if ( decisionIsLL_1 && !foundConfoundingPredicate ) {
// build an LL(1) optimized DFA with edge for each altLook[i]
if ( NFAToDFAConverter.debug ) {
System.out.println("decision "+decision+" is simple LL(1)");
}
DFA lookaheadDFA = new LL1DFA(decision, decisionStartState, altLook);
setLookaheadDFA(decision, lookaheadDFA);
updateLineColumnToLookaheadDFAMap(lookaheadDFA);
return lookaheadDFA;
}
// not LL(1) but perhaps we can solve with simplified predicate search
// even if k=1 set manually, only resolve here if we have preds; i.e.,
// don't resolve etc...
/*
SemanticContext visiblePredicates =
ll1Analyzer.getPredicates(decisionStartState);
boolean foundConfoundingPredicate =
ll1Analyzer.detectConfoundingPredicates(decisionStartState);
*/
// exit if not forced k=1 or we found a predicate situation we
// can't handle: predicates in rules invoked from this decision.
if ( getUserMaxLookahead(decision)!=1 || // not manually set to k=1
!getAutoBacktrackMode(decision) ||
foundConfoundingPredicate )
{
//System.out.println("trying LL(*)");
return null;
}
List<IntervalSet> edges = new ArrayList<IntervalSet>();
for (int i = 1; i < altLook.length; i++) {
LookaheadSet s = altLook[i];
edges.add((IntervalSet)s.tokenTypeSet);
}
List<IntervalSet> disjoint = makeEdgeSetsDisjoint(edges);
//System.out.println("disjoint="+disjoint);
MultiMap<IntervalSet, Integer> edgeMap = new MultiMap<IntervalSet, Integer>();
for (int i = 0; i < disjoint.size(); i++) {
IntervalSet ds = (IntervalSet) disjoint.get(i);
for (int alt = 1; alt < altLook.length; alt++) {
LookaheadSet look = altLook[alt];
if ( !ds.and(look.tokenTypeSet).isNil() ) {
edgeMap.map(ds, alt);
}
}
}
//System.out.println("edge map: "+edgeMap);
// TODO: how do we know we covered stuff?
// build an LL(1) optimized DFA with edge for each altLook[i]
DFA lookaheadDFA = new LL1DFA(decision, decisionStartState, edgeMap);
setLookaheadDFA(decision, lookaheadDFA);
// create map from line:col to decision DFA (for ANTLRWorks)
updateLineColumnToLookaheadDFAMap(lookaheadDFA);