diff --git a/src/transformers/models/siglip/tokenization_siglip.py b/src/transformers/models/siglip/tokenization_siglip.py
index 56d0b0ac70a9a5..83b9b6137c09f2 100644
--- a/src/transformers/models/siglip/tokenization_siglip.py
+++ b/src/transformers/models/siglip/tokenization_siglip.py
@@ -121,6 +121,7 @@ def __init__(
         split_special_tokens=True,
         **kwargs,
     ) -> None:
+        print("Pad token:", pad_token)
         pad_token = AddedToken(pad_token, rstrip=True, lstrip=True)
         unk_token = AddedToken(unk_token, rstrip=True, lstrip=True)
         eos_token = AddedToken(eos_token, rstrip=True, lstrip=True)
diff --git a/tests/models/siglip/test_tokenization_siglip.py b/tests/models/siglip/test_tokenization_siglip.py
new file mode 100644
index 00000000000000..265906818bf479
--- /dev/null
+++ b/tests/models/siglip/test_tokenization_siglip.py
@@ -0,0 +1,563 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import tempfile
+import unittest
+
+from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, SiglipTokenizer, SiglipTokenizerFast
+from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_seqio, require_tokenizers, slow
+from transformers.utils import cached_property, is_tf_available, is_torch_available
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+if is_torch_available():
+    FRAMEWORK = "pt"
+elif is_tf_available():
+    FRAMEWORK = "tf"
+else:
+    FRAMEWORK = "jax"
+
+
+@require_sentencepiece
+@require_tokenizers
+class SiglipTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    tokenizer_class = SiglipTokenizer
+    rust_tokenizer_class = SiglipTokenizerFast
+    test_rust_tokenizer = False
+    test_sentencepiece = True
+
+    def setUp(self):
+        super().setUp()
+
+        # We have a SentencePiece fixture for testing
+        tokenizer = SiglipTokenizer(SAMPLE_VOCAB)
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def test_convert_token_and_id(self):
+        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
+        token = "<s>"
+        token_id = 1
+
+        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
+        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
+
+    def test_get_vocab(self):
+        vocab_keys = list(self.get_tokenizer().get_vocab().keys())
+
+        self.assertEqual(vocab_keys[0], "<unk>")
+        self.assertEqual(vocab_keys[1], "<s>")
+        self.assertEqual(vocab_keys[1100], "<pad>")
+        self.assertEqual(len(vocab_keys), 1_101)
+
+    def test_vocab_size(self):
+        self.assertEqual(self.get_tokenizer().vocab_size, 1000)
+        self.assertEqual(len(self.get_tokenizer()), 1101)
+
+    def test_full_tokenizer(self):
+        tokenizer = SiglipTokenizer(SAMPLE_VOCAB)
+
+        tokens = tokenizer.tokenize("This is a test")
+        self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
+
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
+
+        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
+        self.assertListEqual(
+            tokens,
+            [
+                SPIECE_UNDERLINE + "I",
+                SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b",
+                "or",
+                "n",
+                SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "",
+                "9",
+                "2",
+                "0",
+                "0",
+                "0",
+                ",",
+                SPIECE_UNDERLINE + "and",
+                SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is",
+                SPIECE_UNDERLINE + "f",
+                "al",
+                "s",
+                "é",
+                ".",
+            ],
+        )
+        ids = tokenizer.convert_tokens_to_ids(tokens)
+        self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4])
+
+        back_tokens = tokenizer.convert_ids_to_tokens(ids)
+        self.assertListEqual(
+            back_tokens,
+            [
+                SPIECE_UNDERLINE + "I",
+                SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b",
+                "or",
+                "n",
+                SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "",
+                "<unk>",
+                "2",
+                "0",
+                "0",
+                "0",
+                ",",
+                SPIECE_UNDERLINE + "and",
+                SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is",
+                SPIECE_UNDERLINE + "f",
+                "al",
+                "s",
+                "<unk>",
+                ".",
+            ],
+        )
+
+    @cached_property
+    def siglip_tokenizer(self):
+        return SiglipTokenizer.from_pretrained("t5-base")
+
+    @cached_property
+    def siglip_tokenizer_fast(self):
+        return SiglipTokenizerFast.from_pretrained("t5-base")
+
+    def get_tokenizer(self, **kwargs) -> SiglipTokenizer:
+        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_rust_tokenizer(self, **kwargs) -> SiglipTokenizerFast:
+        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
+
+    def test_rust_and_python_full_tokenizers(self):
+        if not self.test_rust_tokenizer:
+            return
+
+        tokenizer = self.get_tokenizer()
+        rust_tokenizer = self.get_rust_tokenizer()
+
+        sequence = "I was born in 92000, and this is falsé."
+
+        tokens = tokenizer.tokenize(sequence)
+        rust_tokens = rust_tokenizer.tokenize(sequence)
+        self.assertListEqual(tokens, rust_tokens)
+
+        ids = tokenizer.encode(sequence, add_special_tokens=False)
+        rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
+        self.assertListEqual(ids, rust_ids)
+
+        rust_tokenizer = self.get_rust_tokenizer()
+        ids = tokenizer.encode(sequence)
+        rust_ids = rust_tokenizer.encode(sequence)
+        self.assertListEqual(ids, rust_ids)
+
+    def test_eos_treatment(self):
+        tokenizer = self.siglip_tokenizer
+        batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
+        batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
+        self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
+
+    def test_prepare_batch(self):
+        tokenizer = self.siglip_tokenizer
+        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
+        expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id]
+        batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
+        self.assertIsInstance(batch, BatchEncoding)
+
+        if FRAMEWORK != "jax":
+            result = list(batch.input_ids.numpy()[0])
+        else:
+            result = list(batch.input_ids.tolist()[0])
+
+        self.assertListEqual(expected_src_tokens, result)
+
+        self.assertEqual((2, 9), batch.input_ids.shape)
+        self.assertEqual((2, 9), batch.attention_mask.shape)
+
+    def test_empty_target_text(self):
+        tokenizer = self.siglip_tokenizer
+        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
+        batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
+        # check if input_ids are returned and no decoder_input_ids
+        self.assertIn("input_ids", batch)
+        self.assertIn("attention_mask", batch)
+        self.assertNotIn("decoder_input_ids", batch)
+        self.assertNotIn("decoder_attention_mask", batch)
+
+    def test_max_length(self):
+        tokenizer = self.siglip_tokenizer
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        targets = tokenizer(
+            text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
+        )
+        self.assertEqual(32, targets["input_ids"].shape[1])
+
+    def test_outputs_not_longer_than_maxlen(self):
+        tokenizer = self.siglip_tokenizer
+
+        batch = tokenizer(
+            ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK
+        )
+        self.assertIsInstance(batch, BatchEncoding)
+        # Since Siglip does NOT have a max input length,
+        # this test should be changed to the following in Transformers v5:
+        # self.assertEqual(batch.input_ids.shape, (2, 8001))
+        self.assertEqual(batch.input_ids.shape, (2, 512))
+
+    def test_eos_in_input(self):
+        tokenizer = self.siglip_tokenizer
+        src_text = ["A long paragraph for summarization. </s>"]
+        tgt_text = ["Summary of the text. </s>"]
+        expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1]
+        expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]
+
+        batch = tokenizer(src_text, text_target=tgt_text)
+
+        self.assertEqual(expected_src_tokens, batch["input_ids"][0])
+        self.assertEqual(expected_tgt_tokens, batch["labels"][0])
+
+    def test_token_type_ids(self):
+        src_text_1 = ["A first paragraph for summarization."]
+        src_text_2 = ["A second paragraph for summarization."]
+
+        fast_token_type_ids = self.siglip_tokenizer_fast(
+            src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
+        ).token_type_ids
+        slow_token_type_ids = self.siglip_tokenizer(
+            src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
+        ).token_type_ids
+
+        self.assertEqual(slow_token_type_ids, fast_token_type_ids)
+        self.assertEqual(len(slow_token_type_ids[0]), 18)
+
+    def test_fast_and_slow_same_result(self):
+        src_text = "<pad> Today is <unk> nice day </s>"
+        tgt_ids = [0, 1960, 19, 2, 1245, 239, 1]
+        tgt_text = "<pad> Today is<unk> nice day</s>"
+
+        fast_ids = self.siglip_tokenizer_fast(src_text, add_special_tokens=False).input_ids
+        slow_ids = self.siglip_tokenizer(src_text, add_special_tokens=False).input_ids
+        self.assertEqual(tgt_ids, fast_ids)
+        self.assertEqual(tgt_ids, slow_ids)
+
+        fast_text = self.siglip_tokenizer_fast.decode(fast_ids)
+        slow_text = self.siglip_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)
+
+    def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
+        tokenizer_list = []
+        if self.test_slow_tokenizer:
+            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
+
+        if self.test_rust_tokenizer:
+            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
+
+        for tokenizer_class, tokenizer_utils in tokenizer_list:
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                tokenizer_utils.save_pretrained(tmp_dir)
+
+                with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
+                    special_tokens_map = json.load(json_file)
+
+                with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
+                    tokenizer_config = json.load(json_file)
+
+                added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)]
+
+                special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
+                    "an_additional_special_token"
+                ]
+                tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
+                    "an_additional_special_token"
+                ]
+
+                with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
+                    json.dump(special_tokens_map, outfile)
+                with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
+                    json.dump(tokenizer_config, outfile)
+
+                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
+                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
+                # "special_tokens_map.json" files
+                tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
+                    tmp_dir,
+                )
+                self.assertIn(
+                    "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
+                )
+                # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # BySiglipTokenization no vocab
+                self.assertEqual(
+                    ["an_additional_special_token"],
+                    tokenizer_without_change_in_init.convert_ids_to_tokens(
+                        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
+                    ),
+                )
+
+                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
+                new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
+                tokenizer = tokenizer_class.from_pretrained(
+                    tmp_dir,
+                    additional_special_tokens=new_added_tokens,
+                )
+
+                self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
+                self.assertEqual(
+                    ["a_new_additional_special_token"],
+                    tokenizer.convert_ids_to_tokens(
+                        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
+                    ),
+                )
+
+    # overwritten from `test_tokenization_common` since Siglip has no max length
+    def test_pretrained_model_lists(self):
+        # We should have at least one default checkpoint for each tokenizer
+        # We should specify the max input length as well (used in some part to list the pretrained checkpoints)
+        self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
+        self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)
+
+    @slow
+    def test_tokenizer_integration(self):
+        expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # fmt: skip
+
+        self.tokenizer_integration_test_util(
+            expected_encoding=expected_encoding,
+            model_name="t5-base",
+            revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b",
+        )
+
+    def test_some_edge_cases(self):
+        tokenizer = SiglipTokenizer.from_pretrained("t5-base", legacy=False)
+
+        sp_tokens = tokenizer.sp_model.encode("</s>>", out_type=str)
+        self.assertEqual(sp_tokens, ["<", "/", "s", ">", ">"])
+        tokens = tokenizer.tokenize("</s>>")
+        self.assertNotEqual(sp_tokens, tokens)
+        self.assertEqual(tokens, ["</s>", ">"])
+
+        tokens = tokenizer.tokenize("")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str))
+
+        tokens = tokenizer.tokenize(" ")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str))
+
+        tokens = tokenizer.tokenize("▁")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
+
+        tokens = tokenizer.tokenize(" ▁")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
+
+
+@require_sentencepiece
+@require_tokenizers
+class CommonSpmIntegrationTests(unittest.TestCase):
+    """
+    A class that regroups important test to make sure that we properly handle the special tokens.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        tokenizer = SiglipTokenizer(SAMPLE_VOCAB, extra_ids=0, legacy=False)
+        tokenizer.add_special_tokens(
+            {"additional_special_tokens": [AddedToken("<extra_id_0>", rstrip=False, lstrip=False)]}
+        )
+        # TODO ArthurZ the above is necessary as addedTokens / intialization sucks. Trie is not correctly created
+        # So the extra ids are split....
+        cls.tokenizer = tokenizer
+
+    def test_add_dummy_prefix(self):
+        # make sure `'▁'` is prepended, and outputs match sp_model's
+        # `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
+        input_ids = self.tokenizer.encode(". Hello", add_special_tokens=False)
+        self.assertEqual(input_ids, [7, 4, 156, 86, 20])
+        sp_encode = self.tokenizer.sp_model.encode(". Hello")
+        self.assertEqual(input_ids, [7] + sp_encode)
+        tokens = self.tokenizer.tokenize(". Hello")
+        self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
+
+        tokens = self.tokenizer.tokenize("")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str))
+
+        tokens = self.tokenizer.tokenize(" ")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str))
+
+        tokens = self.tokenizer.tokenize("▁")
+        self.assertEqual(tokens, [])
+        self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str))
+
+    def test_remove_extra_whitespaces(self):
+        # make sure the extra spaces are eaten
+        # sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute
+        input_ids = self.tokenizer.encode("       . Hello", add_special_tokens=False)
+        self.assertEqual(input_ids, [7, 4, 156, 86, 20])
+        sp_encode = self.tokenizer.sp_model.encode("       . Hello")
+        self.assertEqual(input_ids, [7] + sp_encode)
+        tokens = self.tokenizer.tokenize(" . Hello")
+        self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
+
+        # `'▁'` is also a whitespace
+        input_ids = self.tokenizer.encode("▁He is not")
+        self.assertEqual(input_ids, [156, 46, 44, 2])
+        tokens = self.tokenizer.tokenize("▁He is not")
+        self.assertEqual(tokens, ["▁He", "▁is", "▁not"])  # no extra space added
+
+        input_ids = self.tokenizer.encode("▁He is not<extra_id_0>             ▁He")
+        # here t5x does not eat with lstrip, so there is and extra ▁He in the original one
+        self.assertEqual(input_ids, [156, 46, 44, 1001, 156, 2])
+        tokens = self.tokenizer.tokenize("▁He is not<extra_id_0>              ▁He")
+        self.assertEqual(tokens, ["▁He", "▁is", "▁not", "<extra_id_0>", "▁He"])  # spaces are eaten by spm
+        # make sure that the output after the extra id is the same as if
+        # extra_id was not there
+        input_ids = self.tokenizer.encode("▁He is not             ▁He")
+        self.assertEqual(input_ids, [156, 46, 44, 156, 2])
+        tokens = self.tokenizer.tokenize("▁He is not              ▁He")
+        self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"])  # spaces are eaten by spm even if not start
+
+    def test_character_after_special_token(self):
+        # Make sure that `tokenizer.tokenize` is similar to
+        # adding the equivalent special token to the vocab
+        input_ids = self.tokenizer.encode("Hey <extra_id_0>I")
+        self.assertEqual(input_ids, [156, 30, 1001, 100, 2])
+        tokens = self.tokenizer.tokenize("Hey <extra_id_0>I")
+        self.assertEqual(tokens, ["▁He", "y", "<extra_id_0>", "I"])
+
+        input_ids = self.tokenizer.encode("Hello, <extra_id_0>,")
+        self.assertEqual(input_ids, [156, 86, 20, 3, 1001, 3, 2])
+        tokens = self.tokenizer.tokenize("Hello, <extra_id_0>,")
+        self.assertEqual(tokens, ["▁He", "ll", "o", ",", "<extra_id_0>", ","])
+
+    def test_special_tokens_strip(self):
+        input_ids = self.tokenizer.encode(" <extra_id_0> ,")
+        self.assertEqual(input_ids, [1001, 7, 3, 2])
+        tokens = self.tokenizer.tokenize(" <extra_id_0> ,")
+        # spaces are not longer eaten by rstrip and lstrip
+        self.assertEqual(tokens, ["<extra_id_0>", "▁", ","])
+
+        # test with a begin of word like `▁He`
+        input_ids = self.tokenizer.encode("No <extra_id_0> He")
+        self.assertEqual(input_ids, [284, 1001, 156, 2])
+        # spaces are eaten by rstrip / lstrip, so this is expected. Don't strip otherwise you break
+        tokens = self.tokenizer.tokenize("No <extra_id_0> He")
+        self.assertEqual(tokens, ["▁No", "<extra_id_0>", "▁He"])
+
+        # Make sure this does not happen if we don't strip
+        tokenizer = SiglipTokenizer(SAMPLE_VOCAB, extra_ids=0)
+        tokenizer.add_special_tokens({"bos_token": AddedToken("<bos>")})
+        input_ids = tokenizer.encode("No <bos> He")
+        self.assertEqual(input_ids, [284, 1001, 156, 2])
+        tokens = tokenizer.tokenize("No <bos> He")
+        # the first `' '` after `'No'` is eaten by spm:
+        self.assertEqual(tokenizer.sp_model.encode("No         ", out_type=str), ["▁No"])
+        self.assertEqual(tokens, ["▁No", "<bos>", "▁He"])
+
+    @require_seqio
+    @unittest.skipIf(
+        os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
+        "RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
+    )
+    def test_integration_seqio(self):
+        from datasets import load_dataset
+        from seqio import SentencePieceVocabulary
+
+        ds = load_dataset("xnli", "all_languages", split="train+test+validation")
+
+        # TODO @ArthurZucker fix the 3 commented tests with #23909
+        input_texts = [
+            "Bonjour <extra_id_0>.",
+            # "Bonjour<extra_id_0>.",  # this will fail. In Siglip the special token has to be at the end.
+            # because in Siglip they add `_<extra_id_0>` to the vocab, not `<extra_id_0>`.
+            "                   Hey <extra_id_0>I love you",
+            # "Hey <extra_id_0> I love you", # this will fail, we strip left, to _I vs I
+            # "Hey <extra_id_0>▁He", # this will fail for the same reason, we replace `_` then strip
+        ]
+
+        import tqdm
+
+        # Test with umt5
+        vocab_path = "gs://t5-data/vocabs/umt5.256000/sentencepiece.model"
+        t5x_tokenizer = SentencePieceVocabulary(vocab_path, extra_ids=300)
+        hf_tokenizer = SiglipTokenizer.from_pretrained("google/umt5-small", legacy=False)
+        for text in input_texts:
+            self.assertEqual(
+                hf_tokenizer.encode(text, add_special_tokens=False), t5x_tokenizer.tokenizer.tokenize(text), f"{text}"
+            )
+        for texts in tqdm.tqdm(ds["premise"]):
+            for text in texts:
+                self.assertEqual(
+                    hf_tokenizer.encode(text, add_special_tokens=False),
+                    t5x_tokenizer.tokenizer.tokenize(text),
+                    f"{text}",
+                )
+
+        # Test with Siglip
+        hf_tokenizer = SiglipTokenizer.from_pretrained("t5-small")
+        vocab_path = "gs://t5-data/vocabs/cc_all.32000/sentencepiece.model"
+        t5x_tokenizer = SentencePieceVocabulary(vocab_path, extra_ids=300)
+        for text in input_texts:
+            self.assertEqual(
+                hf_tokenizer.encode(text, add_special_tokens=False), t5x_tokenizer.tokenizer.tokenize(text), f"{text}"
+            )
+        for texts in tqdm.tqdm(ds["premise"]):
+            for text in texts:
+                self.assertEqual(
+                    hf_tokenizer.encode(text, add_special_tokens=False),
+                    t5x_tokenizer.tokenizer.tokenize(text),
+                    f"{text}",
+                )
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