Experimental parser: cleanup transition iteration a little

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 81 matching lines @@
     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'.
-    f = (lambda acc, (key, states):
-           acc | states if match_func(key, value) else acc)
-    return reduce(f, self.__transitions.items(), set())
+    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 iter(self.__matches(lambda k, v : k.matches_char(v), value))
+    return self.__matches(lambda k, v : k.matches_char(v), value)
 
   def transition_state_iter_for_key(self, value):
-    return iter(self.__matches(lambda k, v : k.is_superset_of_key(v), value))
+    return self.__matches(lambda k, v : k.is_superset_of_key(v), value)
 
 class Nfa(Automaton):
 
   def __init__(self, start, end, nodes_created):
     super(Nfa, self).__init__()
     self.__start = start
     self.__end = end
     self.__verify(nodes_created)
 
   def start_state(self):
@@ skipping 31 matching lines @@
       f = lambda state: state.transition_state_iter_for_key(key)
       match_states |= set(chain(*map(f, nfa_state_set)))
       transition_state = Nfa.__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)