# Complete GUROBIPY implementation

import gurobipy as gp
from gurobipy import GRB

def optimize_swimming_events():
    """Optimize swimmer-event assignments to maximize performance score."""
    
    # 1. MODEL & DATA SETUP
    model = gp.Model("swimming_optimization")
    
    # Example data setup (replace with actual data retrieval logic)
    performance_scores = {
        (1, 101): 9.5,
        (2, 102): 8.7,
        (3, 103): 9.0
    }
    
    stadium_capacities = {
        101: 500,
        102: 1000,
        103: 1500
    }
    
    swimmers = {1, 2, 3}
    events = {101, 102, 103}
    max_events_per_swimmer = 2
    
    # CRITICAL: Validate array lengths before loops
    assert all(len(performance_scores) > 0 for _ in [swimmers, events]), "Data setup error"
    
    # 2. VARIABLES
    x = model.addVars(performance_scores.keys(), vtype=GRB.BINARY, name="x")
    
    # 3. OBJECTIVE FUNCTION
    model.setObjective(gp.quicksum(performance_scores[i, j] * x[i, j] for i, j in performance_scores), GRB.MAXIMIZE)
    
    # 4. CONSTRAINTS - CORRECT SYNTAX PATTERNS
    
    # Maximum Events per Swimmer Constraint
    model.addConstrs((gp.quicksum(x[i, j] for j in events if (i, j) in performance_scores) <= max_events_per_swimmer 
                      for i in swimmers), name="max_events_per_swimmer")
    
    # Stadium Capacity Constraint
    model.addConstrs((gp.quicksum(x[i, j] for i in swimmers if (i, j) in performance_scores) <= stadium_capacities[j] 
                      for j in events), name="stadium_capacity")
    
    # 5. SOLVING & RESULTS
    model.optimize()
    
    if model.status == GRB.OPTIMAL:
        print(f"Optimal value: {model.objVal}")
        for i, j in performance_scores:
            if x[i, j].x > 1e-6:
                print(f"Swimmer {i} assigned to Event {j}")
    elif model.status == GRB.INFEASIBLE:
        print("Problem is infeasible")
    elif model.status == GRB.UNBOUNDED:
        print("Problem is unbounded")
    
    return model

# Run the optimization
optimize_swimming_events()