# Complete PYOMO implementation - Retry Attempt 4

import pyomo.environ as pyo
from pyomo.opt import SolverFactory

def match_season_optimization():
    """Optimize player selection for maximum performance score"""

    # 1. MODEL CREATION
    model = pyo.ConcreteModel()

    # 2. DATA SETUP
    # Example data based on the problem context
    player_ids = [1, 2, 3]
    performance_coefficients = {1: 0.88, 2: 0.92, 3: 0.85}
    draft_picks = {1: 10, 2: 20, 3: 30}  # Example draft pick numbers
    max_players_per_team = 25
    max_draft_pick_sum = 100
    min_defenders = 4
    min_midfielders = 3
    min_forwards = 3

    # Validate data lengths
    assert len(player_ids) == len(performance_coefficients) == len(draft_picks), "Data length mismatch"

    # 3. SETS
    model.Players = pyo.Set(initialize=player_ids)

    # 4. PARAMETERS
    model.performance_coefficient = pyo.Param(model.Players, initialize=performance_coefficients)
    model.draft_pick = pyo.Param(model.Players, initialize=draft_picks)

    # 5. VARIABLES
    model.x = pyo.Var(model.Players, within=pyo.Binary)

    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.performance_coefficient[i] * model.x[i] for i in model.Players)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)

    # 7. CONSTRAINTS
    # Maximum number of players
    def max_players_rule(model):
        return sum(model.x[i] for i in model.Players) <= max_players_per_team
    model.max_players_constraint = pyo.Constraint(rule=max_players_rule)

    # Maximum draft pick sum
    def max_draft_pick_rule(model):
        return sum(model.draft_pick[i] * model.x[i] for i in model.Players) <= max_draft_pick_sum
    model.max_draft_pick_constraint = pyo.Constraint(rule=max_draft_pick_rule)

    # Minimum number of defenders
    # Assuming player positions are predefined
    defenders = [1]  # Example defender IDs
    def min_defenders_rule(model):
        return sum(model.x[i] for i in defenders) >= min_defenders
    model.min_defenders_constraint = pyo.Constraint(rule=min_defenders_rule)

    # Minimum number of midfielders
    midfielders = [2]  # Example midfielder IDs
    def min_midfielders_rule(model):
        return sum(model.x[i] for i in midfielders) >= min_midfielders
    model.min_midfielders_constraint = pyo.Constraint(rule=min_midfielders_rule)

    # Minimum number of forwards
    forwards = [3]  # Example forward IDs
    def min_forwards_rule(model):
        return sum(model.x[i] for i in forwards) >= min_forwards
    model.min_forwards_constraint = pyo.Constraint(rule=min_forwards_rule)

    # 8. SOLVING WITH GUROBI
    solver = SolverFactory('gurobi')

    # Solve the model
    results = solver.solve(model, tee=True)

    # 9. RESULT PROCESSING
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective)}")

        # Extract variable values
        print("\nSelected players:")
        for i in model.Players:
            if pyo.value(model.x[i]) > 0.5:  # Binary variable, check if selected
                print(f"Player {i} selected")
    elif results.solver.termination_condition == pyo.TerminationCondition.infeasible:
        print("Problem is infeasible")
    elif results.solver.termination_condition == pyo.TerminationCondition.unbounded:
        print("Problem is unbounded")
    else:
        print(f"Solver terminated with condition: {results.solver.termination_condition}")

    return model

# Execute the optimization
match_season_optimization()