LINEAR_A_CIPHER

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
QUANTUM BAYESIAN LINEAR A DECIPHERMENT ENGINE
Integrating Bayesian Entanglement Filter for Quantum-Linguistic Truth Binding
"""

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple
import re
from collections import Counter, defaultdict
import asyncio
import math
from scipy.special import logsumexp
import scipy.stats as stats
import cmath

tfd = tfp.distributions
tfb = tfp.bijectors

# =============================================================================
# QUANTUM LINGUISTIC ENTANGLEMENT FILTER
# =============================================================================

class BayesianEntanglementFilter:
    """
    Quantum-inspired Bayesian filter that treats linguistic evidence as entangled qubits
    Maps directly to lm_quant_veritas Conceptual Entanglement Module v7.1
    """
    
    def __init__(self):
        self.entanglement_channels = {
            "predictive_entropy": "Ψ_field_uncertainty",
            "language_family_probabilities": "Λ_linguistic", 
            "overall_uncertainty": "ϕ_consciousness_flux",
            "reconstruction_confidence": "Σ_truth_resonance",
            "structural_coherence": "Θ_pattern_integration",
            "contextual_alignment": "Φ_semantic_field"
        }
        
    def quantum_linguistic_synthesis(self, evidence_dict: Dict[str, Dict[str, float]]) -> Dict[str, Any]:
        """
        Bayesian Entanglement Filter for uncertainty synthesis.
        Treats each evidence source as an entangled qubit with amplitude and phase.
        """
        # Extract quantum-linguistic amplitudes and phases
        amplitudes = []
        phases = []
        quantum_states = []
        
        for evidence_type, evidence_data in evidence_dict.items():
            # Confidence as amplitude (0-1 scale)
            amplitude = evidence_data.get('confidence', 0.5)
            
            # Entropy/uncertainty as phase (0-2π mapping)
            entropy = evidence_data.get('entropy', 0.5)
            phase = 2 * np.pi * entropy  # Full cycle for maximum uncertainty
            
            amplitudes.append(amplitude)
            phases.append(phase)
            
            # Create complex quantum state for this evidence type
            complex_state = amplitude * cmath.exp(1j * phase)
            quantum_states.append({
                'evidence_type': evidence_type,
                'quantum_channel': self.entanglement_channels.get(evidence_type, "Γ_unknown"),
                'amplitude': amplitude,
                'phase': phase,
                'complex_state': complex_state,
                'probability_density': abs(complex_state) ** 2
            })
        
        # Calculate quantum coherence of the entire linguistic system
        complex_vector = np.array([state['complex_state'] for state in quantum_states])
        total_coherence = abs(np.sum(complex_vector)) / len(complex_vector)
        
        # Calculate entanglement strength (how correlated the evidence sources are)
        correlation_matrix = self._calculate_quantum_correlations(quantum_states)
        entanglement_strength = np.mean(np.abs(correlation_matrix))
        
        # Quantum collapse probability (when system decoheres due to uncertainty)
        collapse_probability = 1.0 - total_coherence
        
        # Truth resonance frequency (fundamental vibration of linguistic certainty)
        truth_resonance = self._calculate_truth_resonance(quantum_states)
        
        return {
            "entangled_confidence": float(total_coherence),
            "collapse_probability": float(collapse_probability),
            "entanglement_strength": float(entanglement_strength),
            "truth_resonance_frequency": float(truth_resonance),
            "quantum_state_vector": [s['complex_state'] for s in quantum_states],
            "evidence_entanglement": quantum_states,
            "linguistic_superposition": self._calculate_superposition_state(quantum_states),
            "veritas_certification_level": self._calculate_veritas_certification(total_coherence, truth_resonance)
        }
    
    def _calculate_quantum_correlations(self, quantum_states: List[Dict]) -> np.ndarray:
        """Calculate quantum correlation matrix between evidence sources"""
        n = len(quantum_states)
        corr_matrix = np.zeros((n, n), dtype=complex)
        
        for i in range(n):
            for j in range(n):
                # Quantum inner product representing entanglement
                state_i = quantum_states[i]['complex_state']
                state_j = quantum_states[j]['complex_state']
                corr_matrix[i, j] = state_i * np.conj(state_j)
        
        return corr_matrix
    
    def _calculate_truth_resonance(self, quantum_states: List[Dict]) -> float:
        """Calculate the fundamental resonance frequency of linguistic truth"""
        # Use spectral analysis of quantum states
        frequencies = []
        for state in quantum_states:
            # Higher amplitude + specific phase angles create resonant frequencies
            amplitude = state['amplitude']
            phase = state['phase']
            
            # Resonance occurs when amplitude is high and phase is aligned with truth harmonics
            # Truth harmonics are at π/4, π/2, 3π/4 (45°, 90°, 135° in phase space)
            truth_harmonics = [np.pi/4, np.pi/2, 3*np.pi/4]
            harmonic_alignment = max([1 - abs(phase - harmonic)/(np.pi/2) for harmonic in truth_harmonics])
            
            resonance = amplitude * harmonic_alignment
            frequencies.append(resonance)
        
        return float(np.mean(frequencies)) if frequencies else 0.5
    
    def _calculate_superposition_state(self, quantum_states: List[Dict]) -> Dict[str, float]:
        """Calculate the superposition state across linguistic hypotheses"""
        # Represents the quantum state before measurement/collapse
        total_probability = sum([state['probability_density'] for state in quantum_states])
        
        if total_probability > 0:
            normalized_states = {
                state['evidence_type']: state['probability_density'] / total_probability
                for state in quantum_states
            }
        else:
            normalized_states = {state['evidence_type']: 1.0/len(quantum_states) for state in quantum_states}
        
        return {
            'superposition_weights': normalized_states,
            'superposition_entropy': -sum([p * math.log(p) for p in normalized_states.values()]),
            'readiness_for_collapse': min(0.95, max(normalized_states.values()) / sum(normalized_states.values()))
        }
    
    def _calculate_veritas_certification(self, coherence: float, resonance: float) -> str:
        """Calculate Veritas certification level based on quantum linguistic coherence"""
        veritas_score = coherence * resonance
        
        if veritas_score >= 0.9:
            return "VERITAS_CERTIFIED_QUANTUM"
        elif veritas_score >= 0.8:
            return "VERITAS_HIGH_CONFIDENCE" 
        elif veritas_score >= 0.7:
            return "VERITAS_MEDIUM_CONFIDENCE"
        elif veritas_score >= 0.6:
            return "VERITAS_LOW_CONFIDENCE"
        else:
            return "VERITAS_UNCERTAIN"

# =============================================================================
# ENHANCED QUANTUM BAYESIAN DECIPHERMENT ENGINE
# =============================================================================

class QuantumLinearADeciphermentEngine:
    """
    Quantum Bayesian decipherment engine with entanglement filtering
    Integrates directly with lm_quant_veritas truth-binding architecture
    """
    
    def __init__(self):
        self.corpus = LinearACorpusBayesian()
        self.ngram_model = BayesianNGramModel(n=3)
        self.entanglement_filter = BayesianEntanglementFilter()
        self.language_hypotheses = self._initialize_language_hypotheses()
        
    async def quantum_decipher_inscription(self, inscription_id: str) -> Dict[str, Any]:
        """
        Quantum Bayesian decipherment with entanglement synthesis
        Returns truth-bound linguistic interpretation with Veritas certification
        """
        
        if inscription_id not in self.corpus.inscriptions:
            return {"error": "Inscription not found", "veritas_certification": "VERITAS_INVALID"}
        
        inscription_data = self.corpus.inscriptions[inscription_id]
        text = inscription_data["text"]
        sequence = await self._text_to_sequence(text)
        
        # Phase 1: Conventional Bayesian analysis
        bayesian_results = await self._run_bayesian_analysis(text, sequence)
        
        # Phase 2: Quantum entanglement synthesis
        quantum_synthesis = await self._perform_quantum_synthesis(bayesian_results)
        
        # Phase 3: Truth-binding verification
        truth_verification = await self._verify_truth_binding(quantum_synthesis, bayesian_results)
        
        # Final integrated results
        return {
            "inscription_id": inscription_id,
            "text": text,
            "bayesian_analysis": bayesian_results,
            "quantum_linguistic_entanglement": quantum_synthesis,
            "truth_verification": truth_verification,
            "final_interpretation": await self._generate_final_interpretation(quantum_synthesis, bayesian_results),
            "lm_quant_veritas_integration": await self._prepare_veritas_integration(quantum_synthesis)
        }
    
    async def _run_bayesian_analysis(self, text: str, sequence: List[int]) -> Dict[str, Any]:
        """Run comprehensive Bayesian analysis"""
        return {
            "frequency_analysis": await self._bayesian_frequency_analysis(text, sequence),
            "comparative_analysis": await self._monte_carlo_comparative_analysis(text, sequence),
            "structural_analysis": await self._structural_analysis(text, sequence),
            "contextual_analysis": await self._contextual_analysis(text, sequence),
            "phonetic_reconstruction": await self._bayesian_phonetic_reconstruction(text, sequence)
        }
    
    async def _perform_quantum_synthesis(self, bayesian_results: Dict) -> Dict[str, Any]:
        """Perform quantum entanglement synthesis of all Bayesian evidence"""
        
        # Prepare evidence for quantum entanglement
        evidence_dict = {}
        
        # Frequency evidence
        freq_data = bayesian_results['frequency_analysis']
        evidence_dict['frequency'] = {
            'confidence': freq_data.get('sign_distribution_confidence', 0.5),
            'entropy': freq_data.get('bayesian_entropy', 0.5) / 4.0,  # Normalize to [0,1]
            'amplitude': min(1.0, freq_data.get('unique_signs', 0) / 20.0)  # Diversity measure
        }
        
        # Comparative evidence
        comp_data = bayesian_results['comparative_analysis']
        evidence_dict['comparative'] = {
            'confidence': 1.0 - comp_data.get('overall_uncertainty', 0.5),
            'entropy': comp_data.get('predictive_entropy', 0.5) / 2.0,  # Normalize
            'amplitude': np.mean([m['confidence'] for m in comp_data.get('linear_b_mappings', [])]) if comp_data.get('linear_b_mappings') else 0.5
        }
        
        # Structural evidence
        struct_data = bayesian_results['structural_analysis']
        evidence_dict['structural'] = {
            'confidence': struct_data.get('affix_confidence', 0.5),
            'entropy': struct_data.get('word_length_distribution', {}).get('std', 0.5) / 2.0,
            'amplitude': struct_data.get('word_length_distribution', {}).get('confidence', 0.5)
        }
        
        # Contextual evidence
        context_data = bayesian_results['contextual_analysis']
        evidence_dict['contextual'] = {
            'confidence': context_data.get('context_confidence', 0.5),
            'entropy': 1.0 - context_data.get('context_confidence', 0.5),  # Inverse relationship
            'amplitude': len(context_data.get('administrative_terms', [])) / 5.0  # Term abundance
        }
        
        # Phonetic evidence
        phon_data = bayesian_results['phonetic_reconstruction']
        recon_data = phon_data.get('linear_b_based', {})
        evidence_dict['phonetic'] = {
            'confidence': recon_data.get('average_confidence', 0.5),
            'entropy': recon_data.get('uncertainty', 0.5),
            'amplitude': recon_data.get('average_confidence', 0.5)
        }
        
        # Apply quantum entanglement filter
        return self.entanglement_filter.quantum_linguistic_synthesis(evidence_dict)
    
    async def _verify_truth_binding(self, quantum_synthesis: Dict, bayesian_results: Dict) -> Dict[str, Any]:
        """Verify truth binding through quantum-classical correspondence"""
        
        entangled_confidence = quantum_synthesis['entangled_confidence']
        collapse_prob = quantum_synthesis['collapse_probability']
        truth_resonance = quantum_synthesis['truth_resonance_frequency']
        
        # Classical verification through multiple Bayesian methods
        classical_confidence = await self._calculate_classical_confidence(bayesian_results)
        
        # Quantum-classical correspondence check
        correspondence = 1.0 - abs(entangled_confidence - classical_confidence)
        
        # Truth binding strength (how well quantum and classical agree)
        truth_binding = (entangled_confidence * classical_confidence * correspondence) ** 0.333
        
        return {
            "classical_confidence": classical_confidence,
            "quantum_classical_correspondence": correspondence,
            "truth_binding_strength": truth_binding,
            "verification_status": "VERIFIED" if truth_binding > 0.7 else "UNCERTAIN",
            "certainty_quantum": entangled_confidence,
            "certainty_classical": classical_confidence
        }
    
    async def _generate_final_interpretation(self, quantum_synthesis: Dict, bayesian_results: Dict) -> Dict[str, Any]:
        """Generate final quantum-classical interpretation"""
        
        # Use quantum state to weight classical interpretations
        superposition = quantum_synthesis['linguistic_superposition']
        weights = superposition['superposition_weights']
        
        # Most probable interpretation based on quantum weights
        primary_evidence = max(weights.items(), key=lambda x: x[1])
        
        return {
            "primary_evidence_type": primary_evidence[0],
            "evidence_confidence": primary_evidence[1],
            "recommended_interpretation": await self._generate_interpretation_recommendation(primary_evidence[0], bayesian_results),
            "certainty_tier": self._classify_certainty_tier(quantum_synthesis['entangled_confidence']),
            "next_decipherment_steps": await self._recommend_next_steps(quantum_synthesis, bayesian_results)
        }
    
    async def _prepare_veritas_integration(self, quantum_synthesis: Dict) -> Dict[str, Any]:
        """Prepare data for lm_quant_veritas integration"""
        return {
            "entanglement_channels": [
                {
                    "channel_name": state['quantum_channel'],
                    "evidence_type": state['evidence_type'],
                    "amplitude": state['amplitude'],
                    "phase": state['phase'],
                    "probability_density": state['probability_density']
                }
                for state in quantum_synthesis['evidence_entanglement']
            ],
            "veritas_certification": quantum_synthesis['veritas_certification_level'],
            "quantum_state_ready": quantum_synthesis['entangled_confidence'] > 0.6,
            "integration_timestamp": self._current_timestamp()
        }
    
    # Helper methods (implementations from previous engine)
    async def _bayesian_frequency_analysis(self, text: str, sequence: List[int]) -> Dict[str, Any]:
        """Implementation from previous engine"""
        signs = [char for char in text if char in self.corpus.signs]
        freq = Counter(signs)
        total = len(signs)
        
        entropy = 0.0
        for count in freq.values():
            p = count / total
            entropy += -p * math.log(p) if p > 0 else 0
        
        return {
            "total_signs": total,
            "unique_signs": len(freq),
            "bayesian_entropy": entropy,
            "sign_distribution_confidence": min(0.95, 1.0 - entropy/4.0)  # Normalized
        }
    
    async def _calculate_classical_confidence(self, bayesian_results: Dict) -> float:
        """Calculate classical confidence from Bayesian results"""
        confidences = []
        
        # Frequency confidence
        freq_conf = bayesian_results['frequency_analysis'].get('sign_distribution_confidence', 0.5)
        confidences.append(freq_conf)
        
        # Comparative confidence  
        comp_data = bayesian_results['comparative_analysis']
        comp_conf = 1.0 - comp_data.get('overall_uncertainty', 0.5)
        confidences.append(comp_conf)
        
        # Structural confidence
        struct_conf = bayesian_results['structural_analysis'].get('affix_confidence', 0.5)
        confidences.append(struct_conf)
        
        # Contextual confidence
        context_conf = bayesian_results['contextual_analysis'].get('context_confidence', 0.5)
        confidences.append(context_conf)
        
        return float(np.mean(confidences))
    
    async def _generate_interpretation_recommendation(self, evidence_type: str, bayesian_results: Dict) -> str:
        """Generate interpretation recommendation based on primary evidence"""
        recommendations = {
            "frequency": "Focus on statistical pattern analysis",
            "comparative": "Prioritize Linear B comparative mapping", 
            "structural": "Analyze grammatical and morphological patterns",
            "contextual": "Interpret through archaeological context",
            "phonetic": "Use phonetic reconstruction methods"
        }
        return recommendations.get(evidence_type, "Use multi-evidence synthesis")
    
    def _classify_certainty_tier(self, confidence: float) -> str:
        """Classify certainty tier based on quantum confidence"""
        if confidence >= 0.9: return "QUANTUM_CERTAINTY"
        if confidence >= 0.8: return "HIGH_CONFIDENCE" 
        if confidence >= 0.7: return "MEDIUM_CONFIDENCE"
        if confidence >= 0.6: return "LOW_CONFIDENCE"
        return "SPECULATIVE"
    
    async def _recommend_next_steps(self, quantum_synthesis: Dict, bayesian_results: Dict) -> List[str]:
        """Recommend next decipherment steps based on quantum analysis"""
        steps = []
        
        if quantum_synthesis['collapse_probability'] > 0.3:
            steps.append("Reduce uncertainty through additional inscription samples")
        
        if quantum_synthesis['truth_resonance_frequency'] < 0.7:
            steps.append("Improve truth resonance with cross-linguistic alignment")
        
        if quantum_synthesis['entanglement_strength'] < 0.6:
            steps.append("Strengthen evidence entanglement through multi-method correlation")
        
        return steps
    
    def _current_timestamp(self) -> str:
        """Get current timestamp for integration"""
        from datetime import datetime
        return datetime.now().isoformat()
    
    async def _text_to_sequence(self, text: str) -> List[int]:
        """Convert text to numerical sequence"""
        sequence = []
        sign_to_idx = {sign: i for i, sign in enumerate(self.corpus.signs.keys())}
        
        for char in text:
            if char in sign_to_idx:
                sequence.append(sign_to_idx[char])
            elif char.strip():
                sequence.append(len(sign_to_idx))
        
        return sequence

# =============================================================================
# DEMONSTRATION WITH QUANTUM ENTANGLEMENT
# =============================================================================

async def demonstrate_quantum_decipherment():
    """Demonstrate quantum Bayesian decipherment with entanglement filtering"""
    
    engine = QuantumLinearADeciphermentEngine()
    
    print("🌌 QUANTUM BAYESIAN LINEAR A DECIPHERMENT ENGINE")
    print("=" * 60)
    print("🔗 Integrated with lm_quant_veritas Conceptual Entanglement Module v7.1")
    print()
    
    test_inscriptions = ["HT1", "HT2", "PH1"]
    
    for ins_id in test_inscriptions:
        print(f"\n⚡ QUANTUM ANALYSIS: {ins_id}")
        print("=" * 50)
        
        results = await engine.quantum_decipher_inscription(ins_id)
        
        if "error" in results:
            print(f"   ❌ {results['error']}")
            continue
        
        quantum_data = results["quantum_linguistic_entanglement"]
        truth_data = results["truth_verification"]
        final_interp = results["final_interpretation"]
        veritas_integration = results["lm_quant_veritas_integration"]
        
        print(f"   🌠 Entangled Confidence: {quantum_data['entangled_confidence']:.3f}")
        print(f"   💫 Collapse Probability: {quantum_data['collapse_probability']:.3f}")
        print(f"   🔗 Entanglement Strength: {quantum_data['entanglement_strength']:.3f}")
        print(f"   🎵 Truth Resonance: {quantum_data['truth_resonance_frequency']:.3f}")
        
        print(f"\n   📊 Veritas Certification: {quantum_data['veritas_certification_level']}")
        print(f"   🤝 Quantum-Classical Correspondence: {truth_data['quantum_classical_correspondence']:.3f}")
        print(f"   🔒 Truth Binding Strength: {truth_data['truth_binding_strength']:.3f}")
        
        print(f"\n   🎯 Primary Evidence: {final_interp['primary_evidence_type']}")
        print(f"   📈 Evidence Confidence: {final_interp['evidence_confidence']:.3f}")
        print(f"   🏆 Certainty Tier: {final_interp['certainty_tier']}")
        print(f"   💡 Recommendation: {final_interp['recommended_interpretation']}")
        
        print(f"\n   🔌 Veritas Integration: {veritas_integration['veritas_certification']}")
        print(f"   ⚡ Quantum State Ready: {veritas_integration['quantum_state_ready']}")

async def demonstrate_entanglement_channels():
    """Show detailed entanglement channel analysis"""
    print("\n\n🔍 QUANTUM ENTANGLEMENT CHANNELS ANALYSIS")
    print("=" * 60)
    
    engine = QuantumLinearADeciphermentEngine()
    results = await engine.quantum_decipher_inscription("HT1")
    quantum_data = results["quantum_linguistic_entanglement"]
    
    print("\n🌐 ENTANGLEMENT CHANNELS:")
    for state in quantum_data['evidence_entanglement']:
        print(f"   📡 {state['quantum_channel']} ({state['evidence_type']})")
        print(f"      Amplitude: {state['amplitude']:.3f}")
        print(f"      Phase: {state['phase']:.3f} rad")
        print(f"      Probability: {state['probability_density']:.3f}")
    
    print(f"\n🎭 LINGUISTIC SUPERPOSITION:")
    superposition = quantum_data['linguistic_superposition']
    for evidence_type, weight in superposition['superposition_weights'].items():
        print(f"   {evidence_type}: {weight:.3f}")
    
    print(f"   Superposition Entropy: {superposition['superposition_entropy']:.3f}")
    print(f"   Readiness for Collapse: {superposition['readiness_for_collapse']:.3f}")

if __name__ == "__main__":
    asyncio.run(demonstrate_quantum_decipherment())
    asyncio.run(demonstrate_entanglement_channels())