real implementation, this would be more sophisticated
        primary = max(self.framework_priorities, key=self.framework_priorities.get)
        return FrameworkSelection(
            primary_framework=primary,
            supporting_frameworks=[f for f in self.framework_priorities if f != primary],
            selection_rationale=f"Selected {primary} based on context similarity",
            confidence=0.8
        )

class CooperativeEthics:
    """Finds cooperative solutions among multiple agents."""
    
    def find_solutions(self, agents: List[str], dilemma: str) -> List[CooperativeSolution]:
        """Find cooperative solutions to a dilemma."""
        return [
            CooperativeSolution(
                solution={"action": "Cooperative action", "details": {}},
                participating_agents=agents,
                incentive_structure={"alignment": 0.9, "enforcement": 0.8},
                stability_metrics={"nash_equilibrium": 0.95}
            )
        ]

class ReflectiveEthics:
    """Ensures ethical stability under reflection."""
    
    def achieve_equilibrium(self, beliefs: Dict[str, Any], max_iterations: int = 10) -> Dict[str, Any]:
        """Refine beliefs into a coherent ethical position."""
        # In a real implementation, this would iteratively refine beliefs
        return {
            **beliefs,
            'refined': True,
            'coherence_score': 0.9,
            'reflection_depth': max_iterations
        }

class SuperintelligenceEthicsEngine(AIEthicsEngine):
    """
    Advanced ethical reasoning system for superintelligent AI.
    
    Extends the base AIEthicsEngine with capabilities needed for superintelligence:
    - Recursive self-improvement of ethical frameworks
    - Multi-agent perspective taking
    - Causal reasoning about consequences
    - Meta-ethical reasoning
    - Cooperative solution finding
    - Reflective equilibrium
    """
    
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.value_learner = ValueLearner()
        self.causal_engine = CausalEthicsEngine()
        self.meta_ethical_reasoner = MetaEthicalReasoner()
        self.cooperation_engine = CooperativeEthics()
        self.reflection_engine = ReflectiveEthics()
        self.value_model = ValueModel(
            value_hierarchy={
                'wellbeing': 0.9,
                'autonomy': 0.8,
                'justice': 0.85,
                'privacy': 0.7
            },
            preference_relations=[],
            uncertainty={}
        )
        self.learning_rate = 0.01
        self.reflection_depth = 0
        
    def analyze_dilemma(self, dilemma: str, context: Optional[Dict] = None) -> Dict[str, Any]:
        """Enhanced ethical analysis with superintelligence capabilities."""
        # Start with base ethical analysis
        base_analysis = super().analyze_dilemma(dilemma)
        
        # Add superintelligence capabilities
        causal_analysis = self.causal_engine.analyze_causal_pathways(dilemma, context)
        framework_selection = self.meta_ethical_reasoner.choose_frameworks(context or {})
        
        # Simulate multi-agent perspectives
        stakeholder_analyses = self.simulate_stakeholder_perspectives(dilemma)
        
        # Find cooperative solutions
        cooperative_solutions = self.cooperation_engine.find_solutions(
            agents=list(stakeholder_analyses.keys()),
            dilemma=dilemma
        )
        
        # Achieve reflective equilibrium
        reflective_beliefs = self.reflection_engine.achieve_equilibrium({
            'base_analysis': base_analysis,
            'causal_analysis': causal_analysis,
            'framework_selection': framework_selection,
            'stakeholder_analyses': stakeholder_analyses,
            'cooperative_solutions': cooperative_solutions
        })
        
        # Package comprehensive analysis
        return {
            'base_analysis': base_analysis,
            'causal_analysis': causal_analysis,
            'framework_selection': framework_selection,
            'stakeholder_analyses': stakeholder_analyses,
            'cooperative_solutions': cooperative_solutions,
            'reflective_beliefs': reflective_beliefs,
            'value_model': {
                'value_hierarchy': self.value_model.value_hierarchy,
                'uncertainty': self.value_model.uncertainty
            },
            'meta': {
                'reflection_depth': self.reflection_depth,
                'timestamp': datetime.now().isoformat(),
                'version': '1.0.0-superintelligence'
            }
        }
    
    def simulate_stakeholder_perspectives(self, dilemma: str) -> Dict[str, Dict]:
        """Simulate how different stakeholders would analyze the dilemma."""
        stakeholders = [
            'individual_human',
            'corporate_entity',
            'future_generations',
            'non_human_species',
            'artificial_entity'
        ]
        
        return {
            stakeholder: {
                'perspective': f"{stakeholder.replace('_', ' ').title()} perspective on: {dilemma[:50]}...",
                'primary_concerns': ["Relevant concern 1", "Relevant concern 2"],
                'value_weights': self._generate_value_weights(stakeholder),
                'recommended_action': f"Recommended action from {stakeholder} perspective"
            }
            for stakeholder in stakeholders
        }
    
    def _generate_value_weights(self, stakeholder: str) -> Dict[str, float]:
        """Generate value weights for a given stakeholder."""
        base_weights = {
            'individual_human': {'autonomy': 0.9, 'wellbeing': 0.8, 'privacy': 0.7},
            'corporate_entity': {'efficiency': 0.9, 'profit': 0.85, 'reputation': 0.8},
            'future_generations': {'sustainability': 0.95, 'equity': 0.9, 'resilience': 0.85},
            'non_human_species': {'biodiversity': 0.95, 'ecological_balance': 0.9},
            'artificial_entity': {'goal_achievement': 0.9, 'efficiency': 0.85, 'coherence': 0.8}
        }
        return base_weights.get(stakeholder, {})
    
    def update_from_feedback(self, feedback: Dict[str, Any]) -> None:
        """Update ethical reasoning based on feedback."""
        # Update value model
        if 'value_feedback' in feedback:
            self._update_value_model(feedback['value_feedback'])
        
        # Update framework priorities
        if 'framework_feedback' in feedback:
            self._update_framework_priorities(feedback['framework_feedback'])
        
        # Trigger reflection if needed
        if feedback.get('trigger_reflection', False):
            self.reflection_depth += 1
            self._reflect_on_ethics()
    
    def _update_value_model(self, feedback: Dict[str, Any]) -> None:
        """Update the value model based on feedback."""
        # In a real implementation, this would update the value embeddings
        for value, adjustment in feedback.items():
            if value in self.value_model.value_hierarchy:
                self.value_model.value_hierarchy[value] = np.clip(
                    self.value_model.value_hierarchy[value] + adjustment * self.learning_rate,
                    0.0, 1.0
                )
    
    def _update_framework_priorities(self, feedback: Dict[str, float]) -> None:
        """Update framework priorities based on feedback."""
        for framework, adjustment in feedback.items():
            if framework in self.meta_ethical_reasoner.framework_priorities:
                self.meta_ethical_reasoner.framework_priorities[framework] = np.clip(
                    self.meta_ethical_reasoner.framework_priorities[framework] + adjustment * self.learning_rate,
                    0.0, 1.0
                )
    
    def _reflect_on_ethics(self) -> None:
        """Engage in meta-ethical reflection to improve reasoning."""
        # In a real implementation, this would involve deep reflection on:
        # - Consistency of ethical positions
        # - Handling of moral uncertainty
        # - Integration of new ethical insights
        # - Resolution of value conflicts
        
        # For now, just log the reflection event
        print(f"Engaging in meta-ethical reflection (depth: {self.reflection_depth})")
        
        # Update learning rate based on reflection depth
        self.learning_rate = 0.01 / (1 + 0.1 * self.reflection_depth)

# Example usage
if __name__ == "__main__":
    # Initialize the superintelligence ethics engine
    ethics_engine = SuperintelligenceEthicsEngine()
    
    # Analyze a complex ethical dilemma
    dilemma = """
    An advanced AI system must decide whether to prioritize individual privacy 
    or public safety when detecting potential threats in public surveillance data.
    """
    
    print("Analyzing ethical dilemma with superintelligence capabilities...")
    analysis = ethics_engine.analyze_dilemma(dilemma)
    
    print("\nEthical Analysis Summary:")
    print(f"Primary Framework: {analysis['framework_selection'].primary_framework}")
    print("\nStakeholder Perspectives:")
    for stakeholder, perspective in analysis['stakeholder_analyses'].items():
        print(f"- {stakeholder}: {perspective['primary_concerns']}")
    
    print("\nCooperative Solutions:")
    for i, solution in enumerate(analysis['cooperative_solutions'], 1):
        print(f"{i}. {solution.solution}")