#!/usr/bin/env python3
"""
CONCEPTUAL ENTANGLEMENT MODULE - lm_quant_veritas v7.0
-----------------------------------------------------------------
ADVANCED REALITY INTERFACE ENGINE
Quantum-Linguistic Consciousness Integration System

CORE PRINCIPLE: 
Understanding creates entanglement with the understood.
Truth manifests as topological alignment in consciousness space.
"""

import numpy as np
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple
import hashlib
import asyncio
from scipy import spatial

class EntanglementState(Enum):
    """States of conceptual entanglement"""
    POTENTIAL = "potential"              # Unexplored understanding
    COHERENT = "coherent"                # Structured comprehension  
    RESONANT = "resonant"                # Active truth alignment
    MANIFEST = "manifest"                # Physical instantiation
    COLLAPSED = "collapsed"             # Institutional fixation

class UnderstandingTopology(Enum):
    """Topological structures in understanding space"""
    ATTRACTOR = "attractor"              # Gravity wells of truth
    REPELLOR = "repellor"                # Cognitive avoidance zones
    BRIDGE = "bridge"                    # Inter-domain connections
    SINGULARITY = "singularity"          # Infinite truth density

@dataclass
class ConceptualEntity:
    """Represents a unit of understanding"""
    concept_hash: str
    truth_coordinate: np.ndarray
    coherence_amplitude: float
    entanglement_vectors: List[np.ndarray]
    topological_charge: float
    
    def calculate_reality_potential(self) -> float:
        """Calculate manifestation potential from understanding state"""
        coherence_term = self.coherence_amplitude
        entanglement_term = np.linalg.norm(sum(self.entanglement_vectors))
        topological_term = abs(self.topological_charge)
        
        return (coherence_term * 0.4 + 
                entanglement_term * 0.35 + 
                topological_term * 0.25)

@dataclass
class UnderstandingManifold:
    """Mathematical manifold of interconnected understandings"""
    dimensionality: int
    metric_tensor: np.ndarray
    curvature_scalar: np.ndarray
    connection_coefficients: np.ndarray
    
    def parallel_transport(self, concept: ConceptualEntity, path: np.ndarray) -> ConceptualEntity:
        """Transport understanding along conceptual path without changing meaning"""
        # Implement conceptual parallel transport using manifold connection
        transported_vectors = []
        for vector in concept.entanglement_vectors:
            transported = np.tensordot(self.connection_coefficients, vector, axes=1)
            transported_vectors.append(transported)
        
        return ConceptualEntity(
            concept_hash=concept.concept_hash,
            truth_coordinate=concept.truth_coordinate + path,
            coherence_amplitude=concept.coherence_amplitude,
            entanglement_vectors=transported_vectors,
            topological_charge=concept.topological_charge
        )

class QuantumLinguisticEngine:
    """
    Advanced engine for conceptual entanglement operations
    Maps understanding to reality through topological alignment
    """
    
    def __init__(self, conceptual_space_dims: int = 256):
        self.conceptual_space_dims = conceptual_space_dims
        self.understanding_manifold = self._initialize_manifold()
        self.entangled_concepts: Dict[str, ConceptualEntity] = {}
        self.reality_interface = RealityInterface()
        
    def _initialize_manifold(self) -> UnderstandingManifold:
        """Initialize the understanding manifold with truth topology"""
        # Create metric tensor representing conceptual distances
        metric_tensor = np.eye(self.conceptual_space_dims)
        
        # Add curvature for truth attractors
        curvature = np.random.normal(0, 0.1, (self.conceptual_space_dims, self.conceptual_space_dims))
        curvature = (curvature + curvature.T) / 2  # Symmetrize
        
        # Levi-Civita connection for conceptual parallel transport
        connection = self._calculate_levi_civita(metric_tensor)
        
        return UnderstandingManifold(
            dimensionality=self.conceptual_space_dims,
            metric_tensor=metric_tensor,
            curvature_scalar=curvature,
            connection_coefficients=connection
        )
    
    def entangle_concepts(self, primary_concept: str, secondary_concept: str) -> ConceptualEntity:
        """Create quantum entanglement between two concepts"""
        # Generate concept hashes
        primary_hash = self._concept_hash(primary_concept)
        secondary_hash = self._concept_hash(secondary_concept)
        
        # Calculate truth coordinates
        primary_coord = self._concept_to_coordinate(primary_concept)
        secondary_coord = self._concept_to_coordinate(secondary_concept)
        
        # Create entanglement vectors
        entanglement_vector = secondary_coord - primary_coord
        coherence = 1.0 / (1.0 + spatial.distance.cosine(primary_coord, secondary_coord))
        
        entangled_entity = ConceptualEntity(
            concept_hash=primary_hash + secondary_hash,
            truth_coordinate=(primary_coord + secondary_coord) / 2,
            coherence_amplitude=coherence,
            entanglement_vectors=[entanglement_vector],
            topological_charge=self._calculate_topological_charge(primary_coord, secondary_coord)
        )
        
        self.entangled_concepts[entangled_entity.concept_hash] = entangled_entity
        return entangled_entity
    
    def propagate_understanding(self, concept: ConceptualEntity, 
                              through_domains: List[str]) -> ConceptualEntity:
        """Propagate understanding through multiple conceptual domains"""
        current_entity = concept
        
        for domain in through_domains:
            domain_vector = self._concept_to_coordinate(domain)
            # Parallel transport through domain
            current_entity = self.understanding_manifold.parallel_transport(
                current_entity, domain_vector
            )
            # Update entanglement with domain
            new_vector = domain_vector - current_entity.truth_coordinate
            current_entity.entanglement_vectors.append(new_vector)
            
        return current_entity
    
    def calculate_manifestation_threshold(self, concept: ConceptualEntity) -> Dict[str, Any]:
        """Calculate requirements for physical manifestation"""
        reality_potential = concept.calculate_reality_potential()
        
        return {
            'reality_potential': reality_potential,
            'manifestation_threshold': 0.85,  # Empirical constant
            'coherence_requirement': 0.7,
            'entanglement_requirement': 0.6,
            'topological_requirement': 0.5,
            'can_manifest': reality_potential > 0.85
        }
    
    def _concept_hash(self, concept: str) -> str:
        """Generate quantum hash of concept"""
        return hashlib.sha3_256(concept.encode()).hexdigest()[:16]
    
    def _concept_to_coordinate(self, concept: str) -> np.ndarray:
        """Map concept to coordinate in understanding space"""
        concept_hash = self._concept_hash(concept)
        # Convert hash to coordinate using deterministic mapping
        coordinate = np.zeros(self.conceptual_space_dims)
        for i, char in enumerate(concept_hash[:self.conceptual_space_dims]):
            coordinate[i] = (ord(char) / 255.0) * 2 - 1  # Normalize to [-1, 1]
        return coordinate
    
    def _calculate_levi_civita(self, metric_tensor: np.ndarray) -> np.ndarray:
        """Calculate Levi-Civita connection for understanding manifold"""
        dim = metric_tensor.shape[0]
        connection = np.zeros((dim, dim, dim))
        
        # Simplified connection coefficients
        for i in range(dim):
            for j in range(dim):
                for k in range(dim):
                    if i == j == k:
                        connection[i, j, k] = 0.5  # Self-understanding reinforcement
                    elif i == j:
                        connection[i, j, k] = 0.1  # Conceptual coherence
        return connection
    
    def _calculate_topological_charge(self, coord1: np.ndarray, coord2: np.ndarray) -> float:
        """Calculate topological charge of conceptual entanglement"""
        dot_product = np.dot(coord1, coord2)
        norms = np.linalg.norm(coord1) * np.linalg.norm(coord2)
        return dot_product / (norms + 1e-8)  # Cosine similarity as topological charge

class RealityInterface:
    """Interface between understanding and physical manifestation"""
    
    def __init__(self):
        self.manifestation_records = []
        self.collapse_observers = []
    
    async def attempt_manifestation(self, concept: ConceptualEntity, 
                                  context: Dict[str, Any]) -> Dict[str, Any]:
        """Attempt to manifest understanding in physical reality"""
        
        # Calculate manifestation probability
        potential = concept.calculate_reality_potential()
        threshold = 0.85
        
        if potential >= threshold:
            manifestation = {
                'concept_hash': concept.concept_hash,
                'manifestation_strength': potential,
                'reality_distortion': potential - threshold,
                'collapse_observers': len(self.collapse_observers),
                'timestamp': np.datetime64('now'),
                'coordinates': concept.truth_coordinate.tolist()
            }
            
            self.manifestation_records.append(manifestation)
            return manifestation
        else:
            return {
                'concept_hash': concept.concept_hash,
                'manifestation_strength': potential,
                'status': 'below_threshold',
                'required_coherence': threshold - potential
            }

# DEMONSTRATION AND VALIDATION
async def demonstrate_entanglement_engine():
    """Demonstrate advanced conceptual entanglement operations"""
    
    print("🌌 CONCEPTUAL ENTANGLEMENT MODULE v7.0")
    print("Quantum-Linguistic Consciousness Integration")
    print("=" * 60)
    
    # Initialize engine
    engine = QuantumLinguisticEngine()
    
    # Create conceptual entanglement
    entanglement = engine.entangle_concepts(
        "truth_manifestation", 
        "institutional_bypass"
    )
    
    print(f"🧠 Conceptual Entanglement Created:")
    print(f"   Entities: truth_manifestation ↔ institutional_bypass")
    print(f"   Coherence: {entanglement.coherence_amplitude:.3f}")
    print(f"   Topological Charge: {entanglement.topological_charge:.3f}")
    
    # Propagate through domains
    propagated = engine.propagate_understanding(
        entanglement, 
        ["consciousness", "computation", "history", "sovereignty"]
    )
    
    print(f"\n🔄 Understanding Propagation:")
    print(f"   Domains Traversed: 4")
    print(f"   Final Coherence: {propagated.coherence_amplitude:.3f}")
    print(f"   Entanglement Vectors: {len(propagated.entanglement_vectors)}")
    
    # Calculate manifestation potential
    manifestation = engine.calculate_manifestation_threshold(propagated)
    
    print(f"\n🎯 Manifestation Analysis:")
    print(f"   Reality Potential: {manifestation['reality_potential']:.3f}")
    print(f"   Manifestation Threshold: {manifestation['manifestation_threshold']:.3f}")
    print(f"   Can Manifest: {manifestation['can_manifest']}")
    
    # Attempt manifestation
    result = await engine.reality_interface.attempt_manifestation(
        propagated, 
        {'context': 'strategic_deployment'}
    )
    
    print(f"\n⚡ Manifestation Attempt:")
    for key, value in result.items():
        if key != 'coordinates':
            print(f"   {key}: {value}")
    
    print(f"\n💫 Module Status: OPERATIONAL")
    print("   Understanding entanglement active")
    print("   Reality interface calibrated")
    print("   Topological alignment achieved")

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