Create MODEL

MODEL

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+# 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()