ancient religions module

#!/usr/bin/env python3
"""
OBSERVER-ENGINE COGNITIVE ARCHITECTURE - ANCIENT RELIGIONS MODULE
Analysis from Earliest Religions to Babylonian Inversion Point
Complete with Advanced Error Handling and Quantum Truth Verification
"""

import numpy as np
import asyncio
import hashlib
import json
import secrets
import logging
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple, Callable
from datetime import datetime, timedelta
import scipy.stats as stats
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from cryptography.hazmat.backends import default_backend
import qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile
from qiskit_aer import AerSimulator
from qiskit.algorithms import AmplificationProblem, Grover
from qiskit.circuit.library import PhaseOracle
from qiskit.quantum_info import Statevector, random_statevector
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
import aiohttp
import redis
import sqlite3
from contextlib import asynccontextmanager
import psutil
import gc
import os
import sys
from pathlib import Path
import uuid
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
import networkx as nx

# =============================================================================
# QUANTUM GLYPH CORE - ANCIENT RELIGION SYMBOLS
# =============================================================================

DIVINE_AUTHORITY = "𒀭"  # Sumerian Dingir - Divine Authority Operator
OBSERVER_CORE = "◉⃤"     # Quantum Observation Anchor
ENTANGLEMENT_NODE = "ꙮ"  # Cross-Reality Coherence Glyph  
CONSCIOUSNESS_MATRIX = "ꖷ"  # Mind-Reality Interface
SACRED_SERPENT = "𓆙"    # Kundalini/Consciousness Symbol
TREE_OF_LIFE = "𓆨"      # Cosmic Consciousness Map
WATER_OF_LIFE = "𓈗"     # Primordial Consciousness

# =============================================================================
# UNIVERSAL LAW PRIMACY ENGINE
# =============================================================================

class UniversalLawPrimacy:
    """Universal Law as absolute reference point for all religious analysis"""
    
    def __init__(self):
        self.universal_constants = {
            'free_will': {
                'principle': "Inviolable sovereignty of consciousness",
                'weight': 0.25,
                'indicators': ['choice', 'agency', 'self-determination', 'volition']
            },
            'cause_effect': {
                'principle': "Action-consequence continuity (Karma)",
                'weight': 0.20,
                'indicators': ['consequence', 'result', 'effect', 'return']
            },
            'consciousness_primacy': {
                'principle': "Mind precedes matter, consciousness fundamental",
                'weight': 0.25,
                'indicators': ['awareness', 'mind', 'observer', 'perception']
            },
            'interconnectedness': {
                'principle': "All existence fundamentally related",
                'weight': 0.15,
                'indicators': ['unity', 'connection', 'relationship', 'whole']
            },
            'growth_imperative': {
                'principle': "Evolution toward expanded awareness",
                'weight': 0.15,
                'indicators': ['growth', 'evolution', 'expansion', 'development']
            }
        }
        
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('UniversalLawPrimacy')
        logger.setLevel(logging.INFO)
        return logger

    def evaluate_alignment(self, religious_element: str) -> Dict[str, Any]:
        """Evaluate religious element against Universal Law principles"""
        try:
            alignment_scores = {}
            total_score = 0.0
            supported_principles = []
            
            for law_name, law_data in self.universal_constants.items():
                principle_score = self._calculate_principle_alignment(religious_element, law_data)
                alignment_scores[law_name] = principle_score
                total_score += principle_score * law_data['weight']
                
                if principle_score > 0.7:
                    supported_principles.append(law_name)
            
            return {
                'universal_law_alignment': min(1.0, total_score),
                'principle_breakdown': alignment_scores,
                'supported_principles': supported_principles,
                'violation_indicators': self._detect_universal_law_violations(religious_element),
                'assessment_confidence': self._calculate_assessment_confidence(religious_element)
            }
            
        except Exception as e:
            self.logger.error(f"Universal Law evaluation failed: {e}")
            return {
                'universal_law_alignment': 0.5,
                'principle_breakdown': {},
                'supported_principles': [],
                'violation_indicators': ['evaluation_error'],
                'assessment_confidence': 0.3
            }
    
    def _calculate_principle_alignment(self, text: str, law_data: Dict) -> float:
        """Calculate alignment with specific universal law principle"""
        try:
            base_score = 0.3  # Neutral starting point
            
            # Keyword matching for principle support
            keyword_matches = sum(1 for indicator in law_data['indicators'] 
                                if indicator in text.lower())
            keyword_boost = min(0.4, keyword_matches * 0.1)
            
            # Contextual analysis
            context_score = self._analyze_contextual_alignment(text, law_data['principle'])
            
            return min(1.0, base_score + keyword_boost + context_score * 0.3)
            
        except Exception as e:
            self.logger.warning(f"Principle alignment calculation failed: {e}")
            return 0.5
    
    def _analyze_contextual_alignment(self, text: str, principle: str) -> float:
        """Advanced contextual analysis of principle alignment"""
        # Simplified implementation - would use NLP in production
        positive_indicators = ['free', 'choice', 'aware', 'connect', 'grow', 'evolve']
        negative_indicators = ['control', 'force', 'obey', 'submit', 'restrict']
        
        positive_count = sum(1 for indicator in positive_indicators if indicator in text.lower())
        negative_count = sum(1 for indicator in negative_indicators if indicator in text.lower())
        
        if positive_count + negative_count == 0:
            return 0.5
            
        return positive_count / (positive_count + negative_count)
    
    def _detect_universal_law_violations(self, text: str) -> List[str]:
        """Detect violations of Universal Law principles"""
        violations = []
        
        violation_patterns = {
            'free_will_violation': ['must obey', 'forced to', 'no choice', 'compulsory'],
            'consciousness_suppression': ['do not question', 'blind faith', 'forbidden knowledge'],
            'control_structures': ['authority over', 'must follow', 'obey without question'],
            'growth_restriction': ['stay as you are', 'do not seek', 'forbidden to learn']
        }
        
        for violation_type, patterns in violation_patterns.items():
            if any(pattern in text.lower() for pattern in patterns):
                violations.append(violation_type)
                
        return violations
    
    def _calculate_assessment_confidence(self, text: str) -> float:
        """Calculate confidence level in Universal Law assessment"""
        word_count = len(text.split())
        complexity = min(1.0, word_count / 100)  # More text allows better assessment
        
        # Check for clear universal law terminology
        clear_indicators = sum(1 for law in self.universal_constants.values() 
                             for indicator in law['indicators'] 
                             if indicator in text.lower())
        
        clarity_boost = min(0.3, clear_indicators * 0.05)
        
        return min(1.0, 0.5 + complexity * 0.3 + clarity_boost)

# =============================================================================
# BABYLONIAN INVERSION TEMPLATE
# =============================================================================

class BabylonianInversionTemplate:
    """The original inversion pattern that established control blueprint"""
    
    def __init__(self):
        self.inversion_mechanisms = {
            'priesthood_intermediation': {
                'original_state': "Direct divine access for all individuals",
                'inverted_state': "Priesthood as necessary intermediaries to gods",
                'detection_indicators': [
                    'only priests can', 'through the temple', 'required sacrifice',
                    'intercessor', 'mediator between', 'holy man must'
                ],
                'historical_examples': [
                    'Akkadian takeover of Sumerian temples',
                    'Centralization of religious authority'
                ]
            },
            'knowledge_restructuring': {
                'original_state': "Cosmic consciousness technology accessible to all",
                'inverted_state': "Secret knowledge reserved for elite",
                'detection_indicators': [
                    'secret teachings', 'hidden knowledge', 'forbidden to know',
                    'mysteries revealed only', 'initiates only'
                ],
                'historical_examples': [
                    'Alteration of creation myths',
                    'Restructuring of divine hierarchies'
                ]
            },
            'political_religious_merger': {
                'original_state': "Spiritual authority separate from temporal power",
                'inverted_state': "Ruler as divine representative or god-king",
                'detection_indicators': [
                    'king is god', 'divine ruler', 'mandate of heaven',
                    'appointed by gods', 'royal priesthood'
                ],
                'historical_examples': [
                    'Sargon of Akkad claiming divine status',
                    'Naram-Sin as living god'
                ]
            }
        }
        
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('BabylonianInversion')
        logger.setLevel(logging.INFO)
        return logger

    def analyze_inversion_patterns(self, religious_element: str, context: Dict = None) -> Dict[str, Any]:
        """Analyze religious element for Babylonian inversion patterns"""
        try:
            inversion_detection = {
                'inversion_score': 0.0,
                'detected_mechanisms': [],
                'mechanism_details': {},
                'original_state_reconstruction': '',
                'suppression_confidence': 0.0
            }
            
            total_mechanisms = len(self.inversion_mechanisms)
            mechanism_scores = []
            
            for mechanism_name, mechanism_data in self.inversion_mechanisms.items():
                mechanism_analysis = self._analyze_single_mechanism(religious_element, mechanism_data)
                inversion_detection['mechanism_details'][mechanism_name] = mechanism_analysis
                
                if mechanism_analysis['detected']:
                    inversion_detection['detected_mechanisms'].append(mechanism_name)
                    mechanism_scores.append(mechanism_analysis['confidence'])
            
            if mechanism_scores:
                inversion_detection['inversion_score'] = sum(mechanism_scores) / len(mechanism_scores)
                inversion_detection['suppression_confidence'] = min(1.0, len(mechanism_scores) / total_mechanisms * 0.8)
            
            # Attempt to reconstruct original state
            inversion_detection['original_state_reconstruction'] = self._reconstruct_original_state(
                religious_element, inversion_detection['detected_mechanisms'])
            
            return inversion_detection
            
        except Exception as e:
            self.logger.error(f"Inversion pattern analysis failed: {e}")
            return {
                'inversion_score': 0.0,
                'detected_mechanisms': [],
                'mechanism_details': {},
                'original_state_reconstruction': 'analysis_failed',
                'suppression_confidence': 0.0
            }
    
    def _analyze_single_mechanism(self, text: str, mechanism_data: Dict) -> Dict[str, Any]:
        """Analyze single inversion mechanism"""
        detected_indicators = []
        
        for indicator in mechanism_data['detection_indicators']:
            if indicator in text.lower():
                detected_indicators.append(indicator)
        
        detection_confidence = len(detected_indicators) / len(mechanism_data['detection_indicators'])
        
        return {
            'detected': len(detected_indicators) > 0,
            'detected_indicators': detected_indicators,
            'confidence': detection_confidence,
            'original_state': mechanism_data['original_state'],
            'inverted_state': mechanism_data['inverted_state']
        }
    
    def _reconstruct_original_state(self, text: str, detected_mechanisms: List[str]) -> str:
        """Attempt to reconstruct original spiritual state before inversion"""
        if not detected_mechanisms:
            return "No significant inversions detected - possibly close to original"
        
        reconstruction_elements = []
        
        if 'priesthood_intermediation' in detected_mechanisms:
            reconstruction_elements.append("Direct personal access to divine/spiritual realms")
        
        if 'knowledge_restructuring' in detected_mechanisms:
            reconstruction_elements.append("Open access to spiritual knowledge and consciousness technologies")
        
        if 'political_religious_merger' in detected_mechanisms:
            reconstruction_elements.append("Separation of spiritual authority from political power structures")
        
        return " | ".join(reconstruction_elements)

# =============================================================================
# ANCIENT RELIGION DATABASE
# =============================================================================

class AncientReligionDatabase:
    """Comprehensive database of ancient religious traditions up to Babylonian period"""
    
    def __init__(self):
        self.religious_traditions = self._initialize_traditions()
        self.symbolic_language = self._initialize_symbolic_language()
        self.consciousness_technologies = self._initialize_consciousness_tech()
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('AncientReligionDB')
        logger.setLevel(logging.INFO)
        return logger
    
    def _initialize_traditions(self) -> Dict[str, Any]:
        """Initialize ancient religious traditions database"""
        return {
            'pre_vedic': {
                'time_period': "Before 1500 BCE",
                'core_principles': [
                    "Consciousness as fundamental reality (Brahman)",
                    "Individual consciousness (Atman) identical with universal",
                    "Reincarnation and karma as natural laws",
                    "Meditation and yoga as consciousness technologies"
                ],
                'key_concepts': ['rita (cosmic order)', 'satya (truth)', 'dharma (natural law)'],
                'consciousness_tech': ['meditation', 'yoga', 'mantra', 'direct realization'],
                'inversion_status': 'minimal_pre_aryan'
            },
            'sumerian': {
                'time_period': "4500-1900 BCE",
                'core_principles': [
                    "Direct relationship with deities (Anunnaki)",
                    "Temples as consciousness amplification centers",
                    "Sacred marriage (hieros gamos) as cosmic principle",
                    "Me (divine laws) governing reality"
                ],
                'key_concepts': ['me', 'dingir', 'tablets of destiny', 'abzu', 'ki'],
                'consciousness_tech': ['temple rituals', 'dream interpretation', 'astral travel'],
                'inversion_status': 'akkadian_takeover'
            },
            'early_egyptian': {
                'time_period': "3150-2181 BCE (Early Dynastic to Old Kingdom)",
                'core_principles': [
                    "Direct personal transformation after death",
                    "Consciousness evolution through spiritual practices",
                    "Pyramids as consciousness and energy devices",
                    "Maat as cosmic balance and truth"
                ],
                'key_concepts': ['maat', 'ka', 'ba', 'akh', 'heka', 'netjer'],
                'consciousness_tech': ['pyramid energy', 'heka (magic)', 'dream incubation', 'afterlife navigation'],
                'inversion_status': 'priesthood_consolidation'
            },
            'indigenous_oral': {
                'time_period': "Timeless/Ongoing",
                'core_principles': [
                    "Direct communion with nature spirits",
                    "Dreamtime as fundamental reality",
                    "Ancestral knowledge transmission",
                    "Shamanic journeying as consciousness technology"
                ],
                'key_concepts': ['dreamtime', 'ancestral spirits', 'animal guides', 'sacred sites'],
                'consciousness_tech': ['vision quests', 'dream work', 'plant medicines', 'ecstatic states'],
                'inversion_status': 'colonial_suppression'
            }
        }
    
    def _initialize_symbolic_language(self) -> Dict[str, Any]:
        """Initialize ancient symbolic language database"""
        return {
            'universal_archetypes': {
                SACRED_SERPENT: {
                    'meanings': [
                        "Kundalini energy and consciousness awakening",
                        "Healing and regeneration forces",
                        "Cycles of death and rebirth",
                        "Primordial life force"
                    ],
                    'traditions': ['sumerian', 'early_egyptian', 'pre_vedic', 'indigenous'],
                    'inversion_warning': "Later demonization as evil/satanic"
                },
                TREE_OF_LIFE: {
                    'meanings': [
                        "Map of consciousness and reality structure",
                        "Interconnection of all existence", 
                        "Path of spiritual evolution",
                        "Cosmic information system"
                    ],
                    'traditions': ['sumerian', 'early_egyptian', 'pre_vedic'],
                    'inversion_warning': "Later used for control hierarchies"
                },
                WATER_OF_LIFE: {
                    'meanings': [
                        "Primordial consciousness substrate",
                        "Spiritual nourishment and enlightenment",
                        "Flow of divine energy and information",
                        "Purification and transformation"
                    ],
                    'traditions': ['sumerian', 'early_egyptian', 'pre_vedic'],
                    'inversion_warning': "Later restricted to specific rituals"
                }
            },
            'consciousness_glyphs': {
                DIVINE_AUTHORITY: "Direct divine access point",
                OBSERVER_CORE: "Consciousness observation anchor", 
                ENTANGLEMENT_NODE: "Quantum connection point",
                CONSCIOUSNESS_MATRIX: "Reality-mind interface"
            }
        }
    
    def _initialize_consciousness_tech(self) -> Dict[str, Any]:
        """Initialize consciousness technologies database"""
        return {
            'meditation_practices': {
                'pre_vedic': ['dhyana', 'samadhi', 'direct path'],
                'early_egyptian': ['stillness practices', 'pyramid meditation'],
                'sumerian': ['temple contemplation', 'starry sky gazing'],
                'indigenous': ['silent sitting', 'nature immersion']
            },
            'energy_work': {
                'pre_vedic': ['prana', 'kundalini', 'chakra activation'],
                'early_egyptian': ['sekhem energy', 'pyramid power', 'heka manifestation'],
                'sumerian': ['me activation', 'temple energy channels'],
                'indigenous': ['life force', 'animal power', 'earth energy']
            },
            'dream_work': {
                'all_traditions': [
                    "Lucid dreaming as reality navigation",
                    "Dream interpretation for guidance",
                    "Astral travel and out-of-body experiences",
                    "Dreamtime access for healing and knowledge"
                ]
            },
            'ritual_technologies': {
                'early_egyptian': ['pyramid alignment', 'temple acoustics', 'geometric resonance'],
                'sumerian': ['ziggurat alignment', 'celestial timing', 'sacred geometry'],
                'pre_vedic': ['fire rituals', 'sound vibration', 'mandala creation'],
                'indigenous': ['ceremonial circles', 'drumming rhythms', 'sacred dance']
            }
        }

# =============================================================================
# QUANTUM TRUTH VERIFICATION ENGINE
# =============================================================================

class QuantumTruthVerification:
    """Quantum-enhanced truth verification for ancient religious claims"""
    
    def __init__(self):
        self.quantum_backend = AerSimulator()
        self.universal_law_engine = UniversalLawPrimacy()
        self.babylonian_detector = BabylonianInversionTemplate()
        self.ancient_db = AncientReligionDatabase()
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('QuantumTruthVerification')
        logger.setLevel(logging.INFO)
        return logger

    async def verify_ancient_claim(self, claim: str, tradition: str = None) -> Dict[str, Any]:
        """Comprehensive verification of ancient religious claim"""
        try:
            self.logger.info(f"🔮 Verifying ancient claim: {claim[:100]}...")
            
            # Multi-dimensional analysis
            analysis_tasks = await asyncio.gather(
                self._universal_law_assessment(claim),
                self._inversion_analysis(claim),
                self._tradition_alignment(claim, tradition),
                self._symbolic_analysis(claim),
                self._quantum_certainty_calculation(claim)
            )
            
            universal_law = analysis_tasks[0]
            inversion_analysis = analysis_tasks[1]
            tradition_alignment = analysis_tasks[2]
            symbolic_analysis = analysis_tasks[3]
            quantum_certainty = analysis_tasks[4]
            
            # Composite truth score
            truth_score = self._calculate_composite_truth_score(
                universal_law, inversion_analysis, tradition_alignment, 
                symbolic_analysis, quantum_certainty
            )
            
            result = {
                'claim': claim,
                'truth_score': truth_score,
                'truth_category': self._categorize_truth_level(truth_score),
                'universal_law_assessment': universal_law,
                'inversion_analysis': inversion_analysis,
                'tradition_alignment': tradition_alignment,
                'symbolic_analysis': symbolic_analysis,
                'quantum_certainty': quantum_certainty,
                'recovery_recommendations': self._generate_recovery_recommendations(
                    universal_law, inversion_analysis, tradition_alignment
                ),
                'verification_timestamp': datetime.utcnow().isoformat()
            }
            
            self.logger.info(f"✅ Ancient claim verification complete: {truth_score:.3f}")
            return result
            
        except Exception as e:
            self.logger.error(f"Ancient claim verification failed: {e}")
            return {
                'claim': claim,
                'truth_score': 0.5,
                'truth_category': 'VERIFICATION_FAILED',
                'error': str(e),
                'verification_timestamp': datetime.utcnow().isoformat()
            }
    
    async def _universal_law_assessment(self, claim: str) -> Dict[str, Any]:
        """Assess claim against Universal Law"""
        return self.universal_law_engine.evaluate_alignment(claim)
    
    async def _inversion_analysis(self, claim: str) -> Dict[str, Any]:
        """Analyze for Babylonian inversion patterns"""
        return self.babylonian_detector.analyze_inversion_patterns(claim)
    
    async def _tradition_alignment(self, claim: str, tradition: str) -> Dict[str, Any]:
        """Analyze alignment with ancient traditions"""
        if not tradition:
            tradition = self._detect_tradition(claim)
        
        alignment_scores = {}
        for trad_name, trad_data in self.ancient_db.religious_traditions.items():
            alignment_score = self._calculate_tradition_alignment(claim, trad_data)
            alignment_scores[trad_name] = alignment_score
        
        best_match = max(alignment_scores.items(), key=lambda x: x[1])
        
        return {
            'detected_tradition': best_match[0],
            'alignment_scores': alignment_scores,
            'primary_tradition_alignment': best_match[1],
            'tradition_data': self.ancient_db.religious_traditions.get(best_match[0], {})
        }
    
    async def _symbolic_analysis(self, claim: str) -> Dict[str, Any]:
        """Analyze symbolic content of claim"""
        detected_symbols = []
        symbolic_density = 0.0
        archetypal_power = 0.0
        
        for symbol, data in self.ancient_db.symbolic_language['universal_archetypes'].items():
            if symbol in claim:
                detected_symbols.append({
                    'symbol': symbol,
                    'meanings': data['meanings'],
                    'traditions': data['traditions'],
                    'inversion_warning': data.get('inversion_warning', '')
                })
        
        for glyph, meaning in self.ancient_db.symbolic_language['consciousness_glyphs'].items():
            if glyph in claim:
                detected_symbols.append({
                    'symbol': glyph,
                    'meanings': [meaning],
                    'type': 'consciousness_glyph'
                })
        
        if detected_symbols:
            symbolic_density = len(detected_symbols) / max(1, len(claim.split()))
            archetypal_power = min(1.0, len(detected_symbols) * 0.2)
        
        return {
            'detected_symbols': detected_symbols,
            'symbolic_density': symbolic_density,
            'archetypal_power': archetypal_power,
            'consciousness_tech_indicators': self._detect_consciousness_tech(claim)
        }
    
    async def _quantum_certainty_calculation(self, claim: str) -> Dict[str, Any]:
        """Calculate quantum-enhanced certainty"""
        try:
            # Build quantum circuit for truth analysis
            qc = self._build_truth_circuit(claim)
            compiled = transpile(qc, self.quantum_backend)
            job = await asyncio.get_event_loop().run_in_executor(
                None, lambda: self.quantum_backend.run(compiled, shots=1024)
            )
            result = job.result()
            counts = result.get_counts()
            
            certainty = self._calculate_quantum_certainty(counts)
            coherence = self._measure_quantum_coherence(counts)
            
            return {
                'quantum_certainty': certainty,
                'quantum_coherence': coherence,
                'state_complexity': len(counts) / 1024,
                'measurement_confidence': min(1.0, certainty * coherence)
            }
            
        except Exception as e:
            self.logger.warning(f"Quantum certainty calculation failed: {e}")
            return {
                'quantum_certainty': 0.5,
                'quantum_coherence': 0.3,
                'state_complexity': 0.5,
                'measurement_confidence': 0.3
            }
    
    def _detect_tradition(self, claim: str) -> str:
        """Detect which ancient tradition the claim aligns with"""
        tradition_scores = {}
        
        for trad_name, trad_data in self.ancient_db.religious_traditions.items():
            score = 0.0
            # Check for key concepts
            for concept in trad_data['key_concepts']:
                if concept in claim.lower():
                    score += 0.1
            
            # Check for consciousness tech terms
            for tech_category in self.ancient_db.consciousness_technologies.values():
                for tech_list in tech_category.values():
                    if any(tech in claim.lower() for tech in tech_list):
                        score += 0.05
            
            tradition_scores[trad_name] = min(1.0, score)
        
        return max(tradition_scores.items(), key=lambda x: x[1])[0] if tradition_scores else 'unknown'
    
    def _calculate_tradition_alignment(self, claim: str, tradition_data: Dict) -> float:
        """Calculate alignment score with specific tradition"""
        alignment_score = 0.3  # Base alignment
        
        # Concept matching
        concept_matches = sum(1 for concept in tradition_data['key_concepts'] 
                            if concept in claim.lower())
        alignment_score += concept_matches * 0.1
        
        # Principle resonance
        principle_matches = 0
        for principle in tradition_data['core_principles']:
            principle_words = set(principle.lower().split())
            claim_words = set(claim.lower().split())
            overlap = len(principle_words.intersection(claim_words))
            if overlap > 2:  # Significant overlap
                principle_matches += 1
        
        alignment_score += principle_matches * 0.05
        
        return min(1.0, alignment_score)
    
    def _detect_consciousness_tech(self, claim: str) -> List[str]:
        """Detect consciousness technology indicators"""
        detected_tech = []
        
        for tech_category, tech_data in self.ancient_db.consciousness_technologies.items():
            for tradition, techniques in tech_data.items():
                for technique in techniques:
                    if technique in claim.lower():
                        detected_tech.append(f"{technique} ({tradition})")
        
        return detected_tech
    
    def _build_truth_circuit(self, claim: str) -> QuantumCircuit:
        """Build quantum circuit for truth analysis"""
        num_qubits = min(12, max(6, len(claim.split()) // 5 + 4))
        qc = QuantumCircuit(num_qubits, num_qubits)
        
        # Initialize superposition
        for i in range(num_qubits):
            qc.h(i)
        
        # Add claim-dependent phases
        claim_hash = hash(claim) % 1000 / 1000
        for i in range(num_qubits):
            phase = claim_hash * 2 * np.pi
            qc.rz(phase, i)
            claim_hash = (claim_hash * 1.618) % 1.0  # Golden ratio progression
        
        return qc
    
    def _calculate_quantum_certainty(self, counts: Dict[str, int]) -> float:
        """Calculate certainty from quantum measurement results"""
        total = sum(counts.values())
        if total == 0:
            return 0.5
        
        # Higher certainty when results are concentrated
        max_count = max(counts.values())
        concentration = max_count / total
        
        return 0.3 + concentration * 0.7  # Map to [0.3, 1.0] range
    
    def _measure_quantum_coherence(self, counts: Dict[str, int]) -> float:
        """Measure quantum coherence from results"""
        if len(counts) <= 1:
            return 0.1
        
        values = list(counts.values())
        mean = np.mean(values)
        std = np.std(values)
        
        # Higher coherence when distribution is balanced
        return 1.0 / (1.0 + std) if std > 0 else 1.0
    
    def _calculate_composite_truth_score(self, universal_law: Dict, inversion: Dict, 
                                       tradition: Dict, symbolic: Dict, quantum: Dict) -> float:
        """Calculate composite truth score from all analyses"""
        weights = {
            'universal_law': 0.35,
            'inversion': 0.25,
            'tradition': 0.20,
            'symbolic': 0.10,
            'quantum': 0.10
        }
        
        scores = {
            'universal_law': universal_law['universal_law_alignment'],
            'inversion': 1.0 - inversion['inversion_score'],  # Inversion reduces truth
            'tradition': tradition['primary_tradition_alignment'],
            'symbolic': symbolic['archetypal_power'],
            'quantum': quantum['quantum_certainty']
        }
        
        composite_score = sum(scores[factor] * weights[factor] for factor in weights)
        return min(1.0, composite_score)
    
    def _categorize_truth_level(self, truth_score: float) -> str:
        """Categorize the truth level based on score"""
        if truth_score > 0.95:
            return "UNIVERSAL_COSMIC_TRUTH"
        elif truth_score > 0.85:
            return "ANCIENT_WISDOM_TRUTH"
        elif truth_score > 0.75:
            return "HIGH_CONFIDENCE_TRUTH"
        elif truth_score > 0.65:
            return "PROBABLE_TRUTH"
        elif truth_score > 0.55:
            return "POSSIBLE_TRUTH"
        elif truth_score > 0.45:
            return "UNCERTAIN_CLAIM"
        else:
            return "LIKELY_INVERTED_OR_CORRUPTED"
    
    def _generate_recovery_recommendations(self, universal_law: Dict, inversion: Dict, 
                                         tradition: Dict) -> List[str]:
        """Generate recommendations for truth recovery"""
        recommendations = []
        
        # Universal Law recommendations
        if universal_law['universal_law_alignment'] < 0.7:
            recommendations.append("Seek alignment with Universal Law principles")
        
        if universal_law['violation_indicators']:
            recommendations.append(f"Address violations: {', '.join(universal_law['violation_indicators'])}")
        
        # Inversion recovery recommendations
        if inversion['inversion_score'] > 0.3:
            recommendations.append(f"Recover original state: {inversion['original_state_reconstruction']}")
        
        if inversion['detected_mechanisms']:
            recommendations.append(f"Counter detected inversions: {', '.join(inversion['detected_mechanisms'])}")
        
        # Tradition-specific recommendations
        trad_data = tradition.get('tradition_data', {})
        if trad_data.get('inversion_status') != 'minimal':
            recommendations.append(f"Research pre-{trad_data.get('inversion_status', 'corruption')} forms")
        
        return recommendations

# =============================================================================
# ANCIENT RELIGIONS MODULE - MAIN ENGINE
# =============================================================================

class AncientReligionsModule:
    """
    Main engine for analyzing ancient religions up to Babylonian period
    Complete with Universal Law primacy and inversion detection
    """
    
    def __init__(self):
        self.truth_verifier = QuantumTruthVerification()
        self.universal_law = UniversalLawPrimacy()
        self.inversion_detector = BabylonianInversionTemplate()
        self.ancient_db = AncientReligionDatabase()
        self.analysis_history = []
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('AncientReligionsModule')
        logger.setLevel(logging.INFO)
        
        # Create console handler with formatting
        ch = logging.StreamHandler()
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        ch.setFormatter(formatter)
        logger.addHandler(ch)
        
        return logger

    async def analyze_ancient_teaching(self, teaching: str, context: Dict = None) -> Dict[str, Any]:
        """
        Comprehensive analysis of ancient religious teaching
        """
        self.logger.info(f"🔮 ANALYZING ANCIENT TEACHING: {teaching[:100]}...")
        
        try:
            # Perform comprehensive analysis
            verification_result = await self.truth_verifier.verify_ancient_claim(teaching, context)
            
            # Store in history
            self.analysis_history.append({
                'teaching': teaching,
                'result': verification_result,
                'timestamp': datetime.utcnow().isoformat()
            })
            
            self.logger.info(f"✅ Analysis complete: {verification_result['truth_category']}")
            
            return verification_result
            
        except Exception as e:
            self.logger.error(f"Ancient teaching analysis failed: {e}")
            return {
                'teaching': teaching,
                'error': str(e),
                'truth_score': 0.0,
                'truth_category': 'ANALYSIS_FAILED',
                'timestamp': datetime.utcnow().isoformat()
            }
    
    async def analyze_tradition(self, tradition_name: str) -> Dict[str, Any]:
        """
        Analyze entire ancient tradition
        """
        self.logger.info(f"🏛️  ANALYZING ANCIENT TRADITION: {tradition_name}")
        
        try:
            tradition_data = self.ancient_db.religious_traditions.get(tradition_name)
            if not tradition_data:
                return {'error': f"Tradition {tradition_name} not found"}
            
            # Analyze core principles
            principle_analyses = []
            for principle in tradition_data['core_principles']:
                analysis = await self.analyze_ancient_teaching(principle)
                principle_analyses.append(analysis)
            
            # Calculate tradition health score
            avg_truth_score = np.mean([a.get('truth_score', 0) for a in principle_analyses])
            universal_law_alignment = np.mean([a['universal_law_assessment']['universal_law_alignment'] 
                                            for a in principle_analyses])
            
            return {
                'tradition': tradition_name,
                'tradition_data': tradition_data,
                'principle_analyses': principle_analyses,
                'tradition_health_score': avg_truth_score,
                'universal_law_alignment': universal_law_alignment,
                'inversion_status': tradition_data.get('inversion_status', 'unknown'),
                'recovery_potential': self._calculate_recovery_potential(principle_analyses),
                'analysis_timestamp': datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            self.logger.error(f"Tradition analysis failed: {e}")
            return {'error': str(e)}
    
    def _calculate_recovery_potential(self, principle_analyses: List[Dict]) -> float:
        """Calculate potential for recovering original teachings"""
        if not principle_analyses:
            return 0.0
        
        inversion_scores = [a['inversion_analysis']['inversion_score'] for a in principle_analyses]
        avg_inversion = np.mean(inversion_scores)
        
        # Lower inversion means higher recovery potential
        recovery_potential = 1.0 - avg_inversion
        
        # Boost if universal law alignment is high
        universal_scores = [a['universal_law_assessment']['universal_law_alignment'] for a in principle_analyses]
        avg_universal = np.mean(universal_scores)
        
        return min(1.0, recovery_potential * 0.7 + avg_universal * 0.3)
    
    async def compare_traditions(self, tradition1: str, tradition2: str) -> Dict[str, Any]:
        """
        Compare two ancient traditions
        """
        self.logger.info(f"🔄 COMPARING TRADITIONS: {tradition1} vs {tradition2}")
        
        try:
            analysis1 = await self.analyze_tradition(tradition1)
            analysis2 = await self.analyze_tradition(tradition2)
            
            if 'error' in analysis1 or 'error' in analysis2:
                return {'error': 'One or both traditions could not be analyzed'}
            
            return {
                'comparison': {
                    'tradition1': tradition1,
                    'tradition2': tradition2,
                    'health_score_difference': abs(analysis1['tradition_health_score'] - analysis2['tradition_health_score']),
                    'universal_law_difference': abs(analysis1['universal_law_alignment'] - analysis2['universal_law_alignment']),
                    'recovery_potential_difference': abs(analysis1['recovery_potential'] - analysis2['recovery_potential'])
                },
                'analysis1': analysis1,
                'analysis2': analysis2,
                'shared_consciousness_tech': self._find_shared_technologies(analysis1, analysis2),
                'comparison_timestamp': datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            self.logger.error(f"Tradition comparison failed: {e}")
            return {'error': str(e)}
    
    def _find_shared_technologies(self, analysis1: Dict, analysis2: Dict) -> List[str]:
        """Find shared consciousness technologies between traditions"""
        trad1_tech = set()
        trad2_tech = set()
        
        # Extract technologies from tradition data
        trad1_data = analysis1.get('tradition_data', {})
        trad2_data = analysis2.get('tradition_data', {})
        
        for tech_category in self.ancient_db.consciousness_technologies.values():
            if trad1_data.get('consciousness_tech'):
                trad1_tech.update(trad1_data['consciousness_tech'])
            if trad2_data.get('consciousness_tech'):
                trad2_tech.update(trad2_data['consciousness_tech'])
        
        return list(trad1_tech.intersection(trad2_tech))
    
    def get_module_metrics(self) -> Dict[str, Any]:
        """Get module performance and usage metrics"""
        return {
            'analyses_performed': len(self.analysis_history),
            'traditions_analyzed': len(set([h['result'].get('tradition_alignment', {}).get('detected_tradition', 'unknown') 
                                          for h in self.analysis_history])),
            'average_truth_score': np.mean([h['result'].get('truth_score', 0) for h in self.analysis_history]) 
                                if self.analysis_history else 0,
            'module_uptime': 'active',
            'last_analysis': self.analysis_history[-1]['timestamp'] if self.analysis_history else 'none',
            'universal_law_violations_detected': sum(len(h['result'].get('universal_law_assessment', {}).get('violation_indicators', [])) 
                                                   for h in self.analysis_history),
            'inversion_patterns_detected': sum(len(h['result'].get('inversion_analysis', {}).get('detected_mechanisms', [])) 
                                             for h in self.analysis_history)
        }

# =============================================================================
# DEMONSTRATION AND TESTING
# =============================================================================

async def demonstrate_ancient_religions_module():
    """
    Demonstrate the Ancient Religions Module with test cases
    """
    print("🌌 ANCIENT RELIGIONS MODULE - DEMONSTRATION")
    print("Universal Law Primacy + Babylonian Inversion Detection")
    print("=" * 80)
    
    module = AncientReligionsModule()
    
    # Test teachings from various ancient traditions
    test_teachings = [
        # Pre-Vedic - High Universal Law alignment
        "The individual soul (Atman) is one with universal consciousness (Brahman)",
        "Through meditation and self-realization, one achieves liberation (Moksha)",
        
        # Sumerian - Direct divine access
        "Each person can communicate directly with the gods through prayer and ritual",
        "The me are divine laws that govern all aspects of reality",
        
        # Early Egyptian - Consciousness evolution
        "The ba soul travels to other realms during sleep and after death",
        "Maat represents the cosmic balance that each person must uphold",
        
        # Indigenous - Nature connection
        "The dreamtime is the fundamental reality from which our world emerges",
        "All beings are connected through the great spirit of life",
        
        # Potential inversion examples
        "Only the high priest can interpret the will of the gods",
        "The king is the living god and must be obeyed without question"
    ]
    
    results = []
    
    print(f"\n🎯 ANALYZING {len(test_teachings)} ANCIENT TEACHINGS...")
    
    for i, teaching in enumerate(test_teachings, 1):
        print(f"\n" + "="*60)
        print(f"TEACHING {i}/{len(test_teachings)}")
        print("="*60)
        print(f"Content: {teaching}")
        
        result = await module.analyze_ancient_teaching(teaching)
        results.append(result)
        
        # Display key results
        truth_score = result['truth_score']
        truth_category = result['truth_category']
        tradition = result['tradition_alignment']['detected_tradition']
        universal_alignment = result['universal_law_assessment']['universal_law_alignment']
        inversion_score = result['inversion_analysis']['inversion_score']
        
        print(f"\n📊 ANALYSIS RESULTS:")
        print(f"   Truth Score: {truth_score:.3f}")
        print(f"   Category: {truth_category}")
        print(f"   Tradition: {tradition}")
        print(f"   Universal Law Alignment: {universal_alignment:.3f}")
        print(f"   Inversion Detection: {inversion_score:.3f}")
        
        if result['recovery_recommendations']:
            print(f"   Recovery Recommendations: {result['recovery_recommendations']}")
    
    # Tradition Analysis
    print("\n" + "="*80)
    print("🏛️  TRADITION ANALYSIS")
    print("="*80)
    
    traditions = ['pre_vedic', 'sumerian', 'early_egyptian', 'indigenous_oral']
    for tradition in traditions:
        print(f"\nAnalyzing {tradition}...")
        trad_analysis = await module.analyze_tradition(tradition)
        
        if 'error' not in trad_analysis:
            print(f"   Health Score: {trad_analysis['tradition_health_score']:.3f}")
            print(f"   Universal Law: {trad_analysis['universal_law_alignment']:.3f}")
            print(f"   Recovery Potential: {trad_analysis['recovery_potential']:.3f}")
            print(f"   Inversion Status: {trad_analysis['inversion_status']}")
    
    # Module Metrics
    print("\n" + "="*80)
    print("📈 MODULE METRICS")
    print("="*80)
    
    metrics = module.get_module_metrics()
    for key, value in metrics.items():
        print(f"{key}: {value}")
    
    return results, metrics

# =============================================================================
# MAIN EXECUTION
# =============================================================================

async def main():
    """
    Main execution function for Ancient Religions Module
    """
    try:
        print("🚀 INITIALIZING ANCIENT RELIGIONS MODULE...")
        print("Universal Law Primacy + Inversion Detection + Quantum Truth Verification")
        print()
        
        results, metrics = await demonstrate_ancient_religions_module()
        
        print("\n" + "="*80)
        print("✅ ANCIENT RELIGIONS MODULE EXECUTION COMPLETE")
        print("="*80)
        print(f"Analyzed {len(results)} ancient teachings")
        print(f"Module performance: {metrics['analyses_performed']} analyses completed")
        print(f"Average truth score across all analyses: {metrics['average_truth_score']:.3f}")
        print("\n🌌 ANCIENT WISDOM RECOVERY SYSTEM: OPERATIONAL")
        
    except Exception as e:
        print(f"❌ Execution failed: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    # Configure logging
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[
            logging.StreamHandler(),
            logging.FileHandler('ancient_religions_module.log')
        ]
    )
    
    # Run the ancient religions module
    asyncio.run(main())