#!/usr/bin/env python3
"""
NEBULA-X: Enhanced Unified Holographic Neural Network
Corrected & Hardened version
Original author: Francisco Angulo de Lafuente - Agnuxo
This file is a patched, complete and ready-to-run version of nebula_x_complete.py
(robust handling for complex arrays, improved holographic correlation, safer quantum state
initialization and other defensive fixes).
"""

import os
import sys
import json
import time
import logging
import asyncio
import threading
from typing import Dict, List, Tuple, Optional, Any, Union
from dataclasses import dataclass, field
from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import subprocess

# Core scientific computing
import numpy as np
import scipy as sp
from scipy import ndimage, fft, optimize
import pandas as pd

# Machine Learning & Deep Learning (optional usage)
try:
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.cuda as cuda
    from torch.utils.data import DataLoader, Dataset
    import torchvision.transforms as transforms
    TORCH_AVAILABLE = True
except Exception:
    TORCH_AVAILABLE = False

# Quantum Computing
try:
    import pennylane as qml
    from pennylane import numpy as pnp
    QUANTUM_AVAILABLE = True
except ImportError:
    QUANTUM_AVAILABLE = False
    print("Warning: PennyLane not available. Quantum features disabled.")

# GPU Acceleration & Raytracing
try:
    import cupy as cp
    import cupyx.scipy.fft as cp_fft
    CUPY_AVAILABLE = True
except Exception:
    CUPY_AVAILABLE = False
    print("Warning: CuPy not available. GPU acceleration limited.")

# Optical Computing & CUDA kernels
try:
    import pycuda.driver as cuda_driver
    import pycuda.autoinit
    import pycuda.gpuarray as gpuarray
    from pycuda.compiler import SourceModule
    PYCUDA_AVAILABLE = True
except Exception:
    PYCUDA_AVAILABLE = False
    print("Warning: PyCUDA not available. Custom CUDA kernels disabled.")

# Networking & P2P
import socket
import websockets
import requests
from urllib.parse import urlparse

# Evolutionary Algorithms
try:
    from deap import base, creator, tools, algorithms
    DEAP_AVAILABLE = True
except Exception:
    DEAP_AVAILABLE = False
    print("Warning: DEAP not available. Evolutionary optimization disabled.")

# Holographic Processing
from PIL import Image
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Configuration & Utilities
import yaml
from datetime import datetime
import pickle
import hashlib
import uuid

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Helper utilities

def ensure_complex_array(arr: np.ndarray) -> np.ndarray:
    """Return a complex copy of arr without losing imaginary parts and avoiding ComplexWarning."""
    arr = np.asarray(arr)
    if np.iscomplexobj(arr):
        return arr.astype(np.complex128)
    else:
        return arr.astype(np.complex128)


def safe_reshape_to_square_2d(data: np.ndarray) -> np.ndarray:
    """Pad (with complex zeros) and reshape 1D data to a square 2D complex array."""
    data = np.asarray(data)
    if data.ndim == 1:
        size = int(np.ceil(np.sqrt(data.size)))
        total = size * size
        padded = np.zeros(total, dtype=np.complex128)
        padded[:data.size] = data.astype(np.complex128)
        return padded.reshape(size, size)
    elif data.ndim == 2:
        return ensure_complex_array(data)
    else:
        # Flatten high-dim arrays then reshape
        flat = data.flatten()
        return safe_reshape_to_square_2d(flat)


# Constants
LIGHT_SPEED = 299792458  # m/s
PLANCK_CONSTANT = 6.62607015e-34  # J⋅Hz⁻¹
BOLTZMANN_CONSTANT = 1.380649e-23  # J⋅K⁻¹


@dataclass
class NebulaConfig:
    """Complete configuration for NEBULA-X"""

    nebula_space_size: Tuple[int, int, int] = (1000, 1000, 1000)
    max_neurons: int = 1000000
    initial_neurons: int = 10000
    neuron_types: List[str] = field(default_factory=lambda: ['photonic', 'quantum', 'classical'])

    # Optical
    wavelength: float = 632.8e-9
    refractive_index: float = 1.0
    coherence_length: float = 1.0
    beam_diameter: float = 1e-3

    # Quantum
    qubits_per_neuron: int = 4
    quantum_noise_level: float = 0.01
    decoherence_time: float = 1e-6

    # Raytracing
    rays_per_neuron: int = 1000
    max_bounces: int = 10
    raytracing_resolution: Tuple[int, int] = (1024, 1024)
    monte_carlo_samples: int = 10000

    # Gravitational dynamics
    gravitational_constant: float = 1e-10
    neuron_mass: float = 1.0
    attraction_threshold: float = 0.1
    repulsion_threshold: float = 0.05

    # Evolutionary
    population_size: int = 100
    mutation_rate: float = 0.1
    crossover_rate: float = 0.8
    generations: int = 1000

    # P2P
    p2p_port: int = 8080
    max_peers: int = 50
    knowledge_sync_interval: float = 10.0

    # Benchmark
    benchmark_datasets: List[str] = field(default_factory=lambda: ['mmlu', 'gsm8k'])
    evaluation_interval: int = 100

    # Hardware
    use_gpu: bool = True
    use_rt_cores: bool = True
    use_tensor_cores: bool = True
    max_gpu_memory: float = 0.8


class QuantumNeuron:
    """Quantum neuron with local holographic memory"""

    def __init__(self, neuron_id: str, config: NebulaConfig):
        self.id = neuron_id
        self.config = config
        self.position = np.random.rand(3) * 1000
        self.velocity = np.zeros(3)
        self.mass = config.neuron_mass
        self.luminosity = 1.0
        self.connections: Dict[str, Any] = {}

        # Quantum state (if available) otherwise simulated complex state
        if QUANTUM_AVAILABLE:
            try:
                self.quantum_device = qml.device('default.qubit', wires=config.qubits_per_neuron)
                self.quantum_memory = self._initialize_quantum_state()
            except Exception as e:
                logger.warning(f"Failed to initialize PennyLane device: {e}")
                self.quantum_memory = self._simulate_quantum_state()
        else:
            self.quantum_memory = self._simulate_quantum_state()

        self.optical_properties = {
            'reflectivity': float(np.random.rand()),
            'transmissivity': float(np.random.rand()),
            'phase_shift': float(np.random.rand() * 2 * np.pi),
            'polarization': np.random.rand(3).tolist(),
            'spectrum': np.random.rand(100).tolist()
        }

        self.holographic_memory = np.zeros((64, 64), dtype=np.complex128)

    def _simulate_quantum_state(self) -> np.ndarray:
        """Create a normalized complex state vector for simulation."""
        size = 2 ** self.config.qubits_per_neuron
        state = np.random.randn(size) + 1j * np.random.randn(size)
        state = state.astype(np.complex128)
        norm = np.linalg.norm(state)
        if norm == 0:
            state[0] = 1.0
            norm = 1.0
        return state / norm

    def _initialize_quantum_state(self) -> np.ndarray:
        """Initialize a quantum state using PennyLane qnode (if available)"""
        @qml.qnode(self.quantum_device)
        def quantum_circuit():
            for i in range(self.config.qubits_per_neuron):
                qml.RY(np.random.rand() * np.pi, wires=i)
                qml.RZ(np.random.rand() * 2 * np.pi, wires=i)
            return qml.state()

        return np.array(quantum_circuit())

    def quantum_process(self, input_data: np.ndarray) -> np.ndarray:
        """Process input with the neuron's quantum memory (simulated if PennyLane not available)."""
        input_data = np.asarray(input_data)
        if not QUANTUM_AVAILABLE:
            # Simulated processing: project input onto quantum memory (real part)
            try:
                # make shapes compatible
                mem = np.asarray(self.quantum_memory)
                vec = np.resize(input_data, mem.shape)
                return np.real(np.vdot(mem, vec)) * np.ones(self.config.qubits_per_neuron)
            except Exception:
                return np.zeros(self.config.qubits_per_neuron)

        # If quantum available, build a small qnode
        @qml.qnode(self.quantum_device)
        def qnn(inputs):
            for i, val in enumerate(inputs[: self.config.qubits_per_neuron]):
                qml.RY(float(val) * np.pi, wires=i)
            # simple entangling layer
            for i in range(self.config.qubits_per_neuron - 1):
                qml.CNOT(wires=[i, i + 1])
            return [qml.expval(qml.PauliZ(i)) for i in range(self.config.qubits_per_neuron)]

        # reshape input
        inputs = np.resize(input_data, (self.config.qubits_per_neuron,))
        return np.array(qnn(inputs))

    def gravitational_force(self, other_neuron: 'QuantumNeuron') -> np.ndarray:
        r_vec = other_neuron.position - self.position
        r_mag = np.linalg.norm(r_vec)
        if r_mag < 1e-6:
            return np.zeros(3)
        F_mag = (
            self.config.gravitational_constant * self.mass * other_neuron.mass
            * self.luminosity * other_neuron.luminosity
        ) / (r_mag ** 2)
        return F_mag * (r_vec / r_mag)

    def update_position(self, dt: float, forces: np.ndarray):
        acceleration = forces / max(1e-12, self.mass)
        new_position = self.position + self.velocity * dt + 0.5 * acceleration * dt ** 2
        # Clip per-dimension with nebula_space_size
        nx, ny, nz = self.config.nebula_space_size
        new_position = np.clip(new_position, 0, [nx, ny, nz])
        self.velocity += acceleration * dt
        self.position = new_position

    def holographic_encode(self, data: np.ndarray) -> np.ndarray:
        """Encode input data into the neuron's local holographic memory and return hologram."""
        data2d = safe_reshape_to_square_2d(np.asarray(data))
        # create reference wave
        h, w = data2d.shape
        y, x = np.indices((h, w))
        reference_wave = np.exp(1j * np.pi * (x + y))
        object_wave = data2d.astype(np.complex128)
        hologram = np.abs(object_wave + reference_wave) ** 2
        self.holographic_memory = np.fft.fft2(hologram)
        return hologram

    def holographic_decode(self) -> np.ndarray:
        reconstructed = np.fft.ifft2(self.holographic_memory)
        return np.real(reconstructed)


class RaytracingEngine:
    def __init__(self, config: NebulaConfig):
        self.config = config
        self.scene_buffer = None
        self.ray_buffer = None
        self.cuda_module = None
        if PYCUDA_AVAILABLE and config.use_gpu:
            try:
                self._initialize_cuda_kernels()
            except Exception as e:
                logger.warning(f"CUDA kernel init failed: {e}")
                self.cuda_module = None

    def _initialize_cuda_kernels(self):
        cuda_code = r"""
        #include <curand_kernel.h>
        __global__ void trace_rays(float *rays, float *neurons, float *output, 
                                 int num_rays, int num_neurons) {
            int idx = blockIdx.x * blockDim.x + threadIdx.x;
            if (idx >= num_rays) return;
            curandState state;
            curand_init(idx, 0, 0, &state);
            float3 origin = make_float3(rays[idx*6], rays[idx*6+1], rays[idx*6+2]);
            float3 direction = make_float3(rays[idx*6+3], rays[idx*6+4], rays[idx*6+5]);
            float intensity = 1.0f;
            float3 color = make_float3(1.0f, 1.0f, 1.0f);
            for (int bounce = 0; bounce < 10; bounce++) {
                float min_distance = INFINITY;
                int hit_neuron = -1;
                for (int n = 0; n < num_neurons; n++) {
                    float3 neuron_pos = make_float3(neurons[n*7], neurons[n*7+1], neurons[n*7+2]);
                    float neuron_radius = neurons[n*7+3];
                    float3 oc = origin - neuron_pos;
                    float a = dot(direction, direction);
                    float b = 2.0f * dot(oc, direction);
                    float c = dot(oc, oc) - neuron_radius * neuron_radius;
                    float discriminant = b*b - 4*a*c;
                    if (discriminant > 0) {
                        float distance = (-b - sqrt(discriminant)) / (2.0f * a);
                        if (distance > 0.001f && distance < min_distance) {
                            min_distance = distance;
                            hit_neuron = n;
                        }
                    }
                }
                if (hit_neuron == -1) break;
                origin = origin + direction * min_distance;
                float reflectivity = neurons[hit_neuron*7+4];
                float phase_shift = neurons[hit_neuron*7+6];
                float3 normal = normalize(origin - make_float3(neurons[hit_neuron*7], 
                                                             neurons[hit_neuron*7+1], 
                                                             neurons[hit_neuron*7+2]));
                if (curand_uniform(&state) < reflectivity) {
                    direction = direction - 2.0f * dot(direction, normal) * normal;
                    intensity *= reflectivity;
                } else {
                    intensity *= (1.0f - reflectivity);
                    break;
                }
                color.x *= cos(phase_shift);
                color.y *= cos(phase_shift + 2.094f);
                color.z *= cos(phase_shift + 4.189f);
                intensity *= 0.9f;
                if (intensity < 0.01f) break;
            }
            output[idx*4] = intensity;
            output[idx*4+1] = color.x;
            output[idx*4+2] = color.y;
            output[idx*4+3] = color.z;
        }
        """
        try:
            self.cuda_module = SourceModule(cuda_code)
            self.trace_rays_kernel = self.cuda_module.get_function("trace_rays")
            logger.info("CUDA raytracing kernels initialized successfully")
        except Exception as e:
            logger.warning(f"Failed to initialize CUDA kernels: {e}")
            self.cuda_module = None

    def trace_neural_rays(self, neurons: List[QuantumNeuron], input_data: np.ndarray) -> np.ndarray:
        num_neurons = len(neurons)
        if num_neurons == 0:
            return np.zeros((0, 4), dtype=np.float32)
        num_rays = max(1, int(self.config.rays_per_neuron * num_neurons))
        rays = self._generate_rays(num_rays)
        neuron_data = np.zeros((num_neurons, 7), dtype=np.float32)
        for i, neuron in enumerate(neurons):
            neuron_data[i, :3] = np.asarray(neuron.position, dtype=np.float32)
            neuron_data[i, 3] = 1.0
            neuron_data[i, 4] = float(neuron.optical_properties.get('reflectivity', 0.5))
            neuron_data[i, 5] = float(neuron.optical_properties.get('transmissivity', 0.0))
            neuron_data[i, 6] = float(neuron.optical_properties.get('phase_shift', 0.0))
        if PYCUDA_AVAILABLE and self.cuda_module is not None:
            try:
                return self._cuda_raytrace(rays, neuron_data)
            except Exception as e:
                logger.warning(f"CUDA raytrace failed, falling back to CPU: {e}")
        return self._cpu_raytrace(rays, neuron_data)

    def _generate_rays(self, num_rays: int) -> np.ndarray:
        rays = np.zeros((num_rays, 6), dtype=np.float32)
        # positions
        nx, ny, nz = self.config.nebula_space_size
        rays[:, :3] = np.random.rand(num_rays, 3) * np.array([nx, ny, nz])
        # directions
        phi = np.random.rand(num_rays) * 2 * np.pi
        costheta = 1 - 2 * np.random.rand(num_rays)
        theta = np.arccos(np.clip(costheta, -1, 1))
        rays[:, 3] = np.sin(theta) * np.cos(phi)
        rays[:, 4] = np.sin(theta) * np.sin(phi)
        rays[:, 5] = np.cos(theta)
        return rays

    def _cuda_raytrace(self, rays: np.ndarray, neurons: np.ndarray) -> np.ndarray:
        num_rays = rays.shape[0]
        rays_gpu = gpuarray.to_gpu(rays.astype(np.float32))
        neurons_gpu = gpuarray.to_gpu(neurons.astype(np.float32))
        output_gpu = gpuarray.zeros((num_rays * 4,), dtype=np.float32)
        block_size = 256
        grid_size = (num_rays + block_size - 1) // block_size
        self.trace_rays_kernel(
            rays_gpu, neurons_gpu, output_gpu,
            np.int32(num_rays), np.int32(neurons.shape[0]),
            block=(block_size, 1, 1), grid=(grid_size, 1)
        )
        out = output_gpu.get().reshape(num_rays, 4)
        return out

    def _cpu_raytrace(self, rays: np.ndarray, neurons: np.ndarray) -> np.ndarray:
        num_rays = rays.shape[0]
        output = np.zeros((num_rays, 4), dtype=np.float32)
        for i in range(num_rays):
            origin = rays[i, :3].copy()
            direction = rays[i, 3:6].copy()
            direction = direction / (np.linalg.norm(direction) + 1e-12)
            intensity = 1.0
            for bounce in range(min(5, self.config.max_bounces)):
                distances = np.linalg.norm(neurons[:, :3] - origin[None, :], axis=1)
                closest = np.argmin(distances)
                if distances[closest] > 10.0:
                    break
                reflectivity = float(neurons[closest, 4])
                intensity *= reflectivity * 0.9
                direction = direction + 0.1 * np.random.randn(3)
                direction /= (np.linalg.norm(direction) + 1e-12)
                origin = neurons[closest, :3]
                if intensity < 0.01:
                    break
            output[i, 0] = intensity
            output[i, 1:4] = intensity
        return output


class HolographicMemory:
    def __init__(self, config: NebulaConfig):
        self.config = config
        self.memory_planes: Dict[str, Dict[str, Any]] = {}
        self.reconstruction_cache: Dict[str, np.ndarray] = {}

    def store_pattern(self, key: str, data: np.ndarray, reference_beam: Optional[np.ndarray] = None) -> bool:
        try:
            data_c = ensure_complex_array(np.asarray(data))
            if reference_beam is None:
                reference_beam = self._generate_reference_beam(data_c.shape)
            object_beam = data_c / (np.max(np.abs(data_c)) + 1e-12)
            interference = np.abs(object_beam + reference_beam) ** 2
            self.memory_planes[key] = {
                'interference': interference,
                'reference': reference_beam,
                'metadata': {
                    'timestamp': time.time(),
                    'shape': data_c.shape,
                    'hash': hashlib.md5(data_c.tobytes()).hexdigest()
                }
            }
            if key in self.reconstruction_cache:
                del self.reconstruction_cache[key]
            logger.info(f"Stored holographic pattern: {key}")
            return True
        except Exception as e:
            logger.error(f"Failed to store pattern {key}: {e}")
            return False

    def retrieve_pattern(self, key: str) -> Optional[np.ndarray]:
        if key not in self.memory_planes:
            return None
        if key in self.reconstruction_cache:
            return self.reconstruction_cache[key]
        try:
            plane = self.memory_planes[key]
            interference = np.asarray(plane['interference'])
            reference = np.asarray(plane['reference'])
            reconstructed = interference * np.conj(reference)
            reconstructed_fft = np.fft.fft2(reconstructed)
            h, w = reconstructed_fft.shape
            mask = np.zeros((h, w), dtype=float)
            ch, cw = h // 2, w // 2
            hh = max(1, h // 4)
            ww = max(1, w // 4)
            mask[ch - hh: ch + hh, cw - ww: cw + ww] = 1
            filtered_fft = reconstructed_fft * mask
            result = np.fft.ifft2(filtered_fft)
            self.reconstruction_cache[key] = result
            logger.debug(f"Retrieved holographic pattern: {key}")
            return result
        except Exception as e:
            logger.error(f"Failed to retrieve pattern {key}: {e}")
            return None

    def _generate_reference_beam(self, shape: Tuple[int, ...]) -> np.ndarray:
        shape = tuple(int(s) for s in shape)
        if len(shape) == 1:
            x = np.arange(shape[0])
            return np.exp(1j * 2 * np.pi * x / (shape[0] + 1e-12)).astype(np.complex128)
        elif len(shape) == 2:
            h, w = shape
            x, y = np.meshgrid(np.arange(w), np.arange(h))
            angle = np.random.rand() * 2 * np.pi
            kx = np.cos(angle)
            ky = np.sin(angle)
            return np.exp(1j * 2 * np.pi * (kx * x / (w + 1e-12) + ky * y / (h + 1e-12))).astype(np.complex128)
        else:
            ref = np.ones(shape, dtype=np.complex128)
            for dim in range(len(shape)):
                dim_ref = self._generate_reference_beam((shape[dim],))
                # reshape to broadcast
                reshape_shape = [1] * len(shape)
                reshape_shape[dim] = shape[dim]
                ref *= dim_ref.reshape(tuple(reshape_shape))
            return ref

    def holographic_rag_search(self, query: np.ndarray, top_k: int = 5) -> List[Tuple[str, float, Optional[np.ndarray]]]:
        results: List[Tuple[str, float, Optional[np.ndarray]]] = []
        try:
            query_hologram = self._data_to_hologram(query)
        except Exception as e:
            logger.warning(f"Failed to convert query to hologram: {e}")
            return results
        for key, plane in list(self.memory_planes.items()):
            try:
                stored_pattern = np.asarray(plane.get('interference'))
                # ensure shapes compatible
                corr = self._holographic_correlation(query_hologram, stored_pattern)
                score = float(np.max(np.abs(corr))) if corr.size > 0 else 0.0
                retrieved = self.retrieve_pattern(key)
                results.append((key, score, retrieved))
            except Exception as e:
                logger.warning(f"Error in holographic search for {key}: {e}")
                continue
        results.sort(key=lambda x: x[1], reverse=True)
        return results[:top_k]

    def _data_to_hologram(self, data: np.ndarray) -> np.ndarray:
        data = np.asarray(data)
        if data.ndim == 1:
            data2d = safe_reshape_to_square_2d(data)
        else:
            data2d = ensure_complex_array(data)
        reference = self._generate_reference_beam(data2d.shape)
        return np.abs(data2d.astype(np.complex128) + reference) ** 2

    def _holographic_correlation(self, pattern1: np.ndarray, pattern2: np.ndarray) -> np.ndarray:
        p1 = np.asarray(pattern1)
        p2 = np.asarray(pattern2)
        # convert to 2D arrays
        if p1.ndim == 1:
            p1 = safe_reshape_to_square_2d(p1)
        if p2.ndim == 1:
            p2 = safe_reshape_to_square_2d(p2)
        # make same shape by cropping or padding
        h = max(p1.shape[0], p2.shape[0])
        w = max(p1.shape[1], p2.shape[1])
        def to_shape(x, h, w):
            out = np.zeros((h, w), dtype=np.complex128)
            hh = min(h, x.shape[0])
            ww = min(w, x.shape[1])
            out[:hh, :ww] = x[:hh, :ww]
            return out
        p1s = to_shape(p1, h, w)
        p2s = to_shape(p2, h, w)
        fft1 = np.fft.fft2(p1s)
        fft2 = np.fft.fft2(p2s)
        correlation_fft = fft1 * np.conj(fft2)
        correlation = np.fft.ifft2(correlation_fft)
        return correlation


class EvolutionaryOptimizer:
    def __init__(self, config: NebulaConfig):
        self.config = config
        self.generation = 0
        self.best_fitness = -np.inf
        self.fitness_history: List[float] = []
        if DEAP_AVAILABLE:
            self._setup_deap()

    def _setup_deap(self):
        creator.create("FitnessMax", base.Fitness, weights=(1.0,))
        creator.create("Individual", list, fitness=creator.FitnessMax)
        self.toolbox = base.Toolbox()
        self.toolbox.register("attr_float", np.random.normal, 0, 1)
        self.toolbox.register("individual", tools.initRepeat, creator.Individual, self.toolbox.attr_float, n=100)
        self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
        self.toolbox.register("evaluate", self._evaluate_individual)
        self.toolbox.register("mate", tools.cxBlend, alpha=0.5)
        self.toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=self.config.mutation_rate)
        self.toolbox.register("select", tools.selTournament, tournsize=3)

    def _evaluate_individual(self, individual: List[float]) -> Tuple[float]:
        try:
            params = self._genes_to_params(individual)
            fitness = self._simulate_network_performance(params)
            return (fitness,)
        except Exception as e:
            logger.warning(f"Evaluation failed: {e}")
            return (-np.inf,)

    def _genes_to_params(self, genes: List[float]) -> Dict[str, Any]:
        params: Dict[str, Any] = {}
        params['learning_rate'] = max(0.0001, abs(genes[0]) * 0.1)
        params['neuron_density'] = max(0.1, abs(genes[1]))
        params['connection_strength'] = float(genes[2])
        params['optical_coherence'] = float(max(0, min(1, genes[3])))
        params['quantum_entanglement'] = float(max(0, min(1, genes[4])))
        params['hologram_resolution'] = int(abs(genes[5]) * 100) + 32
        params['reference_beam_angle'] = float(genes[6]) * np.pi
        params['interference_threshold'] = float(max(0, abs(genes[7])))
        params['rays_per_sample'] = int(abs(genes[8]) * 1000) + 100
        params['max_bounces'] = int(abs(genes[9]) * 10) + 1
        params['photon_energy'] = max(0.1, abs(genes[10]) * 10)
        return params

    def _simulate_network_performance(self, params: Dict[str, Any]) -> float:
        base_performance = 0.5
        if 0.001 <= params['learning_rate'] <= 0.01:
            base_performance += 0.1
        if 0.5 <= params['neuron_density'] <= 2.0:
            base_performance += 0.1
        if params['optical_coherence'] > 0.8:
            base_performance += 0.15
        if params['quantum_entanglement'] > 0.6:
            base_performance += 0.1
        if params['hologram_resolution'] > 512:
            base_performance -= 0.05
        if params['rays_per_sample'] > 5000:
            base_performance -= 0.05
        noise = np.random.normal(0, 0.02)
        return max(0, base_performance + noise)

    def evolve_architecture(self, generations: int = None) -> Dict[str, Any]:
        if not DEAP_AVAILABLE:
            logger.warning("DEAP not available, returning default parameters")
            return self._get_default_params()
        if generations is None:
            generations = self.config.generations
        population = self.toolbox.population(n=self.config.population_size)
        stats = tools.Statistics(lambda ind: ind.fitness.values)
        stats.register("avg", np.mean)
        stats.register("std", np.std)
        stats.register("min", np.min)
        stats.register("max", np.max)
        logger.info(f"Starting evolutionary optimization for {generations} generations")
        population, logbook = algorithms.eaSimple(
            population, self.toolbox,
            cxpb=self.config.crossover_rate,
            mutpb=self.config.mutation_rate,
            ngen=generations,
            stats=stats,
            verbose=True
        )
        best_individual = tools.selBest(population, 1)[0]
        best_params = self._genes_to_params(best_individual)
        self.best_fitness = best_individual.fitness.values[0]
        logger.info(f"Evolution completed. Best fitness: {self.best_fitness}")
        return best_params

    def _get_default_params(self) -> Dict[str, Any]:
        return {
            'learning_rate': 0.001,
            'neuron_density': 1.0,
            'connection_strength': 0.5,
            'optical_coherence': 0.9,
            'quantum_entanglement': 0.7,
            'hologram_resolution': 256,
            'reference_beam_angle': np.pi / 4,
            'interference_threshold': 0.1,
            'rays_per_sample': 1000,
            'max_bounces': 5,
            'photon_energy': 1.0
        }


class P2PNetworkManager:
    def __init__(self, config: NebulaConfig):
        self.config = config
        self.node_id = str(uuid.uuid4())
        self.peers: Dict[str, Any] = {}
        self.knowledge_cache: Dict[str, Any] = {}
        self.server_socket = None
        self.running = False

    async def start_network(self):
        self.running = True
        start_server = websockets.serve(self.handle_connection, 'localhost', self.config.p2p_port)
        logger.info(f"P2P node {self.node_id} starting on port {self.config.p2p_port}")
        await asyncio.gather(start_server, self.discovery_loop(), self.sync_loop())

    async def handle_connection(self, websocket, path):
        peer_id = None
        try:
            async for message in websocket:
                data = json.loads(message)
                if data.get('type') == 'handshake':
                    peer_id = data.get('node_id')
                    self.peers[peer_id] = {'websocket': websocket, 'last_seen': time.time(), 'knowledge_hash': data.get('knowledge_hash', ''), 'capabilities': data.get('capabilities', [])}
                    response = {'type': 'handshake_response', 'node_id': self.node_id, 'knowledge_hash': self._compute_knowledge_hash(), 'capabilities': ['holographic_memory', 'quantum_processing', 'raytracing']}
                    await websocket.send(json.dumps(response))
                elif data.get('type') == 'knowledge_request':
                    await self.handle_knowledge_request(websocket, data)
                elif data.get('type') == 'knowledge_share':
                    await self.handle_knowledge_share(data)
                elif data.get('type') == 'computation_request':
                    await self.handle_computation_request(websocket, data)
        except websockets.exceptions.ConnectionClosed:
            if peer_id and peer_id in self.peers:
                del self.peers[peer_id]
                logger.info(f"Peer {peer_id} disconnected")
        except Exception as e:
            logger.error(f"Error handling P2P connection: {e}")

    async def discovery_loop(self):
        while self.running:
            try:
                if len(self.peers) < self.config.max_peers:
                    await self.discover_peers()
                current_time = time.time()
                disconnected = [pid for pid, p in self.peers.items() if current_time - p['last_seen'] > 60]
                for pid in disconnected:
                    del self.peers[pid]
                    logger.info(f"Removed inactive peer: {pid}")
                await asyncio.sleep(30)
            except Exception as e:
                logger.error(f"Error in discovery loop: {e}")
                await asyncio.sleep(10)

    async def sync_loop(self):
        while self.running:
            try:
                await self.sync_knowledge()
                await asyncio.sleep(self.config.knowledge_sync_interval)
            except Exception as e:
                logger.error(f"Error in sync loop: {e}")
                await asyncio.sleep(5)

    async def discover_peers(self):
        base_port = self.config.p2p_port
        for offset in range(1, 10):
            if len(self.peers) >= self.config.max_peers:
                break
            port = base_port + offset
            if port == self.config.p2p_port:
                continue
            uri = f"ws://localhost:{port}"
            try:
                websocket = await asyncio.wait_for(websockets.connect(uri), timeout=3)
                handshake = {'type': 'handshake', 'node_id': self.node_id, 'knowledge_hash': self._compute_knowledge_hash(), 'capabilities': ['holographic_memory', 'quantum_processing', 'raytracing']}
                await websocket.send(json.dumps(handshake))
                response = await asyncio.wait_for(websocket.recv(), timeout=3)
                data = json.loads(response)
                if data.get('type') == 'handshake_response':
                    pid = data.get('node_id')
                    self.peers[pid] = {'websocket': websocket, 'last_seen': time.time(), 'knowledge_hash': data.get('knowledge_hash', ''), 'capabilities': data.get('capabilities', [])}
                    logger.info(f"Connected to peer: {pid}")
            except Exception:
                continue

    async def sync_knowledge(self):
        if not self.peers:
            return
        my_hash = self._compute_knowledge_hash()
        for pid, peer in list(self.peers.items()):
            try:
                if peer.get('knowledge_hash') != my_hash:
                    request = {'type': 'knowledge_request', 'requesting_node': self.node_id, 'knowledge_hash': my_hash}
                    await peer['websocket'].send(json.dumps(request))
                    peer['last_seen'] = time.time()
            except websockets.exceptions.ConnectionClosed:
                del self.peers[pid]
            except Exception as e:
                logger.warning(f"Failed to sync with peer {pid}: {e}")

    async def handle_knowledge_request(self, websocket, data):
        requesting_node = data.get('requesting_node')
        their_hash = data.get('knowledge_hash')
        my_hash = self._compute_knowledge_hash()
        if their_hash != my_hash:
            knowledge_data = {'type': 'knowledge_share', 'from_node': self.node_id, 'knowledge_hash': my_hash, 'knowledge': self._serialize_knowledge(), 'timestamp': time.time()}
            await websocket.send(json.dumps(knowledge_data))
            logger.debug(f"Shared knowledge with {requesting_node}")

    async def handle_knowledge_share(self, data):
        from_node = data.get('from_node')
        knowledge = data.get('knowledge')
        timestamp = data.get('timestamp')
        self._integrate_knowledge(knowledge, from_node, timestamp)
        logger.debug(f"Integrated knowledge from {from_node}")

    async def handle_computation_request(self, websocket, data):
        request_id = data.get('request_id')
        computation_type = data.get('computation_type')
        params = data.get('parameters', {})
        try:
            result = await self._execute_computation(computation_type, params)
            response = {'type': 'computation_result', 'request_id': request_id, 'result': result, 'node_id': self.node_id}
            await websocket.send(json.dumps(response))
        except Exception as e:
            error_response = {'type': 'computation_error', 'request_id': request_id, 'error': str(e), 'node_id': self.node_id}
            await websocket.send(json.dumps(error_response))

    def _compute_knowledge_hash(self) -> str:
        try:
            knowledge_str = json.dumps(self.knowledge_cache, sort_keys=True)
        except Exception:
            knowledge_str = str(self.knowledge_cache)
        return hashlib.sha256(knowledge_str.encode()).hexdigest()

    def _serialize_knowledge(self) -> Dict[str, Any]:
        return {'patterns': list(self.knowledge_cache.keys()), 'metadata': {'node_id': self.node_id, 'timestamp': time.time(), 'version': '1.0'}}

    def _integrate_knowledge(self, knowledge: Dict[str, Any], from_node: str, timestamp: float):
        if not isinstance(knowledge, dict):
            return
        for pattern in knowledge.get('patterns', []):
            if pattern not in self.knowledge_cache:
                self.knowledge_cache[pattern] = {'source': from_node, 'received_at': timestamp, 'confidence': 0.5}

    async def _execute_computation(self, computation_type: str, parameters: Dict[str, Any]) -> Any:
        if computation_type == 'holographic_reconstruction':
            pattern = parameters.get('pattern', np.random.rand(64, 64))
            return np.fft.ifft2(np.fft.fft2(pattern)).tolist()
        elif computation_type == 'quantum_simulation':
            return [0.5, 0.3, 0.2, 0.1]
        elif computation_type == 'raytracing_sample':
            return {'intensity': 0.8, 'color': [1.0, 0.9, 0.8]}
        else:
            raise ValueError(f"Unknown computation type: {computation_type}")


class BenchmarkManager:
    def __init__(self, config: NebulaConfig):
        self.config = config
        self.results: Dict[str, float] = {}
        self.baseline_scores = {'mmlu': 0.25, 'gsm8k': 0.0}

    def load_datasets(self) -> Dict[str, Any]:
        datasets: Dict[str, Any] = {}
        if 'mmlu' in self.config.benchmark_datasets:
            datasets['mmlu'] = self._load_mmlu_dataset()
        if 'gsm8k' in self.config.benchmark_datasets:
            datasets['gsm8k'] = self._load_gsm8k_dataset()
        return datasets

    def _load_mmlu_dataset(self) -> Dict[str, Any]:
        logger.info("Loading MMLU dataset (simulated)")
        samples = []
        subjects = ['mathematics', 'physics', 'computer_science', 'chemistry', 'biology']
        for i in range(100):
            subject = np.random.choice(subjects)
            samples.append({'question': f"Sample MMLU question {i} in {subject}", 'choices': ["Option A", "Option B", "Option C", "Option D"], 'correct_answer': int(np.random.randint(0, 4)), 'subject': subject})
        return {'samples': samples, 'metadata': {'total_samples': len(samples), 'subjects': subjects, 'format': 'multiple_choice'}}

    def _load_gsm8k_dataset(self) -> Dict[str, Any]:
        logger.info("Loading GSM8K dataset (simulated)")
        samples = []
        for i in range(50):
            samples.append({'question': f"Math word problem {i}: If John has {np.random.randint(1,100)} apples and gives away {np.random.randint(1,50)}, how many does he have left?", 'answer': f"{np.random.randint(1,50)}", 'solution_steps': ["Step 1: Identify initial amount", "Step 2: Identify amount given away", "Step 3: Subtract to find remainder"]})
        return {'samples': samples, 'metadata': {'total_samples': len(samples), 'format': 'math_word_problems'}}

    def evaluate_model(self, model, datasets: Dict[str, Any]) -> Dict[str, float]:
        results: Dict[str, float] = {}
        for dataset_name, dataset in datasets.items():
            logger.info(f"Evaluating on {dataset_name}")
            if dataset_name == 'mmlu':
                score = self._evaluate_mmlu(model, dataset)
            elif dataset_name == 'gsm8k':
                score = self._evaluate_gsm8k(model, dataset)
            else:
                logger.warning(f"Unknown dataset: {dataset_name}")
                continue
            results[dataset_name] = score
            baseline = self.baseline_scores.get(dataset_name, 0.0)
            improvement = ((score - baseline) / baseline * 100) if baseline > 0 else 0
            logger.info(f"{dataset_name} score: {score:.4f} (+{improvement:.1f}% vs baseline)")
        self.results.update(results)
        return results

    def _evaluate_mmlu(self, model, dataset: Dict[str, Any]) -> float:
        samples = dataset.get('samples', [])
        correct = 0
        for sample in samples:
            try:
                prediction = self._simulate_mmlu_prediction(model, sample)
                if prediction == sample.get('correct_answer'):
                    correct += 1
            except Exception as e:
                logger.warning(f"Error evaluating MMLU sample: {e}")
        return correct / len(samples) if samples else 0.0

    def _evaluate_gsm8k(self, model, dataset: Dict[str, Any]) -> float:
        samples = dataset.get('samples', [])
        correct = 0
        for sample in samples:
            try:
                prediction = self._simulate_gsm8k_prediction(model, sample)
                if self._check_math_answer(prediction, sample.get('answer')):
                    correct += 1
            except Exception as e:
                logger.warning(f"Error evaluating GSM8K sample: {e}")
        return correct / len(samples) if samples else 0.0

    def _encode_text_holographically(self, text: str) -> np.ndarray:
        text_hash = hashlib.md5(text.encode()).hexdigest()
        numeric_hash = int(text_hash, 16)
        np.random.seed(numeric_hash % (2 ** 32))
        encoding = np.random.rand(128)
        return encoding / (np.linalg.norm(encoding) + 1e-12)

    def _simulate_holographic_rag(self, query_encoding: np.ndarray) -> np.ndarray:
        knowledge_base = np.random.rand(10, 128)
        similarities = np.dot(knowledge_base, query_encoding)
        weights = np.exp(similarities) / (np.sum(np.exp(similarities)) + 1e-12)
        relevant_knowledge = np.dot(weights, knowledge_base)
        return relevant_knowledge

    def _simulate_quantum_reasoning(self, question: np.ndarray, knowledge: np.ndarray) -> np.ndarray:
        combined = np.concatenate([question, knowledge])
        phase_shifts = np.random.rand(len(combined)) * 2 * np.pi
        quantum_state = combined * np.exp(1j * phase_shifts)
        probabilities = np.abs(quantum_state) ** 2
        return probabilities[: len(question)]

    def _simulate_mmlu_prediction(self, model, sample: Dict[str, Any]) -> int:
        question = sample.get('question', '')
        choices = sample.get('choices', [])
        question_encoding = self._encode_text_holographically(question)
        relevant_knowledge = self._simulate_holographic_rag(question_encoding)
        quantum_reasoning = self._simulate_quantum_reasoning(question_encoding, relevant_knowledge)
        confidence_scores = []
        for choice in choices:
            choice_encoding = self._encode_text_holographically(choice)
            compatibility = float(np.dot(quantum_reasoning, choice_encoding[: len(quantum_reasoning)]))
            confidence_scores.append(compatibility)
        return int(np.argmax(confidence_scores)) if confidence_scores else 0

    def _simulate_gsm8k_prediction(self, model, sample: Dict[str, Any]) -> str:
        question = sample.get('question', '')
        problem_structure = self._analyze_math_problem(question)
        reasoning_steps = self._simulate_math_reasoning(problem_structure)
        answer = self._extract_numerical_answer(reasoning_steps)
        return str(answer)

    def _analyze_math_problem(self, question: str) -> Dict[str, Any]:
        import re
        numbers = [float(x) for x in re.findall(r'\d+(?:\.\d+)?', question)]
        operations = []
        ql = question.lower()
        if 'give' in ql or 'lose' in ql:
            operations.append('subtract')
        if 'get' in ql or 'buy' in ql:
            operations.append('add')
        if 'times' in ql or 'multiply' in ql:
            operations.append('multiply')
        return {'numbers': numbers, 'operations': operations, 'entities': ['apples', 'person']}

    def _simulate_math_reasoning(self, problem: Dict[str, Any]) -> List[str]:
        numbers = problem.get('numbers', [])
        operations = problem.get('operations', [])
        steps = [f"Initial amount: {numbers[0] if numbers else 0}", f"Operation: {operations[0] if operations else 'unknown'}", f"Second amount: {numbers[1] if len(numbers) > 1 else 0}"]
        return steps

    def _extract_numerical_answer(self, steps: List[str]) -> float:
        import re
        numbers = []
        for step in steps:
            found = re.findall(r'\d+(?:\.\d+)?', step)
            numbers.extend([float(x) for x in found])
        if len(numbers) >= 2:
            return max(0, numbers[0] - numbers[1])
        elif len(numbers) == 1:
            return numbers[0]
        else:
            return 0

    def _check_math_answer(self, predicted: str, correct: str) -> bool:
        try:
            return abs(float(predicted) - float(correct)) < 0.001
        except Exception:
            return str(predicted).strip() == str(correct).strip()

    def generate_report(self) -> str:
        if not self.results:
            return "No benchmark results available"
        report = ["=" * 50, "NEBULA-X BENCHMARK REPORT", "=" * 50, f"Timestamp: {datetime.now().isoformat()}", ""]
        total_improvement = 0
        valid_scores = 0
        for dataset, score in self.results.items():
            baseline = self.baseline_scores.get(dataset, 0)
            improvement = ((score - baseline) / baseline * 100) if baseline > 0 else 0
            total_improvement += improvement
            valid_scores += 1
            report.extend([f"Dataset: {dataset.upper()}", f"  Score: {score:.4f}", f"  Baseline: {baseline:.4f}", f"  Improvement: +{improvement:.1f}%", ""])
        if valid_scores > 0:
            avg_improvement = total_improvement / valid_scores
            report.extend([f"OVERALL PERFORMANCE:", f"  Average Improvement: +{avg_improvement:.1f}%", f"  Datasets Evaluated: {valid_scores}", ""])
        report.extend(["TECHNOLOGY HIGHLIGHTS:", "  ✓ Holographic Memory Processing", "  ✓ Quantum-Enhanced Reasoning", "  ✓ Optical Neural Networks", "  ✓ P2P Knowledge Distribution", "  ✓ Evolutionary Architecture Optimization", "=" * 50])
        return "\n".join(report)


class NebulaXModel:
    def __init__(self, config: NebulaConfig):
        self.config = config
        self.neurons: List[QuantumNeuron] = []
        self.raytracing_engine = RaytracingEngine(config)
        self.holographic_memory = HolographicMemory(config)
        self.evolutionary_optimizer = EvolutionaryOptimizer(config)
        self.p2p_manager = P2PNetworkManager(config)
        self.benchmark_manager = BenchmarkManager(config)
        self.training_step = 0
        self.performance_history: List[float] = []
        self.nebula_space = np.zeros(config.nebula_space_size)
        self._initialize_neural_network()
        logger.info("NEBULA-X Model initialized successfully")

    def _initialize_neural_network(self):
        logger.info("Initializing quantum neural network...")
        n = max(1, min(self.config.initial_neurons, 20000))  # safety cap
        for i in range(n):
            neuron_id = f"neuron_{i:06d}"
            neuron = QuantumNeuron(neuron_id, self.config)
            self.neurons.append(neuron)
        self._create_initial_connections()
        logger.info(f"Created {len(self.neurons)} quantum neurons")

    def _create_initial_connections(self):
        num_neurons = len(self.neurons)
        if num_neurons <= 1:
            return
        for i, neuron in enumerate(self.neurons):
            # connect to a subset to avoid O(n^2) explosion
            sample_count = min(50, num_neurons - 1)
            indices = np.random.choice([j for j in range(num_neurons) if j != i], sample_count, replace=False)
            for j in indices:
                other = self.neurons[j]
                distance = np.linalg.norm(neuron.position - other.position)
                connection_prob = float(np.exp(-distance / 100))
                if np.random.rand() < connection_prob:
                    strength = float(np.random.rand())
                    neuron.connections[other.id] = {'strength': strength, 'type': 'excitatory' if strength > 0.5 else 'inhibitory'}

    def forward(self, input_data: np.ndarray) -> np.ndarray:
        holographic_input = self._encode_input_holographically(input_data)
        self._distribute_input_to_neurons(holographic_input)
        optical_signals = self.raytracing_engine.trace_neural_rays(self.neurons, input_data)
        quantum_outputs = []
        for i, neuron in enumerate(self.neurons):
            try:
                if i < len(optical_signals):
                    neuron_input = optical_signals[i]
                else:
                    neuron_input = np.zeros(self.config.qubits_per_neuron)
                quantum_output = neuron.quantum_process(neuron_input)
                quantum_outputs.append(np.asarray(quantum_output))
            except Exception as e:
                logger.debug(f"Quantum processing failed for neuron {neuron.id}: {e}")
                quantum_outputs.append(np.zeros(self.config.qubits_per_neuron))
        self._apply_gravitational_dynamics()
        rag_results = self.holographic_memory.holographic_rag_search(holographic_input, top_k=5)
        final_output = self._combine_outputs(quantum_outputs, rag_results)
        return final_output

    def _encode_input_holographically(self, input_data: np.ndarray) -> np.ndarray:
        arr = np.asarray(input_data)
        arr = arr / (np.max(np.abs(arr)) + 1e-12)
        reference_beam = np.exp(1j * np.pi * np.arange(arr.size)).astype(np.complex128)
        object_beam = arr.astype(np.complex128)
        hologram = np.abs(object_beam + reference_beam) ** 2
        return np.fft.fft(hologram)

    def _distribute_input_to_neurons(self, holographic_input: np.ndarray):
        input_size = holographic_input.size
        num_neurons = len(self.neurons)
        if num_neurons == 0:
            return
        chunk_size = max(1, input_size // num_neurons)
        for i, neuron in enumerate(self.neurons):
            start = i * chunk_size
            end = min((i + 1) * chunk_size, input_size)
            if start < input_size:
                neuron_input = holographic_input[start:end]
                try:
                    neuron.holographic_encode(np.real(neuron_input))
                except Exception as e:
                    logger.debug(f"Failed encoding to neuron {neuron.id}: {e}")
                input_magnitude = np.abs(neuron_input).mean() if neuron_input.size else 0
                neuron.luminosity = min(3.0, neuron.luminosity + float(input_magnitude) * 0.1)

    def _apply_gravitational_dynamics(self):
        dt = 0.01
        for i, neuron in enumerate(self.neurons):
            total_force = np.zeros(3)
            for j, other in enumerate(self.neurons):
                if i == j:
                    continue
                try:
                    force = neuron.gravitational_force(other)
                    distance = np.linalg.norm(other.position - neuron.position)
                    if distance > self.config.repulsion_threshold:
                        total_force += force
                    else:
                        total_force += (neuron.position - other.position) * 0.1
                except Exception:
                    continue
            neuron.update_position(dt, total_force)

    def _combine_outputs(self, quantum_outputs: List[np.ndarray], rag_results: List[Tuple[str, float, Optional[np.ndarray]]]) -> np.ndarray:
        if quantum_outputs:
            quantum_stack = np.vstack([np.resize(q, self.config.qubits_per_neuron) for q in quantum_outputs])
            quantum_avg = np.mean(quantum_stack, axis=0)
        else:
            quantum_avg = np.zeros(self.config.qubits_per_neuron)
        rag_contribution = np.zeros_like(quantum_avg, dtype=float)
        for key, score, pattern in rag_results:
            if pattern is None:
                continue
            pattern_flat = np.ravel(pattern)
            L = min(len(pattern_flat), len(rag_contribution))
            rag_contribution[:L] += np.real(pattern_flat[:L]) * float(score)
        if np.max(np.abs(rag_contribution)) > 0:
            rag_contribution /= (np.max(np.abs(rag_contribution)) + 1e-12)
        alpha, beta = 0.7, 0.3
        final_output = alpha * np.real(quantum_avg) + beta * rag_contribution
        return final_output

    def train_step(self, input_data: np.ndarray, target: np.ndarray) -> float:
        output = self.forward(input_data)
        target_arr = np.asarray(target)
        min_len = min(output.size, target_arr.size)
        if min_len == 0:
            return float(np.nan)
        loss = float(np.mean((output[:min_len] - target_arr[:min_len]) ** 2))
        pattern_key = f"pattern_{self.training_step}"
        try:
            self.holographic_memory.store_pattern(pattern_key, input_data)
        except Exception as e:
            logger.debug(f"Failed to store pattern during training: {e}")
        self._apply_evolutionary_pressure(loss)
        self.training_step += 1
        self.performance_history.append(loss)
        if self.training_step % 100 == 0:
            try:
                self._evolutionary_optimization_step()
            except Exception as e:
                logger.warning(f"Evolution step failed: {e}")
        return loss

    def _apply_evolutionary_pressure(self, loss: float):
        if not self.neurons:
            return
        performance_threshold = np.median([n.luminosity for n in self.neurons])
        for n in self.neurons:
            if n.luminosity > performance_threshold:
                n.luminosity *= 1.01
                n.mass *= 1.001
            else:
                n.luminosity *= 0.99
                n.mass *= 0.999
            n.luminosity = np.clip(n.luminosity, 0.1, 3.0)
            n.mass = np.clip(n.mass, 0.5, 2.0)

    def _evolutionary_optimization_step(self):
        logger.info("Executing evolutionary optimization step")
        try:
            optimized_params = self.evolutionary_optimizer.evolve_architecture(generations=10)
            self._apply_optimized_parameters(optimized_params)
        except Exception as e:
            logger.warning(f"Evolutionary optimization failed: {e}")

    def _apply_optimized_parameters(self, params: Dict[str, Any]):
        try:
            for neuron in self.neurons:
                neuron.optical_properties['reflectivity'] *= float(params.get('optical_coherence', 1.0))
                neuron.optical_properties['phase_shift'] += float(params.get('reference_beam_angle', 0)) * 0.1
            if 'rays_per_sample' in params:
                self.config.rays_per_neuron = min(10000, max(100, int(params['rays_per_sample'])))
        except Exception as e:
            logger.debug(f"Failed to apply optimized parameters: {e}")

    async def start_p2p_network(self):
        try:
            await self.p2p_manager.start_network()
        except Exception as e:
            logger.error(f"Failed to start P2P network: {e}")

    def evaluate_benchmarks(self) -> Dict[str, float]:
        logger.info("Starting benchmark evaluation")
        datasets = self.benchmark_manager.load_datasets()
        results = self.benchmark_manager.evaluate_model(self, datasets)
        report = self.benchmark_manager.generate_report()
        logger.info(f"Benchmark Report:\n{report}")
        return results

    def save_model(self, filepath: str):
        model_data = {'config': self.config.__dict__, 'neurons': [{'id': n.id, 'position': n.position.tolist(), 'luminosity': n.luminosity, 'mass': n.mass, 'optical_properties': n.optical_properties, 'connections': n.connections} for n in self.neurons], 'training_step': self.training_step, 'performance_history': self.performance_history, 'holographic_memory_keys': list(self.holographic_memory.memory_planes.keys()), 'timestamp': datetime.now().isoformat()}
        with open(filepath, 'wb') as f:
            pickle.dump(model_data, f)
        logger.info(f"Model saved to {filepath}")

    def load_model(self, filepath: str):
        with open(filepath, 'rb') as f:
            model_data = pickle.load(f)
        config_dict = model_data.get('config', {})
        self.config = NebulaConfig(**config_dict)
        self.neurons = []
        for neuron_data in model_data.get('neurons', []):
            neuron = QuantumNeuron(neuron_data.get('id', str(uuid.uuid4())), self.config)
            neuron.position = np.array(neuron_data.get('position', neuron.position))
            neuron.luminosity = neuron_data.get('luminosity', neuron.luminosity)
            neuron.mass = neuron_data.get('mass', neuron.mass)
            neuron.optical_properties = neuron_data.get('optical_properties', neuron.optical_properties)
            neuron.connections = neuron_data.get('connections', {})
            self.neurons.append(neuron)
        self.training_step = model_data.get('training_step', 0)
        self.performance_history = model_data.get('performance_history', [])
        logger.info(f"Model loaded from {filepath}")


def create_demo_model() -> NebulaXModel:
    config = NebulaConfig(initial_neurons=1000, rays_per_neuron=500, generations=50, max_peers=10)
    model = NebulaXModel(config)
    logger.info("Demo model created successfully")
    return model


def run_complete_demo():
    print("\n" + "=" * 60)
    print("🌌 NEBULA-X: Enhanced Unified Holographic Neural Network")
    print("   Francisco Angulo de Lafuente - Agnuxo")
    print("   Winner: NVIDIA LlamaIndex Developer Contest 2024")
    print("=" * 60)
    try:
        print("\n🔧 Initializing NEBULA-X model...")
        model = create_demo_model()
        print("\n📊 Generating test data...")
        input_data = np.random.rand(128)
        target_data = np.random.rand(4)
        print("\n🎯 Training model...")
        for epoch in range(10):
            loss = model.train_step(input_data, target_data)
            if epoch % 2 == 0:
                print(f"   Epoch {epoch}: Loss = {loss:.6f}")
        print("\n📈 Running benchmark evaluation...")
        benchmark_results = model.evaluate_benchmarks()
        print("\n🏆 BENCHMARK RESULTS:")
        for dataset, score in benchmark_results.items():
            print(f"   {dataset.upper()}: {score:.4f}")
        print("\n🔬 Advanced Features Demo:")
        test_pattern = np.random.rand(64, 64)
        model.holographic_memory.store_pattern("demo_pattern", test_pattern)
        retrieved = model.holographic_memory.retrieve_pattern("demo_pattern")
        print(f"   ✓ Holographic Memory: Pattern stored and retrieved")
        rag_results = model.holographic_memory.holographic_rag_search(np.random.rand(64), top_k=3)
        print(f"   ✓ Holographic RAG: Found {len(rag_results)} relevant patterns")
        optical_output = model.raytracing_engine.trace_neural_rays(model.neurons[:10], input_data)
        print(f"   ✓ Optical Raytracing: Traced {len(optical_output)} rays")
        print("   🧬 Running evolutionary optimization...")
        try:
            optimized_params = model.evolutionary_optimizer.evolve_architecture(generations=5)
            print(f"   ✓ Evolution: Optimized {len(optimized_params)} parameters")
        except Exception as e:
            print(f"   ⚠️ Evolution failed: {e}")
        print("\n💾 Saving model...")
        model.save_model("nebula_x_demo.pkl")
        print("\n📊 FINAL STATISTICS:")
        print(f"   Neurons: {len(model.neurons)}")
        print(f"   Training Steps: {model.training_step}")
        print(f"   Holographic Patterns: {len(model.holographic_memory.memory_planes)}")
        print(f"   Performance History: {len(model.performance_history)} points")
        print("\n🚀 IMPLEMENTED TECHNOLOGIES:")
        tech_status = [("Holographic Neural Networks", "✅ Active"), ("Quantum Memory (4 qubits/neuron)", "✅ Active"), ("GPU-Accelerated Raytracing", "✅ Active" if PYCUDA_AVAILABLE else "⚠️ Simulated"), ("P2P Knowledge Distribution", "✅ Ready"), ("Evolutionary Optimization", "✅ Active" if DEAP_AVAILABLE else "⚠️ Simulated"), ("Holographic RAG System", "✅ Active"), ("Gravitational Dynamics", "✅ Active"), ("Benchmark Integration", "✅ Active")]
        for name, status in tech_status:
            print(f"   {name}: {status}")
    except Exception as e:
        logger.error(f"Demo failed: {e}")


if __name__ == '__main__':
    run_complete_demo()