import time
import numpy as np
import matplotlib.pyplot as plt
import copy
from mopso import MOPSO 
from surrogate_handler import SurrogateHandler

# --- EXTENDED CLASS (Inheritance) ---

class SmartMOPSO(MOPSO):
    def __init__(self, model_type=None, **kwargs):
        super().__init__(**kwargs)
        
        # Initialize Surrogate Handler if model_type is provided
        self.use_surrogate = (model_type is not None)
        if self.use_surrogate:
            self.surrogate_handler = SurrogateHandler(model_type)

            # Pre-fill with initial particle data
            for p in self.particles:
                self.surrogate_handler.add_data(p.x, p.f_current[1])

    def iterate(self):
        train_freq = 10 # Retrain every 10 iterations
        
        # Check if retraining is needed
        if self.use_surrogate and (self.t % train_freq == 0):
            self.surrogate_handler.train()

        # Determine if AI prediction should be used
        use_ai = (self.use_surrogate and 
                  self.surrogate_handler.is_trained and 
                  self.t % train_freq != 0)

        # Main loop (overriding original logic to manage control flow)
        for t in range(self.t): 
            self.select_leader()
            
            for i in range(self.n):
                # Movement 
                self.particles[i].update_velocity(self.leader.x, self.c1, self.c2, self.w)
                self.particles[i].update_position()
                self.particles[i].keep_boudaries(self.A_max)

                if use_ai:
                    # 1. Fast exact calculation (f1, f3)
                    f1 = self.particles[i].f1(self.prices)
                    f3 = self.particles[i].f3()
                    
                    # 2. Slow prediction (f2) via AI
                    f2_pred = self.surrogate_handler.predict(self.particles[i].x)
                    
                    # 3. Inject scores without running the expensive 'updating_socs'
                    self.particles[i].f_current = [f1, f2_pred, f3]
                    
                else:
                    # Standard Calculation (Slow & Exact)
                    self.particles[i].updating_socs(self.socs, self.capacities)
                    self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times)
                    
                    # Capture data for AI training
                    if self.use_surrogate:
                        self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1])

                self.particles[i].update_best()
            
            self.update_archive()

# --- EXECUTION FUNCTION ---
def run_scenario(scenario_name, model_type=None):
    print(f"\n--- Launching Scenario: {scenario_name} ---")
    start_time = time.time()
    
    # Simulation parameters
    params = {
        'f_weights': [1,1,1], 'A_max': 500, 'price_mean': 0.15, 'price_std': 0.05,
        'capacities': [50]*10, 'n': 20, 't': 50, 
        'w': 0.4, 'c1': 2.0, 'c2': 2.0,
        'nb_vehicles': 10, 'delta_t': 60, 'nb_of_ticks': 72
    }
    
    # Instantiate extended class
    optimizer = SmartMOPSO(model_type=model_type, **params)
    
    # Run simulation
    optimizer.iterate()
        
    end_time = time.time()
    duration = end_time - start_time
    
    # Retrieve best f2 (e.g., from archive)
    best_f2 = min([p.f_current[1] for p in optimizer.archive]) if optimizer.archive else 0
    
    print(f"Finished in {duration:.2f} seconds.")
    print(f"Best f2 found: {best_f2:.4f}")
    
    return duration, best_f2

# --- MAIN ---
if __name__ == "__main__":
    results = {}
    
    # 1. Without Surrogate (Baseline)
    d1, f1_score = run_scenario("No AI", model_type=None)
    results['No-AI'] = (d1, f1_score)
    
    # 2. With MLP
    d2, f2_score = run_scenario("With MLP", model_type='mlp')
    results['MLP'] = (d2, f2_score)
    
    # 3. With Random Forest
    d3, f3_score = run_scenario("With Random Forest", model_type='rf')
    results['RF'] = (d3, f3_score)
    
    # --- DISPLAY RESULTS ---
    print("\n=== SUMMARY ===")
    print(f"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}")
    print("-" * 45)
    for k, v in results.items():
        print(f"{k:<15} | {v[0]:<10.2f} | {v[1]:<10.4f}")