import asyncio
import logging
import random
from typing import Dict, Any, List, Tuple
from enum import Enum

class ReasoningFramework(Enum):
    """Cadres de raisonnement disponibles"""
    SWOT = "swot_analysis"
    STRATEGIC = "strategic_planning"
    LOGICAL = "logical_deduction"
    SYSTEMIC = "systemic_thinking"
    CRITICAL = "critical_thinking"
    CREATIVE = "creative_problem_solving"

class ReasoningNeuron:
    """
    Neurone de raisonnement avancé avec multiples cadres d'analyse
    """
    
    def __init__(self):
        self.logger = logging.getLogger("reasoning_neuron")
        self.frameworks = list(ReasoningFramework)
        self.knowledge_base = {}
        self.thinking_models = {}
        self.analysis_cache = {}
        
    async def initialize(self):
        """Initialise le neurone de raisonnement"""
        self.logger.info("🧠 Initialisation du neurone de raisonnement...")
        
        # Chargement des modèles de pensée
        await self._load_thinking_models()
        await self._load_analysis_patterns()
        
        self.logger.info("✅ Neurone de raisonnement initialisé")
        return True

    async def analyze(self, data: Dict, context: Dict, depth: int = 3) -> Dict[str, Any]:
        """
        Analyse les données avec multiples cadres de raisonnement
        """
        self.logger.info(f"🔍 Analyse de raisonnement (profondeur: {depth})")
        
        # Sélection des cadres basée sur le contexte
        selected_frameworks = self._select_frameworks(data, context, depth)
        
        analyses = {}
        for framework in selected_frameworks:
            analysis_method = getattr(self, f"_analyze_{framework.value}")
            analyses[framework.value] = await analysis_method(data, context, depth)
        
        # Synthèse des analyses
        synthesis = await self._synthesize_analyses(analyses, data, context)
        
        return {
            'frameworks_used': [f.value for f in selected_frameworks],
            'detailed_analysis': analyses,
            'synthesis': synthesis,
            'certainty': self._calculate_certainty(analyses),
            'coherence_score': await self._calculate_coherence(analyses),
            'recommendations': await self._generate_recommendations(synthesis),
            'risk_assessment': await self._assess_risks(analyses),
            'decision_path': await self._trace_decision_path(analyses)
        }

    async def _analyze_swot_analysis(self, data: Dict, context: Dict, depth: int) -> Dict:
        """Analyse SWOT approfondie"""
        return {
            'strengths': await self._identify_strengths(data, context, depth),
            'weaknesses': await self._identify_weaknesses(data, context, depth),
            'opportunities': await self._identify_opportunities(data, context, depth),
            'threats': await self._identify_threats(data, context, depth),
            'strategic_implications': await self._derive_strategic_implications(data, context),
            'confidence': random.uniform(0.7, 0.95)
        }

    async def _analyze_strategic_planning(self, data: Dict, context: Dict, depth: int) -> Dict:
        """Planification stratégique avancée"""
        return {
            'vision': await self._define_strategic_vision(data, context),
            'objectives': await self._set_strategic_objectives(data, context, depth),
            'action_plan': await self._create_action_plan(data, context),
            'success_metrics': await self._define_success_metrics(data),
            'timeline': await self._create_strategic_timeline(data, context),
            'resource_allocation': await self._allocate_resources_strategically(data),
            'confidence': random.uniform(0.6, 0.9)
        }

    async def _analyze_logical_deduction(self, data: Dict, context: Dict, depth: int) -> Dict:
        """Déduction logique formelle"""
        premises = await self._extract_premises(data, context)
        conclusions = await self._deduce_conclusions(premises, depth)
        
        return {
            'premises': premises,
            'inferences': await self._make_inferences(premises, depth),
            'conclusions': conclusions,
            'logical_consistency': await self._check_logical_consistency(conclusions),
            'fallacies_detected': await self._detect_logical_fallacies(premises),
            'confidence': random.uniform(0.8, 0.98)
        }

    async def _analyze_systemic_thinking(self, data: Dict, context: Dict, depth: int) -> Dict:
        """Pensée systémique"""
        return {
            'system_components': await self._identify_system_components(data, context),
            'interconnections': await self._map_interconnections(data, context, depth),
            'feedback_loops': await self._identify_feedback_loops(data, context),
            'emergent_properties': await self._analyze_emergent_properties(data, context),
            'system_dynamics': await self._model_system_dynamics(data, context),
            'leverage_points': await self._identify_leverage_points(data, context),
            'confidence': random.uniform(0.7, 0.92)
        }

    async def _synthesize_analyses(self, analyses: Dict, data: Dict, context: Dict) -> Dict:
        """Synthétise les analyses de tous les cadres"""
        synthesis = {
            'key_insights': [],
            'critical_factors': [],
            'strategic_direction': '',
            'risk_level': 'medium',
            'opportunity_areas': [],
            'decision_framework': {}
        }
        
        # Agrégation des insights
        for framework, analysis in analyses.items():
            if 'strengths' in analysis:
                synthesis['key_insights'].extend(analysis['strengths'][:2])
            if 'opportunities' in analysis:
                synthesis['key_insights'].extend(analysis['opportunities'][:2])
                synthesis['opportunity_areas'].extend(analysis['opportunities'][:3])
            if 'threats' in analysis:
                synthesis['critical_factors'].extend(analysis['threats'][:2])
            if 'conclusions' in analysis:
                synthesis['key_insights'].extend(analysis['conclusions'][:2])
        
        # Détermination de la direction stratégique
        synthesis['strategic_direction'] = await self._determine_strategic_direction(analyses)
        
        # Évaluation des risques
        synthesis['risk_level'] = await self._assess_overall_risk(analyses)
        
        # Cadre de décision
        synthesis['decision_framework'] = await self._build_decision_framework(analyses)
        
        return synthesis

    def _calculate_certainty(self, analyses: Dict) -> float:
        """Calcule la certitude globale"""
        confidence_scores = []
        for analysis in analyses.values():
            if 'confidence' in analysis:
                confidence_scores.append(analysis['confidence'])
        
        return sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0.7

    async def _calculate_coherence(self, analyses: Dict) -> float:
        """Calcule le score de cohérence entre les analyses"""
        # Logique simplifiée pour l'exemple
        return min(0.95, 0.7 + (len(analyses) * 0.05))

    def _select_frameworks(self, data: Dict, context: Dict, depth: int) -> List[ReasoningFramework]:
        """Sélectionne les cadres de raisonnement appropriés"""
        # Logique de sélection basée sur le type de problème
        problem_type = data.get('type', 'general')
        
        if problem_type in ['strategic', 'planning']:
            return [ReasoningFramework.STRATEGIC, ReasoningFramework.SYSTEMIC, ReasoningFramework.SWOT]
        elif problem_type in ['logical', 'analytical']:
            return [ReasoningFramework.LOGICAL, ReasoningFramework.CRITICAL]
        elif problem_type in ['creative', 'innovation']:
            return [ReasoningFramework.CREATIVE, ReasoningFramework.SYSTEMIC]
        else:
            # Sélection par défaut
            return random.sample(self.frameworks, min(depth, len(self.frameworks)))

    # Méthodes d'implémentation (simplifiées pour l'exemple)
    async def _load_thinking_models(self):
        """Charge les modèles de pensée"""
        self.thinking_models = {
            'first_principles': {
                'description': "Décomposition aux principes fondamentaux",
                'steps': ['identify_assumptions', 'break_down_fundamentals', 'rebuild_from_scratch']
            },
            'inversion_thinking': {
                'description': "Résolution par inversion du problème",
                'steps': ['define_opposite_goal', 'identify_prevention_measures', 'invert_solution']
            }
        }

    async def _load_analysis_patterns(self):
        """Charge les patterns d'analyse"""
        self.analysis_patterns = {
            'problem_solving': ['define', 'analyze', 'generate', 'evaluate', 'implement'],
            'decision_making': ['options', 'criteria', 'evaluation', 'selection', 'execution']
        }

    async def _identify_strengths(self, data: Dict, context: Dict, depth: int) -> List[str]:
        return ["Capacité d'analyse multidimensionnelle", "Accès à des connaissances étendues"]

    async def _identify_weaknesses(self, data: Dict, context: Dict, depth: int) -> List[str]:
        return ["Dépendance à la qualité des données d'entrée", "Complexité des problèmes ambigus"]

    async def _identify_opportunities(self, data: Dict, context: Dict, depth: int) -> List[str]:
        return ["Amélioration continue par l'apprentissage", "Synergies avec d'autres systèmes"]

    async def _identify_threats(self, data: Dict, context: Dict, depth: int) -> List[str]:
        return ["Biais potentiels dans l'analyse", "Variables externes imprévisibles"]

    async def _generate_recommendations(self, synthesis: Dict) -> List[str]:
        return [
            "Adopter une approche équilibrée intégrant multiples perspectives",
            "Mettre en place des mécanismes de feedback continu",
            "Planifier des revues stratégiques périodiques"
        ]

    async def _assess_risks(self, analyses: Dict) -> Dict:
        return {
            'level': 'medium',
            'mitigation_strategies': ['Surveillance active', 'Plans de contingence'],
            'monitoring_metrics': ['Indicateurs de performance', 'Signaux d'alerte précoce']
        }

    async def get_status(self) -> Dict[str, Any]:
        """Retourne le statut du neurone"""
        return {
            'active': True,
            'frameworks_loaded': len(self.frameworks),
            'thinking_models': len(self.thinking_models),
            'analysis_cache_size': len(self.analysis_cache)
        }