# Complete PYOMO implementation - Retry Attempt 4

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

def entertainment_awards_optimization():
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    # Engagement scores data
    engagement_scores = {
        (1, 1): 0.85,
        (2, 1): 0.75,
        (3, 1): 0.65
    }
    
    # Festival nominations data
    festival_nominations = {
        1: 5,
        2: 6,
        3: 4
    }
    
    # Artwork types data
    artwork_types = {
        1: 0.9,  # Film
        2: 0.8,  # Short
        3: 0.7   # Documentary
    }
    
    # Validate array lengths before indexing
    Artwork_ID = [1, 2, 3]
    Festival_ID = [1, 2, 3]
    
    assert len(festival_nominations) == len(Festival_ID), "Festival nominations data mismatch"
    assert all((i, j) in engagement_scores for i in Artwork_ID for j in Festival_ID), "Engagement scores data mismatch"
    assert all(i in artwork_types for i in Artwork_ID), "Artwork types data mismatch"
    
    # 3. SETS
    model.Artwork_ID = pyo.Set(initialize=Artwork_ID)
    model.Festival_ID = pyo.Set(initialize=Festival_ID)
    
    # 4. PARAMETERS
    model.engagement_scores = pyo.Param(model.Artwork_ID, model.Festival_ID, initialize=engagement_scores)
    model.max_nominations = pyo.Param(model.Festival_ID, initialize=festival_nominations)
    model.diversity_scores = pyo.Param(model.Artwork_ID, initialize=artwork_types)
    
    # 5. VARIABLES
    model.x = pyo.Var(model.Artwork_ID, model.Festival_ID, within=pyo.Binary)
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.engagement_scores[i, j] * model.x[i, j] for i in model.Artwork_ID for j in model.Festival_ID)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)
    
    # 7. CONSTRAINTS
    # Maximum Nominations per Festival
    def max_nominations_rule(model, j):
        return sum(model.x[i, j] for i in model.Artwork_ID) <= model.max_nominations[j]
    model.max_nominations_constraint = pyo.Constraint(model.Festival_ID, rule=max_nominations_rule)
    
    # Minimum Diversity Requirement
    def min_diversity_rule(model, j):
        return sum(model.diversity_scores[i] * model.x[i, j] for i in model.Artwork_ID) >= 0.75
    model.min_diversity_constraint = pyo.Constraint(model.Festival_ID, rule=min_diversity_rule)
    
    # 8. SOLVING WITH GUROBI
    solver = SolverFactory('gurobi')
    
    # Optional: Set solver options
    solver.options['TimeLimit'] = 300  # 5 minutes
    solver.options['MIPGap'] = 0.01    # 1% gap
    
    # Solve the model
    results = solver.solve(model, tee=True)  # tee=True shows solver output
    
    # 9. RESULT PROCESSING
    # Check solver status
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective)}")
        
        # Extract variable values
        print("\nVariable values:")
        for i in model.Artwork_ID:
            for j in model.Festival_ID:
                x_val = pyo.value(model.x[i, j])
                if x_val > 1e-6:  # Only print non-zero values
                    print(f"x[{i}, {j}] = {int(x_val)}")
        
    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
if __name__ == "__main__":
    entertainment_awards_optimization()