# Complete GUROBIPY implementation - Retry Attempt 4

import gurobipy as gp
from gurobipy import GRB

def optimize_team_selection():
    # 1. MODEL & DATA SETUP
    model = gp.Model("match_season")
    
    # Example data setup (replace with actual data retrieval logic)
    player_ids = [1, 2, 3]
    performance_coefficients = [0.88, 0.92, 0.85]
    draft_picks = [10, 20, 30]  # Example draft pick numbers
    positions = ['defender', 'midfielder', 'forward']  # Example positions
    
    # Constraint bounds (replace with actual data retrieval logic)
    max_players_per_team = 25
    max_draft_pick_sum = 100
    min_defenders = 4
    min_midfielders = 3
    min_forwards = 3
    
    # Validate array lengths
    assert len(player_ids) == len(performance_coefficients) == len(draft_picks) == len(positions), "Array length mismatch"
    
    # 2. VARIABLES
    x = {i: model.addVar(vtype=GRB.BINARY, name=f"x_{i}") for i in player_ids}
    
    # 3. OBJECTIVE FUNCTION
    model.setObjective(gp.quicksum(performance_coefficients[i] * x[player_ids[i]] for i in range(len(player_ids))), GRB.MAXIMIZE)
    
    # 4. CONSTRAINTS
    # Maximum number of players
    model.addConstr(gp.quicksum(x[player_ids[i]] for i in range(len(player_ids))) <= max_players_per_team, name="max_players")
    
    # Maximum draft pick sum
    model.addConstr(gp.quicksum(draft_picks[i] * x[player_ids[i]] for i in range(len(player_ids))) <= max_draft_pick_sum, name="max_draft_pick_sum")
    
    # Minimum number of defenders
    model.addConstr(gp.quicksum(x[player_ids[i]] for i in range(len(player_ids)) if positions[i] == 'defender') >= min_defenders, name="min_defenders")
    
    # Minimum number of midfielders
    model.addConstr(gp.quicksum(x[player_ids[i]] for i in range(len(player_ids)) if positions[i] == 'midfielder') >= min_midfielders, name="min_midfielders")
    
    # Minimum number of forwards
    model.addConstr(gp.quicksum(x[player_ids[i]] for i in range(len(player_ids)) if positions[i] == 'forward') >= min_forwards, name="min_forwards")
    
    # 5. SOLVING & RESULTS
    model.optimize()
    
    if model.status == GRB.OPTIMAL:
        print(f"Optimal value: {model.objVal}")
        for i in player_ids:
            if x[i].x > 1e-6:
                print(f"Player {i} selected")
    elif model.status == GRB.INFEASIBLE:
        print("Problem is infeasible")
    elif model.status == GRB.UNBOUNDED:
        print("Problem is unbounded")
    
    return model

# Run the optimization
optimize_team_selection()