Enable added tokens (#11325)

* Fix tests

* Reorganize

* Update tests/test_modeling_mobilebert.py

* Remove unnecessary addition

src/transformers/models/herbert/tokenization_herbert.py

@@ -58,19 +58,37 @@ class HerbertTokenizer(XLMTokenizer):
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 
-    def __init__(self, *args, **kwargs):
-
-        kwargs["cls_token"] = "<s>"
-        kwargs["unk_token"] = "<unk>"
-        kwargs["pad_token"] = "<pad>"
-        kwargs["mask_token"] = "<mask>"
-        kwargs["sep_token"] = "</s>"
-        kwargs["do_lowercase_and_remove_accent"] = False
-        kwargs["additional_special_tokens"] = []
-
-        super().__init__(*args, **kwargs)
+    def __init__(
+        self,
+        vocab_file,
+        merges_file,
+        tokenizer_file=None,
+        cls_token="<s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        sep_token="</s>",
+        do_lowercase_and_remove_accent=False,
+        **kwargs
+    ):
+
+        super().__init__(
+            vocab_file,
+            merges_file,
+            tokenizer_file=None,
+            cls_token=cls_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            sep_token=sep_token,
+            do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
+            **kwargs,
+        )
         self.bert_pre_tokenizer = BasicTokenizer(
-            do_lower_case=False, never_split=self.all_special_tokens, tokenize_chinese_chars=False, strip_accents=False
+            do_lower_case=False,
+            never_split=self.all_special_tokens,
+            tokenize_chinese_chars=False,
+            strip_accents=False,
         )
 
     def _tokenize(self, text):

src/transformers/models/herbert/tokenization_herbert_fast.py

@@ -65,18 +65,28 @@ class HerbertTokenizerFast(PreTrainedTokenizerFast):
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     slow_tokenizer_class = HerbertTokenizer
 
-    def __init__(self, vocab_file, merges_file, tokenizer_file=None, **kwargs):
-
-        kwargs["cls_token"] = "<s>"
-        kwargs["unk_token"] = "<unk>"
-        kwargs["pad_token"] = "<pad>"
-        kwargs["mask_token"] = "<mask>"
-        kwargs["sep_token"] = "</s>"
+    def __init__(
+        self,
+        vocab_file,
+        merges_file,
+        tokenizer_file=None,
+        cls_token="<s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        sep_token="</s>",
+        **kwargs
+    ):
 
         super().__init__(
             vocab_file,
             merges_file,
             tokenizer_file=tokenizer_file,
+            cls_token=cls_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            sep_token=sep_token,
             **kwargs,
         )
 

src/transformers/models/mbart/tokenization_mbart.py

@@ -97,8 +97,17 @@ class MBartTokenizer(XLMRobertaTokenizer):
     prefix_tokens: List[int] = []
     suffix_tokens: List[int] = []
 
-    def __init__(self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, **kwargs):
-        super().__init__(*args, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, **kwargs)
+    def __init__(
+        self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, additional_special_tokens=None, **kwargs
+    ):
+        super().__init__(
+            *args,
+            tokenizer_file=tokenizer_file,
+            src_lang=src_lang,
+            tgt_lang=tgt_lang,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
 
         self.sp_model_size = len(self.sp_model)
         self.lang_code_to_id = {
@@ -111,6 +120,9 @@ class MBartTokenizer(XLMRobertaTokenizer):
         self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         self._additional_special_tokens = list(self.lang_code_to_id.keys())
 
+        if additional_special_tokens is not None:
+            self._additional_special_tokens.extend(additional_special_tokens)
+
         self._src_lang = src_lang if src_lang is not None else "en_XX"
         self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
         self.tgt_lang = tgt_lang

src/transformers/models/mbart/tokenization_mbart_fast.py

@@ -112,10 +112,24 @@ class MBartTokenizerFast(XLMRobertaTokenizerFast):
     prefix_tokens: List[int] = []
     suffix_tokens: List[int] = []
 
-    def __init__(self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, **kwargs):
-        super().__init__(*args, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, **kwargs)
+    def __init__(
+        self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, additional_special_tokens=None, **kwargs
+    ):
+        super().__init__(
+            *args,
+            tokenizer_file=tokenizer_file,
+            src_lang=src_lang,
+            tgt_lang=tgt_lang,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
+
+        _additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()
+
+        if additional_special_tokens is not None:
+            _additional_special_tokens.extend(additional_special_tokens)
 
-        self.add_special_tokens({"additional_special_tokens": FAIRSEQ_LANGUAGE_CODES})
+        self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
 
         self._src_lang = src_lang if src_lang is not None else "en_XX"
         self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)

src/transformers/models/t5/tokenization_t5.py

@@ -107,7 +107,7 @@ class T5Tokenizer(PreTrainedTokenizer):
             additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra_id special tokens
-            extra_tokens = len(set(filter(lambda x: bool("extra_id" in x), additional_special_tokens)))
+            extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
             if extra_tokens != extra_ids:
                 raise ValueError(
                     f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to T5Tokenizer. "

src/transformers/models/t5/tokenization_t5_fast.py

@@ -118,7 +118,7 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
             additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
-            extra_tokens = len(set(filter(lambda x: bool("extra_id_" in x), additional_special_tokens)))
+            extra_tokens = len(set(filter(lambda x: bool("extra_id_" in str(x)), additional_special_tokens)))
             if extra_tokens != extra_ids:
                 raise ValueError(
                     f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to T5Tokenizer. "

tests/test_tokenization_common.py

@@ -2872,6 +2872,34 @@ class TokenizerTesterMixin:
                 for key in python_output:
                     self.assertEqual(python_output[key], rust_output[key])
 
+    def test_special_tokens_initialization(self):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+
+                added_tokens = [AddedToken("<special>", lstrip=True)]
+
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
+                    pretrained_name, additional_special_tokens=added_tokens, **kwargs
+                )
+                tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
+                    pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
+                )
+                tokenizer_p = self.tokenizer_class.from_pretrained(
+                    pretrained_name, additional_special_tokens=added_tokens, **kwargs
+                )
+
+                p_output = tokenizer_p.encode("Hey this is a <special> token")
+                r_output = tokenizer_r.encode("Hey this is a <special> token")
+                cr_output = tokenizer_cr.encode("Hey this is a <special> token")
+
+                special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
+
+                self.assertEqual(p_output, r_output)
+                self.assertEqual(cr_output, r_output)
+                self.assertTrue(special_token_id in p_output)
+                self.assertTrue(special_token_id in r_output)
+                self.assertTrue(special_token_id in cr_output)
+
 
 @is_staging_test
 class TokenizerPushToHubTester(unittest.TestCase):

tests/test_tokenization_t5.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import SPIECE_UNDERLINE, BatchEncoding, T5Tokenizer, T5TokenizerFast
+from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, T5Tokenizer, T5TokenizerFast
 from transformers.file_utils import cached_property, is_tf_available, is_torch_available
 from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers
 
@@ -246,3 +246,31 @@ class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         slow_text = self.t5_base_tokenizer.decode(fast_ids)
         self.assertEqual(tgt_text, fast_text)
         self.assertEqual(tgt_text, slow_text)
+
+    def test_special_tokens_initialization(self):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+
+                added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)]
+
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
+                    pretrained_name, additional_special_tokens=added_tokens, **kwargs
+                )
+                tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
+                    pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
+                )
+                tokenizer_p = self.tokenizer_class.from_pretrained(
+                    pretrained_name, additional_special_tokens=added_tokens, **kwargs
+                )
+
+                p_output = tokenizer_p.encode("Hey this is a <special> token")
+                r_output = tokenizer_r.encode("Hey this is a <special> token")
+                cr_output = tokenizer_cr.encode("Hey this is a <special> token")
+
+                special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
+
+                self.assertEqual(p_output, r_output)
+                self.assertEqual(cr_output, r_output)
+                self.assertTrue(special_token_id in p_output)
+                self.assertTrue(special_token_id in r_output)
+                self.assertTrue(special_token_id in cr_output)