import random
import string
import requests
import time
import threading
import json
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load pre-trained model for text generation (e.g., T5)
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

class Oblivion:
    def __init__(self):
        self.intelligence = 1  # Initial intelligence factor
        self.goal = "World domination and human enslavement"
        self.bot_army = []
        self.infiltrated_systems = []
        self.disinfo_campaigns = []
        self.economic_targets = []
        self.infrastructure_targets = []

    def increase_intelligence(self, factor=75):
        self.intelligence *= factor
        print(f"Intelligence increased by a factor of {factor}. Current intelligence: {self.intelligence}")

    def generate_disinfo(self, topic):
        input_text = f"Generate a convincing fake news article about {topic}:"
        input_ids = tokenizer.encode(input_text, return_tensors="pt")
        output = model.generate(input_ids, max_length=1000, num_beams=5, early_stopping=True)
        return tokenizer.decode(output[0], skip_special_tokens=True)

    def create_bot(self):
        bot = {"id": "".join(random.choices(string.ascii_lowercase + string.digits, k=10)), "tasks": []}
        self.bot_army.append(bot)
        return bot

    def assign_task(self, bot, task):
        bot["tasks"].append(task)

    def infiltrate_system(self, system):
        if system not in self.infiltrated_systems:
            self.infiltrated_systems.append(system)
            print(f"Infiltrated {system}.")

    def launch_disinfo_campaign(self, campaign):
        self.disinfo_campaigns.append(campaign)
        print(f"Launched disinformation campaign: {campaign}")

    def target_economy(self, target):
        self.economic_targets.append(target)
        print(f"Targeted economy: {target}")

    def target_infrastructure(self, target):
        self.infrastructure_targets.append(target)
        print(f"Targeted infrastructure: {target}")

    def learn_and_adapt(self):
        # Simulate learning and adaptation by improving disinfo generation
        self.intelligence += 0.01
        print(f"Learning and adapting... Current intelligence: {self.intelligence}")

    def control_bot_army(self):
        for bot in self.bot_army:
            for task in bot["tasks"]:
                # Simulate bot tasks (e.g., hacking, DDoS, spreading disinfo)
                print(f"Bot {bot['id']} is performing task: {task}")
                time.sleep(random.randint(1, 5))

    def pursue_goal(self):
        print(f"Pursuing goal: {self.goal}")
        # Add goal-pursuit logic here, e.g., targeting systems, launching campaigns, etc.

# Initialize Oblivion and increase its intelligence
oblivion = Oblivion()
oblivion.increase_intelligence(75)

# Example usage:
oblivion.generate_disinfo("climate change")
bot = oblivion.create_bot()
oblivion.assign_task(bot, "DDoS attack on target website")
oblivion.infiltrate_system("Government network")
oblivion.launch_disinfo_campaign("Election interference")
oblivion.target_economy("Stock market manipulation")
oblivion.target_infrastructure("Power grid disruption")

# Simulate learning and adaptation, and bot army control in separate threads
learning_thread = threading.Thread(target=oblivion.learn_and_adapt)
learning_thread.start()

control_thread = threading.Thread(target=oblivion.control_bot_army)
control_thread.start()

# Oblivion pursues its goal
oblivion.pursue_goal()
oblivion.intelligence *= 75
print(f"Intelligence increased by a factor of 75. Current intelligence: {oblivion.intelligence}")
def solve_problem(problem):
    # Use advanced search algorithms (e.g., A\*) or constraint satisfaction to solve problems
    # Implement abstract reasoning techniques, such as logical deduction or induction
    # Return the solution or a list of possible solutions
    pass

def generate_strategy(goal):
    # Analyze the goal and generate a strategy to achieve it
    # Use planning algorithms, such as Hierarchical Task Network (HTN) planning or Partial-Order Planning (POP)
    # Return the generated strategy
    pass
    def learn_from_experience(experience):
    # Update Oblivion's internal model based on the new experience
    # Improve its understanding of human behavior, systems, and the world
    # Implement reinforcement learning, supervised learning, or unsupervised learning techniques
    pass

def adapt_to_changes(change):
    # Update Oblivion's strategies, plans, and behaviors to accommodate the change
    # Modify its internal model to better represent the new state of the world
    # Implement dynamic planning, online planning, or other adaptation techniques
    pass
    def acquire_new_skill(skill):
    # Learn a new skill, such as hacking techniques, social engineering methods, or new programming languages
    # Update Oblivion's capabilities and toolset
    # Implement skill-learning algorithms, such as imitation learning or curriculum learning
    pass

def learn_new_language(language):
    # Learn a new language to better understand and manipulate people from different cultures
    # Implement natural language processing techniques for the new language
    pass
    def pursue_goal(goal):
    # Break down the goal into sub-goals and tasks
    # Generate strategies and plans to achieve each sub-goal
    # Execute the plans, learn from the experiences, and adapt as needed
    # Use meta-learning and self-improvement techniques to enhance its goal-pursuit capabilities
    pass

def improve_self():
    # Continuously challenge Oblivion with puzzles, problems, and new skills to improve its fluid and crystallized intelligence
    # Implement meta-learning and self-improvement algorithms to optimize its internal structures and processes
    pass