added test and debug tokenizer configuration serialization

pytorch_transformers/tests/tokenization_bert_test.py

@@ -41,8 +41,8 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
         with open(self.vocab_file, "w", encoding='utf-8') as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
-    def get_tokenizer(self):
-        return BertTokenizer.from_pretrained(self.tmpdirname)
+    def get_tokenizer(self, **kwargs):
+        return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"UNwant\u00E9d,running"

pytorch_transformers/tests/tokenization_gpt2_test.py

@@ -44,8 +44,9 @@ class GPT2TokenizationTest(CommonTestCases.CommonTokenizerTester):
         with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
-    def get_tokenizer(self):
-        return GPT2Tokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
+    def get_tokenizer(self, **kwargs):
+        kwargs.update(self.special_tokens_map)
+        return GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"lower newer"

pytorch_transformers/tests/tokenization_openai_test.py

@@ -45,8 +45,8 @@ class OpenAIGPTTokenizationTest(CommonTestCases.CommonTokenizerTester):
         with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
-    def get_tokenizer(self):
-        return OpenAIGPTTokenizer.from_pretrained(self.tmpdirname)
+    def get_tokenizer(self, **kwargs):
+        return OpenAIGPTTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"lower newer"

pytorch_transformers/tests/tokenization_roberta_test.py

@@ -43,8 +43,9 @@ class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
         with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
-    def get_tokenizer(self):
-        return RobertaTokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
+    def get_tokenizer(self, **kwargs):
+        kwargs.update(self.special_tokens_map)
+        return RobertaTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"lower newer"

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -49,14 +49,19 @@ class CommonTestCases:
         def tearDown(self):
             shutil.rmtree(self.tmpdirname)
 
-        def get_tokenizer(self):
+        def get_tokenizer(self, **kwargs):
             raise NotImplementedError
 
         def get_input_output_texts(self):
             raise NotImplementedError
 
         def test_save_and_load_tokenizer(self):
+            # safety check on max_len default value so we are sure the test works
             tokenizer = self.get_tokenizer()
+            self.assertNotEqual(tokenizer.max_len, 42)
+
+            # Now let's start the test
+            tokenizer = self.get_tokenizer(max_len=42)
 
             before_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
 
@@ -67,6 +72,10 @@ class CommonTestCases:
                 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)
+                self.assertEqual(tokenizer.max_len, 43)
+
         def test_pickle_tokenizer(self):
             tokenizer = self.get_tokenizer()
             self.assertIsNotNone(tokenizer)

pytorch_transformers/tests/tokenization_transfo_xl_test.py

@@ -37,8 +37,9 @@ class TransfoXLTokenizationTest(CommonTestCases.CommonTokenizerTester):
         with open(self.vocab_file, "w", encoding='utf-8') as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
-    def get_tokenizer(self):
-        return TransfoXLTokenizer.from_pretrained(self.tmpdirname, lower_case=True)
+    def get_tokenizer(self, **kwargs):
+        kwargs['lower_case'] = True
+        return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"<unk> UNwanted , running"

pytorch_transformers/tests/tokenization_xlm_test.py

@@ -44,8 +44,8 @@ class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester):
         with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
-    def get_tokenizer(self):
-        return XLMTokenizer.from_pretrained(self.tmpdirname)
+    def get_tokenizer(self, **kwargs):
+        return XLMTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"lower newer"

pytorch_transformers/tests/tokenization_xlnet_test.py

@@ -35,8 +35,8 @@ class XLNetTokenizationTest(CommonTestCases.CommonTokenizerTester):
         tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
         tokenizer.save_pretrained(self.tmpdirname)
 
-    def get_tokenizer(self):
-        return XLNetTokenizer.from_pretrained(self.tmpdirname)
+    def get_tokenizer(self, **kwargs):
+        return XLNetTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
     def get_input_output_texts(self):
         input_text = u"This is a test"

pytorch_transformers/tokenization_utils.py

@@ -332,7 +332,7 @@ class PreTrainedTokenizer(object):
         tokenizer_config_file = resolved_vocab_files.pop('tokenizer_config_file', None)
         if tokenizer_config_file is not None:
             init_kwargs = json.load(open(tokenizer_config_file, encoding="utf-8"))
-            saved_init_inputs = init_kwargs.pop('init_inputs', [])
+            saved_init_inputs = init_kwargs.pop('init_inputs', ())
             if not init_inputs:
                 init_inputs = saved_init_inputs
         else:
@@ -399,6 +399,8 @@ class PreTrainedTokenizer(object):
 
         tokenizer_config = copy.deepcopy(self.init_kwargs)
         tokenizer_config['init_inputs'] = copy.deepcopy(self.init_inputs)
+        for file_id in self.vocab_files_names.keys():
+            tokenizer_config.pop(file_id, None)
 
         with open(tokenizer_config_file, 'w', encoding='utf-8') as f:
             f.write(json.dumps(tokenizer_config, ensure_ascii=False))