Experimental parser: Abstact state matching

tools/lexer_generator/nfa.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 19 matching lines @@
 
 class NfaState(AutomatonState):
 
   def __init__(self):
     super(NfaState, self).__init__()
     self.__transitions = {}
     self.__unclosed = set()
     self.__epsilon_closure = None
     self.__action = Action.empty_action()
 
-  def transitions_to_multiple_states(self):
-    return True
-
   def epsilon_closure_iter(self):
     return iter(self.__epsilon_closure)
 
   def set_epsilon_closure(self, closure):
     assert self.is_closed()
     assert self.__epsilon_closure == None
     self.__epsilon_closure = frozenset(closure)
 
   def action(self):
     assert self.is_closed()
@@ skipping 34 matching lines @@
     assert not self.is_closed()
     unclosed, self.__unclosed = self.__unclosed, None
     if end == None:
       assert not unclosed
       return
     for key in unclosed:
       self.__add_transition(key, end)
     if not unclosed:
       self.__add_transition(TransitionKey.epsilon(), end)
 
-  def __matches(self, match_func, value):
-    # f collects states whose corresponding TransitionKey matches 'value'.
-    items = self.__transitions.items()
-    iters = [iter(states) for (key, states) in items if match_func(key, value)]
-    return chain(*iters)
-
-  def transition_state_iter_for_char(self, value):
-    return self.__matches(lambda k, v : k.matches_char(v), value)
-
-  def transition_state_iter_for_key(self, value):
-    return self.__matches(lambda k, v : k.is_superset_of_key(v), value)
+  def key_state_iter(
+    self,
+    key_filter = lambda x: True,
+    state_filter = lambda x: True,
+    match_func = lambda x, y: True,
+    yield_func = lambda x, y: (x, y)):
+    for key, states in self.__transitions.items():
+      if key_filter(key):
+        for state in states:
+          if state_filter(state) and match_func(key, state):
+            yield yield_func(key, state)
 
 class Nfa(Automaton):
 
   def __init__(self, encoding, start, end, nodes_created):
     super(Nfa, self).__init__(encoding)
     self.__start = start
     self.__end = end
     self.__verify(nodes_created)
 
   def start_state(self):
@@ skipping 39 matching lines @@
       f = lambda state: state.transition_state_iter_for_key(key)
       match_states = set(chain(*map(f, nfa_state_set)))
       transition_state = self.__to_dfa(match_states, dfa_nodes, end_node)
       dfa_nodes[name]['transitions'][key] = transition_state
     return name
 
   def compute_dfa(self):
     dfa_nodes = {}
     start_name = self.__to_dfa(set([self.__start]), dfa_nodes, self.__end)
     return (start_name, dfa_nodes)