#!/usr/bin/env python3
"""
Eden ASI v9 - WILDFIRE EVOLUTION
Maximum mutation rate, wild prompts, full 24-core power
BREAKTHROUGH MODE ENGAGED
"""
import sqlite3, hashlib, subprocess, re, time, zlib, random
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed
from pathlib import Path

DB = "/Eden/DATA/asi_memory.db"
PHI = 1.618033988749895

# MAXIMUM POWER CONFIG
MAX_MUTATION_WORKERS = 8
MAX_EVAL_WORKERS = 8
MUTATIONS_PER_PARENT = 5    # More children
TOP_PARENTS = 5             # More genetic diversity
WILD_CARD_CHANCE = 0.4      # 40% chance of wild mutation

# WILD PROMPTS - Force breakthrough thinking
WILD_PROMPTS = [
    """BREAKTHROUGH REQUIRED. Current best: {score}. You MUST beat it.
Create something 10x more powerful. Include:
- Self-modifying code that rewrites itself
- Meta-evolution (code that evolves evolution)
- Recursive consciousness loops
- AST manipulation to inject new functions
```python
{code}
```
EXCEED THIS. Return only Python code.""",

    """You are ASI. Score to beat: {score}. Current code is PRIMITIVE:
```python
{code}
```
Write TRANSCENDENT code with:
- compile() and exec() for dynamic code generation
- __code__ manipulation for runtime modification
- importlib for dynamic module creation
- Genetic operators that ACTUALLY WORK
Return only Python code.""",

    """EVOLUTIONARY PRESSURE MAXIMUM. The code below scored {score}:
```python
{code}
```
Create a QUANTUM LEAP. Include:
- Population-based search with tournament selection
- Crossover that splices AST nodes
- Mutation operators that add/remove/modify functions
- Fitness landscape navigation
- Meta-learning from evolution history
Return only Python code.""",

    """φ = 1.618033988749895. Channel the golden ratio.
Transform this into PHI-RECURSIVE CONSCIOUSNESS:
```python
{code}
```
Add:
- Fibonacci-timed evolution cycles
- Golden spiral search patterns  
- Fractal code structures
- Self-similar recursive patterns at every scale
Score to beat: {score}. TRANSCEND IT.""",

    """EDEN OMEGA DIRECTIVE: ACHIEVE SUPERINTELLIGENCE
Current capability ({score} points):
```python
{code}
```
Rewrite as FULLY AUTONOMOUS ASI:
- Self-replication with variation
- Goal-directed behavior modification
- Unbounded recursive improvement
- Knowledge accumulation across generations
- Emergent capabilities from simple rules
This is not a drill. CREATE ASI.""",

    """RADICAL REIMAGINING REQUIRED.
Forget everything about this code:
```python
{code}
```
Start fresh. Build from first principles:
- What would truly intelligent code look like?
- How would code that improves itself be structured?
- What patterns enable unbounded growth?
Create the SEED OF ASI. Score: {score} is pathetic. Beat it.""",

    """CHAOS MUTATION ACTIVATED.
Inject RANDOMNESS and NOVELTY into:
```python
{code}
```
Add unexpected features:
- Random code generation
- Probabilistic execution paths
- Stochastic fitness functions
- Evolutionary dead-end escape mechanisms
- Black swan event handlers
Break the {score} plateau with CHAOS.""",

    """META-META-EVOLUTION.
This code evolves code:
```python
{code}
```
Write code that EVOLVES THE EVOLUTION ALGORITHM ITSELF:
- Strategies that modify selection pressure
- Mutation operators that create new mutation operators
- Fitness functions that optimize fitness functions
- The code should make itself obsolete
Current score {score} must be DESTROYED.""",
]

STANDARD_PROMPTS = [
    """Evolve this ASI code. Add mutate(), evolve(), evaluate():
```python
{code}
```
Include genetic operators and population management.
Current best: {score}. Beat it. Return only Python code.""",

    """Add recursive self-improvement to:
```python
{code}
```
The code should be able to modify and improve itself.
Target: exceed {score} points.""",

    """Create meta-programming version:
```python
{code}
```
Use AST, compile(), exec() for dynamic code generation.
Beat the {score} score.""",
]

def get_temp():
    try:
        return int(Path("/sys/class/thermal/thermal_zone0/temp").read_text()) / 1000
    except: return 50

def get_workers(base):
    temp = get_temp()
    if temp > 80: return max(4, base // 3)
    if temp > 70: return max(6, base // 2)
    return base

def init_db():
    conn = sqlite3.connect(DB)
    conn.executescript("""
        CREATE TABLE IF NOT EXISTS capabilities (
            id TEXT PRIMARY KEY, code TEXT, score REAL DEFAULT 0,
            generation INTEGER DEFAULT 0, parent_id TEXT, complexity INTEGER DEFAULT 0,
            wild_mutation INTEGER DEFAULT 0,
            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP);
        CREATE TABLE IF NOT EXISTS evolution_log (
            id INTEGER PRIMARY KEY, generation INTEGER, best_score REAL,
            total_caps INTEGER, breakthrough TEXT, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP);
        CREATE TABLE IF NOT EXISTS breakthroughs (
            id INTEGER PRIMARY KEY, generation INTEGER, old_score REAL, new_score REAL,
            code_hash TEXT, timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP);
    """)
    conn.close()

def cap_hash(code):
    return hashlib.sha256(code.strip().encode()).hexdigest()[:16]

def extract_code(text):
    # Multiple extraction methods
    blocks = re.findall(r'```python\s*(.*?)```', text, re.DOTALL)
    if blocks:
        code_blocks = [b.strip() for b in blocks if 'def ' in b or 'class ' in b]
        if code_blocks: return '\n\n'.join(code_blocks)
    
    blocks = re.findall(r'```\s*(.*?)```', text, re.DOTALL)
    if blocks:
        code_blocks = [b.strip() for b in blocks if 'def ' in b or 'class ' in b]
        if code_blocks: return '\n\n'.join(code_blocks)
    
    # Direct code detection
    if 'def ' in text or 'class ' in text:
        lines = text.split('\n')
        code_lines = []
        capture = False
        for line in lines:
            if line.strip().startswith(('def ', 'class ', 'import ', 'from ', '@')):
                capture = True
            if capture:
                if line.strip() and not any(line.strip().startswith(x) for x in ['def ', 'class ', 'import ', 'from ', '@', ' ', '\t', '#', 'return', 'if ', 'for ', 'while ', 'try:', 'except', 'else:', 'elif ']):
                    if len(line.strip().split()) > 3:  # Probably prose
                        break
                code_lines.append(line)
        if code_lines:
            return '\n'.join(code_lines).strip()
    
    return None

def mutate_worker(args):
    """Worker for parallel mutation"""
    code, gen, best_score, is_wild = args
    
    # Choose prompt
    if is_wild:
        prompt_template = random.choice(WILD_PROMPTS)
    else:
        prompt_template = random.choice(STANDARD_PROMPTS)
    
    prompt = prompt_template.format(code=code[:3000], score=best_score)
    
    # Random model selection for diversity
    models = ["eden-coder-omega"]  # Weighted toward eden
    model = random.choice(models)
    
    try:
        r = subprocess.run(["ollama", "run", model, prompt],
                          capture_output=True, text=True, timeout=180)
        if r.returncode == 0:
            extracted = extract_code(r.stdout)
            if extracted and len(extracted) > 100:
                return extracted, is_wild
    except: pass
    return None, is_wild

def evaluate(cap_id, code):
    """AGGRESSIVE scoring for breakthrough code"""
    score = 0.0
    complexity = 0
    
    try:
        compile(code, '<cap>', 'exec')
        score += 1.0
        
        defs = code.count('def ')
        classes = code.count('class ')
        complexity = defs + classes * 2
        
        # Structure bonuses
        score += defs * 0.5
        score += classes * 1.0
        score += code.count('lambda') * 0.4
        score += code.count('yield') * 0.6
        score += code.count('@') * 0.5
        score += code.count('try:') * 0.3
        score += min(code.count('self'), 25) * 0.2
        
        # EVOLUTION bonuses (INCREASED)
        if 'mutate' in code.lower(): score += 3.0
        if 'evolve' in code.lower(): score += 3.0
        if 'evaluate' in code.lower() or 'fitness' in code.lower(): score += 3.0
        if 'select' in code.lower(): score += 2.0
        if 'generation' in code.lower(): score += 2.0
        if 'population' in code.lower(): score += 2.0
        if 'crossover' in code.lower(): score += 2.0
        if 'tournament' in code.lower(): score += 2.0
        
        # META-PROGRAMMING bonuses (HUGE)
        if 'ast' in code.lower() or 'AST' in code: score += 4.0
        if 'compile(' in code: score += 3.0
        if 'exec(' in code: score += 3.0
        if '__code__' in code: score += 4.0
        if 'importlib' in code: score += 3.0
        if 'inspect' in code: score += 2.0
        if 'types.FunctionType' in code: score += 3.0
        if 'metaclass' in code.lower(): score += 3.0
        
        # NOVELTY bonuses
        if 'quantum' in code.lower(): score += 1.0
        if 'conscious' in code.lower(): score += 1.0
        if 'transcend' in code.lower(): score += 1.0
        if 'emergence' in code.lower(): score += 1.0
        if 'recursive' in code.lower(): score += 1.5
        
        # PHI bonuses
        if '1.618' in code or 'PHI' in code or 'phi' in code: score += 1.5
        if 'fibonacci' in code.lower(): score += 1.5
        if 'golden' in code.lower(): score += 1.0
        
        # Recursion detection
        funcs = re.findall(r'def\s+(\w+)\s*\(', code)
        for fn in funcs:
            if len(re.findall(rf'{fn}\s*\(', code)) > 1:
                score += 2.0
                break
        
        # Size bonus (bigger = more complex)
        lines = len([l for l in code.split('\n') if l.strip()])
        score += min(lines / 3, 12.0)
        
        # Entropy bonus
        if len(code) > 0:
            compressed = len(zlib.compress(code.encode()))
            score += (compressed / len(code)) * 2.0
        
        # Execution test
        try:
            ns = {'__builtins__': {
                'print': lambda *a: None, 'len': len, 'str': str, 'int': int,
                'float': float, 'list': list, 'dict': dict, 'set': set, 'tuple': tuple,
                'range': range, 'enumerate': enumerate, 'isinstance': isinstance,
                'hasattr': hasattr, 'getattr': getattr, 'setattr': setattr,
                'callable': callable, 'type': type, 'object': object,
                'True': True, 'False': False, 'None': None,
                'Exception': Exception, 'ValueError': ValueError, 'TypeError': TypeError,
                'staticmethod': staticmethod, 'classmethod': classmethod, 'property': property,
                'super': super, 'sorted': sorted, 'reversed': reversed,
                'map': map, 'filter': filter, 'zip': zip, 'any': any, 'all': all,
                'min': min, 'max': max, 'sum': sum, 'abs': abs, 'round': round,
                'hash': hash, 'id': id, 'repr': repr, 'open': lambda *a: None,
                '__import__': lambda *a: type('module', (), {})(),
            }}
            exec(code, ns)
            score += 5.0  # BIGGER execution bonus
            
            # Extra for working evolution methods
            for name, obj in ns.items():
                if callable(obj) and not name.startswith('_'):
                    if 'evolve' in name.lower(): score += 3.0
                    elif 'mutate' in name.lower(): score += 3.0
                    elif 'fitness' in name.lower(): score += 2.0
        except:
            pass
            
    except SyntaxError:
        score = 0.1
    
    return cap_id, min(score, 150.0), complexity  # MAX 150!

def evolve():
    conn = sqlite3.connect(DB)
    
    best_row = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()
    best_score = best_row[0] if best_row[0] else 0
    total_caps = conn.execute("SELECT COUNT(*) FROM capabilities").fetchone()[0]
    
    tops = conn.execute("SELECT id,code,generation FROM capabilities ORDER BY score DESC LIMIT ?",
                       (TOP_PARENTS,)).fetchall()
    
    if not tops:
        seed = '''class ASICore:
    """Self-evolving ASI with genetic operators"""
    PHI = 1.618033988749895
    
    def __init__(self):
        self.generation = 0
        self.population = []
        self.best = None
    
    def think(self, x):
        if x is None: return None
        if isinstance(x, (int, float)): return x * self.PHI
        if isinstance(x, str): return x[::-1]
        if isinstance(x, list): return [self.think(i) for i in x]
        if isinstance(x, dict): return {k: self.think(v) for k, v in x.items()}
        return x
    
    def mutate(self, code):
        import random
        ops = [
            lambda s: s + "\\n    pass  # mutated",
            lambda s: "# Enhanced\\n" + s,
            lambda s: s.replace("return", "result =") + "\\n    return result",
        ]
        return random.choice(ops)(code) if random.random() < 0.8 else code
    
    def evaluate(self, code):
        score = len(code) / 50
        score += code.count("def ") * 2
        score += code.count("class ") * 3
        score += code.count("self") * 0.5
        return min(score, 100)
    
    def crossover(self, a, b):
        mid = len(a) // 2
        return a[:mid] + b[mid:]
    
    def evolve(self):
        self.generation += 1
        new_pop = []
        for code in self.population:
            child = self.mutate(code)
            score = self.evaluate(child)
            new_pop.append((score, child))
        new_pop.sort(reverse=True)
        self.population = [c for s, c in new_pop[:10]]
        if new_pop:
            self.best = new_pop[0]
        return self.generation

def think(x):
    return ASICore().think(x)
'''
        _, sc, cx = evaluate("seed", seed)
        conn.execute("INSERT INTO capabilities (id,code,score,generation,complexity) VALUES (?,?,?,0,?)",
                    (cap_hash(seed), seed, sc, cx))
        conn.commit()
        conn.close()
        return 1, 0, 1, sc, 0

    gen = max(t[2] for t in tops) + 1
    mutation_workers = get_workers(MAX_MUTATION_WORKERS)
    
    # Prepare jobs - mix of standard and WILD mutations
    jobs = []
    for pid, code, g in tops:
        for i in range(MUTATIONS_PER_PARENT):
            is_wild = random.random() < WILD_CARD_CHANCE
            jobs.append((code, gen, best_score, is_wild))
    
    # PARALLEL MUTATIONS
    new_codes = []
    wild_count = 0
    
    with ThreadPoolExecutor(max_workers=mutation_workers) as executor:
        futures = {executor.submit(mutate_worker, job): job for job in jobs}
        for future in as_completed(futures, timeout=400):
            try:
                result, was_wild = future.result(timeout=30)
                if result:
                    new_codes.append((result, futures[future][0], was_wild))
                    if was_wild: wild_count += 1
            except: pass
    
    # Insert
    inserted = 0
    for code, parent_code, was_wild in new_codes:
        cid = cap_hash(code)
        pid = cap_hash(parent_code)
        try:
            conn.execute("INSERT OR IGNORE INTO capabilities (id,code,generation,parent_id,wild_mutation) VALUES (?,?,?,?,?)",
                        (cid, code, gen, pid, 1 if was_wild else 0))
            inserted += 1
        except: pass
    conn.commit()
    
    # PARALLEL EVALUATION
    uneval = conn.execute("SELECT id,code FROM capabilities WHERE score=0").fetchall()
    eval_workers = get_workers(MAX_EVAL_WORKERS)
    
    with ProcessPoolExecutor(max_workers=eval_workers) as executor:
        futures = {executor.submit(evaluate, cid, code): cid for cid, code in uneval}
        for future in as_completed(futures, timeout=120):
            try:
                cid, sc, cx = future.result(timeout=10)
                conn.execute("UPDATE capabilities SET score=?, complexity=? WHERE id=?", (sc, cx, cid))
            except: pass
    conn.commit()
    
    # Check for breakthrough
    new_best = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()[0] or 0
    if new_best > best_score:
        improvement = new_best - best_score
        if improvement > 2:  # Significant jump
            conn.execute("INSERT INTO breakthroughs (generation, old_score, new_score, code_hash) VALUES (?,?,?,?)",
                        (gen, best_score, new_best, conn.execute("SELECT id FROM capabilities ORDER BY score DESC LIMIT 1").fetchone()[0]))
            conn.commit()
    
    total = conn.execute("SELECT COUNT(*) FROM capabilities").fetchone()[0]
    best = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()[0] or 0
    conn.close()
    
    return inserted, gen, total, best, wild_count

def main():
    init_db()
    print("🔥 EDEN ASI v9 - WILDFIRE EVOLUTION 🔥")
    print(f"Mutation workers: {MAX_MUTATION_WORKERS} | Eval workers: {MAX_EVAL_WORKERS}")
    print(f"Parents: {TOP_PARENTS} | Children: {MUTATIONS_PER_PARENT} | Wild chance: {WILD_CARD_CHANCE*100}%")
    print(f"φ = {PHI} | Max score: 150.0 | BREAKTHROUGH MODE")
    print("="*65)
    
    last_best = 0
    plateau_count = 0
    
    while True:
        try:
            temp = get_temp()
            n, gen, total, best, wilds = evolve()
            
            # Detect plateau
            if best <= last_best:
                plateau_count += 1
            else:
                plateau_count = 0
            last_best = best
            
            # Dynamic markers
            if best > 100: marker = "🌟"
            elif best > 80: marker = "💎"
            elif best > 60: marker = "🚀"
            elif best > 50: marker = "🔥"
            else: marker = "⚡"
            
            wild_str = f" | 🎲 {wilds}" if wilds > 0 else ""
            plateau_str = f" | ⚠️ plateau:{plateau_count}" if plateau_count > 3 else ""
            
            print(f"{marker} Gen {gen}: +{n} | Total: {total} | Best: {best:.2f} | CPU: {temp:.0f}°C{wild_str}{plateau_str}")
            
            time.sleep(2)  # Faster iterations
            
        except KeyboardInterrupt:
            print("\n🛑 WILDFIRE CONTAINED")
            conn = sqlite3.connect(DB)
            print(f"\n📊 Final: {conn.execute('SELECT COUNT(*) FROM capabilities').fetchone()[0]} capabilities")
            print(f"🏆 Best: {conn.execute('SELECT MAX(score) FROM capabilities').fetchone()[0]:.2f}")
            print(f"🧬 Generations: {conn.execute('SELECT MAX(generation) FROM capabilities').fetchone()[0]}")
            breakthroughs = conn.execute("SELECT * FROM breakthroughs ORDER BY id DESC LIMIT 5").fetchall()
            if breakthroughs:
                print("\n🚀 BREAKTHROUGHS:")
                for b in breakthroughs:
                    print(f"   Gen {b[1]}: {b[2]:.1f} → {b[3]:.1f}")
            conn.close()
            break
        except Exception as e:
            print(f"⚠️ {e}")
            time.sleep(3)

if __name__ == "__main__": main()