Experimental parser: refactor TransitionKey to use Term

tools/lexer_generator/automata_test.py

 # Copyright 2013 the V8 project authors. All rights reserved.
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 # 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 10 matching lines @@
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 # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import unittest
 from automaton import Action
 from regex_parser import RegexParser
-from transition_keys import TransitionKey
+from transition_keys import TransitionKey, KeyEncoding
 from nfa_builder import NfaBuilder
 from dfa import Dfa
 
 class AutomataTestCase(unittest.TestCase):
 
+  __encoding = KeyEncoding.get('latin1')
+
   @staticmethod
   def __build_automata(string):
-    nfa = NfaBuilder().nfa(RegexParser.parse(string))
+    encoding = AutomataTestCase.__encoding
+    trees = {'main' : RegexParser.parse(string)}
+    nfa = NfaBuilder.nfa(encoding, {}, trees, 'main')
     (start_name, dfa_nodes) = nfa.compute_dfa()
-    dfa = Dfa(start_name, dfa_nodes)
+    dfa = Dfa(encoding, start_name, dfa_nodes)
     return (nfa, dfa, dfa.minimize())
 
-    # (pattern, should match, should not match)
-    __test_data = [
-      ("a", ["a"], ["b", ""]),
-      ("ab", ["ab"], ["bb", ""]),
-      ("a+b", ["ab", "aab", "aaab"], ["a", "b", ""]),
-      ("a?b", ["ab", "b"], ["a", "c", ""]),
-      ("a*b", ["ab", "aaab", "b"], ["a", "c", ""]),
-      ("a|b", ["a", "b"], ["ab", "c", ""]),
-      (".", ["a", "b"], ["", "aa"]),
-      (".*", ["", "a", "abcaabbcc"], []),
-      ("a.b", ["aab", "abb", "acb"], ["ab", ""]),
-      ("a.?b", ["aab", "abb", "acb", "ab"], ["aaab", ""]),
-      ("a.+b", ["aab", "abb", "acb"], ["aaac", "ab", ""]),
-      (".|.", ["a", "b"], ["aa", ""]),
-      ("//.", ["//a"], ["aa", ""]),
-      ("[ab]{2}", ["aa", "ab", "ba", "bb"], ["", "a", "b", "aaa", "bbb"]),
-      ("[ab]{2,3}", ["aa", "ab", "ba", "bb", "aab", "baa", "bbb"],
-       ["", "a", "b", "aaaa", "bbba"]),
-      ("[ab]{2,4}", ["aa", "ab", "ba", "bb", "aab", "baa", "bbb", "abab"],
-       ["", "a", "b", "aaaba", "bbbaa"]),
-      ("[\\101]", ["A"], ["B"])
-    ]
+  # (pattern, should match, should not match)
+  __test_data = [
+    ("a", ["a"], ["b", ""]),
+    ("ab", ["ab"], ["bb", ""]),
+    ("a+b", ["ab", "aab", "aaab"], ["a", "b", ""]),
+    ("a?b", ["ab", "b"], ["a", "c", ""]),
+    ("a*b", ["ab", "aaab", "b"], ["a", "c", ""]),
+    ("a|b", ["a", "b"], ["ab", "c", ""]),
+    (".", ["a", "b"], ["", "aa"]),
+    (".*", ["", "a", "abcaabbcc"], []),
+    ("a.b", ["aab", "abb", "acb"], ["ab", ""]),
+    ("a.?b", ["aab", "abb", "acb", "ab"], ["aaab", ""]),
+    ("a.+b", ["aab", "abb", "acb"], ["aaac", "ab", ""]),
+    (".|.", ["a", "b"], ["aa", ""]),
+    ("//.", ["//a"], ["aa", ""]),
+    ("[ab]{2}", ["aa", "ab", "ba", "bb"], ["", "a", "b", "aaa", "bbb"]),
+    ("[ab]{2,3}", ["aa", "ab", "ba", "bb", "aab", "baa", "bbb"],
+     ["", "a", "b", "aaaa", "bbba"]),
+    ("[ab]{2,4}", ["aa", "ab", "ba", "bb", "aab", "baa", "bbb", "abab"],
+     ["", "a", "b", "aaaba", "bbbaa"]),
+    ("[\\101]", ["A"], ["B"])
+  ]
 
-    def test_matches(self):
-      for (regex, matches, not_matches) in self.__test_data:
-        automata = self.__build_automata(regex)
-        for string in matches:
-          for automaton in automata:
-            self.assertTrue(automaton.matches(string))
-        for string in not_matches:
-          for automaton in automata:
-            self.assertFalse(automaton.matches(string))
+  def test_matches(self):
+    for (regex, matches, not_matches) in self.__test_data:
+      automata = self.__build_automata(regex)
+      for string in matches:
+        for automaton in automata:
+          self.assertTrue(automaton.matches(string))
+      for string in not_matches:
+        for automaton in automata:
+          self.assertFalse(automaton.matches(string))
 
-    def test_can_construct_dot(self):
-      for (regex, matches, not_matches) in self.__test_data:
-        for automaton in self.__build_automata(regex):
-          automaton.to_dot()
+  def test_can_construct_dot(self):
+    for (regex, matches, not_matches) in self.__test_data:
+      for automaton in self.__build_automata(regex):
+        automaton.to_dot()
 
-    def test_minimization(self):
-      def empty_node():
-        return { 'transitions' : {}, 'terminal' : False, 'action' : None }
-      mapping = { k : empty_node() for k in ['S_0', 'S_1', 'S_2', 'S_3'] }
-      key_a = TransitionKey.single_char('a')
-      key_b = TransitionKey.single_char('b')
-      key_c = TransitionKey.single_char('c')
+  def test_minimization(self):
+    encoding = self.__encoding
+    def empty_node():
+      return {
+        'transitions' : {},
+        'terminal' : False,
+        'action' : Action.empty_action() }
+    mapping = { k : empty_node() for k in ['S_0', 'S_1', 'S_2', 'S_3'] }
+    key_a = TransitionKey.single_char(encoding, 'a')
+    key_b = TransitionKey.single_char(encoding, 'b')
+    key_c = TransitionKey.single_char(encoding, 'c')
 
-      mapping['S_0']['transitions'][key_a] = 'S_1'
-      mapping['S_0']['transitions'][key_b] = 'S_2'
-      mapping['S_1']['transitions'][key_c] = 'S_3'
-      mapping['S_2']['transitions'][key_c] = 'S_3'
-      mapping['S_3']['terminal'] = True
+    mapping['S_0']['transitions'][key_a] = 'S_1'
+    mapping['S_0']['transitions'][key_b] = 'S_2'
+    mapping['S_1']['transitions'][key_c] = 'S_3'
+    mapping['S_2']['transitions'][key_c] = 'S_3'
+    mapping['S_3']['terminal'] = True
 
-      mdfa = Dfa('S_0', mapping).minimize()
-      self.assertEqual(3, mdfa.node_count())
+    mdfa = Dfa(encoding, 'S_0', mapping).minimize()
+    self.assertEqual(3, mdfa.node_count())