Fix llama tokenizer (#22402)

* draft

* update tokenization limma and conversion script

* more udpates

* initial commit

* style

* default pad to None

* draft tokenization tests

* update test

* update tokenization tests

* nits

* update

* versioning test

* major fix

* fix more testst

* finish fixing special masks

* last nit

* more nits

* add encode decode tests

* add more

* fix token type ids

* style

src/transformers/models/llama/convert_llama_weights_to_hf.py

@@ -20,7 +20,7 @@ import shutil
 
 import torch
 
-from transformers import LlamaConfig, LlamaForCausalLM
+from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
 
 
 """
@@ -233,19 +233,9 @@ def write_model(model_path, input_base_path, model_size):
 
 def write_tokenizer(tokenizer_path, input_tokenizer_path):
     print(f"Fetching the tokenizer from {input_tokenizer_path}.")
-    os.makedirs(tokenizer_path, exist_ok=True)
+    # Initialize the tokenizer based on the `spm` model
-    write_json({}, os.path.join(tokenizer_path, "special_tokens_map.json"))
+    tokenizer = LlamaTokenizer(input_tokenizer_path)
-    write_json(
+    tokenizer.save_pretrained(tokenizer_path)
-        {
-            "bos_token": "",
-            "eos_token": "",
-            "model_max_length": int(1e30),
-            "tokenizer_class": "LlamaTokenizer",
-            "unk_token": "",
-        },
-        os.path.join(tokenizer_path, "tokenizer_config.json"),
-    )
-    shutil.copyfile(input_tokenizer_path, os.path.join(tokenizer_path, "tokenizer.model"))
 
 
 def main():

src/transformers/models/llama/tokenization_llama.py

@@ -25,7 +25,7 @@ from typing import Any, Dict, List, Optional, Tuple
 
 import sentencepiece as spm
 
-from ...tokenization_utils import PreTrainedTokenizer
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
 from ...utils import logging
 
 
@@ -33,7 +33,17 @@ logger = logging.get_logger(__name__)
 
 VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
 
-PRETRAINED_VOCAB_FILES_MAP = {}
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
+    },
+    "tokenizer_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
+    },
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "hf-internal-testing/llama-tokenizer": 2048,
+}
 
 
 class LlamaTokenizer(PreTrainedTokenizer):
@@ -47,6 +57,7 @@ class LlamaTokenizer(PreTrainedTokenizer):
 
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     model_input_names = ["input_ids", "attention_mask"]
 
     def __init__(
@@ -55,51 +66,50 @@ class LlamaTokenizer(PreTrainedTokenizer):
         unk_token="<unk>",
         bos_token="<s>",
         eos_token="</s>",
+        pad_token=None,
         sp_model_kwargs: Optional[Dict[str, Any]] = None,
         add_bos_token=True,
         add_eos_token=False,
-        decode_with_prefix_space=False,
         clean_up_tokenization_spaces=False,
         **kwargs,
     ):
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
         super().__init__(
             bos_token=bos_token,
             eos_token=eos_token,
             unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
             **kwargs,
         )
         self.vocab_file = vocab_file
         self.add_bos_token = add_bos_token
         self.add_eos_token = add_eos_token
-        self.decode_with_prefix_space = decode_with_prefix_space
         self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(vocab_file)
-        self._no_prefix_space_tokens = None
 
-        """ Initialisation"""
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
 
-    @property
+    def __setstate__(self, d):
-    def no_prefix_space_tokens(self):
+        self.__dict__ = d
-        if self._no_prefix_space_tokens is None:
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+        self.sp_model.Load(self.vocab_file)
-            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
-        return self._no_prefix_space_tokens
 
     @property
     def vocab_size(self):
         """Returns vocab size"""
         return self.sp_model.get_piece_size()
 
-    @property
-    def bos_token_id(self) -> Optional[int]:
-        return self.sp_model.bos_id()
-
-    @property
-    def eos_token_id(self) -> Optional[int]:
-        return self.sp_model.eos_id()
-
     def get_vocab(self):
         """Returns vocab as a dict"""
         vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
@@ -119,21 +129,15 @@ class LlamaTokenizer(PreTrainedTokenizer):
         token = self.sp_model.IdToPiece(index)
         return token
 
-    def _maybe_add_prefix_space(self, tokens, decoded):
-        if tokens and tokens[0] not in self.no_prefix_space_tokens:
-            return " " + decoded
-        else:
-            return decoded
-
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
         current_sub_tokens = []
         out_string = ""
         prev_is_special = False
-        for token in tokens:
+        for i, token in enumerate(tokens):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
-                if not prev_is_special:
+                if not prev_is_special and i != 0:
                     out_string += " "
                 out_string += self.sp_model.decode(current_sub_tokens) + token
                 prev_is_special = True
@@ -142,7 +146,6 @@ class LlamaTokenizer(PreTrainedTokenizer):
                 current_sub_tokens.append(token)
                 prev_is_special = False
         out_string += self.sp_model.decode(current_sub_tokens)
-        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
         return out_string
 
     def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
@@ -173,18 +176,13 @@ class LlamaTokenizer(PreTrainedTokenizer):
         return (out_vocab_file,)
 
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        if self.add_bos_token:
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-            bos_token_ids = [self.bos_token_id]
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-        else:
-            bos_token_ids = []
 
-        output = bos_token_ids + token_ids_0
+        output = bos_token_id + token_ids_0 + eos_token_id
 
         if token_ids_1 is not None:
-            output = output + token_ids_1
+            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        if self.add_eos_token:
-            output = output + [self.eos_token_id]
 
         return output
 
@@ -211,28 +209,46 @@ class LlamaTokenizer(PreTrainedTokenizer):
                 token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
             )
 
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
         if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
 
     def create_token_type_ids_from_sequences(
         self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
     ) -> List[int]:
         """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
-        use of token type ids, therefore a list of zeros is returned.
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
 
         Args:
             token_ids_0 (`List[int]`):
-                List of IDs.
+                List of ids.
             token_ids_1 (`List[int]`, *optional*):
                 Optional second list of IDs for sequence pairs.
 
         Returns:
-            `List[int]`: List of zeros.
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
         """
-        eos = [self.eos_token_id]
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
 
         if token_ids_1 is None:
-            return len(token_ids_0 + eos) * [0]
+            return len(cls + token_ids_0 + sep) * [0]
-        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

tests/models/llama/test_tokenization_llama.py

@@ -0,0 +1,412 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# 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 os
+import shutil
+import tempfile
+import unittest
+
+from datasets import load_dataset
+
+from transformers import (
+    SPIECE_UNDERLINE,
+    AddedToken,
+    LlamaTokenizer,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    get_tests_dir,
+    nested_simplify,
+    require_sentencepiece,
+    require_tokenizers,
+    require_torch,
+    slow,
+)
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+if is_torch_available():
+    pass
+
+
+@require_sentencepiece
+@require_tokenizers
+class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    tokenizer_class = LlamaTokenizer
+    test_rust_tokenizer = False
+    test_sentencepiece = True
+    from_pretrained_kwargs = {}
+
+    def setUp(self):
+        super().setUp()
+
+        # We have a SentencePiece fixture for testing
+        tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def test_full_tokenizer(self):
+        tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
+
+        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>",
+                ".",
+            ],
+        )
+
+    @unittest.skip("Let's wait for the fast tokenizer!")
+    def test_save_pretrained(self):
+        self.tokenizers_list += (self.rust_tokenizer_class, "hf-internal-testing/llama-tokenizer", {})
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+
+                tmpdirname2 = tempfile.mkdtemp()
+
+                tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
+                tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
+
+                # Checks it save with the same files + the tokenizer.json file for the fast one
+                self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
+                tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
+                self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
+
+                # Checks everything loads correctly in the same way
+                tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
+                tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
+
+                # Check special tokens are set accordingly on Rust and Python
+                for key in tokenizer_pp.special_tokens_map:
+                    self.assertTrue(hasattr(tokenizer_rp, key))
+
+                shutil.rmtree(tmpdirname2)
+
+                # Save tokenizer rust, legacy_format=True
+                tmpdirname2 = tempfile.mkdtemp()
+
+                tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
+                tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
+
+                # Checks it save with the same files
+                self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
+
+                # Checks everything loads correctly in the same way
+                tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
+                tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
+
+                # Check special tokens are set accordingly on Rust and Python
+                for key in tokenizer_pp.special_tokens_map:
+                    self.assertTrue(hasattr(tokenizer_rp, key))
+
+                shutil.rmtree(tmpdirname2)
+
+                # Save tokenizer rust, legacy_format=False
+                tmpdirname2 = tempfile.mkdtemp()
+
+                tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
+                tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
+
+                # Checks it saved the tokenizer.json file
+                self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
+
+                # Checks everything loads correctly in the same way
+                tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
+                tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
+
+                # Check special tokens are set accordingly on Rust and Python
+                for key in tokenizer_pp.special_tokens_map:
+                    self.assertTrue(hasattr(tokenizer_rp, key))
+
+                shutil.rmtree(tmpdirname2)
+
+    @require_torch
+    def test_batch_tokenization(self):
+        if not self.test_seq2seq:
+            return
+
+        tokenizers = self.get_tokenizers()
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                # Longer text that will definitely require truncation.
+                text = [
+                    " UN Chief Says There Is No Military Solution in Syria",
+                    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
+                    " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
+                    " will only worsen the violence and misery for millions of people.",
+                ]
+                try:
+                    batch = tokenizer(
+                        text=text,
+                        max_length=3,
+                        max_target_length=10,
+                        return_tensors="pt",
+                    )
+                except NotImplementedError:
+                    return
+                self.assertEqual(batch.input_ids.shape[1], 3)
+                # max_target_length will default to max_length if not specified
+                batch = tokenizer(text, max_length=3, return_tensors="pt")
+                self.assertEqual(batch.input_ids.shape[1], 3)
+
+                batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
+                self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
+                self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
+                self.assertNotIn("decoder_input_ids", batch_encoder_only)
+
+    @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
+    def test_save_slow_from_fast_and_reload_fast(self):
+        pass
+
+    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
+                )
+                r_output = tokenizer_r.encode("Hey this is a <special> token")
+
+                special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
+
+                self.assertTrue(special_token_id in r_output)
+
+                if self.test_slow_tokenizer:
+                    tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
+                        pretrained_name,
+                        additional_special_tokens=added_tokens,
+                        **kwargs,  # , from_slow=True <- unfortunately too slow to convert
+                    )
+                    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")
+
+                    cr_output = tokenizer_cr.encode("Hey this is a <special> token")
+
+                    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 cr_output)
+
+    @slow
+    def test_tokenizer_integration(self):
+        # fmt: off
+        expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
+        # fmt: on
+
+        self.tokenizer_integration_test_util(
+            expected_encoding=expected_encoding,
+            model_name="hf-internal-testing/llama-tokenizer",
+            revision="0984d03108b1a041ed679bd253b6519b7e1a4778",
+            padding=False,
+        )
+
+
+@require_torch
+@require_sentencepiece
+@require_tokenizers
+class LlamaIntegrationTest(unittest.TestCase):
+    checkpoint_name = "hf-internal-testing/llama-tokenizer"
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tokenizer: LlamaTokenizer = LlamaTokenizer.from_pretrained(cls.checkpoint_name)
+        cls.rust_tokenizer = cls.tokenizer  # TODO @narsil replace with the rust one
+        cls.pad_token_id = 1
+        return cls
+
+    @require_torch
+    def integration_tests(self):
+        inputs = self.tokenizer(
+            ["The following string should be properly encoded: Hello.", "But ird and ปี   ird   ด"],
+            return_tensors="pt",
+        )
+
+        self.assertEqual(
+            nested_simplify(inputs),
+            {
+                "input_ids": [
+                    [1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
+                    [1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
+                ],
+                "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]],
+            },
+        )
+
+    def test_simple_encode_decode(self):
+        pyth_tokenizer = self.tokenizer
+        rust_tokenizer = self.rust_tokenizer
+
+        self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
+        self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
+        self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
+        self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
+
+        # bytefallback showcase
+        self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
+        self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
+        self.assertEqual(
+            pyth_tokenizer.decode(
+                [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
+            ),
+            "生活的真谛是",
+        )
+        self.assertEqual(
+            rust_tokenizer.decode(
+                [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
+            ),
+            "生活的真谛是",
+        )
+
+        # Inner spaces showcase
+        self.assertEqual(pyth_tokenizer.encode("Hi  Hello"), [1, 6324, 29871, 15043])
+        self.assertEqual(rust_tokenizer.encode("Hi  Hello"), [1, 6324, 29871, 15043])
+        self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi  Hello")
+        self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi  Hello")
+
+        self.assertEqual(pyth_tokenizer.encode("Hi   Hello"), [1, 6324, 259, 15043])
+        self.assertEqual(rust_tokenizer.encode("Hi   Hello"), [1, 6324, 259, 15043])
+        self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi   Hello")
+        self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi   Hello")
+
+        self.assertEqual(pyth_tokenizer.encode(""), [1])
+        self.assertEqual(rust_tokenizer.encode(""), [1])
+
+        self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
+        self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
+
+        self.assertEqual(pyth_tokenizer.encode("  "), [1, 1678])
+        self.assertEqual(rust_tokenizer.encode("  "), [1, 1678])
+
+        self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
+        self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
+
+        self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
+        self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
+
+        self.assertEqual(pyth_tokenizer.encode(""), [1])
+        self.assertEqual(rust_tokenizer.encode(""), [1])
+
+        self.assertEqual(pyth_tokenizer.decode([869]), ".")
+        self.assertEqual(rust_tokenizer.decode([869]), ".")
+
+        self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .")
+        self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .")
+
+    @unittest.skipIf(
+        os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
+        "RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
+    )
+    def test_integration_test_xnli(self):
+        import tqdm
+
+        pyth_tokenizer = self.tokenizer
+        rust_tokenizer = self.rust_tokenizer
+
+        dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
+        for item in tqdm.tqdm(dataset["validation"]):
+            string = item["code"]
+            encoded1 = pyth_tokenizer.encode(string)
+            encoded2 = rust_tokenizer.encode(string)
+
+            self.assertEqual(encoded1, encoded2)
+
+            decoded1 = pyth_tokenizer.decode(encoded1)
+            decoded2 = rust_tokenizer.decode(encoded2)
+
+            self.assertEqual(decoded1, decoded2)
+
+        dataset = load_dataset("xnli", "all_languages")
+
+        for item in tqdm.tqdm(dataset["train"]):
+            for string in item["premise"].values():
+                encoded1 = pyth_tokenizer.encode(string)
+                encoded2 = rust_tokenizer.encode(string)
+
+                self.assertEqual(encoded1, encoded2)
+
+                decoded1 = pyth_tokenizer.decode(encoded1)
+                decoded2 = rust_tokenizer.decode(encoded2)
+
+                self.assertEqual(decoded1, decoded2)

tests/test_tokenization_common.py

@@ -3932,7 +3932,7 @@ class TokenizerTesterMixin:
             tokenizer_fast.save_pretrained(tmp_dir_2)
             tokenizer = BertTokenizer.from_pretrained(tmp_dir_2)
 
-        assert tokenizer_fast.clean_up_tokenization_spaces is False
+        assert tokenizer.clean_up_tokenization_spaces is False
         decoded = tokenizer.decode(tokens)
         assert decoded == "[CLS] this shouldn ' t be ! he ' ll go . [SEP]"