change the way sentinel tokens can retrived (#20373)

* change the way sentinel tokens can retrived

* Fix line length for doc string

* Fix line length for doc string

* Add more stronger test for t5 tokenization

* Format file changes

* Make a stronger test for filtering sentinel tokens

* fix file format issues

src/transformers/models/t5/tokenization_t5.py

@@ -79,12 +79,11 @@ class T5Tokenizer(PreTrainedTokenizer):
         pad_token (`str`, *optional*, defaults to `"<pad>"`):
             The token used for padding, for example when batching sequences of different lengths.
         extra_ids (`int`, *optional*, defaults to 100):
-            Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
-            accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
-            indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
-            like in T5 preprocessing see
-            [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
-        additional_special_tokens (`List[str]`, *optional*):
+           Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are
+            accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be
+            retrieved by calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids
+            method
+         additional_special_tokens (`List[str]`, *optional*):
             Additional special tokens used by the tokenizer.
         sp_model_kwargs (`dict`, *optional*):
             Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
@@ -213,6 +212,14 @@ class T5Tokenizer(PreTrainedTokenizer):
             return ([0] * len(token_ids_0)) + [1]
         return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
 
+    def get_sentinel_tokens(self):
+        return list(
+            set(filter(lambda x: bool(re.search("<extra_id_\d+>", x)) is not None, self.additional_special_tokens))
+        )
+
+    def get_sentinel_token_ids(self):
+        return [self._convert_token_to_id(token) for token in self.get_sentinel_tokens()]
+
     def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
         """Do not add eos again if user already added it."""
         if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:

src/transformers/models/t5/tokenization_t5_fast.py

@@ -16,6 +16,7 @@
 
 
 import os
+import re
 import warnings
 from shutil import copyfile
 from typing import List, Optional, Tuple
@@ -90,11 +91,9 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
         pad_token (`str`, *optional*, defaults to `"<pad>"`):
             The token used for padding, for example when batching sequences of different lengths.
         extra_ids (`int`, *optional*, defaults to 100):
-            Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
-            accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
-            indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
-            like in T5 preprocessing see
-            [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
+            Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are accessible as
+            "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be retrieved by
+            calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids method
         additional_special_tokens (`List[str]`, *optional*):
             Additional special tokens used by the tokenizer.
     """
@@ -235,3 +234,11 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
         if token_ids_1 is None:
             return len(token_ids_0 + eos) * [0]
         return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+
+    def get_sentinel_tokens(self):
+        return list(
+            set(filter(lambda x: bool(re.search("<extra_id_\d+>", x)) is not None, self.additional_special_tokens))
+        )
+
+    def get_sentinel_token_ids(self):
+        return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()]

tests/models/t5/test_tokenization_t5.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 import json
 import os
+import re
 import tempfile
 import unittest
 
@@ -379,3 +380,25 @@ class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
             model_name="t5-base",
             revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b",
         )
+
+    def test_get_sentinel_tokens(self):
+        tokenizer = T5Tokenizer(SAMPLE_VOCAB, extra_ids=10)
+        sentinel_tokens = tokenizer.get_sentinel_tokens()
+        self.assertEquals(len(sentinel_tokens), 10)
+        self.assertListEqual(sorted(sentinel_tokens), sorted([f"<extra_id_{str(i)}>" for i in range(0, 10)]))
+        self.assertTrue([re.search("<extra_id_\d+>", token) is not None for token in sentinel_tokens])
+
+    def test_get_sentinel_token_ids(self):
+        tokenizer = T5Tokenizer(SAMPLE_VOCAB, extra_ids=10)
+        self.assertListEqual(sorted(tokenizer.get_sentinel_token_ids()), sorted([i for i in range(1000, 1010)]))
+
+    def test_get_sentinel_tokens_for_fasttokenizer(self):
+        tokenizer = T5TokenizerFast(SAMPLE_VOCAB, extra_ids=10)
+        sentinel_tokens = tokenizer.get_sentinel_tokens()
+        self.assertEquals(len(sentinel_tokens), 10)
+        self.assertListEqual(sorted(sentinel_tokens), sorted([f"<extra_id_{str(i)}>" for i in range(0, 10)]))
+        self.assertTrue([re.search("<extra_id_\d+>", token) is not None for token in sentinel_tokens])
+
+    def test_get_sentinel_token_ids_for_fasttokenizer(self):
+        tokenizer = T5TokenizerFast(SAMPLE_VOCAB, extra_ids=10)
+        self.assertListEqual(sorted(tokenizer.get_sentinel_token_ids()), sorted([i for i in range(1000, 1010)]))