fix tokenizers serialization

pytorch_transformers/tests/tokenization_dilbert_test.py

@@ -27,8 +27,8 @@ class DistilBertTokenizationTest(BertTokenizationTest):
 
     tokenizer_class = DistilBertTokenizer
 
-    def get_tokenizer(self):
-        return DistilBertTokenizer.from_pretrained(self.tmpdirname)
+    def get_tokenizer(self, **kwargs):
+        return DistilBertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def test_sequence_builders(self):
         tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -67,13 +67,13 @@ class CommonTestCases:
 
             with TemporaryDirectory() as tmpdirname:
                 tokenizer.save_pretrained(tmpdirname)
-                tokenizer = tokenizer.from_pretrained(tmpdirname)
+                tokenizer = self.tokenizer_class.from_pretrained(tmpdirname)
 
                 after_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
                 self.assertListEqual(before_tokens, after_tokens)
 
                 self.assertEqual(tokenizer.max_len, 42)
-                tokenizer = tokenizer.from_pretrained(tmpdirname, max_len=43)
+                tokenizer = self.tokenizer_class.from_pretrained(tmpdirname, max_len=43)
                 self.assertEqual(tokenizer.max_len, 43)
 
         def test_pickle_tokenizer(self):

pytorch_transformers/tokenization_transfo_xl.py

@@ -95,7 +95,8 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
             # in a library like ours, at all.
             vocab_dict = torch.load(pretrained_vocab_file)
             for key, value in vocab_dict.items():
-                self.__dict__[key] = value
+                if key not in self.__dict__:
+                    self.__dict__[key] = value
 
         if vocab_file is not None:
             self.build_vocab()

pytorch_transformers/tokenization_xlnet.py

@@ -61,7 +61,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
-    def __init__(self, vocab_file, max_len=None,
+    def __init__(self, vocab_file,
                  do_lower_case=False, remove_space=True, keep_accents=False,
                  bos_token="<s>", eos_token="</s>", unk_token="<unk>", sep_token="<sep>",
                  pad_token="<pad>", cls_token="<cls>", mask_token="<mask>",