add dilbert tokenizer and tests

pytorch_transformers/tests/tokenization_bert_test.py

@@ -42,7 +42,7 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
     def get_tokenizer(self):
-        return BertTokenizer.from_pretrained(self.tmpdirname)
+        return self.tokenizer_class.from_pretrained(self.tmpdirname)
 
     def get_input_output_texts(self):
         input_text = u"UNwant\u00E9d,running"
@@ -50,7 +50,7 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
         return input_text, output_text
 
     def test_full_tokenizer(self):
-        tokenizer = BertTokenizer(self.vocab_file)
+        tokenizer = self.tokenizer_class(self.vocab_file)
 
         tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
         self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
@@ -126,7 +126,7 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
         self.assertFalse(_is_punctuation(u" "))
 
     def test_sequence_builders(self):
-        tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+        tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
 
         text = tokenizer.encode("sequence builders")
         text_2 = tokenizer.encode("multi-sequence build")

pytorch_transformers/tests/tokenization_dilbert_test.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import unittest
+from io import open
+
+from pytorch_transformers.tokenization_dilbert import (DilBertTokenizer)
+
+from .tokenization_tests_commons import CommonTestCases
+from .tokenization_bert_test import BertTokenizationTest
+
+class DilBertTokenizationTest(BertTokenizationTest):
+
+    tokenizer_class = DilBertTokenizer
+
+    def get_tokenizer(self):
+        return DilBertTokenizer.from_pretrained(self.tmpdirname)
+
+    def test_sequence_builders(self):
+        tokenizer = DilBertTokenizer.from_pretrained("dilbert-base-uncased")
+
+        text = tokenizer.encode("sequence builders")
+        text_2 = tokenizer.encode("multi-sequence build")
+
+        encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
+        encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
+
+        assert encoded_sentence == [101] + text + [102]
+        assert encoded_pair == [101] + text + [102] + text_2 + [102]
+
+if __name__ == '__main__':
+    unittest.main()