const fs = require('fs');
const vm = require('vm');

const context = {
  TDGBENCH_FEATURES:{showKggen:true},
  DATASETS_METADATA:{domains:{
    biomed:{graphs:{cg:{},GT2KG_tdg:{},kggen:{}}},
    dbpedia:{graphs:{cg:{},GT2KG_tdg:{}}}
  }}
};
vm.createContext(context);
vm.runInContext(fs.readFileSync('assets/js/pages/evaluate-gnn.js','utf8'),context);

function state(overrides){
  return Object.assign({
    dataset:'biomed',graph:'GT2KG_tdg',variant:'default',modelSource:'existing',
    existingModel:'GCN',pygModel:'SAGEConv',customModule:'my_model',customSymbol:'MyGNN',
    emb:'sentence-transformers/all-MiniLM-L6-v2',results:'tdgbench_results'
  },overrides||{});
}

let code=context.genC2(state({modelSource:'pyg',pygModel:'SAGEConv'}));
if(!code.includes('from torch_geometric.nn import SAGEConv'))throw new Error('Import PyG absent.');
if(!code.includes('model=SAGEConv'))throw new Error('La convolution PyG doit être passée comme classe.');

code=context.genC2(state({modelSource:'existing',existingModel:'GCN'}));
if(!code.includes('model="GCN"')||code.includes('torch_geometric'))throw new Error('Modèle TDGBench incorrect.');

code=context.genC2(state({modelSource:'custom',customModule:'models',customSymbol:'ResearchGNN'}));
if(!code.includes('from models import ResearchGNN')||!code.includes('model=ResearchGNN'))throw new Error('Modèle personnalisé incorrect.');

code=context.genC2(state({modelSource:'existing',existingModel:'MLP'}));
if(code.includes('    graph=')||!code.includes('model="MLP"'))throw new Error('Baseline MLP incorrecte.');

code=context.genC2(state({dataset:'dbpedia',graph:'all',modelSource:'pyg',pygModel:'SAGEConv'}));
if(code.includes('available_graphs')||code.includes('for graph in graphs'))throw new Error('Tous les graphes doivent produire des appels explicites.');
if(!code.includes('tdg = bench.unified_evaluation(')||!code.includes('graph="GT2KG_tdg"'))throw new Error('L\'évaluation explicite du TDG DBpedia est absente.');
if(!code.includes('cg = bench.unified_evaluation(')||!code.includes('graph="cg"'))throw new Error('L\'évaluation explicite du graphe curé DBpedia est absente.');
if(!code.includes('print(tdg.best_results().to_string(index=False))')||!code.includes('print(cg.best_results().to_string(index=False))'))throw new Error('Les meilleurs résultats des deux graphes ne sont pas affichés.');
if((code.match(/model=SAGEConv/g)||[]).length!==2)throw new Error('SAGEConv doit être transmis aux deux évaluations.');

code=context.genC2(state({dataset:'biomed',graph:'all',modelSource:'pyg',pygModel:'SAGEConv'}));
if(!code.includes('graph="kggen"'))throw new Error('KGGen doit être inclus dans l’évaluation de tous les graphes biomédicaux.');

console.log('Evaluation GNN valide : TDGBench, PyG, modèle personnalisé, MLP et tous les graphes.');