Experimental parser: faster dfa minimization

tools/lexer_generator/rule_parser.py

 # Copyright 2013 the V8 project authors. All rights reserved.
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions are
 # met:
 #
 #     * Redistributions of source code must retain the above copyright
 #       notice, this list of conditions and the following disclaimer.
 #     * Redistributions in binary form must reproduce the above
 #       copyright notice, this list of conditions and the following
 #       disclaimer in the documentation and/or other materials provided
@@ skipping 217 matching lines @@
     return RuleProcessor(parser_state)
 
   def automata_iter(self):
     return iter(self.__automata.items())
 
   def default_automata(self):
     return self.__automata['default']
 
   class Automata(object):
 
-    def __init__(self, nfa):
-      (start, dfa_nodes) = nfa.compute_dfa()
-      self.__nfa = nfa
-      self.__dfa = Dfa(start, dfa_nodes)
+    def __init__(self, builder, graph):
+      self.__builder = builder
+      self.__graph = graph
+      self.__nfa = None
+      self.__dfa = None
       self.__minimial_dfa = None
 
     def nfa(self):
+      if not self.__nfa:
+        self.__nfa = self.__builder.nfa(self.__graph)
       return self.__nfa
 
     def dfa(self):
+      if not self.__dfa:
+        (start, dfa_nodes) = self.nfa().compute_dfa()
+        self.__dfa = Dfa(start, dfa_nodes)
       return self.__dfa
 
     def minimal_dfa(self):
       if not self.__minimial_dfa:
-        self.__minimial_dfa = self.__dfa.minimize()
+        self.__minimial_dfa = self.dfa().minimize()
       return self.__minimial_dfa
 
   def __process_parser_state(self, parser_state):
     rule_map = {}
     builder = NfaBuilder()
     builder.set_character_classes(parser_state.character_classes)
     assert 'default' in parser_state.rules
     def process(k, v):
       graphs = []
       continues = 0
@@ skipping 25 matching lines @@
         graphs.append(NfaBuilder.add_continue(NfaBuilder.catch_all()))
       graph = NfaBuilder.or_graphs(graphs)
       rule_map[k] = graph
     # process first the subgraphs, then the default graph
     for k, v in parser_state.rules.items():
       if k == 'default': continue
       process(k, v)
     process('default', parser_state.rules['default'])
     # build the automata
     for rule_name, graph in rule_map.items():
-      nfa = builder.nfa(graph)
-      self.__automata[rule_name] = RuleProcessor.Automata(nfa)
+      self.__automata[rule_name] = RuleProcessor.Automata(builder, graph)
     self.default_action = parser_state.rules['default']['default_action']