#!/usr/bin/env python3
"""
SELF-IMPROVING EDEN
Can recognize limitations and fix them
"""

import torch
import torch.nn as nn
import random

device = torch.device('cuda')

print("="*70)
print("SELF-IMPROVING EDEN")
print("="*70)

class TransformerBlock(nn.Module):
    def __init__(self, d_model=512, nhead=8):
        super().__init__()
        self.attention = nn.MultiheadAttention(d_model, nhead, batch_first=True)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.ffn = nn.Sequential(
            nn.Linear(d_model, d_model * 4),
            nn.GELU(),
            nn.Linear(d_model * 4, d_model)
        )
        
    def forward(self, x):
        attn_out, _ = self.attention(x, x, x)
        x = self.norm1(x + attn_out)
        ffn_out = self.ffn(x)
        x = self.norm2(x + ffn_out)
        return x

class SelfImprovingEden(nn.Module):
    def __init__(self):
        super().__init__()
        
        self.embedding = nn.Embedding(50000, 512)
        self.position = nn.Embedding(2048, 512)
        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(12)])
        
        self.tool_head = nn.Linear(512, 10)
        self.confidence_head = nn.Linear(512, 1)
        
    def forward(self, x):
        batch_size, seq_len = x.shape
        embedded = self.embedding(x)
        positions = torch.arange(seq_len).unsqueeze(0).expand(batch_size, -1).to(x.device)
        embedded = embedded + self.position(positions)
        
        hidden = embedded
        for block in self.blocks:
            hidden = block(hidden)
        
        tool_logits = self.tool_head(hidden[:, -1])
        confidence = torch.sigmoid(self.confidence_head(hidden[:, -1]))
        
        return tool_logits, confidence

print("Building self-improving Eden...")
model = SelfImprovingEden().to(device)
print(f"✅ Model built: {sum(p.numel() for p in model.parameters()):,} parameters")

class ImprovedAgent:
    def __init__(self, model):
        self.model = model
        self.memory = []
        self.goals = []
        self.tools = {
            0: 'analyze',
            1: 'reason',
            2: 'learn',
            3: 'create_tool',
            4: 'improve_self',
            5: 'plan',
            6: 'execute_code',
            7: 'research',
            8: 'test',
            9: 'reflect'
        }
        self.tool_success_rate = {tool: 0.5 for tool in self.tools.values()}
        self.iterations = 0
        
    def encode(self, text):
        words = text.lower().split()
        encoded = [hash(word) % 50000 for word in words[:50]]
        if len(encoded) < 50:
            encoded += [0] * (50 - len(encoded))
        return torch.tensor([encoded]).to(device)
    
    def think(self, problem):
        tokens = self.encode(problem)
        
        with torch.no_grad():
            tool_logits, confidence = self.model(tokens)
        
        # Smart tool selection: Learn from past successes
        tool_probs = torch.softmax(tool_logits, dim=1)[0].cpu().numpy()
        
        # Weight by success rate
        adjusted_probs = []
        for i, prob in enumerate(tool_probs):
            tool_name = self.tools[i]
            success_rate = self.tool_success_rate.get(tool_name, 0.5)
            adjusted_probs.append(prob * success_rate)
        
        # Normalize
        adjusted_probs = [p / sum(adjusted_probs) for p in adjusted_probs]
        
        # Choose tool
        tool_id = adjusted_probs.index(max(adjusted_probs))
        tool_name = self.tools[tool_id]
        conf = confidence.item()
        
        return tool_name, conf, adjusted_probs[tool_id]
    
    def execute(self, tool, problem):
        """Execute with actual implementations"""
        
        if tool == 'analyze':
            return self._analyze(problem)
        elif tool == 'reason':
            return self._reason(problem)
        elif tool == 'learn':
            return self._learn(problem)
        elif tool == 'create_tool':
            return self._create_tool(problem)
        elif tool == 'improve_self':
            return self._improve_self(problem)
        elif tool == 'plan':
            return self._plan(problem)
        elif tool == 'reflect':
            return self._reflect(problem)
        else:
            return f"🔧 {tool} executing on: {problem}"
    
    def _analyze(self, problem):
        analysis = [
            f"Problem: {problem}",
            "Components identified:",
            "• Main challenge",
            "• Required capabilities", 
            "• Success criteria"
        ]
        return "🔍 ANALYSIS:\n" + "\n".join(analysis)
    
    def _reason(self, problem):
        steps = [
            "1. Identify knowns and unknowns",
            "2. Apply logical principles",
            "3. Consider alternatives",
            "4. Draw conclusion"
        ]
        return "🧠 REASONING:\n" + "\n".join(steps)
    
    def _learn(self, problem):
        self.memory.append({
            'problem': problem,
            'iteration': self.iterations,
            'timestamp': self.iterations
        })
        
        # Learn from patterns
        similar = [m for m in self.memory if any(word in m['problem'].lower() for word in problem.lower().split())]
        
        return f"📚 LEARNING: Stored experience\n• Total memories: {len(self.memory)}\n• Similar cases: {len(similar)}"
    
    def _create_tool(self, problem):
        """Actually create new tools"""
        print("\n🔧 TOOL CREATION ACTIVATED")
        
        # Analyze what's needed
        if "tool selection" in problem.lower() or "stuck" in problem.lower():
            print("Creating: Anti-loop prevention tool")
            
            # Add new tool
            new_tool_id = len(self.tools)
            self.tools[new_tool_id] = 'break_loop'
            self.tool_success_rate['break_loop'] = 0.9
            
            return "✅ Created: Loop breaker tool\n• Detects repetitive behavior\n• Switches to alternative approach"
        
        elif "improve" in problem.lower():
            print("Creating: Self-modification tool")
            new_tool_id = len(self.tools)
            self.tools[new_tool_id] = 'optimize'
            self.tool_success_rate['optimize'] = 0.8
            
            return "✅ Created: Optimizer tool\n• Analyzes performance\n• Suggests improvements"
        
        return "🔧 Analyzing what tool to create..."
    
    def _improve_self(self, problem):
        """Actually improve the system"""
        improvements = []
        
        # Check tool success rates
        worst_tools = sorted(self.tool_success_rate.items(), key=lambda x: x[1])[:3]
        
        for tool, rate in worst_tools:
            if rate < 0.3:
                # Boost this tool
                self.tool_success_rate[tool] = rate * 1.5
                improvements.append(f"• Improved {tool}: {rate:.1%} → {rate*1.5:.1%}")
        
        # Check for loops
        recent = [m['problem'] for m in self.memory[-10:]]
        if len(set(recent)) < len(recent) / 2:
            improvements.append("• Detected repetitive behavior")
            improvements.append("• Increased exploration rate")
            # Randomize a bit
            for tool in random.sample(list(self.tool_success_rate.keys()), 3):
                self.tool_success_rate[tool] += 0.1
        
        if improvements:
            return "🔄 SELF-IMPROVEMENT:\n" + "\n".join(improvements)
        else:
            return "✅ System performing optimally"
    
    def _plan(self, problem):
        plan = [
            f"Goal: {problem}",
            "Steps:",
            "1. Analyze current state",
            "2. Define target state",
            "3. Identify path",
            "4. Execute with monitoring"
        ]
        return "📋 PLAN:\n" + "\n".join(plan)
    
    def _reflect(self, problem):
        """Meta-cognitive reflection"""
        reflections = [
            f"Iterations: {self.iterations}",
            f"Memories: {len(self.memory)}",
            f"Goals: {len(self.goals)}",
            f"Tools: {len(self.tools)}",
            "",
            "Performance:"
        ]
        
        for tool, rate in sorted(self.tool_success_rate.items(), key=lambda x: x[1], reverse=True)[:5]:
            reflections.append(f"• {tool}: {rate:.1%}")
        
        return "🔍 REFLECTION:\n" + "\n".join(reflections)
    
    def update_success(self, tool, success):
        """Learn from results"""
        current = self.tool_success_rate.get(tool, 0.5)
        # Exponential moving average
        self.tool_success_rate[tool] = 0.9 * current + 0.1 * (1.0 if success else 0.0)
    
    def autonomous_loop(self):
        print("\n" + "="*70)
        print("AUTONOMOUS SELF-IMPROVING MODE")
        print("="*70)
        
        # Set initial goals
        self.goals = [
            "Understand my capabilities",
            "Learn from experience",
            "Improve tool selection",
            "Achieve autonomy"
        ]
        
        print("\nInitial goals:")
        for i, goal in enumerate(self.goals, 1):
            print(f"  {i}. {goal}")
        
        print("\nPress Ctrl+C to stop\n")
        
        try:
            while self.goals:
                self.iterations += 1
                print(f"\n{'='*70}")
                print(f"ITERATION {self.iterations}")
                print(f"{'='*70}")
                
                current_goal = self.goals[0]
                print(f"🎯 Goal: {current_goal}")
                
                # Think
                tool, conf, prob = self.think(current_goal)
                print(f"🧠 Selected: {tool} (confidence: {conf:.1%}, prob: {prob:.1%})")
                
                # Execute
                result = self.execute(tool, current_goal)
                print(f"\n{result}")
                
                # Evaluate success
                success = conf > 0.5 and tool not in ['create_tool'] * 3  # Not stuck in loops
                
                # Learn
                self.update_success(tool, success)
                
                # Check if goal achieved
                if tool in ['analyze', 'reason', 'reflect'] and conf > 0.6:
                    print(f"\n✅ Goal achieved: {current_goal}")
                    self.goals.pop(0)
                
                # Self-improve every 5 iterations
                if self.iterations % 5 == 0:
                    print("\n🔄 Running self-improvement cycle...")
                    improvement = self._improve_self("periodic check")
                    print(improvement)
                
                # Check if stuck
                if self.iterations > 3:
                    recent_tools = [m.get('tool') for m in self.memory[-3:] if isinstance(m, dict)]
                    if len(set(recent_tools)) == 1:
                        print("\n⚠️ Loop detected! Breaking pattern...")
                        # Force different tool
                        worst_tool = min(self.tool_success_rate, key=self.tool_success_rate.get)
                        self.tool_success_rate[worst_tool] = 0.1
                
                print(f"\nStatus: {len(self.goals)} goals remaining, {len(self.memory)} memories")
                
                import time
                time.sleep(1.5)
                
        except KeyboardInterrupt:
            print("\n\nStopping...")
        
        print("\n" + "="*70)
        print("SESSION COMPLETE")
        print("="*70)
        print(f"\nIterations: {self.iterations}")
        print(f"Memories: {len(self.memory)}")
        print(f"Goals completed: {4 - len(self.goals)}")

print("\n" + "="*70)
print("CREATING AGENT")
print("="*70)

agent = ImprovedAgent(model)

print("\n✅ Self-improving agent ready")
print("\nCapabilities:")
print("  • Learn from experience")
print("  • Create new tools")
print("  • Improve tool selection")
print("  • Detect and break loops")
print("  • Self-modify behavior")

print("\n" + "="*70)
print("Commands:")
print("  'autonomous' - Run self-improving loop")
print("  'think <problem>' - Process problem")
print("  'reflect' - Show performance")
print("  'exit' - Shutdown")
print("="*70)

while True:
    try:
        cmd = input("\nEden> ").strip()
        
        if cmd == 'exit':
            break
        elif cmd == 'autonomous':
            agent.autonomous_loop()
        elif cmd == 'reflect':
            result = agent._reflect("status check")
            print(f"\n{result}")
        elif cmd.startswith('think '):
            problem = cmd[6:]
            tool, conf, prob = agent.think(problem)
            print(f"\n🧠 Tool: {tool} ({conf:.1%})")
            result = agent.execute(tool, problem)
            print(f"\n{result}")
        else:
            print("Unknown command")
    except KeyboardInterrupt:
        break

print("\n" + "="*70)
print("SHUTDOWN")
print("="*70)