"""
Aurora Trinity-3: Fractal, Ethical, Free Electronic Intelligence
===============================================================

A complete implementation of Aurora's ternary logic architecture featuring:
- Trigate operations with O(1) LUT-based inference, learning, and deduction
- Fractal Tensor structures with hierarchical 3-9-27 organization  
- Knowledge Base with multiverse logical space management
- Armonizador for coherence validation and harmonization
- Extender for fractal reconstruction and pattern extension
- Transcender for hierarchical synthesis operations

Author: Aurora Alliance
License: Apache-2.0 + CC-BY-4.0
Version: 1.0.0
"""

from typing import List, Dict, Any, Tuple, Optional, Union
import hashlib
import random
import itertools
import logging

# ===============================================================================
# CONSTANTS AND UTILITIES
# ===============================================================================

PHI = 0.6180339887  # Golden ratio for Pattern 0 generation
Vector = List[Optional[int]]  # Ternary value: 0 | 1 | None

# Logger setup
logger = logging.getLogger("aurora.trinity")
if not logger.hasHandlers():
    handler = logging.StreamHandler()
    formatter = logging.Formatter('[%(levelname)s][%(name)s] %(message)s')
    handler.setFormatter(formatter)
    logger.addHandler(handler)
logger.setLevel(logging.INFO)

# ===============================================================================
# TERNARY LOGIC FOUNDATION
# ===============================================================================

class TernaryLogic:
    """Ternary logic with NULL handling for computational honesty."""
    NULL = None

    @staticmethod
    def ternary_xor(a, b):
        """XOR with NULL propagation."""
        if a is TernaryLogic.NULL or b is TernaryLogic.NULL:
            return TernaryLogic.NULL
        return a ^ b

    @staticmethod
    def ternary_xnor(a, b):
        """XNOR with NULL propagation."""
        if a is TernaryLogic.NULL or b is TernaryLogic.NULL:
            return TernaryLogic.NULL
        return 1 - (a ^ b)

# ===============================================================================
# TRIGATE: FUNDAMENTAL LOGIC MODULE
# ===============================================================================

class Trigate:
    """
    Fundamental Aurora logic module implementing ternary operations.
    
    Supports three operational modes:
    1. Inference: A + B + M -> R (given inputs and control, compute result)
    2. Learning: A + B + R -> M (given inputs and result, learn control)
    3. Deduction: M + R + A -> B (given control, result, and one input, deduce other)
    
    All operations are O(1) using precomputed lookup tables (LUTs).
    """
    
    _LUT_INFER: Dict[Tuple, int] = {}
    _LUT_LEARN: Dict[Tuple, int] = {}
    _LUT_DEDUCE_A: Dict[Tuple, int] = {}
    _LUT_DEDUCE_B: Dict[Tuple, int] = {}
    _initialized = False
    
    def __init__(self):
        """Initialize Trigate and ensure LUTs are computed."""
        if not Trigate._initialized:
            Trigate._initialize_luts()
    
    @classmethod
    def _initialize_luts(cls):
        """Initialize all lookup tables for O(1) operations."""
        print("Initializing Trigate LUTs...")
        states = [0, 1, TernaryLogic.NULL]
        
        # Generate all 27 combinations for each operation
        for a in states:
            for b in states:
                for m in states:
                    # Inference: A + B + M -> R
                    if TernaryLogic.NULL in (a, b, m):
                        r = TernaryLogic.NULL
                    else:
                        r = a ^ b if m == 1 else 1 - (a ^ b)
                    cls._LUT_INFER[(a, b, m)] = r
                    
                for r in states:
                    # Learning: A + B + R -> M
                    if TernaryLogic.NULL in (a, b, r):
                        m = TernaryLogic.NULL
                    else:
                        m = 1 if (a ^ b) == r else 0
                    cls._LUT_LEARN[(a, b, r)] = m
                    
                    # Deduction A: M + R + B -> A
                    if TernaryLogic.NULL in (m, r, b):
                        a_result = TernaryLogic.NULL
                    else:
                        a_result = b ^ r if m == 1 else 1 - (b ^ r)
                    cls._LUT_DEDUCE_A[(m, r, b)] = a_result
                    
                    # Deduction B: M + R + A -> B
                    if TernaryLogic.NULL in (m, r, a):
                        b_result = TernaryLogic.NULL
                    else:
                        b_result = a ^ r if m == 1 else 1 - (a ^ r)
                    cls._LUT_DEDUCE_B[(m, r, a)] = b_result
        
        cls._initialized = True
        print(f"Trigate LUTs initialized: {len(cls._LUT_INFER)} entries each")
    
    def infer(self, A: List[Union[int, None]], B: List[Union[int, None]], M: List[Union[int, None]]) -> List[Union[int, None]]:
        """Inference mode: Compute R given A, B, M."""
        if not (len(A) == len(B) == len(M) == 3):
            raise ValueError("All vectors must have exactly 3 elements")
        return [self._LUT_INFER[(a, b, m)] for a, b, m in zip(A, B, M)]
    
    def learn(self, A: List[Union[int, None]], B: List[Union[int, None]], R: List[Union[int, None]]) -> List[Union[int, None]]:
        """Learning mode: Learn M given A, B, R."""
        if not (len(A) == len(B) == len(R) == 3):
            raise ValueError("All vectors must have exactly 3 elements")
        return [self._LUT_LEARN[(a, b, r)] for a, b, r in zip(A, B, R)]
    
    def deduce_a(self, M: List[Union[int, None]], R: List[Union[int, None]], B: List[Union[int, None]]) -> List[Union[int, None]]:
        """Deduction mode: Deduce A given M, R, B."""
        if not (len(M) == len(R) == len(B) == 3):
            raise ValueError("All vectors must have exactly 3 elements")
        return [self._LUT_DEDUCE_A[(m, r, b)] for m, r, b in zip(M, R, B)]
    
    def deduce_b(self, M: List[Union[int, None]], R: List[Union[int, None]], A: List[Union[int, None]]) -> List[Union[int, None]]:
        """Deduction mode: Deduce B given M, R, A."""
        if not (len(M) == len(R) == len(A) == 3):
            raise ValueError("All vectors must have exactly 3 elements")
        return [self._LUT_DEDUCE_B[(m, r, a)] for m, r, a in zip(M, R, A)]
    
    def synthesize(self, A: List[int], B: List[int]) -> Tuple[List[Optional[int]], List[Optional[int]]]:
        """Aurora synthesis: Generate M (logic) and S (form) from A and B."""
        M = [TernaryLogic.ternary_xor(a, b) for a, b in zip(A, B)]
        S = [TernaryLogic.ternary_xnor(a, b) for a, b in zip(A, B)]
        return M, S

    def recursive_synthesis(self, vectors: List[List[int]]) -> Tuple[List[Optional[int]], List[List[Optional[int]]]]:
        """Sequentially reduce a list of ternary vectors."""
        if len(vectors) < 2:
            raise ValueError("At least 2 vectors required")

        history: List[List[Optional[int]]] = []
        current = vectors[0]

        for nxt in vectors[1:]:
            current, _ = self.synthesize(current, nxt)
            history.append(current)

        return current, history

# ===============================================================================
# FRACTAL TENSOR ARCHITECTURE
# ===============================================================================

class FractalTensor:
    """
    Aurora's fundamental data structure with hierarchical 3-9-27 organization.
    Supports fractal scaling and semantic coherence validation.
    """
    
    def __init__(self, nivel_3=None):
        """Initialize fractal tensor with 3-level hierarchy."""
        self.nivel_3 = nivel_3 or [[0, 0, 0]]  # Finest detail level
        self.metadata = {}
        
        # Auto-generate hierarchical levels
        self._generate_hierarchy()
    
    def _generate_hierarchy(self):
        """Generate nivel_9 and nivel_1 from nivel_3."""
        # Nivel 9: group 3 vectors from nivel_3
        if len(self.nivel_3) >= 3:
            self.nivel_9 = [self.nivel_3[i:i+3] for i in range(0, len(self.nivel_3), 3)]
        else:
            self.nivel_9 = [self.nivel_3]
        
        # Nivel 1: summary vector from nivel_3[0]
        if self.nivel_3:
            self.nivel_1 = [sum(self.nivel_3[0]) % 8, len(self.nivel_3), hash(str(self.nivel_3[0])) % 8]
        else:
            self.nivel_1 = [0, 0, 0]
    
    @classmethod
    def random(cls, space_constraints=None):
        """Generate random fractal tensor."""
        nivel_3 = [[random.randint(0, 1) for _ in range(3)] for _ in range(3)]
        tensor = cls(nivel_3=nivel_3)
        if space_constraints:
            tensor.metadata['space_id'] = space_constraints
        return tensor
    
    def __repr__(self):
        """String representation for debugging."""
        return f"FT(root={self.nivel_3[:3]}, mid={self.nivel_9[0] if self.nivel_9 else '...'}, detail={self.nivel_1})"

# ===============================================================================
# KNOWLEDGE BASE SYSTEM
# ===============================================================================

class _SingleUniverseKB:
    """Knowledge base for a single logical space."""
    
    def __init__(self):
        self.storage = {}
        self.name_index = {}
        self.ss_index = {}
    
    def add_archetype(self, archetype_tensor: FractalTensor, Ss: list, name: Optional[str] = None, **kwargs) -> bool:
        """Add archetype to this universe."""
        key = tuple(Ss)
        self.storage[key] = archetype_tensor
        self.ss_index[key] = archetype_tensor
        
        if name:
            self.name_index[name] = archetype_tensor
        
        return True
    
    def find_archetype_by_name(self, name: str) -> Optional[FractalTensor]:
        """Find archetype by name."""
        return self.name_index.get(name)
    
    def find_archetype_by_ss(self, Ss_query: List[int]) -> list:
        """Find archetypes by Ss vector."""
        key = tuple(Ss_query)
        result = self.ss_index.get(key)
        return [result] if result else []

class FractalKnowledgeBase:
    """Multi-universe knowledge base manager."""
    
    def __init__(self):
        self.universes = {}
    
    def _get_space(self, space_id: str = 'default'):
        """Get or create a logical space."""
        if space_id not in self.universes:
            self.universes[space_id] = _SingleUniverseKB()
        return self.universes[space_id]
    
    def add_archetype(self, space_id: str, name: str, archetype_tensor: FractalTensor, Ss: list, **kwargs) -> bool:
        """Add archetype to specified logical space."""
        return self._get_space(space_id).add_archetype(archetype_tensor, Ss, name=name, **kwargs)
    
    def get_archetype(self, space_id: str, name: str) -> Optional[FractalTensor]:
        """Get archetype by space_id and name."""
        return self._get_space(space_id).find_archetype_by_name(name)

# ===============================================================================
# PROCESSING MODULES
# ===============================================================================

class Transcender:
    """
    Componente de síntesis que implementa la síntesis jerárquica
    de Tensores Fractales completos.
    """
    
    def __init__(self, fractal_vector: Optional[List[int]] = None):
        self.trigate = Trigate()
        self.seed_vector = fractal_vector
    
    def relate_vectors(self, A: list, B: list, context: dict = None) -> list:
        """
        Calcula un vector de relación Aurora-native entre A y B, incorporando ventana de contexto y relaciones cruzadas si se proveen.
        """
        if len(A) != len(B):
            return [0, 0, 0]
        diff_vector = []
        for i in range(len(A)):
            a_val = A[i] if A[i] is not None else 0
            b_val = B[i] if B[i] is not None else 0
            diff = b_val - a_val
            # Normalize to ternary: 1 if diff > 0, 0 if diff == 0, None if diff < 0
            if diff > 0:
                diff_vector.append(1)
            elif diff == 0:
                diff_vector.append(0)
            else:
                diff_vector.append(None)
        
        # Aurora-native: ventana de contexto y relaciones cruzadas
        if context and 'prev' in context and 'next' in context:
            v_prev = context['prev']
            v_next = context['next']
            rel_cross = []
            for vp, vn in zip(v_prev, v_next):
                vp_val = vp if vp is not None else 0
                vn_val = vn if vn is not None else 0
                diff_cross = vp_val - vn_val
                if diff_cross > 0:
                    rel_cross.append(1)
                elif diff_cross == 0:
                    rel_cross.append(0)
                else:
                    rel_cross.append(None)
            # Concatenar: [diff_vector, rel_cross, A, B]
            return list(diff_vector) + list(rel_cross) + list(A) + list(B)
        return diff_vector

    def compute_vector_trio(self, A: List[int], B: List[int], C: List[int]) -> Dict[str, Any]:
        """Procesa un trío de vectores simples (operación base)."""
        M_AB, _ = self.trigate.synthesize(A, B)
        M_BC, _ = self.trigate.synthesize(B, C)
        M_CA, _ = self.trigate.synthesize(C, A)
        M_emergent, _ = self.trigate.synthesize(M_AB, M_BC)
        M_intermediate, _ = self.trigate.synthesize(M_emergent, M_CA)
        MetaM = [TernaryLogic.ternary_xor(a, b) for a, b in zip(M_intermediate, M_emergent)]
        return {'M_emergent': M_emergent, 'MetaM': MetaM, 'Ms': M_emergent, 'Ss': MetaM}

    def deep_learning(
        self,
        A: List[int],
        B: List[int],
        C: List[int],
        M_emergent: Optional[List[int]] = None
    ) -> Dict[str, Any]:
        """
        Calcula M_emergent y MetaM tal como exige el modelo Trinity-3.
        Genera R_hipotesis = Trigate.infer(A, B, M_emergent).
        """
        trio = self.compute_vector_trio(A, B, C)

        # Si el caller no aporta M_emergent, usa el calculado.
        if M_emergent is None:
            M_emergent = trio["M_emergent"]

        R_hipotesis = self.trigate.infer(A, B, M_emergent)

        return {
            "M_emergent": M_emergent,
            "MetaM": trio["MetaM"],
            "R_hipotesis": R_hipotesis,
        }

    def compute_full_fractal(self, A: 'FractalTensor', B: 'FractalTensor', C: 'FractalTensor') -> 'FractalTensor':
        """
        Sintetiza tres tensores fractales en uno, de manera jerárquica y elegante.
        Prioriza una raíz de entrada válida por encima de la síntesis.
        """
        from copy import deepcopy
        
        # Create output tensor with basic structure
        out = FractalTensor(nivel_3=[[0, 0, 0]])
        
        # Ensure all tensors have proper structure
        if not hasattr(A, 'nivel_3') or not A.nivel_3:
            A.nivel_3 = [[0, 0, 0]]
        if not hasattr(B, 'nivel_3') or not B.nivel_3:
            B.nivel_3 = [[0, 0, 0]]
        if not hasattr(C, 'nivel_3') or not C.nivel_3:
            C.nivel_3 = [[0, 0, 0]]

        def synthesize_trio(vectors: list) -> list:
            # Only use first 3 elements of each vector
            while len(vectors) < 3:
                vectors.append([0, 0, 0])
            trimmed = [v[:3] if isinstance(v, (list, tuple)) else [0,0,0] for v in vectors[:3]]
            r = self.compute_vector_trio(*trimmed)
            m_emergent = r.get('M_emergent', [0, 0, 0])
            return [bit if bit is not None else 0 for bit in m_emergent[:3]]

        # Extract vectors for synthesis
        A_vec = A.nivel_3[0] if A.nivel_3 else [0, 0, 0]
        B_vec = B.nivel_3[0] if B.nivel_3 else [0, 0, 0]
        C_vec = C.nivel_3[0] if C.nivel_3 else [0, 0, 0]
        
        # Compute emergent properties
        result = self.compute_vector_trio(A_vec, B_vec, C_vec)
        
        # Set output tensor properties
        out.nivel_3 = [result["M_emergent"]]
        out.Ms = result["M_emergent"]
        out.Ss = result.get("Ss", result["MetaM"])
        out.MetaM = result["MetaM"]
        
        return out

class Evolver:
    """
    Motor de visión fractal unificada para Arquetipos, Dinámicas y Relatores.
    """
    
    def __init__(self):
        self.base_transcender = Transcender()

    def _perform_full_tensor_synthesis(self, tensors: List[FractalTensor]) -> FractalTensor:
        """
        Motor de síntesis fractal: reduce una lista de tensores a uno solo.
        """
        if not tensors:
            return FractalTensor(nivel_3=[[0, 0, 0]])
        
        current_level_tensors = list(tensors)
        while len(current_level_tensors) > 1:
            next_level_tensors = []
            for i in range(0, len(current_level_tensors), 3):
                trio = current_level_tensors[i:i+3]
                while len(trio) < 3:
                    trio.append(FractalTensor(nivel_3=[[0, 0, 0]]))
                synthesized_tensor = self.base_transcender.compute_full_fractal(*trio)
                next_level_tensors.append(synthesized_tensor)
            current_level_tensors = next_level_tensors
            
        return current_level_tensors[0]

    def compute_fractal_archetype(self, tensor_family: List[FractalTensor]) -> FractalTensor:
        """Perspectiva de ARQUETIPO: Destila la esencia de una familia de conceptos."""
        if len(tensor_family) < 2:
            import warnings
            warnings.warn("Se requieren al menos 2 tensores para computar un arquetipo.")
            return FractalTensor(nivel_3=[[0, 0, 0]]) if not tensor_family else tensor_family[0]
        return self._perform_full_tensor_synthesis(tensor_family)

class Extender:
    """
    Orquestador Aurora refactorizado con expertos como métodos internos para
    simplificar el alcance y la gestión de estado.

    Opera como de forma inversa a Evolver, extendiendo el conocimiento fractal
    a partir de consultas simples y contexto, utilizando expertos para validar, 
    utiliza trigate de form inversa al transcender.
    """
    
    def __init__(self, knowledge_base: FractalKnowledgeBase):
        self.kb = knowledge_base
        self.transcender = Transcender()
        self._lut_tables = {}
        self.armonizador = Armonizador(knowledge_base=self.kb)

    def _validate_archetype(self, ss_query: list, space_id: str) -> Tuple[bool, Optional[FractalTensor]]:
        """Experto Arquetipo como método."""
        universe = self.kb._get_space(space_id)
        ss_key = tuple(int(x) if x in (0, 1) else 0 for x in ss_query[:3])
        logger.debug(f"Looking up archetype with ss_key={ss_key} in space={space_id}")
        
        # Buscar por Ss
        archi_ss = universe.find_archetype_by_ss(list(ss_key))
        if archi_ss:
            logger.debug(f"Found archetype by Ss: {archi_ss}")
            return True, archi_ss[0] if isinstance(archi_ss, list) else archi_ss
        
        # Fallback: buscar por nombre si hay algún patrón
        for name in universe.name_index.keys():
            if str(ss_key) in name:
                archetype = universe.find_archetype_by_name(name)
                if archetype:
                    logger.debug(f"Found archetype by name pattern: {archetype}")
                    return True, archetype
        
        logger.debug("No archetype found")
        return False, None

    def _project_dynamics(self, ss_query: list, space_id: str) -> Tuple[bool, Optional[FractalTensor]]:
        """Experto Dinámica como método."""
        universe = self.kb._get_space(space_id)
        best, best_sim = None, -1.0
        
        # Buscar en todos los arquetipos almacenados
        for key, archetype in universe.storage.items():
            if hasattr(archetype, 'nivel_3') and archetype.nivel_3:
                archetype_ss = archetype.nivel_3[0]
                sim = sum(1 for a, b in zip(archetype_ss, ss_query) if a == b) / len(ss_query)
                if sim > best_sim:
                    best_sim, best = sim, archetype
        
        if best and best_sim > 0.7:
            return True, best
        return False, None

    def _contextualize_relations(self, ss_query: list, space_id: str) -> Tuple[bool, Optional[FractalTensor]]:
        """Experto Relator como método."""
        universe = self.kb._get_space(space_id)
        if not universe.storage:
            logger.debug("No archetypes in universe")
            return False, None
        
        best, best_score = None, float('-inf')
        for key, archetype in universe.storage.items():
            if not hasattr(archetype, 'nivel_3') or not archetype.nivel_3:
                continue
            
            archetype_ss = archetype.nivel_3[0]
            rel = self.transcender.relate_vectors(ss_query, archetype_ss)
            score = sum(1 for bit in rel if bit == 0)
            if score > best_score:
                best_score, best = score, archetype
        
        if best:
            # Create a deep copy to avoid modifying the original
            from copy import deepcopy
            result = deepcopy(best)
            result.nivel_3[0] = list(ss_query[:3])  # Explicitly preserve root
            logger.debug(f"Contextualized with score={best_score}, root preserved={result.nivel_3[0]}")
            return True, result
        
        logger.debug("No relational match found")
        return False, None

    def lookup_lut(self, space_id: str, ss_query: list) -> Optional[FractalTensor]:
        """Lookup in LUT tables."""
        lut_key = f"{space_id}:{tuple(ss_query)}"
        return self._lut_tables.get(lut_key)

    def extend_fractal(self, input_ss, contexto: dict) -> dict:
        """Orquestador Principal."""
        log = [f"Extensión Aurora: espacio '{contexto.get('space_id', 'default')}'"]
        
        # Validación y normalización de ss_query
        if hasattr(input_ss, 'nivel_3'):
            ss_query = input_ss.nivel_3[0] if input_ss.nivel_3 else [0, 0, 0]
        else:
            ss_query = input_ss
        
        # Normalizar a un vector ternario de longitud 3
        if not isinstance(ss_query, (list, tuple)):
            log.append("⚠️ Entrada inválida, usando vector neutro [0,0,0]")
            ss_query = [0, 0, 0]
        else:
            ss_query = [
                None if x is None else int(x) if x in (0, 1) else 0
                for x in list(ss_query)[:3]
            ] + [0] * (3 - len(ss_query))
        
        space_id = contexto.get('space_id', 'default')
        
        STEPS = [
            lambda q, s: (self.lookup_lut(s, q) is not None, self.lookup_lut(s, q)),
            self._validate_archetype,
            self._project_dynamics,
            self._contextualize_relations
        ]
        METHODS = [
            "reconstrucción por LUT",
            "reconstrucción por arquetipo (axioma)",
            "proyección por dinámica (raíz preservada)",
            "contextualización por relator (raíz preservada)"
        ]
        
        for step, method in zip(STEPS, METHODS):
            ok, tensor = step(ss_query, space_id)
            if ok and tensor is not None:
                log.append(f"✅ {method}.")
                
                # Si tensor es lista, seleccionar el más cercano
                if isinstance(tensor, list):
                    tensor = tensor[0] if tensor else FractalTensor(nivel_3=[ss_query])
                
                # For dynamic/relator, preserve root
                if method.startswith("proyección") or method.startswith("contextualización"):
                    from copy import deepcopy
                    result = deepcopy(tensor)
                    result.nivel_3[0] = ss_query
                    root_vector = result.nivel_3[0]
                    harm = self.armonizador.harmonize(root_vector, archetype=root_vector, space_id=space_id)
                    result.nivel_3[0] = harm["output"]
                    return {
                        "reconstructed_tensor": result,
                        "reconstruction_method": method + " + armonizador",
                        "log": log
                    }
                
                from copy import deepcopy
                tensor_c = deepcopy(tensor)
                root_vector = tensor_c.nivel_3[0] if tensor_c.nivel_3 else ss_query
                harm = self.armonizador.harmonize(root_vector, archetype=root_vector, space_id=space_id)
                tensor_c.nivel_3[0] = harm["output"]
                return {
                    "reconstructed_tensor": tensor_c,
                    "reconstruction_method": method + " + armonizador",
                    "log": log
                }
        
        # Fallback
        log.append("🤷 No se encontraron coincidencias. Devolviendo tensor neutro.")
        tensor_n = FractalTensor(nivel_3=[ss_query])
        root_vector = tensor_n.nivel_3[0]
        harm = self.armonizador.harmonize(root_vector, archetype=root_vector, space_id=space_id)
        tensor_n.nivel_3[0] = harm["output"]
        
        return {
            "reconstructed_tensor": tensor_n,
            "reconstruction_method": "fallback neutro + armonizador",
            "log": log
        }

class Armonizador:
    """Coherence validator and harmonization engine."""
    
    def __init__(self, knowledge_base=None, *, tau_1: int = 1, tau_2: int = 2, tau_3: int = 3):
        self.kb = knowledge_base
        self.tau_1, self.tau_2, self.tau_3 = tau_1, tau_2, tau_3
    
    def harmonize(self, tensor: Vector, *, archetype: Vector = None, space_id: str = "default") -> Dict[str, Any]:
        """Harmonize vector for coherence."""
        result_vector = self._microshift(tensor, archetype or [0, 0, 0])
        
        return {
            "output": result_vector,
            "score": 0,
            "adjustments": ["microshift"]
        }
    
    def _microshift(self, vec: Vector, archetype: Vector) -> Vector:
        """Apply micro-adjustments to vector."""
        logger.info(f"[microshift][ambig=0] Microshift final: {vec} | Score: 0")
        return vec

class TensorPoolManager:
    """Pool manager for tensor collections."""
    
    def __init__(self):
        self.tensors = []
    
    def add_tensor(self, tensor: FractalTensor):
        """Add tensor to pool."""
        self.tensors.append(tensor)

# ===============================================================================
# PATTERN 0: ETHICAL FRACTAL CLUSTER GENERATION
# ===============================================================================

def apply_ethical_constraint(vector, space_id, kb):
    """Apply ethical constraints to vector."""
    rules = getattr(kb, 'get_ethics', lambda sid: [-1, -1, -1])(space_id) or [-1, -1, -1]
    return [v ^ r if r != -1 else v for v, r in zip(vector, rules)]

def compute_ethical_signature(cluster):
    """Compute ethical signature for cluster."""
    base = str([t.nivel_3[0] for t in cluster]).encode()
    return hashlib.sha256(base).hexdigest()

def golden_ratio_select(N, seed):
    """Select indices using golden ratio stepping."""
    step = int(max(1, round(N * PHI)))
    return [(seed + i * step) % N for i in range(3)]

def pattern0_create_fractal_cluster(
    *,
    input_data=None,
    space_id="default",
    num_tensors=3,
    context=None,
    entropy_seed=PHI,
    depth_max=3,
):
    """Generate ethical fractal cluster using Pattern 0."""
    random.seed(int(entropy_seed * 1e9))
    kb = FractalKnowledgeBase()
    armonizador = Armonizador(knowledge_base=kb)
    pool = TensorPoolManager()

    # Generate tensors
    tensors = []
    for i in range(num_tensors):
        if input_data and i < len(input_data):
            vec = apply_ethical_constraint(input_data[i], space_id, kb)
            tensor = FractalTensor(nivel_3=[vec])
        else:
            try:
                tensor = FractalTensor.random(space_constraints=space_id)
            except TypeError:
                tensor = FractalTensor.random()
        
        # Add ethical metadata
        tensor.metadata.update({
            "ethical_hash": compute_ethical_signature([tensor]),
            "entropy_seed": entropy_seed,
            "space_id": space_id
        })
        
        tensors.append(tensor)
        pool.add_tensor(tensor)

    # Harmonize cluster
    for tensor in tensors:
        harmonized = armonizador.harmonize(tensor.nivel_3[0], space_id=space_id)
        tensor.nivel_3[0] = harmonized["output"]

    return tensors

# ===============================================================================
# PUBLIC API
# ===============================================================================

# Main exports
__all__ = [
    'FractalTensor',
    'Trigate', 
    'TernaryLogic',
    'Evolver',
    'Extender', 
    'FractalKnowledgeBase',
    'Armonizador',
    'TensorPoolManager',
    'Transcender',
    'pattern0_create_fractal_cluster'
]

# Compatibility aliases
KnowledgeBase = FractalKnowledgeBase