# Complete GUROBIPY implementation - Retry Attempt 4

import gurobipy as gp
from gurobipy import GRB

def entertainment_awards_optimization():
    # 1. MODEL & DATA SETUP
    model = gp.Model("entertainment_awards")

    # Sample data based on the provided schema and realistic values
    engagement_scores = {
        (1, 1): 0.85,
        (2, 1): 0.75,
        (3, 1): 0.65
    }
    
    festival_nominations = {
        1: 5,
        2: 6,
        3: 4
    }
    
    artwork_types = {
        1: 0.9,  # Assuming Artwork_ID 1 is 'Film'
        2: 0.8,  # Assuming Artwork_ID 2 is 'Short'
        3: 0.7   # Assuming Artwork_ID 3 is 'Documentary'
    }

    # Extract unique Artwork_ID and Festival_ID
    Artwork_ID = list(set(i for i, j in engagement_scores.keys()))
    Festival_ID = list(set(j for i, j in engagement_scores.keys()))

    # Validate array lengths before loops
    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"

    # 2. VARIABLES
    x = model.addVars(Artwork_ID, Festival_ID, vtype=GRB.BINARY, name="x")

    # 3. OBJECTIVE FUNCTION
    model.setObjective(
        gp.quicksum(engagement_scores[i, j] * x[i, j] for i in Artwork_ID for j in Festival_ID),
        GRB.MAXIMIZE
    )

    # 4. CONSTRAINTS
    # Maximum Nominations per Festival
    for j in Festival_ID:
        model.addConstr(
            gp.quicksum(x[i, j] for i in Artwork_ID) <= festival_nominations[j],
            name=f"max_nominations_{j}"
        )

    # Minimum Diversity Requirement
    for j in Festival_ID:
        model.addConstr(
            gp.quicksum(artwork_types[i] * x[i, j] for i in Artwork_ID) >= 0.75,
            name=f"min_diversity_{j}"
        )

    # 5. SOLVING & RESULTS
    model.optimize()

    if model.status == GRB.OPTIMAL:
        print(f"Optimal value: {model.objVal}")
        for i in Artwork_ID:
            for j in Festival_ID:
                if x[i, j].x > 1e-6:
                    print(f"Artwork {i} nominated for Festival {j}")
    elif model.status == GRB.INFEASIBLE:
        print("Problem is infeasible")
    elif model.status == GRB.UNBOUNDED:
        print("Problem is unbounded")

    return model

# Execute the optimization
entertainment_awards_optimization()