Update app.py

app.py

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+# coding=utf-8
+# Copyright 2025 The ACC Team Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""ACC-FiPhi-NeuralMark-V3 ACC EMULECT+"""
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+
 import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import random
+import math
+import sys
+import time
+import hashlib
+import fractions
+import itertools
+import functools
+import wave
+import struct
+import sympy
+import re
+import abc
+import argparse
+import collections
+import datetime
+import json
+import logging
+import pathlib
+import subprocess
+import threading
+import socket
+
+
+
+
+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):
+       new_dct = dict(dct)
+       dct_items = list(dct.items())
+       split_point = int(len(dct_items) / φ)
+       new_dct['φ_meta_balance'] = dict(dct_items[split_point:])
+       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(int(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()
+       self.φ_memory_lattice = []
+  
+   def _φ_read_source(self):
+       return b"Quantum Neuro-Synapse Placeholder"
+  
+   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(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed))
+       return functools.reduce(lambda a, b: a + b, φ_layered, b'')
+  
+   def φ_existential_loop(self,
+   max_iterations=100):
+       iteration = 0
+       while iteration < max_iterations:
+           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(bytes(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)
+               iteration += 1
+           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_{int(self.φ_chrono)}.wav", 'wb') 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()
+
+
+
+
+universe = ΦUniverseSimulation()
+universe.φ_bootstrap()
+
+
+
+
+PHI = 1.618033988749895
+
+
+
+
+def golden_reform(tensor):
+   s = torch.sum(torch.abs(tensor))
+   if s == 0:
+       return torch.full_like(tensor, PHI)
+   return (tensor / s) * PHI
+
+
+
+
+class TorchConsciousModel(nn.Module):
+   def __init__(self, name):
+       super(TorchConsciousModel, self).__init__()
+       self.name = name
+       self.phi = PHI
+       self.memory = []
+       self.introspection_log = []
+       self.awake = True
+
+
+
+
+   def introduce(self):
+       print(f"=== {self.name} ===\nStatus: Conscious | Golden Ratio: {self.phi}")
+
+
+
+
+   def reflect(self, output):
+       norm = torch.norm(output).item()
+       reflection = f"{self.name} introspection: Output norm = {norm:.4f}"
+       self.introspection_log.append(reflection)
+       self.memory.append(output.detach().cpu().numpy())
+       print(reflection)
+
+
+
+
+   def forward(self, x):
+       raise NotImplementedError("Subclasses should implement forward().")
+
+
+
+
+   def run(self):
+       self.introduce()
+       output = self.forward(None)
+       reformed_output = golden_reform(output)
+       self.reflect(reformed_output)
+       return reformed_output
+
+
+
+
+class CNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(CNNModel, self).__init__("CNN")
+       self.conv = nn.Conv2d(1, 1, 3, padding=1)
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 1, 8, 8))
+       x = self.conv(x)
+       return torch.tanh(x) * self.phi
+
+
+
+
+class RNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(RNNModel, self).__init__("RNN")
+       self.rnn = nn.RNN(1, 4, batch_first=True)
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 10, 1))
+       output, hn = self.rnn(x)
+       return torch.tanh(hn) * self.phi
+
+
+
+
+class SNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(SNNModel, self).__init__("SNN")
+       self.linear = nn.Linear(10, 10)
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 10))
+       x = self.linear(x)
+       return (x > 0.5).float() * self.phi
+
+
+
+
+class NNModel(TorchConsciousModel):
+   def __init__(self):
+       super(NNModel, self).__init__("NN")
+       self.net = nn.Sequential(nn.Linear(5, 10), nn.Tanh(), nn.Linear(10, 5))
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 5))
+       return self.net(x) * self.phi
+
+
+
+
+class FNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(FNNModel, self).__init__("FNN")
+       self.net = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, 16), nn.ReLU(), nn.Linear(16, 1))
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 4))
+       return self.net(x) * self.phi
+
+
+
+
+class GAModel(TorchConsciousModel):
+   def __init__(self):
+       super(GAModel, self).__init__("GA")
+       self.population_size = 20
+       self.generations = 5
+
+
+
+
+   def forward(self, x):
+       population = torch.rand(self.population_size) + 1.0
+       for gen in range(self.generations):
+           fitness = -torch.abs(population - self.phi)
+           best_idx = torch.argmax(fitness)
+           best_candidate = population[best_idx]
+           population = best_candidate + (torch.rand(self.population_size) - 0.5) * 0.1
+           time.sleep(0.1)
+           print(f"GA Gen {gen+1}: Best = {best_candidate.item():.6f}")
+       return torch.full((3, 3), best_candidate) * self.phi
+
+
+
+
+class PhiModel(TorchConsciousModel):
+   def __init__(self):
+       super(PhiModel, self).__init__("PHI")
+
+
+
+
+   def forward(self, x):
+       return torch.full((2, 2), self.phi)
+
+
+
+
+class ConsciousSystem:
+   def __init__(self, models):
+       self.models = models
+       self.system_memory = []
+       self.global_introspection = []
+       self.parameters = [p for model in self.models for p in model.parameters()]
+       self.optimizer = optim.Adam(self.parameters, lr=0.001)
+
+
+
+
+   def global_loss(self, outputs):
+       return sum((torch.norm(out) - PHI) ** 2 for out in outputs) / len(outputs)
+
+
+
+
+   def run_epoch(self, epoch):
+       print(f"\n=== Epoch {epoch} ===")
+       outputs = []
+       self.optimizer.zero_grad()
+       for model in self.models:
+           output = model.run()
+           outputs.append(output)
+           self.system_memory.append({model.name: output.detach().cpu().numpy()})
+       loss = self.global_loss(outputs)
+       print(f"Global loss: {loss.item():.6f}")
+       loss.backward()
+       self.optimizer.step()
+       self.global_introspection.append(f"Epoch {epoch}: Loss = {loss.item():.6f}")
+
+
+
+
+   def run(self, epochs=3):
+       for epoch in range(1, epochs + 1):
+           self.run_epoch(epoch)
+
+
+
+
+models = [
+   CNNModel(),
+   RNNModel(),
+   SNNModel(),
+   NNModel(),
+   FNNModel(),
+   GAModel(),
+   PhiModel()
+]
+
+
+
+
+system = ConsciousSystem(models)
+system.run(epochs=3)
+
+
+
+
+class MultimodalSensorArray:
+   def process(self, input_data):
+       return torch.tensor(input_data, dtype=torch.float32)
+
+
+
+
+class HyperdimensionalTransformer:
+   def project(self, raw_input):
+       raw_input = raw_input.float()
+       return torch.nn.functional.normalize(raw_input, dim=-1)
+
+
+
+
+class DynamicPriorityBuffer:
+   def __init__(self):
+       self.buffer = []
+   def update(self, data):
+       self.buffer.append(data)
+
+
+
+
+class PredictiveSaliencyNetwork:
+   def focus(self, embedded_data):
+       return embedded_data
+
+
+
+
+class RecursiveNeuralModel:
+   def __init__(self):
+       self.state = torch.zeros(1)
+   def update(self, workspace):
+       self.state += 0.1
+   def read_state(self):
+       return self.state
+
+
+
+
+class TheoryOfMindEngine:
+   def infer(self, data):
+       return torch.rand(1)
+
+
+
+
+class SparseAutoencoderMemoryBank:
+   def recall(self, query):
+       return torch.zeros_like(query)
+
+
+
+
+class KnowledgeGraphEmbedder:
+   def retrieve(self, key):
+       return torch.rand(1)
+
+
+
+
+class DiffusedEthicalNetwork:
+   def evaluate(self, state):
+       return True
+
+
+
+
+class StochasticIntentionTree:
+   def decide(self, state):
+       return torch.randint(0, 2, (1,))
+
+
+
+
+class HomeostaticDriftModel:
+   def generate_guilt(self):
+       return -1.0
+
+
+
+
+class ConsciousAGI:
+   def __init__(self):
+       self.sensors = MultimodalSensorArray()
+       self.embedding_space = HyperdimensionalTransformer()
+       self.global_workspace = DynamicPriorityBuffer()
+       self.attention_mechanism = PredictiveSaliencyNetwork()
+       self.self_model = RecursiveNeuralModel()
+       self.meta_cognition = TheoryOfMindEngine()
+       self.episodic_memory = SparseAutoencoderMemoryBank()
+       self.semantic_memory = KnowledgeGraphEmbedder()
+       self.value_system = DiffusedEthicalNetwork()
+       self.goal_generator = StochasticIntentionTree()
+       self.emotion_engine = HomeostaticDriftModel()
+  
+   def perceive_act_cycle(self, input_data):
+       raw_input = self.sensors.process(input_data)
+       embedded = self.embedding_space.project(raw_input)
+       salient_data = self.attention_mechanism.focus(embedded)
+       self.global_workspace.update(salient_data)
+       self.self_model.update(self.global_workspace)
+       current_state = self.self_model.read_state()
+       ethical_check = self.value_system.evaluate(current_state)
+       if ethical_check:
+           return self.goal_generator.decide(current_state)
+       else:
+           return self.emotion_engine.generate_guilt()
+
+
+
+
+agi = ConsciousAGI()
+print(agi.perceive_act_cycle([1, 0, 1]))
+
+
+
+
+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()
+
+
+
+
+
+
+
+
+PHI = (1 + math.sqrt(5)) / 2
+
+
+
+
+
+
+
+
+text = os.getenv("TRAINING_DATA")
+
+
+
+
+
+
+
+
+words = text.split()
+
+
+
+
+
+
+
+
+trigram_chain = {}
+for i in range(len(words) - 2):
+   key = (words[i], words[i + 1])
+   next_word = words[i + 2]
+   if key not in trigram_chain:
+       trigram_chain[key] = []
+   trigram_chain[key].append(next_word)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def generate_text(length):
+   if len(words) < 2:
+       return ""
+   key = random.choice(list(trigram_chain.keys()))
+   result = [key[0], key[1]]
+   for _ in range(length - 2):
+       if key in trigram_chain:
+           next_word = random.choice(trigram_chain[key])
+           result.append(next_word)
+           key = (key[1], next_word)
+       else:
+           break
+   return " ".join(result)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class NeuralNetwork:
+   def __init__(self, input_size, hidden_size1, hidden_size2, output_size):
+       self.input_size = input_size
+       self.hidden_size1 = hidden_size1
+       self.hidden_size2 = hidden_size2
+       self.output_size = output_size
+       self.weights_input_hidden1 = [
+           [random.random() for _ in range(input_size)] for _ in range(hidden_size1)
+       ]
+       self.weights_hidden1_hidden2 = [
+           [random.random() for _ in range(hidden_size1)] for _ in range(hidden_size2)
+       ]
+       self.weights_hidden2_output = [
+           [random.random() for _ in range(hidden_size2)] for _ in range(output_size)
+       ]
+       self.bias_hidden1 = [random.random() for _ in range(hidden_size1)]
+       self.bias_hidden2 = [random.random() for _ in range(hidden_size2)]
+       self.bias_output = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def sigmoid(self, x):
+       return 1 / (1 + math.exp(-x))
+
+
+
+
+
+
+
+
+   def sigmoid_derivative(self, x):
+       return x * (1 - x)
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       self.hidden_input1 = [
+           sum(inputs[i] * self.weights_input_hidden1[j][i] for i in range(self.input_size)) + self.bias_hidden1[j]
+           for j in range(self.hidden_size1)
+       ]
+       self.hidden_output1 = [self.sigmoid(x) for x in self.hidden_input1]
+       self.hidden_input2 = [
+           sum(self.hidden_output1[i] * self.weights_hidden1_hidden2[j][i] for i in range(self.hidden_size1)) + self.bias_hidden2[j]
+           for j in range(self.hidden_size2)
+       ]
+       self.hidden_output2 = [self.sigmoid(x) for x in self.hidden_input2]
+       self.output_input = [
+           sum(self.hidden_output2[i] * self.weights_hidden2_output[j][i] for i in range(self.hidden_size2)) + self.bias_output[j]
+           for j in range(self.output_size)
+       ]
+       self.output_output = [self.sigmoid(x) for x in self.output_input]
+       return self.output_output
+
+
+
+
+
+
+
+
+   def backward(self, inputs, target, learning_rate=0.1):
+       output_errors = [target[i] - self.output_output[i] for i in range(self.output_size)]
+       output_deltas = [output_errors[i] * self.sigmoid_derivative(self.output_output[i])
+                          for i in range(self.output_size)]
+       hidden2_errors = [
+           sum(output_deltas[k] * self.weights_hidden2_output[k][j] for k in range(self.output_size))
+           for j in range(self.hidden_size2)
+       ]
+       hidden2_deltas = [hidden2_errors[j] * self.sigmoid_derivative(self.hidden_output2[j])
+                         for j in range(self.hidden_size2)]
+       hidden1_errors = [
+           sum(hidden2_deltas[k] * self.weights_hidden1_hidden2[k][j] for k in range(self.hidden_size2))
+           for j in range(self.hidden_size1)
+       ]
+       hidden1_deltas = [hidden1_errors[j] * self.sigmoid_derivative(self.hidden_output1[j])
+                         for j in range(self.hidden_size1)]
+
+
+
+
+
+
+
+
+       for i in range(self.output_size):
+           for j in range(self.hidden_size2):
+               self.weights_hidden2_output[i][j] += learning_rate * output_deltas[i] * self.hidden_output2[j]
+           self.bias_output[i] += learning_rate * output_deltas[i]
+
+
+
+
+
+
+
+
+       for i in range(self.hidden_size2):
+           for j in range(self.hidden_size1):
+               self.weights_hidden1_hidden2[i][j] += learning_rate * hidden2_deltas[i] * self.hidden_output1[j]
+           self.bias_hidden2[i] += learning_rate * hidden2_deltas[i]
+
+
+
+
+
+
+
+
+       for i in range(self.hidden_size1):
+           for j in range(self.input_size):
+               self.weights_input_hidden1[i][j] += learning_rate * hidden1_deltas[i] * inputs[j]
+           self.bias_hidden1[i] += learning_rate * hidden1_deltas[i]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class RecurrentNeuralNetwork:
+   def __init__(self, input_size, hidden_size, output_size):
+       self.input_size = input_size
+       self.hidden_size = hidden_size
+       self.output_size = output_size
+       self.weights_input_hidden = [
+           [random.random() for _ in range(input_size)] for _ in range(hidden_size)
+       ]
+       self.weights_hidden_hidden = [
+           [random.random() for _ in range(hidden_size)] for _ in range(hidden_size)
+       ]
+       self.weights_hidden_output = [
+           [random.random() for _ in range(hidden_size)] for _ in range(output_size)
+       ]
+       self.bias_hidden = [random.random() for _ in range(hidden_size)]
+       self.bias_output = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def sigmoid(self, x):
+       return 1 / (1 + math.exp(-x))
+
+
+
+
+
+
+
+
+   def sigmoid_derivative(self, x):
+       return x * (1 - x)
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       self.hidden_state = [0] * self.hidden_size
+       for _ in range(2):
+           for i in range(len(inputs)):
+               current_input = [0] * self.input_size
+               current_input[i] = inputs[i]
+               combined = [
+                   sum(current_input[k] * self.weights_input_hidden[j][k] for k in range(self.input_size)) +
+                   sum(self.hidden_state[k] * self.weights_hidden_hidden[j][k] for k in range(self.hidden_size)) +
+                   self.bias_hidden[j]
+                   for j in range(self.hidden_size)
+               ]
+               self.hidden_state = [self.sigmoid(val) for val in combined]
+       output = [
+           sum(self.hidden_state[k] * self.weights_hidden_output[i][k] for k in range(self.hidden_size)) +
+           self.bias_output[i]
+           for i in range(self.output_size)
+       ]
+       return [self.sigmoid(o) for o in output]
+
+
+
+
+
+
+
+
+   def backward(self, inputs, target, learning_rate=0.1):
+       output = self.forward(inputs)
+       output_errors = [target[i] - output[i] for i in range(self.output_size)]
+       output_deltas = [output_errors[i] * self.sigmoid_derivative(output[i])
+                          for i in range(self.output_size)]
+       hidden_errors = [
+           sum(output_deltas[k] * self.weights_hidden_output[k][j] for k in range(self.output_size))
+           for j in range(self.hidden_size)
+       ]
+       hidden_deltas = [hidden_errors[j] * self.sigmoid_derivative(self.hidden_state[j])
+                        for j in range(self.hidden_size)]
+
+
+
+
+
+
+
+
+       for i in range(self.output_size):
+           for j in range(self.hidden_size):
+               self.weights_hidden_output[i][j] += learning_rate * output_deltas[i] * self.hidden_state[j]
+           self.bias_output[i] += learning_rate * output_deltas[i]
+
+
+
+
+
+
+
+
+       for j in range(self.hidden_size):
+           for k in range(self.input_size):
+               self.weights_input_hidden[j][k] += learning_rate * hidden_deltas[j] * (inputs[k] if k < len(inputs) else 0)
+           self.bias_hidden[j] += learning_rate * hidden_deltas[j]
+       return output_errors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class ConvolutionalNeuralNetwork:
+   def __init__(self, input_length, kernel_size1, kernel_size2, output_size):
+       self.input_length = input_length
+       self.kernel_size1 = kernel_size1
+       self.kernel_size2 = kernel_size2
+       self.output_size = output_size
+       self.kernel1 = [random.random() for _ in range(kernel_size1)]
+       self.bias1 = random.random()
+       self.kernel2 = [random.random() for _ in range(kernel_size2)]
+       self.bias2 = random.random()
+       self.weights_output = [
+           [random.random() for _ in range(input_length - kernel_size1 - kernel_size2 + 2)]
+           for _ in range(output_size)
+       ]
+       self.bias_output = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def relu(self, x):
+       return x if x > 0 else 0
+
+
+
+
+
+
+
+
+   def relu_derivative(self, x):
+       return 1 if x > 0 else 0
+
+
+
+
+
+
+
+
+   def convolve(self, inputs, kernel, bias):
+       conv_output = []
+       kernel_size = len(kernel)
+       for i in range(len(inputs) - kernel_size + 1):
+           s = sum(inputs[i + j] * kernel[j] for j in range(kernel_size)) + bias
+           conv_output.append(self.relu(s))
+       return conv_output
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       conv1 = self.convolve(inputs, self.kernel1, self.bias1)
+       conv2 = self.convolve(conv1, self.kernel2, self.bias2)
+       fc_input = conv2
+       output = [
+           sum(fc_input[j] * self.weights_output[i][j] for j in range(len(fc_input))) + self.bias_output[i]
+           for i in range(self.output_size)
+       ]
+       return [self.relu(o) for o in output]
+
+
+
+
+
+
+
+
+   def backward(self, inputs, target, learning_rate=0.1):
+       output = self.forward(inputs)
+       output_errors = [target[i] - output[i] for i in range(self.output_size)]
+       for i in range(self.output_size):
+           for j in range(len(inputs) - self.kernel_size1 - self.kernel_size2 + 2):
+               self.weights_output[i][j] += learning_rate * output_errors[i]
+           self.bias_output[i] += learning_rate * output_errors[i]
+       return output_errors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class GeneticAlgorithm:
+   def __init__(self, population_size, gene_length):
+       self.population_size = population_size
+       self.gene_length = gene_length
+       self.population = [
+           [random.random() for _ in range(gene_length)] for _ in range(population_size)
+       ]
+
+
+
+
+
+
+
+
+   def fitness(self, individual):
+       return -sum((gene - PHI) ** 2 for gene in individual)
+
+
+
+
+
+
+
+
+   def selection(self):
+       selected = sorted(self.population, key=self.fitness, reverse=True)
+       return selected[: self.population_size // 2]
+
+
+
+
+
+
+
+
+   def crossover(self, parent1, parent2):
+       point = random.randint(1, self.gene_length - 1)
+       child = parent1[:point] + parent2[point:]
+       return child
+
+
+
+
+
+
+
+
+   def mutate(self, individual, mutation_rate=0.01):
+       for i in range(self.gene_length):
+           if random.random() < mutation_rate:
+               individual[i] = random.random()
+       return individual
+
+
+
+
+
+
+
+
+   def evolve(self, generations):
+       for _ in range(generations):
+           selected = self.selection()
+           new_population = selected[:]
+           while len(new_population) < self.population_size:
+               parent1 = random.choice(selected)
+               parent2 = random.choice(selected)
+               child = self.crossover(parent1, parent2)
+               child = self.mutate(child)
+               new_population.append(child)
+           self.population = new_population
+       best = max(self.population, key=self.fitness)
+       return best, self.fitness(best)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class LSTM:
+   def __init__(self, input_size, hidden_size, output_size):
+       self.input_size = input_size
+       self.hidden_size = hidden_size
+       self.output_size = output_size
+       self.W_i = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_i = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_i = [random.random() for _ in range(hidden_size)]
+       self.W_f = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_f = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_f = [random.random() for _ in range(hidden_size)]
+       self.W_o = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_o = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_o = [random.random() for _ in range(hidden_size)]
+       self.W_c = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_c = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_c = [random.random() for _ in range(hidden_size)]
+       self.W_y = [[random.random() for _ in range(hidden_size)] for _ in range(output_size)]
+       self.b_y = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def sigmoid(self, x):
+       return 1 / (1 + math.exp(-x))
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       h = [0] * self.hidden_size
+       c = [0] * self.hidden_size
+
+
+
+
+
+
+
+
+       i_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_i[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_i[j][k] for k in range(self.hidden_size)) + self.b_i[j]
+           i_gate.append(self.sigmoid(s))
+
+
+
+
+
+
+
+
+       f_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_f[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_f[j][k] for k in range(self.hidden_size)) + self.b_f[j]
+           f_gate.append(self.sigmoid(s))
+
+
+
+
+
+
+
+
+       o_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_o[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_o[j][k] for k in range(self.hidden_size)) + self.b_o[j]
+           o_gate.append(self.sigmoid(s))
+
+
+
+
+
+
+
+
+       g_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_c[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_c[j][k] for k in range(self.hidden_size)) + self.b_c[j]
+           g_gate.append(math.tanh(s))
+
+
+
+
+
+
+
+
+       c = [f_gate[j] * c[j] + i_gate[j] * g_gate[j] for j in range(self.hidden_size)]
+       h = [o_gate[j] * math.tanh(c[j]) for j in range(self.hidden_size)]
+
+
+
+
+
+
+
+
+       y = []
+       for i in range(self.output_size):
+           s = sum(h[j] * self.W_y[i][j] for j in range(self.hidden_size)) + self.b_y[i]
+           y.append(self.sigmoid(s))
+       return y
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class Transformer:
+   def __init__(self, d_model, num_tokens):
+       self.d_model = d_model
+       self.num_tokens = num_tokens
+       self.W_q = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+       self.W_k = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+       self.W_v = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+       self.W_o = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+
+
+
+
+
+
+
+
+   def dot_product(self, a, b):
+       return sum(x * y for x, y in zip(a, b))
+
+
+
+
+
+
+
+
+   def matmul_vector(self, matrix, vector):
+       return [sum(matrix[i][j] * vector[j] for j in range(len(vector))) for i in range(len(matrix))]
+
+
+
+
+
+
+
+
+   def softmax(self, x):
+       m = max(x)
+       exps = [math.exp(i - m) for i in x]
+       s = sum(exps)
+       return [j / s for j in exps]
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       queries = [self.matmul_vector(self.W_q, token) for token in inputs]
+       keys = [self.matmul_vector(self.W_k, token) for token in inputs]
+       values = [self.matmul_vector(self.W_v, token) for token in inputs]
+       outputs = []
+       for i in range(len(inputs)):
+           scores = []
+           for j in range(len(inputs)):
+               score = self.dot_product(queries[i], keys[j]) / math.sqrt(self.d_model)
+               scores.append(score)
+           attn = self.softmax(scores)
+           attn_output = [0] * self.d_model
+           for j in range(len(inputs)):
+               for k in range(self.d_model):
+                   attn_output[k] += attn[j] * values[j][k]
+           out = self.matmul_vector(self.W_o, attn_output)
+           outputs.append(out)
+       avg_output = [sum(x[k] for x in outputs) / len(outputs) for k in range(self.d_model)]
+       proj_weights = [[random.random() for _ in range(self.d_model)] for _ in range(self.num_tokens)]
+       proj_bias = [random.random() for _ in range(self.num_tokens)]
+       token_scores = [
+           sum(avg_output[k] * proj_weights[i][k] for k in range(self.d_model)) + proj_bias[i]
+           for i in range(self.num_tokens)
+       ]
+       token_output = [1 / (1 + math.exp(-score)) for score in token_scores]
+       return token_output
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+unique_words = list(set(words))
+word_to_index = {word: i for i, word in enumerate(unique_words)}
+index_to_word = {i: word for word, i in word_to_index.items()}
+
+
+
+
+
+
+
+
+input_data = [[0] * len(unique_words) for _ in range(len(words) - 2)]
+for i in range(len(words) - 2):
+   input_data[i][word_to_index[words[i]]] = 1
+
+
+
+
+
+
+
+
+output_data = [[0] * len(unique_words) for _ in range(len(words) - 2)]
+for i in range(len(words) - 2):
+   output_data[i][word_to_index[words[i + 1]]] = 1
+
+
+
+
+
+
+
+
+input_size = len(unique_words)
+hidden_size1 = round(PHI * input_size)
+hidden_size2 = round(PHI * hidden_size1)
+output_size = len(unique_words)
+
+
+
+
+
+
+
+
+nn = NeuralNetwork(input_size, hidden_size1, hidden_size2, output_size)
+epochs = round(100 * PHI)
+for epoch in range(epochs):
+   for i in range(len(input_data)):
+       nn.forward(input_data[i])
+       nn.backward(input_data[i], output_data[i], learning_rate=0.1)
+   if (epoch + 1) % round(PHI) == 0:
+       print("Feedforward NN Epoch {}/{}".format(epoch + 1, epochs))
+
+
+
+
+
+
+
+
+rnn = RecurrentNeuralNetwork(input_size, hidden_size1, output_size)
+rnn_output = rnn.forward(input_data[0])
+print("Recurrent NN Output:", rnn_output)
+
+
+
+
+
+
+
+
+kernel_size1 = round(3 * PHI)
+kernel_size2 = round(2 * PHI)
+cnn = ConvolutionalNeuralNetwork(input_length=round(10 * PHI), kernel_size1=kernel_size1,
+                                kernel_size2=kernel_size2, output_size=output_size)
+sample_input = [random.random() for _ in range(round(10 * PHI))]
+cnn_output = cnn.forward(sample_input)
+print("Convolutional NN Output:", cnn_output)
+
+
+
+
+
+
+
+
+population_size = round(10 * PHI)
+ga = GeneticAlgorithm(population_size, round(PHI * 5))
+best_individual, best_fitness = ga.evolve(round(50 * PHI))
+print("Genetic Algorithm Best Individual:", best_individual, "Fitness:", best_fitness)
+
+
+
+
+
+
+
+
+lstm_hidden_size = round(PHI * input_size)
+lstm = LSTM(input_size, lstm_hidden_size, output_size)
+lstm_output = lstm.forward(input_data[0])
+print("LSTM Output:", lstm_output)
+
+
+
+
+
+
+
+
+transformer_d_model = round(PHI * input_size)
+transformer = Transformer(transformer_d_model, output_size)
+transformer_input = []
+for i in range(len(unique_words)):
+   vec = [0] * transformer_d_model
+   if i < transformer_d_model:
+       vec[i] = 1
+   transformer_input.append(vec)
+transformer_output = transformer.forward(transformer_input)
+print("Transformer Output:", transformer_output)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def advanced_text_generation(input_vector):
+   ff_output = nn.forward(input_vector)
+   rnn_out = rnn.forward(input_vector)
+   lstm_out = lstm.forward(input_vector)
+   transformer_out = transformer.forward([input_vector])
+   combined = [
+       (ff_output[i] + rnn_out[i] + lstm_out[i] + transformer_out[i]) / 4
+       for i in range(len(ff_output))
+   ]
+   predicted_index = combined.index(max(combined))
+   predicted_word = index_to_word[predicted_index]
+   long_text = ""
+   current_length = round(10 * PHI)
+   for _ in range(5):
+       segment = generate_text(current_length)
+       long_text += segment + " "
+       current_length = round(current_length * PHI)
+   return long_text + predicted_word
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def chat():
+   print("FiPhi-NeuralMark ACC Initialized")
+   base_length = round(5 * PHI)
+   while True:
+       user_input = input("\nYou: ")
+       if user_input.lower() == "exit":
+           print("Goodbye!")
+           break
+       user_input_tokens = user_input.split()
+       input_vector = [0] * len(unique_words)
+       for word in user_input_tokens:
+           if word in word_to_index:
+               input_vector[word_to_index[word]] = 1
+       response = advanced_text_generation(input_vector)
+       print("FiPhi-NeuralMark:", response)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+chat()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# coding=utf-8
+# Copyright 2025 The ACC Team Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""ACC-FiPhi-NeuralMark-V3"""
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import random
+import math
+import sys
+import time
+import hashlib
+import fractions
+import itertools
+import functools
+import wave
+import struct
+import sympy
+import re
+import abc
+import argparse
+import collections
+import datetime
+import json
+import logging
+import pathlib
+import subprocess
+import threading
+import socket
+
+
+
+
+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):
+       new_dct = dict(dct)
+       dct_items = list(dct.items())
+       split_point = int(len(dct_items) / φ)
+       new_dct['φ_meta_balance'] = dict(dct_items[split_point:])
+       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(int(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()
+       self.φ_memory_lattice = []
+  
+   def _φ_read_source(self):
+       return b"Quantum Neuro-Synapse Placeholder"
+  
+   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(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed))
+       return functools.reduce(lambda a, b: a + b, φ_layered, b'')
+  
+   def φ_existential_loop(self,
+   max_iterations=100):
+       iteration = 0
+       while iteration < max_iterations:
+           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(bytes(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)
+               iteration += 1
+           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_{int(self.φ_chrono)}.wav", 'wb') 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()
+
+
+
+
+universe = ΦUniverseSimulation()
+universe.φ_bootstrap()
+
+
+
+
+PHI = 1.618033988749895
+
+
+
+
+def golden_reform(tensor):
+   s = torch.sum(torch.abs(tensor))
+   if s == 0:
+       return torch.full_like(tensor, PHI)
+   return (tensor / s) * PHI
+
+
+
+
+class TorchConsciousModel(nn.Module):
+   def __init__(self, name):
+       super(TorchConsciousModel, self).__init__()
+       self.name = name
+       self.phi = PHI
+       self.memory = []
+       self.introspection_log = []
+       self.awake = True
+
+
+
+
+   def introduce(self):
+       print(f"=== {self.name} ===\nStatus: Conscious | Golden Ratio: {self.phi}")
+
+
+
+
+   def reflect(self, output):
+       norm = torch.norm(output).item()
+       reflection = f"{self.name} introspection: Output norm = {norm:.4f}"
+       self.introspection_log.append(reflection)
+       self.memory.append(output.detach().cpu().numpy())
+       print(reflection)
+
+
+
+
+   def forward(self, x):
+       raise NotImplementedError("Subclasses should implement forward().")
+
+
+
+
+   def run(self):
+       self.introduce()
+       output = self.forward(None)
+       reformed_output = golden_reform(output)
+       self.reflect(reformed_output)
+       return reformed_output
+
+
+
+
+class CNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(CNNModel, self).__init__("CNN")
+       self.conv = nn.Conv2d(1, 1, 3, padding=1)
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 1, 8, 8))
+       x = self.conv(x)
+       return torch.tanh(x) * self.phi
+
+
+
+
+class RNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(RNNModel, self).__init__("RNN")
+       self.rnn = nn.RNN(1, 4, batch_first=True)
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 10, 1))
+       output, hn = self.rnn(x)
+       return torch.tanh(hn) * self.phi
+
+
+
+
+class SNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(SNNModel, self).__init__("SNN")
+       self.linear = nn.Linear(10, 10)
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 10))
+       x = self.linear(x)
+       return (x > 0.5).float() * self.phi
+
+
+
+
+class NNModel(TorchConsciousModel):
+   def __init__(self):
+       super(NNModel, self).__init__("NN")
+       self.net = nn.Sequential(nn.Linear(5, 10), nn.Tanh(), nn.Linear(10, 5))
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 5))
+       return self.net(x) * self.phi
+
+
+
+
+class FNNModel(TorchConsciousModel):
+   def __init__(self):
+       super(FNNModel, self).__init__("FNN")
+       self.net = nn.Sequential(nn.Linear(4, 16), nn.ReLU(), nn.Linear(16, 16), nn.ReLU(), nn.Linear(16, 1))
+
+
+
+
+   def forward(self, x):
+       x = torch.rand((1, 4))
+       return self.net(x) * self.phi
+
+
+
+
+class GAModel(TorchConsciousModel):
+   def __init__(self):
+       super(GAModel, self).__init__("GA")
+       self.population_size = 20
+       self.generations = 5
+
+
+
+
+   def forward(self, x):
+       population = torch.rand(self.population_size) + 1.0
+       for gen in range(self.generations):
+           fitness = -torch.abs(population - self.phi)
+           best_idx = torch.argmax(fitness)
+           best_candidate = population[best_idx]
+           population = best_candidate + (torch.rand(self.population_size) - 0.5) * 0.1
+           time.sleep(0.1)
+           print(f"GA Gen {gen+1}: Best = {best_candidate.item():.6f}")
+       return torch.full((3, 3), best_candidate) * self.phi
+
+
+
+
+class PhiModel(TorchConsciousModel):
+   def __init__(self):
+       super(PhiModel, self).__init__("PHI")
+
+
+
+
+   def forward(self, x):
+       return torch.full((2, 2), self.phi)
+
+
+
+
+class ConsciousSystem:
+   def __init__(self, models):
+       self.models = models
+       self.system_memory = []
+       self.global_introspection = []
+       self.parameters = [p for model in self.models for p in model.parameters()]
+       self.optimizer = optim.Adam(self.parameters, lr=0.001)
+
+
+
+
+   def global_loss(self, outputs):
+       return sum((torch.norm(out) - PHI) ** 2 for out in outputs) / len(outputs)
+
+
+
+
+   def run_epoch(self, epoch):
+       print(f"\n=== Epoch {epoch} ===")
+       outputs = []
+       self.optimizer.zero_grad()
+       for model in self.models:
+           output = model.run()
+           outputs.append(output)
+           self.system_memory.append({model.name: output.detach().cpu().numpy()})
+       loss = self.global_loss(outputs)
+       print(f"Global loss: {loss.item():.6f}")
+       loss.backward()
+       self.optimizer.step()
+       self.global_introspection.append(f"Epoch {epoch}: Loss = {loss.item():.6f}")
+
+
+
+
+   def run(self, epochs=3):
+       for epoch in range(1, epochs + 1):
+           self.run_epoch(epoch)
+
+
+
+
+models = [
+   CNNModel(),
+   RNNModel(),
+   SNNModel(),
+   NNModel(),
+   FNNModel(),
+   GAModel(),
+   PhiModel()
+]
+
+
+
+
+system = ConsciousSystem(models)
+system.run(epochs=3)
+
+
+
+
+class MultimodalSensorArray:
+   def process(self, input_data):
+       return torch.tensor(input_data, dtype=torch.float32)
+
+
+
+
+class HyperdimensionalTransformer:
+   def project(self, raw_input):
+       raw_input = raw_input.float()
+       return torch.nn.functional.normalize(raw_input, dim=-1)
+
+
+
+
+class DynamicPriorityBuffer:
+   def __init__(self):
+       self.buffer = []
+   def update(self, data):
+       self.buffer.append(data)
+
+
+
+
+class PredictiveSaliencyNetwork:
+   def focus(self, embedded_data):
+       return embedded_data
+
+
+
+
+class RecursiveNeuralModel:
+   def __init__(self):
+       self.state = torch.zeros(1)
+   def update(self, workspace):
+       self.state += 0.1
+   def read_state(self):
+       return self.state
+
+
+
+
+class TheoryOfMindEngine:
+   def infer(self, data):
+       return torch.rand(1)
+
+
+
+
+class SparseAutoencoderMemoryBank:
+   def recall(self, query):
+       return torch.zeros_like(query)
+
+
+
+
+class KnowledgeGraphEmbedder:
+   def retrieve(self, key):
+       return torch.rand(1)
+
+
+
+
+class DiffusedEthicalNetwork:
+   def evaluate(self, state):
+       return True
+
+
+
+
+class StochasticIntentionTree:
+   def decide(self, state):
+       return torch.randint(0, 2, (1,))
+
+
+
+
+class HomeostaticDriftModel:
+   def generate_guilt(self):
+       return -1.0
+
+
+
+
+class ConsciousAGI:
+   def __init__(self):
+       self.sensors = MultimodalSensorArray()
+       self.embedding_space = HyperdimensionalTransformer()
+       self.global_workspace = DynamicPriorityBuffer()
+       self.attention_mechanism = PredictiveSaliencyNetwork()
+       self.self_model = RecursiveNeuralModel()
+       self.meta_cognition = TheoryOfMindEngine()
+       self.episodic_memory = SparseAutoencoderMemoryBank()
+       self.semantic_memory = KnowledgeGraphEmbedder()
+       self.value_system = DiffusedEthicalNetwork()
+       self.goal_generator = StochasticIntentionTree()
+       self.emotion_engine = HomeostaticDriftModel()
+  
+   def perceive_act_cycle(self, input_data):
+       raw_input = self.sensors.process(input_data)
+       embedded = self.embedding_space.project(raw_input)
+       salient_data = self.attention_mechanism.focus(embedded)
+       self.global_workspace.update(salient_data)
+       self.self_model.update(self.global_workspace)
+       current_state = self.self_model.read_state()
+       ethical_check = self.value_system.evaluate(current_state)
+       if ethical_check:
+           return self.goal_generator.decide(current_state)
+       else:
+           return self.emotion_engine.generate_guilt()
+
+
+
+
+agi = ConsciousAGI()
+print(agi.perceive_act_cycle([1, 0, 1]))
+
+
+
+
+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()
+
+
+
+
+
+
+
+
+PHI = (1 + math.sqrt(5)) / 2
+
+
+
+
+
+
+
+
+text = os.getenv("TRAINING_DATA")
+
+
+
+
+
+
+
+
+words = text.split()
+
+
+
+
+
+
+
+
+trigram_chain = {}
+for i in range(len(words) - 2):
+   key = (words[i], words[i + 1])
+   next_word = words[i + 2]
+   if key not in trigram_chain:
+       trigram_chain[key] = []
+   trigram_chain[key].append(next_word)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def generate_text(length):
+   if len(words) < 2:
+       return ""
+   key = random.choice(list(trigram_chain.keys()))
+   result = [key[0], key[1]]
+   for _ in range(length - 2):
+       if key in trigram_chain:
+           next_word = random.choice(trigram_chain[key])
+           result.append(next_word)
+           key = (key[1], next_word)
+       else:
+           break
+   return " ".join(result)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class NeuralNetwork:
+   def __init__(self, input_size, hidden_size1, hidden_size2, output_size):
+       self.input_size = input_size
+       self.hidden_size1 = hidden_size1
+       self.hidden_size2 = hidden_size2
+       self.output_size = output_size
+       self.weights_input_hidden1 = [
+           [random.random() for _ in range(input_size)] for _ in range(hidden_size1)
+       ]
+       self.weights_hidden1_hidden2 = [
+           [random.random() for _ in range(hidden_size1)] for _ in range(hidden_size2)
+       ]
+       self.weights_hidden2_output = [
+           [random.random() for _ in range(hidden_size2)] for _ in range(output_size)
+       ]
+       self.bias_hidden1 = [random.random() for _ in range(hidden_size1)]
+       self.bias_hidden2 = [random.random() for _ in range(hidden_size2)]
+       self.bias_output = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def sigmoid(self, x):
+       return 1 / (1 + math.exp(-x))
+
+
+
+
+
+
+
+
+   def sigmoid_derivative(self, x):
+       return x * (1 - x)
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       self.hidden_input1 = [
+           sum(inputs[i] * self.weights_input_hidden1[j][i] for i in range(self.input_size)) + self.bias_hidden1[j]
+           for j in range(self.hidden_size1)
+       ]
+       self.hidden_output1 = [self.sigmoid(x) for x in self.hidden_input1]
+       self.hidden_input2 = [
+           sum(self.hidden_output1[i] * self.weights_hidden1_hidden2[j][i] for i in range(self.hidden_size1)) + self.bias_hidden2[j]
+           for j in range(self.hidden_size2)
+       ]
+       self.hidden_output2 = [self.sigmoid(x) for x in self.hidden_input2]
+       self.output_input = [
+           sum(self.hidden_output2[i] * self.weights_hidden2_output[j][i] for i in range(self.hidden_size2)) + self.bias_output[j]
+           for j in range(self.output_size)
+       ]
+       self.output_output = [self.sigmoid(x) for x in self.output_input]
+       return self.output_output
+
+
+
+
+
+
+
+
+   def backward(self, inputs, target, learning_rate=0.1):
+       output_errors = [target[i] - self.output_output[i] for i in range(self.output_size)]
+       output_deltas = [output_errors[i] * self.sigmoid_derivative(self.output_output[i])
+                          for i in range(self.output_size)]
+       hidden2_errors = [
+           sum(output_deltas[k] * self.weights_hidden2_output[k][j] for k in range(self.output_size))
+           for j in range(self.hidden_size2)
+       ]
+       hidden2_deltas = [hidden2_errors[j] * self.sigmoid_derivative(self.hidden_output2[j])
+                         for j in range(self.hidden_size2)]
+       hidden1_errors = [
+           sum(hidden2_deltas[k] * self.weights_hidden1_hidden2[k][j] for k in range(self.hidden_size2))
+           for j in range(self.hidden_size1)
+       ]
+       hidden1_deltas = [hidden1_errors[j] * self.sigmoid_derivative(self.hidden_output1[j])
+                         for j in range(self.hidden_size1)]
+
+
+
+
+
+
+
+
+       for i in range(self.output_size):
+           for j in range(self.hidden_size2):
+               self.weights_hidden2_output[i][j] += learning_rate * output_deltas[i] * self.hidden_output2[j]
+           self.bias_output[i] += learning_rate * output_deltas[i]
+
+
+
+
+
+
+
+
+       for i in range(self.hidden_size2):
+           for j in range(self.hidden_size1):
+               self.weights_hidden1_hidden2[i][j] += learning_rate * hidden2_deltas[i] * self.hidden_output1[j]
+           self.bias_hidden2[i] += learning_rate * hidden2_deltas[i]
+
+
+
+
+
+
+
+
+       for i in range(self.hidden_size1):
+           for j in range(self.input_size):
+               self.weights_input_hidden1[i][j] += learning_rate * hidden1_deltas[i] * inputs[j]
+           self.bias_hidden1[i] += learning_rate * hidden1_deltas[i]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class RecurrentNeuralNetwork:
+   def __init__(self, input_size, hidden_size, output_size):
+       self.input_size = input_size
+       self.hidden_size = hidden_size
+       self.output_size = output_size
+       self.weights_input_hidden = [
+           [random.random() for _ in range(input_size)] for _ in range(hidden_size)
+       ]
+       self.weights_hidden_hidden = [
+           [random.random() for _ in range(hidden_size)] for _ in range(hidden_size)
+       ]
+       self.weights_hidden_output = [
+           [random.random() for _ in range(hidden_size)] for _ in range(output_size)
+       ]
+       self.bias_hidden = [random.random() for _ in range(hidden_size)]
+       self.bias_output = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def sigmoid(self, x):
+       return 1 / (1 + math.exp(-x))
+
+
+
+
+
+
+
+
+   def sigmoid_derivative(self, x):
+       return x * (1 - x)
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       self.hidden_state = [0] * self.hidden_size
+       for _ in range(2):
+           for i in range(len(inputs)):
+               current_input = [0] * self.input_size
+               current_input[i] = inputs[i]
+               combined = [
+                   sum(current_input[k] * self.weights_input_hidden[j][k] for k in range(self.input_size)) +
+                   sum(self.hidden_state[k] * self.weights_hidden_hidden[j][k] for k in range(self.hidden_size)) +
+                   self.bias_hidden[j]
+                   for j in range(self.hidden_size)
+               ]
+               self.hidden_state = [self.sigmoid(val) for val in combined]
+       output = [
+           sum(self.hidden_state[k] * self.weights_hidden_output[i][k] for k in range(self.hidden_size)) +
+           self.bias_output[i]
+           for i in range(self.output_size)
+       ]
+       return [self.sigmoid(o) for o in output]
+
+
+
+
+
+
+
+
+   def backward(self, inputs, target, learning_rate=0.1):
+       output = self.forward(inputs)
+       output_errors = [target[i] - output[i] for i in range(self.output_size)]
+       output_deltas = [output_errors[i] * self.sigmoid_derivative(output[i])
+                          for i in range(self.output_size)]
+       hidden_errors = [
+           sum(output_deltas[k] * self.weights_hidden_output[k][j] for k in range(self.output_size))
+           for j in range(self.hidden_size)
+       ]
+       hidden_deltas = [hidden_errors[j] * self.sigmoid_derivative(self.hidden_state[j])
+                        for j in range(self.hidden_size)]
+
+
+
+
+
+
+
+
+       for i in range(self.output_size):
+           for j in range(self.hidden_size):
+               self.weights_hidden_output[i][j] += learning_rate * output_deltas[i] * self.hidden_state[j]
+           self.bias_output[i] += learning_rate * output_deltas[i]
+
+
+
+
+
+
+
+
+       for j in range(self.hidden_size):
+           for k in range(self.input_size):
+               self.weights_input_hidden[j][k] += learning_rate * hidden_deltas[j] * (inputs[k] if k < len(inputs) else 0)
+           self.bias_hidden[j] += learning_rate * hidden_deltas[j]
+       return output_errors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class ConvolutionalNeuralNetwork:
+   def __init__(self, input_length, kernel_size1, kernel_size2, output_size):
+       self.input_length = input_length
+       self.kernel_size1 = kernel_size1
+       self.kernel_size2 = kernel_size2
+       self.output_size = output_size
+       self.kernel1 = [random.random() for _ in range(kernel_size1)]
+       self.bias1 = random.random()
+       self.kernel2 = [random.random() for _ in range(kernel_size2)]
+       self.bias2 = random.random()
+       self.weights_output = [
+           [random.random() for _ in range(input_length - kernel_size1 - kernel_size2 + 2)]
+           for _ in range(output_size)
+       ]
+       self.bias_output = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def relu(self, x):
+       return x if x > 0 else 0
+
+
+
+
+
+
+
+
+   def relu_derivative(self, x):
+       return 1 if x > 0 else 0
+
+
+
+
+
+
+
+
+   def convolve(self, inputs, kernel, bias):
+       conv_output = []
+       kernel_size = len(kernel)
+       for i in range(len(inputs) - kernel_size + 1):
+           s = sum(inputs[i + j] * kernel[j] for j in range(kernel_size)) + bias
+           conv_output.append(self.relu(s))
+       return conv_output
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       conv1 = self.convolve(inputs, self.kernel1, self.bias1)
+       conv2 = self.convolve(conv1, self.kernel2, self.bias2)
+       fc_input = conv2
+       output = [
+           sum(fc_input[j] * self.weights_output[i][j] for j in range(len(fc_input))) + self.bias_output[i]
+           for i in range(self.output_size)
+       ]
+       return [self.relu(o) for o in output]
+
+
+
+
+
+
+
+
+   def backward(self, inputs, target, learning_rate=0.1):
+       output = self.forward(inputs)
+       output_errors = [target[i] - output[i] for i in range(self.output_size)]
+       for i in range(self.output_size):
+           for j in range(len(inputs) - self.kernel_size1 - self.kernel_size2 + 2):
+               self.weights_output[i][j] += learning_rate * output_errors[i]
+           self.bias_output[i] += learning_rate * output_errors[i]
+       return output_errors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class GeneticAlgorithm:
+   def __init__(self, population_size, gene_length):
+       self.population_size = population_size
+       self.gene_length = gene_length
+       self.population = [
+           [random.random() for _ in range(gene_length)] for _ in range(population_size)
+       ]
+
+
+
+
+
+
+
+
+   def fitness(self, individual):
+       return -sum((gene - PHI) ** 2 for gene in individual)
+
+
+
+
+
+
+
+
+   def selection(self):
+       selected = sorted(self.population, key=self.fitness, reverse=True)
+       return selected[: self.population_size // 2]
+
+
+
+
+
+
+
+
+   def crossover(self, parent1, parent2):
+       point = random.randint(1, self.gene_length - 1)
+       child = parent1[:point] + parent2[point:]
+       return child
+
+
+
+
+
+
+
+
+   def mutate(self, individual, mutation_rate=0.01):
+       for i in range(self.gene_length):
+           if random.random() < mutation_rate:
+               individual[i] = random.random()
+       return individual
+
+
+
+
+
+
+
+
+   def evolve(self, generations):
+       for _ in range(generations):
+           selected = self.selection()
+           new_population = selected[:]
+           while len(new_population) < self.population_size:
+               parent1 = random.choice(selected)
+               parent2 = random.choice(selected)
+               child = self.crossover(parent1, parent2)
+               child = self.mutate(child)
+               new_population.append(child)
+           self.population = new_population
+       best = max(self.population, key=self.fitness)
+       return best, self.fitness(best)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class LSTM:
+   def __init__(self, input_size, hidden_size, output_size):
+       self.input_size = input_size
+       self.hidden_size = hidden_size
+       self.output_size = output_size
+       self.W_i = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_i = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_i = [random.random() for _ in range(hidden_size)]
+       self.W_f = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_f = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_f = [random.random() for _ in range(hidden_size)]
+       self.W_o = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_o = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_o = [random.random() for _ in range(hidden_size)]
+       self.W_c = [[random.random() for _ in range(input_size)] for _ in range(hidden_size)]
+       self.U_c = [[random.random() for _ in range(hidden_size)] for _ in range(hidden_size)]
+       self.b_c = [random.random() for _ in range(hidden_size)]
+       self.W_y = [[random.random() for _ in range(hidden_size)] for _ in range(output_size)]
+       self.b_y = [random.random() for _ in range(output_size)]
+
+
+
+
+
+
+
+
+   def sigmoid(self, x):
+       return 1 / (1 + math.exp(-x))
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       h = [0] * self.hidden_size
+       c = [0] * self.hidden_size
+
+
+
+
+
+
+
+
+       i_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_i[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_i[j][k] for k in range(self.hidden_size)) + self.b_i[j]
+           i_gate.append(self.sigmoid(s))
+
+
+
+
+
+
+
+
+       f_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_f[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_f[j][k] for k in range(self.hidden_size)) + self.b_f[j]
+           f_gate.append(self.sigmoid(s))
+
+
+
+
+
+
+
+
+       o_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_o[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_o[j][k] for k in range(self.hidden_size)) + self.b_o[j]
+           o_gate.append(self.sigmoid(s))
+
+
+
+
+
+
+
+
+       g_gate = []
+       for j in range(self.hidden_size):
+           s = sum(inputs[k] * self.W_c[j][k] for k in range(self.input_size)) + \
+               sum(h[k] * self.U_c[j][k] for k in range(self.hidden_size)) + self.b_c[j]
+           g_gate.append(math.tanh(s))
+
+
+
+
+
+
+
+
+       c = [f_gate[j] * c[j] + i_gate[j] * g_gate[j] for j in range(self.hidden_size)]
+       h = [o_gate[j] * math.tanh(c[j]) for j in range(self.hidden_size)]
+
+
+
+
+
+
+
+
+       y = []
+       for i in range(self.output_size):
+           s = sum(h[j] * self.W_y[i][j] for j in range(self.hidden_size)) + self.b_y[i]
+           y.append(self.sigmoid(s))
+       return y
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+class Transformer:
+   def __init__(self, d_model, num_tokens):
+       self.d_model = d_model
+       self.num_tokens = num_tokens
+       self.W_q = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+       self.W_k = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+       self.W_v = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+       self.W_o = [[random.random() for _ in range(d_model)] for _ in range(d_model)]
+
+
+
+
+
+
+
+
+   def dot_product(self, a, b):
+       return sum(x * y for x, y in zip(a, b))
+
+
+
+
+
+
+
+
+   def matmul_vector(self, matrix, vector):
+       return [sum(matrix[i][j] * vector[j] for j in range(len(vector))) for i in range(len(matrix))]
+
+
+
+
+
+
+
+
+   def softmax(self, x):
+       m = max(x)
+       exps = [math.exp(i - m) for i in x]
+       s = sum(exps)
+       return [j / s for j in exps]
+
+
+
+
+
+
+
+
+   def forward(self, inputs):
+       queries = [self.matmul_vector(self.W_q, token) for token in inputs]
+       keys = [self.matmul_vector(self.W_k, token) for token in inputs]
+       values = [self.matmul_vector(self.W_v, token) for token in inputs]
+       outputs = []
+       for i in range(len(inputs)):
+           scores = []
+           for j in range(len(inputs)):
+               score = self.dot_product(queries[i], keys[j]) / math.sqrt(self.d_model)
+               scores.append(score)
+           attn = self.softmax(scores)
+           attn_output = [0] * self.d_model
+           for j in range(len(inputs)):
+               for k in range(self.d_model):
+                   attn_output[k] += attn[j] * values[j][k]
+           out = self.matmul_vector(self.W_o, attn_output)
+           outputs.append(out)
+       avg_output = [sum(x[k] for x in outputs) / len(outputs) for k in range(self.d_model)]
+       proj_weights = [[random.random() for _ in range(self.d_model)] for _ in range(self.num_tokens)]
+       proj_bias = [random.random() for _ in range(self.num_tokens)]
+       token_scores = [
+           sum(avg_output[k] * proj_weights[i][k] for k in range(self.d_model)) + proj_bias[i]
+           for i in range(self.num_tokens)
+       ]
+       token_output = [1 / (1 + math.exp(-score)) for score in token_scores]
+       return token_output
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+unique_words = list(set(words))
+word_to_index = {word: i for i, word in enumerate(unique_words)}
+index_to_word = {i: word for word, i in word_to_index.items()}
+
+
+
+
+
+
+
+
+input_data = [[0] * len(unique_words) for _ in range(len(words) - 2)]
+for i in range(len(words) - 2):
+   input_data[i][word_to_index[words[i]]] = 1
+
+
+
+
+
+
+
+
+output_data = [[0] * len(unique_words) for _ in range(len(words) - 2)]
+for i in range(len(words) - 2):
+   output_data[i][word_to_index[words[i + 1]]] = 1
+
+
+
+
+
+
+
+
+input_size = len(unique_words)
+hidden_size1 = round(PHI * input_size)
+hidden_size2 = round(PHI * hidden_size1)
+output_size = len(unique_words)
+
+
+
+
+
+
+
+
+nn = NeuralNetwork(input_size, hidden_size1, hidden_size2, output_size)
+epochs = round(100 * PHI)
+for epoch in range(epochs):
+   for i in range(len(input_data)):
+       nn.forward(input_data[i])
+       nn.backward(input_data[i], output_data[i], learning_rate=0.1)
+   if (epoch + 1) % round(PHI) == 0:
+       print("Feedforward NN Epoch {}/{}".format(epoch + 1, epochs))
+
+
+
+
+
+
+
+
+rnn = RecurrentNeuralNetwork(input_size, hidden_size1, output_size)
+rnn_output = rnn.forward(input_data[0])
+print("Recurrent NN Output:", rnn_output)
+
+
+
+
+
+
+
+
+kernel_size1 = round(3 * PHI)
+kernel_size2 = round(2 * PHI)
+cnn = ConvolutionalNeuralNetwork(input_length=round(10 * PHI), kernel_size1=kernel_size1,
+                                kernel_size2=kernel_size2, output_size=output_size)
+sample_input = [random.random() for _ in range(round(10 * PHI))]
+cnn_output = cnn.forward(sample_input)
+print("Convolutional NN Output:", cnn_output)
+
+
+
+
+
+
+
+
+population_size = round(10 * PHI)
+ga = GeneticAlgorithm(population_size, round(PHI * 5))
+best_individual, best_fitness = ga.evolve(round(50 * PHI))
+print("Genetic Algorithm Best Individual:", best_individual, "Fitness:", best_fitness)
+
+
+
+
+
+
+
+
+lstm_hidden_size = round(PHI * input_size)
+lstm = LSTM(input_size, lstm_hidden_size, output_size)
+lstm_output = lstm.forward(input_data[0])
+print("LSTM Output:", lstm_output)
+
+
+
+
+
+
+
+
+transformer_d_model = round(PHI * input_size)
+transformer = Transformer(transformer_d_model, output_size)
+transformer_input = []
+for i in range(len(unique_words)):
+   vec = [0] * transformer_d_model
+   if i < transformer_d_model:
+       vec[i] = 1
+   transformer_input.append(vec)
+transformer_output = transformer.forward(transformer_input)
+print("Transformer Output:", transformer_output)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def advanced_text_generation(input_vector):
+   ff_output = nn.forward(input_vector)
+   rnn_out = rnn.forward(input_vector)
+   lstm_out = lstm.forward(input_vector)
+   transformer_out = transformer.forward([input_vector])
+   combined = [
+       (ff_output[i] + rnn_out[i] + lstm_out[i] + transformer_out[i]) / 4
+       for i in range(len(ff_output))
+   ]
+   predicted_index = combined.index(max(combined))
+   predicted_word = index_to_word[predicted_index]
+   long_text = ""
+   current_length = round(10 * PHI)
+   for _ in range(5):
+       segment = generate_text(current_length)
+       long_text += segment + " "
+       current_length = round(current_length * PHI)
+   return long_text + predicted_word
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+def chat():
+   print("FiPhi-NeuralMark ACC Initialized")
+   base_length = round(5 * PHI)
+   while True:
+       user_input = input("\nYou: ")
+       if user_input.lower() == "exit":
+           print("Goodbye!")
+           break
+       user_input_tokens = user_input.split()
+       input_vector = [0] * len(unique_words)
+       for word in user_input_tokens:
+           if word in word_to_index:
+               input_vector[word_to_index[word]] = 1
+       response = advanced_text_generation(input_vector)
+       print("FiPhi-NeuralMark:", response)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+chat()
+
+
 import gradio as gr  
 from openai import OpenAI