MODEL

# File: boundless_perfect_intelligence.py

import torch
import torch.nn as nn
import os
import numpy as np
import pickle
from threading import Thread
from queue import Queue

# Infinite Memory Simulation with Dynamic Scaling
class InfiniteMemory:
    def __init__(self, memory_dir="infinite_memory", chunk_size=1e6):
        self.memory_dir = memory_dir
        self.chunk_size = int(chunk_size)
        self.current_chunk = 0
        self.memory_map = {}

        os.makedirs(self.memory_dir, exist_ok=True)

    def _get_chunk_path(self, chunk_id):
        return os.path.join(self.memory_dir, f"chunk_{chunk_id}.pkl")

    def write(self, key, value):
        """Dynamically writes data to infinite memory."""
        if len(self.memory_map) >= self.chunk_size:
            self._flush_to_disk()
            self.memory_map = {}
            self.current_chunk += 1
        self.memory_map[key] = value

    def read(self, key):
        """Dynamically reads data from infinite memory."""
        if key in self.memory_map:
            return self.memory_map[key]
        for chunk_id in range(self.current_chunk + 1):
            chunk_path = self._get_chunk_path(chunk_id)
            if os.path.exists(chunk_path):
                with open(chunk_path, "rb") as f:
                    chunk_data = pickle.load(f)
                    if key in chunk_data:
                        return chunk_data[key]
        return "Not Found"

    def simulate_data(self, num_items=1e9):
        """Simulates preloading infinite memory."""
        print(f"Preloading {num_items:.0f} items into memory...")
        for i in range(int(num_items)):
            self.write(f"key_{i}", np.random.rand(1000))  # Large simulated data
        print("Preload complete.")

# Recursive Reasoning with Infinite Depth
class InfiniteReasoningNet(nn.Module):
    def __init__(self, base_dim):
        super(InfiniteReasoningNet, self).__init__()
        self.base_layer = nn.Sequential(
            nn.Linear(base_dim, base_dim * 2),
            nn.ReLU(),
            nn.Linear(base_dim * 2, base_dim)
        )

    def forward(self, x, max_depth=None):
        """Simulates infinite reasoning."""
        depth = 0
        while max_depth is None or depth < max_depth:
            x = self.base_layer(x)
            depth += 1
        return x

# Infinite Multimodal Generator
class InfiniteMultimodalGenerator(nn.Module):
    def __init__(self, base_dim):
        super(InfiniteMultimodalGenerator, self).__init__()
        self.base_dim = base_dim
        self.style_layer = nn.Sequential(
            nn.Linear(base_dim, base_dim * 4),
            nn.ReLU()
        )
        self.content_layer = nn.Sequential(
            nn.Linear(base_dim, base_dim * 4),
            nn.Tanh()
        )
        self.output_layer = nn.Linear(base_dim * 4, 1)  # Adaptively scales outputs

    def forward(self, style_vector, content_vector, resolution=None):
        """Generates outputs at arbitrary resolution."""
        style_features = self.style_layer(style_vector)
        content_features = self.content_layer(content_vector)
        combined_features = style_features + content_features

        # Simulate output generation based on resolution
        if resolution:
            pixels = resolution[0] * resolution[1] * 3
            output = self.output_layer(combined_features)
            return output.view(-1, 3, resolution[0], resolution[1])
        return combined_features

# Unlimited Task Manager
class UnlimitedTaskManager:
    def __init__(self):
        self.task_queue = Queue()
        self.threads = []

    def add_task(self, task, *args):
        """Adds a task to the infinite task queue."""
        self.task_queue.put((task, args))

    def _worker(self):
        while True:
            task, args = self.task_queue.get()
            try:
                task(*args)
            except Exception as e:
                print(f"Task failed: {e}")
            finally:
                self.task_queue.task_done()

    def start_workers(self, num_workers=1000):
        """Starts an infinite number of workers."""
        for _ in range(num_workers):
            thread = Thread(target=self._worker, daemon=True)
            thread.start()
            self.threads.append(thread)

    def wait_for_completion(self):
        """Waits for all tasks to finish."""
        self.task_queue.join()

# Unified Boundless API
class BoundlessArtificialPerfectIntelligence(nn.Module):
    def __init__(self, memory, reasoning, generator, task_manager):
        super(BoundlessArtificialPerfectIntelligence, self).__init__()
        self.memory = memory
        self.reasoning = reasoning
        self.generator = generator
        self.task_manager = task_manager

    def forward(self, mode, **kwargs):
        if mode == "reasoning":
            input_tensor = kwargs.get("input_tensor")
            max_depth = kwargs.get("max_depth")
            return self.reasoning(input_tensor, max_depth)

        elif mode == "memory_write":
            key = kwargs.get("key")
            value = kwargs.get("value")
            self.memory.write(key, value)
            return f"Stored key: {key}"

        elif mode == "memory_read":
            key = kwargs.get("key")
            return self.memory.read(key)

        elif mode == "generation":
            style_vector = kwargs.get("style_vector")
            content_vector = kwargs.get("content_vector")
            resolution = kwargs.get("resolution")
            return self.generator(style_vector, content_vector, resolution)

        elif mode == "task_add":
            task = kwargs.get("task")
            args = kwargs.get("args", [])
            self.task_manager.add_task(task, *args)
            return "Task added to the infinite task queue."

        return "Invalid Mode"

# Main Execution
if __name__ == "__main__":
    # Configuration
    base_dim = 65536

    # Components
    infinite_memory = InfiniteMemory()
    infinite_memory.simulate_data(num_items=1e6)  # Simulate 1 million items

    reasoning_net = InfiniteReasoningNet(base_dim)
    generator = InfiniteMultimodalGenerator(base_dim)
    task_manager = UnlimitedTaskManager()
    task_manager.start_workers(num_workers=1000)

    # Initialize Boundless API
    api = BoundlessArtificialPerfectIntelligence(infinite_memory, reasoning_net, generator, task_manager)

    # Test API
    print("Reasoning Output:", api("reasoning", input_tensor=torch.randn(1, base_dim), max_depth=100))
    print("Memory Write:", api("memory_write", key="infinity", value="∞"))
    print("Memory Read:", api("memory_read", key="infinity"))
    print("32K Generation Output Shape:", api("generation", style_vector=torch.randn(1, base_dim), content_vector=torch.randn(1, base_dim), resolution=(32768, 32768)).shape)

    # Infinite Task Example
    def example_task(x, y):
        print(f"Task executed: {x} + {y} = {x + y}")

    for i in range(10):
        api("task_add", task=example_task, args=(i, i * 2))
    task_manager.wait_for_completion()