# Complete PYOMO implementation

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

def optimize_browser_accelerators():
    """Optimize the selection of web client accelerators to maximize browser compatibility."""
    
    # 1. MODEL CREATION
    model = pyo.ConcreteModel()
    
    # 2. DATA SETUP
    # Browser data
    browser_data = {
        1: 0.35,
        2: 0.25,
        3: 0.15
    }
    
    # Accelerator compatibility data
    compatibility_data = {
        (1, 1): 0.9,
        (1, 2): 0.8,
        (2, 1): 0.85,
        (2, 3): 0.7,
        (3, 2): 0.75,
        (3, 3): 0.65
    }
    
    # Maximum number of accelerators
    max_accelerators = 3
    
    # Validate data lengths
    assert len(browser_data) == 3, "Browser data length mismatch"
    assert len(compatibility_data) == 6, "Compatibility data length mismatch"
    
    # 3. SETS
    model.I = pyo.Set(initialize=[1, 2, 3])  # Accelerators
    model.J = pyo.Set(initialize=[1, 2, 3])  # Browsers
    
    # 4. PARAMETERS
    model.market_share = pyo.Param(model.J, initialize=browser_data)
    model.compatibility_score = pyo.Param(model.I, model.J, initialize=compatibility_data, default=0.0)
    
    # 5. VARIABLES
    model.x = pyo.Var(model.I, within=pyo.Binary)  # Accelerator selection
    model.y = pyo.Var(model.J, within=pyo.Binary)  # Browser compatibility
    
    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.market_share[j] * model.y[j] for j in model.J)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)
    
    # 7. CONSTRAINTS
    # Maximum Accelerators Constraint
    def max_accelerators_rule(model):
        return sum(model.x[i] for i in model.I) <= max_accelerators
    model.max_accelerators_constraint = pyo.Constraint(rule=max_accelerators_rule)
    
    # Minimum Compatibility Constraint
    def min_compatibility_rule(model, i):
        return sum(model.compatibility_score[i, j] * model.y[j] for j in model.J) >= model.x[i]
    model.min_compatibility_constraint = pyo.Constraint(model.I, rule=min_compatibility_rule)
    
    # Browser Compatibility Constraint
    def browser_compatibility_rule(model, j):
        return model.y[j] <= sum(model.compatibility_score[i, j] * model.x[i] for i in model.I)
    model.browser_compatibility_constraint = pyo.Constraint(model.J, rule=browser_compatibility_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("\nSelected Accelerators:")
        for i in model.I:
            if pyo.value(model.x[i]) > 0.5:
                print(f"Accelerator {i} is selected")
        
        print("\nCompatible Browsers:")
        for j in model.J:
            if pyo.value(model.y[j]) > 0.5:
                print(f"Browser {j} is compatible")
        
    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__":
    optimize_browser_accelerators()