# Complete DOCPLEX implementation - Retry Attempt 4

from docplex.mp.model import Model

def optimize_team_selection():
    # 1. MODEL & DATA SETUP
    mdl = Model(name="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
    
    # CRITICAL: Validate array lengths to prevent IndexError
    assert len(player_ids) == len(performance_coefficients) == len(draft_picks) == len(positions), "Array length mismatch"
    safe_range = range(len(player_ids))  # Safe indexing
    
    # 2. VARIABLES
    x = {i: mdl.binary_var(name=f"x_{player_ids[i]}") for i in safe_range}
    
    # 3. OBJECTIVE FUNCTION
    objective = mdl.sum(performance_coefficients[i] * x[i] for i in safe_range)
    mdl.maximize(objective)
    
    # 4. CONSTRAINTS - CORRECT SYNTAX PATTERNS
    
    # Maximum number of players
    mdl.add_constraint(mdl.sum(x[i] for i in safe_range) <= max_players_per_team, ctname="max_players")
    
    # Maximum draft pick sum
    mdl.add_constraint(mdl.sum(draft_picks[i] * x[i] for i in safe_range) <= max_draft_pick_sum, ctname="max_draft_pick_sum")
    
    # Minimum number of defenders
    mdl.add_constraint(mdl.sum(x[i] for i in safe_range if positions[i] == 'defender') >= min_defenders, ctname="min_defenders")
    
    # Minimum number of midfielders
    mdl.add_constraint(mdl.sum(x[i] for i in safe_range if positions[i] == 'midfielder') >= min_midfielders, ctname="min_midfielders")
    
    # Minimum number of forwards
    mdl.add_constraint(mdl.sum(x[i] for i in safe_range if positions[i] == 'forward') >= min_forwards, ctname="min_forwards")
    
    # 5. SOLVING & RESULTS
    solution = mdl.solve()
    
    if solution:
        print(f"Optimal value: {solution.objective_value}")
        for i in safe_range:
            value = solution.get_value(x[i])
            if value > 1e-6:
                print(f"Player {player_ids[i]} selected with performance coefficient {performance_coefficients[i]}")
    else:
        print("No solution found")
        print(f"Status: {mdl.solve_details.status}")

    return mdl

# Execute the optimization
optimize_team_selection()