# evo_transformer.py
import random

class EvoTransformer:
    def __init__(self, config=None):
        # Initialize with default or passed config
        self.config = config or {
            "layers": 4,
            "attention_heads": 4,
            "ffn_dim": 1024,
            "dropout": 0.1,
            "memory": False,
        }
        self.history = [self.config.copy()]

    def mutate(self):
        new_config = self.config.copy()
        trait = random.choice(list(new_config.keys()))
        
        if trait == "layers":
            new_config[trait] = max(1, new_config[trait] + random.choice([-1, 1]))
        elif trait == "attention_heads":
            new_config[trait] = random.choice([2, 4, 6, 8])
        elif trait == "ffn_dim":
            new_config[trait] = random.choice([512, 1024, 2048])
        elif trait == "dropout":
            new_config[trait] = round(min(max(0.0, new_config[trait] + random.uniform(-0.05, 0.05)), 0.5), 2)
        elif trait == "memory":
            new_config[trait] = not new_config[trait]

        self.config = new_config
        self.history.append(new_config.copy())

    def evolve(self, generations=3):
        for _ in range(generations):
            self.mutate()

    def get_history(self):
        return self.history

    def evaluate(self):
        # Simulate an accuracy score for demo purposes
        score = round(random.uniform(0.85, 0.95), 4)
        return {"accuracy": score, "params": self.estimate_params()}

    def estimate_params(self):
        # Simulated parameter count based on config
        return 10 + self.config["layers"] * self.config["ffn_dim"] * 0.001