import random
import json
import os
import uuid
import csv
import torch.nn as nn

GENOME_LOG = "genome_log.csv"
BEST_GENOME_FILE = "best_genome.json"

# Mutation bounds
MUTATION_LIMITS = {
    "num_layers": (2, 12),
    "ffn_dim": (256, 4096),
    "num_heads": (2, 16),
}

def default_config():
    return {
        "genome_id": str(uuid.uuid4()),
        "num_layers": 6,
        "ffn_dim": 1024,
        "num_heads": 8,
        "memory_enabled": True
    }

def mutate_genome(base_config, exploration_rate=0.5):
    config = base_config.copy()
    config["genome_id"] = str(uuid.uuid4())
    mutation_type = random.choice(["num_layers", "ffn_dim", "num_heads", "memory_enabled"])

    if mutation_type == "memory_enabled":
        config["memory_enabled"] = not config["memory_enabled"]
    else:
        min_val, max_val = MUTATION_LIMITS[mutation_type]
        change = int((max_val - min_val) * exploration_rate)
        delta = random.randint(-change, change)
        config[mutation_type] = max(min_val, min(max_val, config[mutation_type] + delta))

    # ✅ Ensure num_heads divides d_model cleanly
    embed_dim = 512
    if config["num_heads"] > embed_dim:
        config["num_heads"] = max(1, embed_dim // 64)
    while embed_dim % config["num_heads"] != 0:
        config["num_heads"] -= 1
        if config["num_heads"] <= 0:
            config["num_heads"] = 1
            break

    return config

def log_genome(config, score=None):
    row = [
        config.get("genome_id", ""),
        config.get("num_layers", ""),
        config.get("ffn_dim", ""),
        config.get("num_heads", ""),
        config.get("memory_enabled", ""),
        score if score is not None else ""
    ]
    file_exists = os.path.exists(GENOME_LOG)
    with open(GENOME_LOG, "a", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        if not file_exists:
            writer.writerow(["genome_id", "num_layers", "ffn_dim", "num_heads", "memory_enabled", "score"])
        writer.writerow(row)

def save_best_genome(config):
    with open(BEST_GENOME_FILE, "w", encoding="utf-8") as f:
        json.dump(config, f)

def load_best_genome():
    if os.path.exists(BEST_GENOME_FILE):
        with open(BEST_GENOME_FILE, "r", encoding="utf-8") as f:
            return json.load(f)
    return default_config()

def build_model_from_config(config):
    from evo_model import EvoTransformerV22
    return EvoTransformerV22(config)