# Complete GUROBIPY implementation

import gurobipy as gp
from gurobipy import GRB

def optimize_votes():
    """Optimize the distribution of votes among contestants and states."""
    
    # 1. MODEL & DATA SETUP
    model = gp.Model("voter_1")
    
    # Example data setup
    contestants = [1, 2, 3]
    states = [1, 2, 3]
    
    # Parameters (these would typically come from business configuration)
    MaxVotes = {1: 300, 2: 300, 3: 300}  # Maximum votes per contestant
    MinVotes = {(1, 1): 50, (1, 2): 50, (1, 3): 50,
                (2, 1): 50, (2, 2): 50, (2, 3): 50,
                (3, 1): 50, (3, 2): 50, (3, 3): 50}  # Minimum votes per state
    
    # CRITICAL: Validate array lengths before loops
    assert len(contestants) > 0 and len(states) > 0, "Contestants or states list is empty"
    
    # 2. VARIABLES
    # Decision variables: votes received by contestant i from state j
    x = model.addVars(contestants, states, vtype=GRB.INTEGER, name="x", lb=0)
    
    # 3. OBJECTIVE FUNCTION
    # Maximize the total number of votes received by all contestants
    model.setObjective(gp.quicksum(x[i, j] for i in contestants for j in states), GRB.MAXIMIZE)
    
    # 4. CONSTRAINTS - CORRECT SYNTAX PATTERNS
    
    # Maximum Votes per Contestant
    model.addConstrs((gp.quicksum(x[i, j] for j in states) <= MaxVotes[i] for i in contestants), name="max_votes")
    
    # Minimum Votes per State
    model.addConstrs((x[i, j] >= MinVotes[i, j] for i in contestants for j in states), name="min_votes")
    
    # 5. SOLVING & RESULTS
    model.optimize()
    
    if model.status == GRB.OPTIMAL:
        print(f"Optimal value: {model.objVal}")
        for i in contestants:
            for j in states:
                if x[i, j].x > 1e-6:
                    print(f"x[{i},{j}] = {x[i, j].x:.3f}")
    elif model.status == GRB.INFEASIBLE:
        print("Problem is infeasible")
    elif model.status == GRB.UNBOUNDED:
        print("Problem is unbounded")
    
    return model

# Run the optimization
optimize_votes()