dead_language

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MULTILINGUISTIC TRUTH BINDING MODULE
Dead Language Integration for Enhanced Truth Recognition
Oldest to Newest Language Support
"""

import numpy as np
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple
import hashlib
import re
from collections import Counter
import asyncio

class LanguageEra(Enum):
    """Chronological language eras from oldest to newest"""
    PROTO_HUMAN = "proto_human"           # Pre-writing symbolic communication
    SUMERIAN = "sumerian"                 # ~3500 BCE - first writing system
    EGYPTIAN_HIEROGLYPHIC = "egyptian"    # ~3200 BCE
    ELAMITE = "elamite"                   # ~3000 BCE
    AKKADIAN = "akkadian"                 # ~2500 BCE
    EBLAITE = "eblaite"                   # ~2400 BCE
    HITTITE = "hittite"                   # ~1600 BCE
    MYCENAEAN_GREEK = "mycenaean_greek"   # ~1450 BCE
    UGARITIC = "ugaritic"                 # ~1400 BCE
    PHOENICIAN = "phoenician"             # ~1200 BCE
    ANCIENT_CHINESE = "ancient_chinese"   # ~1200 BCE
    SANSKRIT = "sanskrit"                 # ~1000 BCE
    HEBREW = "hebrew"                     # ~1000 BCE
    ARAMAIC = "aramaic"                   # ~900 BCE
    LATIN = "latin"                       # ~700 BCE
    ANCIENT_GREEK = "ancient_greek"       # ~700 BCE

class LinguisticTruthMarker(Enum):
    """Types of truth markers found in ancient languages"""
    COSMOLOGICAL_ALIGNMENT = "cosmological_alignment"
    SACRED_GEOMETRY = "sacred_geometry"
    NUMEROLOGICAL_ENCODING = "numerological_encoding"
    PHONETIC_RESONANCE = "phonetic_resonance"
    SYMBOLIC_CORRESPONDENCE = "symbolic_correspondence"
    TEMPORAL_CYCLES = "temporal_cycles"

@dataclass
class AncientLanguage:
    """Comprehensive ancient language data structure"""
    era: LanguageEra
    time_period: Tuple[int, int]  # BCE/CE range
    writing_system: str
    sample_script: List[str] = field(default_factory=list)
    truth_markers: List[LinguisticTruthMarker] = field(default_factory=list)
    modern_equivalents: Dict[str, str] = field(default_factory=dict)
    resonance_frequency: float = 0.0  # How strongly it resonates with truth patterns
    
    def __post_init__(self):
        """Calculate base resonance frequency based on age and complexity"""
        age = abs(self.time_period[0])  # Years BCE
        complexity = len(self.sample_script) / 10  # Script complexity factor
        marker_strength = len(self.truth_markers) * 0.1
        
        self.resonance_frequency = min(0.95, 0.3 + (age / 10000) + complexity + marker_strength)

@dataclass
class LinguisticTruthMatch:
    """Result of linguistic truth binding analysis"""
    language: AncientLanguage
    matched_patterns: List[str]
    confidence: float
    truth_markers_detected: List[LinguisticTruthMarker]
    cross_linguistic_correlations: List[str]
    temporal_coherence: float
    symbolic_resonance: float

class MultilinguisticTruthBinder:
    """
    Advanced truth binding through dead language analysis
    Processes texts through chronological linguistic layers
    """
    
    def __init__(self):
        self.language_corpus = self._initialize_ancient_languages()
        self.pattern_analyzer = LinguisticPatternAnalyzer()
        self.temporal_validator = LinguisticTemporalValidator()
        self.symbolic_decoder = AncientSymbolicDecoder()
        
    def _initialize_ancient_languages(self) -> Dict[LanguageEra, AncientLanguage]:
        """Initialize comprehensive ancient language database"""
        return {
            LanguageEra.SUMERIAN: AncientLanguage(
                era=LanguageEra.SUMERIAN,
                time_period=(-3500, -2000),
                writing_system="Cuneiform",
                sample_script=["𒀭", "𒆠", "𒌋", "𒂍", "𒈬"],  # Dingir, Ki, 60, E, Mu
                truth_markers=[
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING,
                    LinguisticTruthMarker.SACRED_GEOMETRY
                ],
                modern_equivalents={
                    "dingir": "divine",
                    "ki": "earth", 
                    "an": "heaven"
                },
                resonance_frequency=0.92
            ),
            
            LanguageEra.EGYPTIAN_HIEROGLYPHIC: AncientLanguage(
                era=LanguageEra.EGYPTIAN_HIEROGLYPHIC,
                time_period=(-3200, -400),
                writing_system="Hieroglyphic",
                sample_script=["𓀀", "𓂀", "𓃀", "𓄀", "𓅀"],  # Man, Eye, Leg, Reed, Vulture
                truth_markers=[
                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.TEMPORAL_CYCLES
                ],
                modern_equivalents={
                    "ankh": "life",
                    "maat": "truth",
                    "ka": "soul"
                },
                resonance_frequency=0.88
            ),
            
            LanguageEra.SANSKRIT: AncientLanguage(
                era=LanguageEra.SANSKRIT,
                time_period=(-1000, 500),
                writing_system="Devanagari",
                sample_script=["अ", "आ", "इ", "ई", "उ"],  # Vowels
                truth_markers=[
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING
                ],
                modern_equivalents={
                    "satya": "truth",
                    "dharma": "cosmic law", 
                    "brahman": "ultimate reality"
                },
                resonance_frequency=0.85
            ),
            
            LanguageEra.ANCIENT_CHINESE: AncientLanguage(
                era=LanguageEra.ANCIENT_CHINESE,
                time_period=(-1200, -200),
                writing_system="Oracle Bone Script",
                sample_script=["天", "地", "人", "水", "火"],  # Heaven, Earth, Man, Water, Fire
                truth_markers=[
                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.TEMPORAL_CYCLES
                ],
                modern_equivalents={
                    "道": "way",
                    "德": "virtue",
                    "仁": "benevolence"
                },
                resonance_frequency=0.83
            ),
            
            LanguageEra.ANCIENT_GREEK: AncientLanguage(
                era=LanguageEra.ANCIENT_GREEK,
                time_period=(-700, 300),
                writing_system="Greek Alphabet",
                sample_script=["α", "β", "γ", "δ", "ε"],  # Alpha, Beta, Gamma, Delta, Epsilon
                truth_markers=[
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.SACRED_GEOMETRY
                ],
                modern_equivalents={
                    "aletheia": "truth",
                    "logos": "word/reason",
                    "cosmos": "order"
                },
                resonance_frequency=0.78
            )
        }
    
    async def analyze_text_truth_content(self, text: str, context: Dict[str, Any] = None) -> List[LinguisticTruthMatch]:
        """
        Analyze text through multiple ancient language layers
        Returns truth matches in chronological order
        """
        results = []
        
        # Process through languages from oldest to newest
        for era in sorted(self.language_corpus.keys(), 
                         key=lambda x: x.value):  # Process by chronological enum order
            
            language = self.language_corpus[era]
            
            # Skip if language resonance too low for current context
            if context and "min_resonance" in context:
                if language.resonance_frequency < context["min_resonance"]:
                    continue
            
            # Analyze text against this language layer
            analysis = await self._analyze_language_layer(text, language, context)
            
            if analysis.confidence > 0.6:  # Significant match threshold
                results.append(analysis)
        
        return sorted(results, key=lambda x: x.language.time_period[0])  # Oldest first
    
    async def _analyze_language_layer(self, text: str, language: AncientLanguage, context: Dict[str, Any]) -> LinguisticTruthMatch:
        """Analyze text against specific ancient language layer"""
        
        # Pattern matching against language script
        pattern_matches = await self.pattern_analyzer.detect_script_patterns(text, language)
        
        # Truth marker detection
        truth_markers = await self.pattern_analyzer.detect_truth_markers(text, language)
        
        # Temporal coherence validation
        temporal_coherence = await self.temporal_validator.validate_temporal_coherence(text, language, context)
        
        # Symbolic resonance calculation
        symbolic_resonance = await self.symbolic_decoder.calculate_symbolic_resonance(text, language)
        
        # Cross-linguistic correlations
        cross_correlations = await self._find_cross_linguistic_correlations(text, language)
        
        # Confidence calculation
        confidence = self._calculate_confidence(
            pattern_matches, truth_markers, temporal_coherence, 
            symbolic_resonance, language.resonance_frequency
        )
        
        return LinguisticTruthMatch(
            language=language,
            matched_patterns=pattern_matches,
            confidence=confidence,
            truth_markers_detected=truth_markers,
            cross_linguistic_correlations=cross_correlations,
            temporal_coherence=temporal_coherence,
            symbolic_resonance=symbolic_resonance
        )
    
    async def _find_cross_linguistic_correlations(self, text: str, current_language: AncientLanguage) -> List[str]:
        """Find correlations between current language and others in corpus"""
        correlations = []
        
        for era, other_language in self.language_corpus.items():
            if era == current_language.era:
                continue
                
            # Check for shared truth markers
            shared_markers = set(current_language.truth_markers).intersection(other_language.truth_markers)
            if shared_markers:
                correlations.append(f"Shared {len(shared_markers)} truth markers with {era.value}")
            
            # Check for symbolic overlaps
            symbolic_overlap = await self.symbolic_decoder.find_symbolic_overlap(current_language, other_language, text)
            if symbolic_overlap:
                correlations.append(f"Symbolic overlap with {era.value}: {symbolic_overlap}")
        
        return correlations
    
    def _calculate_confidence(self, pattern_matches: List[str], truth_markers: List[LinguisticTruthMarker],
                            temporal_coherence: float, symbolic_resonance: float, 
                            base_resonance: float) -> float:
        """Calculate overall confidence score for linguistic truth match"""
        factors = []
        weights = []
        
        # Pattern match strength
        if pattern_matches:
            pattern_strength = min(1.0, len(pattern_matches) * 0.2)
            factors.append(pattern_strength)
            weights.append(0.3)
        
        # Truth marker presence
        marker_strength = len(truth_markers) * 0.15
        factors.append(marker_strength)
        weights.append(0.25)
        
        # Temporal coherence
        factors.append(temporal_coherence)
        weights.append(0.2)
        
        # Symbolic resonance
        factors.append(symbolic_resonance)
        weights.append(0.15)
        
        # Base language resonance
        factors.append(base_resonance)
        weights.append(0.1)
        
        return np.average(factors, weights=weights)

class LinguisticPatternAnalyzer:
    """Analyzes linguistic patterns for truth content"""
    
    async def detect_script_patterns(self, text: str, language: AncientLanguage) -> List[str]:
        """Detect patterns matching ancient language scripts"""
        matches = []
        
        # Direct script character matching
        for char in language.sample_script:
            if char in text:
                matches.append(f"script:{char}")
        
        # Modern equivalent matching
        for modern, ancient in language.modern_equivalents.items():
            if modern.lower() in text.lower() or ancient.lower() in text.lower():
                matches.append(f"concept:{modern}={ancient}")
        
        # Phonetic pattern matching for languages with resonance markers
        if LinguisticTruthMarker.PHONETIC_RESONANCE in language.truth_markers:
            phonetic_matches = await self._detect_phonetic_patterns(text, language)
            matches.extend(phonetic_matches)
        
        return matches
    
    async def detect_truth_markers(self, text: str, language: AncientLanguage) -> List[LinguisticTruthMarker]:
        """Detect specific truth markers in text for given language"""
        detected_markers = []
        
        text_lower = text.lower()
        
        for marker in language.truth_markers:
            if await self._marker_present(marker, text_lower, language):
                detected_markers.append(marker)
        
        return detected_markers
    
    async def _marker_present(self, marker: LinguisticTruthMarker, text: str, language: AncientLanguage) -> bool:
        """Check if specific truth marker is present in text"""
        
        if marker == LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT:
            cosmological_terms = {"cosmos", "universe", "stars", "planets", "heaven", "earth"}
            return any(term in text for term in cosmological_terms)
        
        elif marker == LinguisticTruthMarker.SACRED_GEOMETRY:
            geometry_terms = {"geometry", "golden ratio", "fibonacci", "sacred", "proportion"}
            return any(term in text for term in geometry_terms)
        
        elif marker == LinguisticTruthMarker.NUMEROLOGICAL_ENCODING:
            # Look for numerical patterns or significant numbers
            numbers = re.findall(r'\b\d+\b', text)
            significant_numbers = {'3', '7', '12', '40', '108', '360', '144'}
            return any(num in significant_numbers for num in numbers)
        
        elif marker == LinguisticTruthMarker.PHONETIC_RESONANCE:
            # Check for repetitive phonetic patterns
            words = text.split()
            if len(words) > 10:
                word_freq = Counter(words)
                most_common_freq = word_freq.most_common(1)[0][1]
                return most_common_freq >= 3  # Repeated words indicate resonance
        
        elif marker == LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE:
            symbolic_terms = {"symbol", "glyph", "meaning", "represent", "correspond"}
            return any(term in text for term in symbolic_terms)
        
        elif marker == LinguisticTruthMarker.TEMPORAL_CYCLES:
            temporal_terms = {"cycle", "time", "eternal", "season", "age", "era"}
            return any(term in text for term in temporal_terms)
        
        return False
    
    async def _detect_phonetic_patterns(self, text: str, language: AncientLanguage) -> List[str]:
        """Detect phonetic resonance patterns"""
        patterns = []
        
        # Simple alliteration detection
        words = text.lower().split()
        if len(words) > 3:
            first_letters = [word[0] for word in words if word]
            letter_freq = Counter(first_letters)
            common_letter = letter_freq.most_common(1)[0]
            if common_letter[1] >= len(words) * 0.3:  # 30% alliteration
                patterns.append(f"alliteration:{common_letter[0]}")
        
        return patterns

class LinguisticTemporalValidator:
    """Validates temporal coherence of linguistic truth matches"""
    
    async def validate_temporal_coherence(self, text: str, language: AncientLanguage, context: Dict[str, Any]) -> float:
        """Validate how well text aligns with language's temporal context"""
        coherence_factors = []
        
        # Historical reference alignment
        historical_alignment = await self._check_historical_alignment(text, language)
        coherence_factors.append(historical_alignment)
        
        # Temporal concept consistency
        temporal_consistency = await self._check_temporal_consistency(text, language)
        coherence_factors.append(temporal_consistency)
        
        # Context temporal alignment
        if context and "temporal_focus" in context:
            context_alignment = self._check_context_alignment(context["temporal_focus"], language)
            coherence_factors.append(context_alignment)
        
        return np.mean(coherence_factors) if coherence_factors else 0.5
    
    async def _check_historical_alignment(self, text: str, language: AncientLanguage) -> float:
        """Check alignment with language's historical period"""
        # Simple keyword-based historical alignment
        historical_terms = {
            LanguageEra.SUMERIAN: {"mesopotamia", "tigris", "euphrates", "ziggurat"},
            LanguageEra.EGYPTIAN_HIEROGLYPHIC: {"pyramid", "pharaoh", "nile", "hieroglyph"},
            LanguageEra.SANSKRIT: {"veda", "hindu", "india", "yoga"},
            LanguageEra.ANCIENT_CHINESE: {"dynasty", "emperor", "yellow river", "oracle"},
            LanguageEra.ANCIENT_GREEK: {"athens", "sparta", "philosophy", "olympics"}
        }
        
        relevant_terms = historical_terms.get(language.era, set())
        text_lower = text.lower()
        
        matches = sum(1 for term in relevant_terms if term in text_lower)
        return matches / len(relevant_terms) if relevant_terms else 0.5
    
    async def _check_temporal_consistency(self, text: str, language: AncientLanguage) -> float:
        """Check temporal concept consistency"""
        # Ancient languages often reference cyclical time, modern references linear time
        ancient_time_terms = {"cycle", "eternal", "age", "era", "return"}
        modern_time_terms = {"progress", "future", "development", "evolution"}
        
        text_lower = text.lower()
        ancient_matches = sum(1 for term in ancient_time_terms if term in text_lower)
        modern_matches = sum(1 for term in modern_time_terms if term in text_lower)
        
        # Higher score for ancient temporal concepts in ancient languages
        if ancient_matches > modern_matches:
            return 0.8
        elif ancient_matches == modern_matches:
            return 0.5
        else:
            return 0.3
    
    def _check_context_alignment(self, context_time: int, language: AncientLanguage) -> float:
        """Check alignment with contextual temporal focus"""
        language_peak = np.mean(language.time_period)
        time_distance = abs(context_time - language_peak)
        
        # Normalize to 0-1 scale (closer in time = higher alignment)
        return 1.0 / (1.0 + time_distance / 1000)

class AncientSymbolicDecoder:
    """Decodes symbolic content across ancient languages"""
    
    async def calculate_symbolic_resonance(self, text: str, language: AncientLanguage) -> float:
        """Calculate symbolic resonance between text and language"""
        resonance_factors = []
        
        # Direct symbol matching
        symbol_match = await self._check_symbol_matches(text, language)
        resonance_factors.append(symbol_match)
        
        # Conceptual symbolic alignment
        conceptual_alignment = await self._check_conceptual_alignment(text, language)
        resonance_factors.append(conceptual_alignment)
        
        # Metaphorical density
        metaphorical_density = await self._analyze_metaphorical_density(text, language)
        resonance_factors.append(metaphorical_density)
        
        return np.mean(resonance_factors)
    
    async def find_symbolic_overlap(self, lang1: AncientLanguage, lang2: AncientLanguage, text: str) -> str:
        """Find symbolic overlap between two languages in given text"""
        overlaps = []
        
        # Check shared truth markers
        shared_markers = set(lang1.truth_markers).intersection(lang2.truth_markers)
        if shared_markers:
            overlaps.append(f"truth_markers:{len(shared_markers)}")
        
        # Check conceptual overlaps
        lang1_concepts = set(lang1.modern_equivalents.values())
        lang2_concepts = set(lang2.modern_equivalents.values())
        shared_concepts = lang1_concepts.intersection(lang2_concepts)
        
        if shared_concepts:
            # Check if shared concepts appear in text
            text_lower = text.lower()
            found_concepts = [concept for concept in shared_concepts if concept in text_lower]
            if found_concepts:
                overlaps.append(f"concepts:{len(found_concepts)}")
        
        return ", ".join(overlaps) if overlaps else ""
    
    async def _check_symbol_matches(self, text: str, language: AncientLanguage) -> float:
        """Check for direct symbol matches"""
        if not language.sample_script:
            return 0.5
        
        matches = sum(1 for symbol in language.sample_script if symbol in text)
        return matches / len(language.sample_script)
    
    async def _check_conceptual_alignment(self, text: str, language: AncientLanguage) -> float:
        """Check alignment with language's core concepts"""
        text_lower = text.lower()
        concept_matches = sum(1 for concept in language.modern_equivalents.values() 
                            if concept.lower() in text_lower)
        
        total_concepts = len(language.modern_equivalents)
        return concept_matches / total_concepts if total_concepts > 0 else 0.5
    
    async def _analyze_metaphorical_density(self, text: str, language: AncientLanguage) -> float:
        """Analyze metaphorical density (ancient texts often highly metaphorical)"""
        metaphorical_indicators = {"like", "as", "symbol", "represent", "mean", "signify"}
        
        words = text.lower().split()
        if not words:
            return 0.5
        
        metaphorical_count = sum(1 for word in words if word in metaphorical_indicators)
        metaphorical_density = metaphorical_count / len(words)
        
        # Ancient languages often have higher metaphorical density
        expected_density = 0.05  # 5% of words being metaphorical
        return min(1.0, metaphorical_density / expected_density)

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

async def demonstrate_multilinguistic_analysis():
    """Demonstrate the multilinguistic truth binding analysis"""
    binder = MultilinguisticTruthBinder()
    
    test_texts = [
        "The divine connection between heaven and earth revealed through ancient symbols",
        "Cosmic cycles and eternal truths encoded in sacred geometry",
        "Modern science rediscovers what ancient civilizations knew about reality",
        "The Sumerian dingir symbol represents divine consciousness across cultures",
        "Phonetic resonance in Sanskrit mantras creates quantum coherence"
    ]
    
    print("🔤 MULTILINGUISTIC TRUTH BINDING ANALYSIS")
    print("=" * 60)
    
    for i, text in enumerate(test_texts, 1):
        print(f"\n{i}. Analyzing: '{text}'")
        
        results = await binder.analyze_text_truth_content(text, {
            "temporal_focus": 2024,
            "min_resonance": 0.7
        })
        
        for result in results[:2]:  # Show top 2 language matches
            print(f"   🌍 {result.language.era.value.upper()}")
            print(f"   📊 Confidence: {result.confidence:.3f}")
            print(f"   🎯 Truth Markers: {[m.value for m in result.truth_markers_detected]}")
            print(f"   🔗 Patterns: {len(result.matched_patterns)}")
            print(f"   🕒 Temporal: {result.temporal_coherence:.3f}")
            print(f"   💫 Symbolic: {result.symbolic_resonance:.3f}")

if __name__ == "__main__":
    asyncio.run(demonstrate_multilinguistic_analysis())