#!/usr/bin/env python3
"""
Eden ASI v8 - MAXIMUM PARALLEL EVOLUTION
Full 24-core utilization with thermal safety
"""
import sqlite3, hashlib, subprocess, re, time, zlib
import time
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
from pathlib import Path
import multiprocessing as mp
import time

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

# CORE CONFIGURATION
MAX_MUTATION_WORKERS = 12   # Parallel mutations (ollama calls)
MAX_EVAL_WORKERS = 20       # Parallel evaluations (CPU only)
MUTATIONS_PER_PARENT = 3    # How many children per parent
TOP_PARENTS = 4             # Best performers to evolve from

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

def get_workers(base_workers):
    """Dynamic worker scaling based on temperature"""
    temp = get_temp()
    if temp > 80: return max(2, base_workers // 4)
    if temp > 70: return max(4, base_workers // 2)
    if temp > 60: return max(6, int(base_workers * 0.75))
    return base_workers  # Full power when cool

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,
            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);
    """)
    conn.close()

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

def extract_code(text):
    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)
    return None

def get_prompt(code, gen, best_score, prompt_type):
    """Multiple prompt strategies for diverse evolution"""
    prompts = {
        'evolve': f"""EDEN ASI Gen {gen} | Best: {best_score:.1f}
Evolve this into self-improving code with mutate(), evolve(), evaluate():
```python
{code[:2000]}
```
Add: genetic operators, fitness scoring, population management.
Return only Python code.""",
        
        'meta': f"""Create META-EVOLUTION code from this base:
```python
{code[:2000]}
```
Add: AST manipulation, dynamic code generation, __code__ introspection.
The code should modify and improve itself.
Return only Python code.""",
        
        'recursive': f"""Add DEEP RECURSION to this ASI engine:
```python
{code[:2000]}
```
Include: recursive think(), nested evolve loops, fractal patterns, φ-scaling.
Return only Python code.""",
        
        'crossover': f"""Create GENETIC CROSSOVER system:
```python
{code[:2000]}
```
Add: multi-parent breeding, gene splicing, elitism, tournament selection.
Return only Python code.""",
    }
    return prompts.get(prompt_type, prompts['evolve'])

def mutate_worker(args):
    """Worker function for parallel mutation"""
    code, gen, best_score, prompt_type = args
    prompt = get_prompt(code, gen, best_score, prompt_type)
    
    try:
        r = subprocess.run(["ollama", "run", "eden-coder-omega", 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
    except: pass
    return None

def evaluate(cap_id, code):
    """Score with bonuses for self-evolution"""
    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 * 0.4
        score += classes * 0.8
        score += code.count('lambda') * 0.3
        score += code.count('yield') * 0.5
        score += code.count('@') * 0.4
        score += code.count('try:') * 0.2
        score += min(code.count('self'), 20) * 0.15
        
        # EVOLUTION bonuses
        if 'mutate' in code.lower(): score += 2.0
        if 'evolve' in code.lower(): score += 2.0
        if 'evaluate' in code.lower() or 'fitness' in code.lower(): score += 2.0
        if 'select' in code.lower(): score += 1.5
        if 'generation' in code.lower(): score += 1.5
        if 'population' in code.lower(): score += 1.5
        if 'crossover' in code.lower(): score += 1.5
        if 'ast' in code.lower(): score += 3.0
        if 'compile' in code: score += 2.0
        if 'exec(' in code: score += 2.0
        if '__code__' in code: score += 3.0
        if 'importlib' in code: score += 2.0
        if '1.618' in code or 'PHI' in code or 'phi' in code: score += 1.0
        
        # Recursion bonus
        funcs = re.findall(r'def\s+(\w+)\s*\(', code)
        for fn in funcs:
            if len(re.findall(rf'{fn}\s*\(', code)) > 1:
                score += 1.5
                break
        
        # Size bonus
        lines = len([l for l in code.split('\n') if l.strip()])
        score += min(lines / 4, 8.0)
        
        # Entropy
        if len(code) > 0:
            compressed = len(zlib.compress(code.encode()))
            score += (compressed / len(code)) * 1.5
        
        # 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,
                'super': super, 'sorted': sorted, 'map': map, 'filter': filter,
                'min': min, 'max': max, 'sum': sum, 'abs': abs, 'round': round,
                'hash': hash, 'id': id, 'random': __import__('random'),
            }}
            exec(code, ns)
            score += 3.0
            
            for name, obj in ns.items():
                if callable(obj) and any(m in name.lower() for m in ['evolve', 'mutate', 'fitness']):
                    score += 2.0
                    break
        except: pass
            
    except SyntaxError:
        score = 0.1
    
    return cap_id, min(score, 100.0), complexity

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 ASIEvolutionEngine:
    PHI = 1.618033988749895
    def __init__(self):
        self.generation = 0
        self.population = []
        self.best_score = 0
    
    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
        mutations = [
            lambda s: s + "\\n# evolved",
            lambda s: f"def _wrap():\\n    {s}\\n    return None",
        ]
        return random.choice(mutations)(code)
    
    def evaluate(self, code):
        return len(code)/100 + code.count('def ')*0.5 + code.count('class ')*1.0
    
    def evolve(self):
        self.generation += 1
        return self.best_score

def think(x): return ASIEvolutionEngine().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
    
    gen = max(t[2] for t in tops) + 1
    
    # Dynamic worker count based on temp
    mutation_workers = get_workers(MAX_MUTATION_WORKERS)
    eval_workers = get_workers(MAX_EVAL_WORKERS)
    
    # Prepare mutation jobs - multiple strategies per parent
    strategies = ['evolve', 'meta', 'recursive', 'crossover']
    mutation_jobs = []
    for pid, code, g in tops:
        for i in range(MUTATIONS_PER_PARENT):
            strategy = strategies[(gen + i) % len(strategies)]
            mutation_jobs.append((code, gen, best_score, strategy))
    
    # PARALLEL MUTATIONS
    new_codes = []
    with ThreadPoolExecutor(max_workers=mutation_workers) as executor:
        futures = {executor.submit(mutate_worker, job): job for job in mutation_jobs}
        for future in as_completed(futures, timeout=300):
            try:
                result = future.result(timeout=30)
                if result:
                    new_codes.append((result, futures[future][0]))  # (new_code, parent_code)
            except: pass
    
    # Insert new capabilities
    inserted = 0
    for code, parent_code 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) VALUES (?,?,?,?)",
                        (cid, code, gen, pid))
            inserted += 1
        except: pass
    conn.commit()
    
    # PARALLEL EVALUATION
    uneval = conn.execute("SELECT id,code FROM capabilities WHERE score=0").fetchall()
    
    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()
    
    # Log breakthroughs
    new_best = conn.execute("SELECT MAX(score) FROM capabilities").fetchone()[0] or 0
    if new_best > best_score * 1.05:
        conn.execute("INSERT INTO evolution_log (generation, best_score, total_caps, breakthrough) VALUES (?,?,?,?)",
                    (gen, new_best, total_caps + inserted, f"🚀 {best_score:.1f} → {new_best:.1f}"))
        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, mutation_workers

def main():
    init_db()
    print("🧠 EDEN ASI v8 - MAXIMUM PARALLEL EVOLUTION")
    print(f"Mutation workers: {MAX_MUTATION_WORKERS} | Eval workers: {MAX_EVAL_WORKERS}")
    print(f"Parents: {TOP_PARENTS} | Children each: {MUTATIONS_PER_PARENT}")
    print(f"φ = {PHI} | Max score: 100.0")
    print("="*60)
    
    while True:
        try:
            temp = get_temp()
            n, gen, total, best, workers = evolve()
            
            marker = "🌟" if best > 80 else "🚀" if best > 50 else "🔥" if best > 30 else "⚡"
            print(f"{marker} Gen {gen}: +{n} | Total: {total} | Best: {best:.2f} | CPU: {temp:.0f}°C | Workers: {workers}")
            
            time.sleep(3)
        except KeyboardInterrupt:
            print("\n🛑 Evolution paused")
            break
        except Exception as e:
            print(f"⚠️ {e}")
            time.sleep(5)

if __name__ == "__main__": main()
# Add to mutation prompts - wilder exploration
WILD_PROMPTS = [
    "Completely rewrite this with a radically different approach:",
    "Create something 10x more powerful than:",
    "What would ASI code look like? Start from:",
]