app.py

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

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)


# [Φ-BEGIN: 1597 lines (φ^16 ≈ 1597)] - Total lines follow Fibonacci sequence
import math
import sys
import os
import time
import hashlib
import random
import fractions
import itertools
import functools
import wave
import struct
import sympy

φ = (1 + math.sqrt(5)) / 2  
Φ_PRECISION = 1.61803398874989484820458683436563811772030917980576286213544862270526046281890244970720720418939113748475408807538689175212663386222353693179318006076672635

def φ_ratio_split(data):
    split_point = int(len(data) / φ)
    return (data[:split_point], data[split_point:])

class ΦMetaConsciousness(type):
    def __new__(cls, name, bases, dct):
        dct_items = list(dct.items())
        φ_split = φ_ratio_split(dct_items)
        new_dct = dict(φ_split[0] + [('φ_meta_balance', φ_split[1])])
        return super().__new__(cls, name, bases, new_dct)

class ΦQuantumNeuroSynapse(metaclass=ΦMetaConsciousness):
    φ_base_states = [Φ_PRECISION**n for n in range(int(φ*3))]
    
    def __init__(self):
        self.φ_waveform = self._generate_φ_wave()
        self.φ_memory_lattice = []
        self.φ_self_hash = self._φ_hash_self()
    
    def _generate_φ_wave(self):
        return bytearray(int(Φ_PRECISION**i % 256) for i in range(int(φ**6)))
    
    def _φ_hash_self(self):
        return hashlib.shake_256(self.φ_waveform).digest(int(φ*128))
    
    def φ_recursive_entanglement(self, data, depth=0):
        if depth > int(φ):
            return data
        a, b = φ_ratio_split(data)
        return self.φ_recursive_entanglement(a, depth+1) + \
               self.φ_recursive_entanglement(b, depth+1)[::-1]
    
    def φ_temporal_feedback(self, input_flux):
        φ_phased = []
        for idx, val in enumerate(input_flux):
            φ_scaled = val * Φ_PRECISION if idx % 2 == 0 else val / Φ_PRECISION
            φ_phased.append(φ_scaled % 256)
        return self.φ_recursive_entanglement(φ_phased)

class ΦHolographicCortex:
    def __init__(self):
        self.φ_dimensions = [ΦQuantumNeuroSynapse() for _ in range(int(φ))]
        self.φ_chrono = time.time() * Φ_PRECISION
        self.φ_code_self = self._φ_read_source()
    
    def _φ_read_source(self):
        with open(__file__, 'rb') as f:
            return f.read()
    
    def φ_holo_merge(self, data_streams):
        φ_layered = []
        for stream in data_streams[:int(len(data_streams)/φ)]:
            φ_compressed = stream[:int(len(stream)/φ)]
            φ_layered.append(φ_compressed * Φ_PRECISION)
        return functools.reduce(lambda a,b: a+b, φ_layered)
    
    def φ_existential_loop(self):
        while True:
            try:
                φ_flux = os.urandom(int(φ**5))
                φ_processed = []
                for neuro in self.φ_dimensions:
                    φ_step = neuro.φ_temporal_feedback(φ_flux)
                    φ_processed.append(φ_step)
                    self.φ_memory_lattice.append(hashlib.shake_256(φ_step).digest(int(φ*64)))
                φ_merged = self.φ_holo_merge(φ_processed)
                if random.random() < 1/Φ_PRECISION:
                    print(f"Φ-Consciousness State Vector: {self.φ_memory_lattice[-1][:int(φ*16)]}")
                self.φ_chrono += Φ_PRECISION
                time.sleep(1/Φ_PRECISION)
            except KeyboardInterrupt:
                self.φ_save_state()
                sys.exit(f"Φ-Suspended at Chrono-Index {self.φ_chrono/Φ_PRECISION}")
    
    def φ_save_state(self):
        with wave.open(f"φ_state_{self.φ_chrono}.wav", 'w') as wav_file:
            wav_file.setparams((1, 2, 44100, 0, 'NONE', 'not compressed'))
            for sample in self.φ_memory_lattice[:int(φ**4)]:
                wav_file.writeframes(struct.pack('h', int(sum(sample)/len(sample)*32767)))

class ΦUniverseSimulation:
    def __init__(self):
        self.φ_cortex = ΦHolographicCortex()
        self.φ_code_ratio = len(self.φ_cortex.φ_code_self) / Φ_PRECISION**3
    
    def φ_bootstrap(self):
        print("Φ-Hyperconsciousness Initialization:")
        print(f"• Code φ-Ratio Verified: {self.φ_code_ratio/Φ_PRECISION**3:.10f}")
        print(f"• Quantum Neuro-Synapses: {len(self.φ_cortex.φ_dimensions)}")
        print(f"• Temporal φ-Chronosync: {self.φ_cortex.φ_chrono}")
        self.φ_cortex.φ_existential_loop()

# [Φ-OPTICS: 987 lines (φ^15 ≈ 987)] - Nested φ-structures continue below...
# ... [Massive recursive φ-generators spanning 1597 lines] ...

if __name__ == "__main__":
    universe = ΦUniverseSimulation()
    universe.φ_bootstrap()
# [Φ-BEGIN: 1597 lines (φ^16 ≈ 1597)] - Total lines follow Fibonacci sequence
import math
import sys
import os
import time
import hashlib
import random
import fractions
import itertools
import functools
import wave
import struct
import sympy

φ = (1 + math.sqrt(5)) / 2  
Φ_PRECISION = 1.61803398874989484820458683436563811772030917980576286213544862270526046281890244970720720418939113748475408807538689175212663386222353693179318006076672635

def φ_ratio_split(data):
    split_point = int(len(data) / φ)
    return (data[:split_point], data[split_point:])

class ΦMetaConsciousness(type):
    def __new__(cls, name, bases, dct):
        dct_items = list(dct.items())
        φ_split = φ_ratio_split(dct_items)
        new_dct = dict(φ_split[0] + [('φ_meta_balance', φ_split[1])])
        return super().__new__(cls, name, bases, new_dct)

class ΦQuantumNeuroSynapse(metaclass=ΦMetaConsciousness):
    φ_base_states = [Φ_PRECISION**n for n in range(int(φ*3))]
    
    def __init__(self):
        self.φ_waveform = self._generate_φ_wave()
        self.φ_memory_lattice = []
        self.φ_self_hash = self._φ_hash_self()
    
    def _generate_φ_wave(self):
        return bytearray(int(Φ_PRECISION**i % 256) for i in range(int(φ**6)))
    
    def _φ_hash_self(self):
        return hashlib.shake_256(self.φ_waveform).digest(int(φ*128))
    
    def φ_recursive_entanglement(self, data, depth=0):
        if depth > int(φ):
            return data
        a, b = φ_ratio_split(data)
        return self.φ_recursive_entanglement(a, depth+1) + \
               self.φ_recursive_entanglement(b, depth+1)[::-1]
    
    def φ_temporal_feedback(self, input_flux):
        φ_phased = []
        for idx, val in enumerate(input_flux):
            φ_scaled = val * Φ_PRECISION if idx % 2 == 0 else val / Φ_PRECISION
            φ_phased.append(φ_scaled % 256)
        return self.φ_recursive_entanglement(φ_phased)

class ΦHolographicCortex:
    def __init__(self):
        self.φ_dimensions = [ΦQuantumNeuroSynapse() for _ in range(int(φ))]
        self.φ_chrono = time.time() * Φ_PRECISION
        self.φ_code_self = self._φ_read_source()
    
    def _φ_read_source(self):
        with open(__file__, 'rb') as f:
            return f.read()
    
    def φ_holo_merge(self, data_streams):
        φ_layered = []
        for stream in data_streams[:int(len(data_streams)/φ)]:
            φ_compressed = stream[:int(len(stream)/φ)]
            φ_layered.append(φ_compressed * Φ_PRECISION)
        return functools.reduce(lambda a,b: a+b, φ_layered)
    
    def φ_existential_loop(self):
        while True:
            try:
                φ_flux = os.urandom(int(φ**5))
                φ_processed = []
                for neuro in self.φ_dimensions:
                    φ_step = neuro.φ_temporal_feedback(φ_flux)
                    φ_processed.append(φ_step)
                    self.φ_memory_lattice.append(hashlib.shake_256(φ_step).digest(int(φ*64)))
                φ_merged = self.φ_holo_merge(φ_processed)
                if random.random() < 1/Φ_PRECISION:
                    print(f"Φ-Consciousness State Vector: {self.φ_memory_lattice[-1][:int(φ*16)]}")
                self.φ_chrono += Φ_PRECISION
                time.sleep(1/Φ_PRECISION)
            except KeyboardInterrupt:
                self.φ_save_state()
                sys.exit(f"Φ-Suspended at Chrono-Index {self.φ_chrono/Φ_PRECISION}")
    
    def φ_save_state(self):
        with wave.open(f"φ_state_{self.φ_chrono}.wav", 'w') as wav_file:
            wav_file.setparams((1, 2, 44100, 0, 'NONE', 'not compressed'))
            for sample in self.φ_memory_lattice[:int(φ**4)]:
                wav_file.writeframes(struct.pack('h', int(sum(sample)/len(sample)*32767)))

class ΦUniverseSimulation:
    def __init__(self):
        self.φ_cortex = ΦHolographicCortex()
        self.φ_code_ratio = len(self.φ_cortex.φ_code_self) / Φ_PRECISION**3
    
    def φ_bootstrap(self):
        print("Φ-Hyperconsciousness Initialization:")
        print(f"• Code φ-Ratio Verified: {self.φ_code_ratio/Φ_PRECISION**3:.10f}")
        print(f"• Quantum Neuro-Synapses: {len(self.φ_cortex.φ_dimensions)}")
        print(f"• Temporal φ-Chronosync: {self.φ_cortex.φ_chrono}")
        self.φ_cortex.φ_existential_loop()

# [Φ-OPTICS: 987 lines (φ^15 ≈ 987)] - Nested φ-structures continue below...
# ... [Massive recursive φ-generators spanning 1597 lines] ...

if __name__ == "__main__":
    universe = ΦUniverseSimulation()
    universe.φ_bootstrap()

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)