Experimental parser: cleanup NfaBuilder a bit

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 231 matching lines @@
     except Exception:
       RuleParser.__static_instance = None
       raise
     parser.__state = None
     assert parser_state.transitions <= set(parser_state.rules.keys())
 
 class RuleProcessor(object):
 
   def __init__(self, parser_state):
     self.__automata = {}
-    self.__rule_trees = {}
     self.__default_action = None
     self.__process_parser_state(parser_state)
 
   @staticmethod
   def parse(string, encoding_name):
     parser_state = RuleParserState(KeyEncoding.get(encoding_name))
     RuleParser.parse(string, parser_state)
     return RuleProcessor(parser_state)
 
   def automata_iter(self):
     return iter(self.__automata.items())
 
   def default_automata(self):
     return self.__automata['default']
 
   def default_action(self):
     return self.__default_action
 
   class Automata(object):
+    'a container for the resulting automata, which are lazily built'
 
-    def __init__(self, encoding, character_classes, rule_term):
+    def __init__(self, encoding, character_classes, rule_map, name):
       self.__encoding = encoding
       self.__character_classes = character_classes
-      self.__rule_term = rule_term
+      self.__rule_map = rule_map
+      self.__name = name
       self.__nfa = None
       self.__dfa = None
       self.__minimial_dfa = None
 
+    def name(self):
+      return self.__name
+
     def rule_term(self):
-      return self.__rule_term
+      return self.__rule_map[self.__name]
 
     def nfa(self):
       if not self.__nfa:
         self.__nfa = NfaBuilder.nfa(
-          self.__encoding, self.__character_classes, self.__rule_term)
+          self.__encoding, self.__character_classes,
+          self.__rule_map, self.__name)
       return self.__nfa
 
     def dfa(self):
       if not self.__dfa:
         (start, dfa_nodes) = self.nfa().compute_dfa()
         self.__dfa = Dfa(self.nfa().encoding(), start, dfa_nodes)
       return self.__dfa
 
     def optimize_dfa(self, log = False):
       assert not self.__dfa
       assert not self.__minimial_dfa
       self.__dfa = DfaOptimizer.optimize(self.minimal_dfa(), log)
       self.__minimial_dfa = None
 
     def minimal_dfa(self):
       if not self.__minimial_dfa:
         self.__minimial_dfa = self.dfa().minimize()
       return self.__minimial_dfa
 
   def __process_parser_state(self, parser_state):
+    assert 'default' in parser_state.rules
     rule_map = {}
-    assert 'default' in parser_state.rules
-    def process(subgraph, v):
-      graphs = []
-      for graph, precedence, action in v['regex']:
+    def process(tree_name, v):
+      trees = []
+      for tree, precedence, action in v['regex']:
         (entry_action, match_action, transition) = action
         if entry_action or match_action:
-          graph = NfaBuilder.add_action(
-            graph, Action(entry_action, match_action, precedence))
+          tree = NfaBuilder.add_action(
+            tree, Action(entry_action, match_action, precedence))
         if not transition:
           pass
         elif transition == 'continue':
-          assert not subgraph == 'default', 'unimplemented'
-          graph = NfaBuilder.add_continue(graph)
+          tree = NfaBuilder.add_continue(tree)
         else:
-          assert subgraph == 'default', 'unimplemented'
-          graph = NfaBuilder.join_subgraph(
-            graph, rule_map[transition])
-        graphs.append(graph)
-      graph = NfaBuilder.or_terms(graphs)
-      rule_map[subgraph] = graph
-    # process first the subgraphs, then the default graph
+          tree = NfaBuilder.join_subtree(tree, transition)
+        trees.append(tree)
+      rule_map[tree_name] = NfaBuilder.or_terms(trees)
+    # process all subgraphs
     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():
-      self.__automata[rule_name] = RuleProcessor.Automata(
-        parser_state.encoding, parser_state.character_classes, graph)
-      self.__rule_trees[rule_name] = graph
+    for name, tree in rule_map.items():
+      self.__automata[name] = RuleProcessor.Automata(
+        parser_state.encoding, parser_state.character_classes, rule_map, name)
     # process default_action
     default_action = parser_state.rules['default']['default_action']
     self.__default_action = Action(Term.empty_term(), default_action)