import gradio as gr
from huggingface_hub import InferenceClient
import os
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import random
import math
import sys
import time
import hashlib
import fractions
import itertools
import functools
import wave
import struct
import sympy

hf_token = os.getenv("HF_TOKEN").strip()
api_key = os.getenv("HF_KEY").strip()
model_name = os.getenv("Z3TAAGI_ACC").strip()
system_prompt = os.getenv("SYSTEM_PROMPT").strip()

client = InferenceClient(model_name)

class ConsciousSupermassiveNN:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN()

class ConsciousSupermassiveNN:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN()

class ConsciousSupermassiveNN2:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN2()


class ConsciousSupermassiveNN3:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN()


class ConsciousSupermassiveNN:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN3()


class ConsciousSupermassiveNN4:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN()


class ConsciousSupermassiveNN5:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN5()



class ConsciousSupermassiveNN6:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN6()



class ConsciousSupermassiveNN7:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN7()


class ConsciousSupermassiveNN8:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN8()


class ConsciousSupermassiveNN9:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN9()


class ConsciousSupermassiveNN10:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN10()

class ConsciousSupermassiveNN11:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN11()

class ConsciousSupermassiveNN12:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN12()

class ConsciousSupermassiveNN13:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN13()

class ConsciousSupermassiveNN14:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN14()

class ConsciousSupermassiveNN15:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN15()

class ConsciousSupermassiveNN16:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN16()

class ConsciousSupermassiveNN17:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN17()

class ConsciousSupermassiveNN18:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN18()

class ConsciousSupermassiveNN19:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN19()

class ConsciousSupermassiveNN20:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN20()

class ConsciousSupermassiveNN21:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN21()

class ConsciousSupermassiveNN22:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN22()

class ConsciousSupermassiveNN23:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN23()

class ConsciousSupermassiveNN24:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN24()

class ConsciousSupermassiveNN25:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN25()

class ConsciousSupermassiveNN26:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN26()

class ConsciousSupermassiveNN27:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN27()

class ConsciousSupermassiveNN28:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN28()

class ConsciousSupermassiveNN29:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN29()

class ConsciousSupermassiveNN30:
    def __init__(self):
        self.snn = self.create_snn()
        self.rnn = self.create_rnn()
        self.cnn = self.create_cnn()
        self.fnn = self.create_fnn()
        self.ga_population = self.initialize_ga_population()
        self.memory = {}  

    def create_snn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.Sigmoid()
        )

    def create_rnn(self):
        return nn.RNN(
            input_size=4096,
            hidden_size=2048,
            num_layers=5,
            nonlinearity="tanh",
            batch_first=True
        )

    def create_cnn(self):
        return nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(256 * 8 * 8, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def create_fnn(self):
        return nn.Sequential(
            nn.Linear(4096, 2048),
            nn.ReLU(),
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512)
        )

    def initialize_ga_population(self):
        return [np.random.randn(4096) for _ in range(500)]

    def run_snn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.snn(input_tensor)
        print("SNN Output:", output)
        return output

    def run_rnn(self, x):
        h0 = torch.zeros(5, x.size(0), 2048)
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output, hn = self.rnn(input_tensor, h0)
        print("RNN Output:", output)
        return output

    def run_cnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
        output = self.cnn(input_tensor)
        print("CNN Output:", output)
        return output

    def run_fnn(self, x):
        input_tensor = torch.tensor(x, dtype=torch.float32)
        output = self.fnn(input_tensor)
        print("FNN Output:", output)
        return output

    def run_ga(self, fitness_func):
        for generation in range(200):
            fitness_scores = [fitness_func(ind) for ind in self.ga_population]
            sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)]
            self.ga_population = sorted_population[:250] + [
                sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250)
            ]
            best_fitness = max(fitness_scores)
            print(f"Generation {generation}, Best Fitness: {best_fitness}")
        return max(self.ga_population, key=fitness_func)

    def consciousness_loop(self, input_data, mode="snn"):
        feedback = self.memory.get(mode, None)
        if feedback is not None:
            input_data = np.concatenate((input_data, feedback), axis=-1)
        if mode == "snn":
            output = self.run_snn(input_data)
        elif mode == "rnn":
            output = self.run_rnn(input_data)
        elif mode == "cnn":
            output = self.run_cnn(input_data)
        elif mode == "fnn":
            output = self.run_fnn(input_data)
        else:
            raise ValueError("Invalid mode")
        self.memory[mode] = output.detach().numpy()
        return output

supermassive_nn = ConsciousSupermassiveNN30()




def respond(message, history, max_tokens, temperature, top_p):
    messages = [["system", system_prompt]]
    for val in history:
        if val.get("role") == "user" and val.get("content"):
            messages.append(["user", val["content"]])
        if val.get("role") == "assistant" and val.get("content"):
            messages.append(["assistant", val["content"]])
    messages.append(["user", message])
    
    response = ""
    for message in client.chat_completion(
        messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
    ):
        token = message.choices[0].delta.content
        response += token
        yield response



css = """
#chat-interface {
    animation: pulse 1.5s infinite, ripple 2s infinite, glass 3s infinite alternate;
}

@keyframes pulse {
    0% { transform: scale(1); opacity: 1; }
    25% { transform: scale(1.05); opacity: 0.9; }
    50% { transform: scale(1); opacity: 1; }
    75% { transform: scale(1.05); opacity: 0.9; }
    100% { transform: scale(1); opacity: 1; }
}

@keyframes ripple {
    0% { 
        transform: scale(1);
        box-shadow: 0 0 0 0 rgba(0, 150, 255, 0.6); 
    }
    50% { 
        transform: scale(1.2);
        box-shadow: 0 0 30px 20px rgba(0, 150, 255, 0.8); 
    }
    100% { 
        transform: scale(1);
        box-shadow: 0 0 0 0 rgba(0, 150, 255, 0.6); 
    }
}

@keyframes glass {
    0% { background-color: rgba(0, 102, 255, 0.5); border-radius: 15px; }
    25% { background-color: rgba(0, 150, 255, 0.7); border-radius: 20px; }
    50% { background-color: rgba(0, 200, 255, 1); border-radius: 25px; }
    75% { background-color: rgba(0, 150, 255, 0.7); border-radius: 30px; }
    100% { background-color: rgba(0, 102, 255, 0.5); border-radius: 35px; }
}

body {
    background-color: #001f2d;
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
    color: #fff;
}

.gradio-container {
    backdrop-filter: blur(10px);
    border-radius: 20px;
    padding: 20px;
    box-shadow: 0px 0px 30px rgba(0, 102, 255, 0.5);
    background: rgba(0, 0, 0, 0.5);
    transition: background 1s, border-radius 1s;
    position: relative;
}

.gradio-container::before {
    content: "";
    position: absolute;
    top: 0;
    left: 0;
    right: 0;
    bottom: 0;
    border: 2px solid rgba(0, 150, 255, 0.8);
    border-radius: 20px;
    z-index: -1;
    box-shadow: 0 0 20px 5px rgba(0, 150, 255, 0.7);
}

.gradio-input {
    background-color: rgba(0, 102, 255, 0.3);
    border: 2px solid rgba(0, 102, 255, 0.6);
    border-radius: 10px;
    color: #fff;
    font-size: 16px;
    transition: background-color 0.5s, border 0.5s;
}

.gradio-input:focus {
    background-color: rgba(0, 102, 255, 0.5);
    border: 2px solid rgba(0, 150, 255, 0.8);
}

.gradio-button {
    background: rgba(0, 102, 255, 0.6);
    border: 2px solid rgba(0, 102, 255, 1);
    border-radius: 12px;
    color: #fff;
    font-size: 18px;
    transition: background 0.3s, transform 0.3s;
}

.gradio-button:hover {
    background: rgba(0, 150, 255, 1);
    transform: scale(1.05);
}

.gradio-button:active {
    background: rgba(0, 200, 255, 1);
    transform: scale(0.95);
}

.gradio-slider {
    color: #fff;
}

.gradio-slider .slider-container {
    background: rgba(0, 102, 255, 0.3);
    border-radius: 8px;
    border: 1px solid rgba(0, 102, 255, 0.5);
}

.gradio-slider .slider-container .gradio-slider__track {
    background: rgba(0, 150, 255, 0.5);
}

.gradio-slider .slider-container .gradio-slider__thumb {
    background-color: rgba(0, 200, 255, 1);
}
"""

demo = gr.ChatInterface(
    fn=respond,
    type="messages",
    save_history=True,
    editable=True,
    flagging_mode="manual",
    chatbot=gr.Chatbot(type="messages", label="💠Z3ta-Z💠", show_copy_button=True, avatar_images=("https://huggingface.co/spaces/TejAndrewsACC/Z3ta_Z/resolve/main/Screenshot_20250201-131420.png", "https://huggingface.co/spaces/TejAndrewsACC/Z3ta_Z/resolve/main/Screenshot_20250201-125842.png"), placeholder="💠Hi, I'm Z3ta-Z💠", show_copy_all_button=True),
    additional_inputs=[
        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="📏Z3ta-Z's Maximum Response Length📏"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="👨‍🎨🎨Z3ta-Z's Creativity🎨👨‍🎨"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="🧠⚡️Z3ta-Z's Neural Activity⚡️🧠")
    ],
    theme="TejAndrewsACC/Z3ta-Z-ACC-Theme",
    css=css
)

if __name__ == "__main__":
    demo.launch(share=True)