[Core Tokenization] Support a fix for spm fast models (#26678)

* fix

* last attempt

* current work

* fix forward compatibility

* save all special tokens

* current state

* revert additional changes

* updates

* remove tokenizer.model

* add a test and the fix

* nit

* revert one more break

* fix typefield issue

* quality

* more tests

* fix fields for FC

* more nits?

* new additional changes

* how

* some updates

* the fix

* where do we stand

* nits

* nits

* revert unrelated changes

* nits nits nits

* styling

* don't break llama just yet

* revert llama changes

* safe arg check

* fixup

* Add a test for T5

* Necessary changes

* Tests passing, added tokens need to not be normalized. If the added tokens are normalized, it will the stripping which seems to be unwanted for a normal functioning

* Add even more tests, when normalization is set to True (which does not work 😓 )

* Add even more tests, when normalization is set to True (which does not work 😓 )

* Update to main

* nits

* fmt

* more and more test

* comments

* revert change as tests are failing

* make the test more readble

* nits

* refactor the test

* nit

* updates

* simplify

* style

* style

* style convert slow

* Update src/transformers/convert_slow_tokenizer.py

src/transformers/convert_slow_tokenizer.py

@@ -552,15 +552,22 @@ class SpmConverter(Converter):
 
     def normalizer(self, proto):
         precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
+        _normalizers = [
+            normalizers.Strip(left=False, right=True),  # stripping is important
+            normalizers.Replace(Regex(" {2,}"), "▁"),
+        ]
         if not precompiled_charsmap:
-            return normalizers.Sequence([normalizers.Replace(Regex(" {2,}"), " ")])
+            return normalizers.Sequence(_normalizers)
         else:
-            return normalizers.Sequence(
+            return normalizers.Sequence([normalizers.Precompiled(precompiled_charsmap)] + _normalizers)
-                [normalizers.Precompiled(precompiled_charsmap), normalizers.Replace(Regex(" {2,}"), " ")]
-            )
 
     def pre_tokenizer(self, replacement, add_prefix_space):
-        return pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)
+        prepend_scheme = "always"
+        if hasattr(self.original_tokenizer, "legacy") and not self.original_tokenizer.legacy:
+            prepend_scheme = "first"
+        return pre_tokenizers.Metaspace(
+            replacement=replacement, add_prefix_space=add_prefix_space, prepend_scheme=prepend_scheme
+        )
 
     def post_processor(self):
         return None

tests/models/t5/test_tokenization_t5.py

@@ -424,6 +424,41 @@ class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         self.assertEqual(tokens, [])
         self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
 
+    def test_fast_slow_edge_cases(self):
+        # We are testing spaces before and spaces after special tokens + space transformations
+        slow_tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False)
+        fast_tokenizer = T5TokenizerFast.from_pretrained("t5-base", legacy=False, from_slow=True)
+        slow_tokenizer.add_tokens(AddedToken("<new_token_test_>", rstrip=False, lstrip=False, normalized=False))
+        fast_tokenizer.add_tokens(AddedToken("<new_token_test_>", rstrip=False, lstrip=False, normalized=False))
+
+        edge_case = "Hey!<new_token_test_>. How</s>Hey <new_token_test_>!"
+        EXPECTED_SLOW = ["▁Hey", "!", "<new_token_test_>", ".", "▁How", "</s>", "He", "y", "<new_token_test_>", "!"]  # fmt: skip
+        with self.subTest(f"slow {edge_case} normalized = False"):
+            self.assertEqual(slow_tokenizer.tokenize(edge_case), EXPECTED_SLOW)
+        with self.subTest(f"Fast {edge_case} normalized = False"):
+            self.assertEqual(fast_tokenizer.tokenize(edge_case), EXPECTED_SLOW)
+
+        hard_case = "Hey! <new_token_test_>. How</s>   Hey   <new_token_test_>  !     .     "
+        EXPECTED_SLOW = ["▁Hey", "!", "<new_token_test_>", ".", "▁How", "</s>", "▁Hey", "<new_token_test_>", "▁", "!", "▁", "."]  # fmt: skip
+        with self.subTest(f"slow {edge_case} normalized = False"):
+            self.assertEqual(slow_tokenizer.tokenize(hard_case), EXPECTED_SLOW)
+        with self.subTest(f"fast {edge_case} normalized = False"):
+            self.assertEqual(fast_tokenizer.tokenize(hard_case), EXPECTED_SLOW)
+
+        fast_tokenizer = T5TokenizerFast.from_pretrained("t5-base", legacy=False, from_slow=True)
+        fast_tokenizer.add_tokens(AddedToken("<new_token_test_>", rstrip=False, lstrip=False, normalized=True))
+
+        # `normalized=True` is the default normalization scheme when adding a token. Normalize -> don't strip the space.
+        # the issue now is that our slow tokenizer should NOT strip the space if we want to simulate sentencepiece token addition.
+
+        EXPECTED_FAST = ["▁Hey", "!", "<new_token_test_>", ".", "▁How", "</s>", "He", "y", "▁", "<new_token_test_>", "!"]  # fmt: skip
+        with self.subTest(f"fast {edge_case} normalized = True"):
+            self.assertEqual(fast_tokenizer.tokenize(edge_case), EXPECTED_FAST)
+
+        EXPECTED_FAST = ['▁Hey', '!', '▁', '<new_token_test_>', '.', '▁How', '</s>', '▁Hey','▁', '<new_token_test_>', '▁', '!', '▁', '.']  # fmt: skip
+        with self.subTest(f"fast {edge_case} normalized = False"):
+            self.assertEqual(fast_tokenizer.tokenize(hard_case), EXPECTED_FAST)
+
 
 @require_sentencepiece
 @require_tokenizers