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test_tokenization_common.py 202 KB
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# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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.
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import inspect
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import itertools
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import json
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import os
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import pickle
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import re
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import shutil
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import sys
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import tempfile
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import unittest
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import unittest.mock as mock
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from collections import OrderedDict
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from itertools import takewhile
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union
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from huggingface_hub import HfFolder, delete_repo, set_access_token
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from parameterized import parameterized
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from requests.exceptions import HTTPError
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from transformers import (
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    AlbertTokenizer,
    AlbertTokenizerFast,
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    AutoTokenizer,
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    BertTokenizer,
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    BertTokenizerFast,
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    PreTrainedTokenizer,
    PreTrainedTokenizerBase,
    PreTrainedTokenizerFast,
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    SpecialTokensMixin,
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    Trainer,
    TrainingArguments,
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    is_tf_available,
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    is_tokenizers_available,
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    is_torch_available,
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    logging,
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)
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from transformers.testing_utils import (
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    TOKEN,
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    USER,
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    check_json_file_has_correct_format,
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    get_tests_dir,
    is_pt_tf_cross_test,
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    is_staging_test,
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    require_tf,
    require_tokenizers,
    require_torch,
    slow,
)
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from transformers.tokenization_utils import AddedToken, Trie
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if is_torch_available():
    import torch.nn as nn


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if TYPE_CHECKING:
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    from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel
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sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
    from test_module.custom_tokenization_fast import CustomTokenizerFast


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logger = logging.get_logger(__name__)

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NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]

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SMALL_TRAINING_CORPUS = [
    ["This is the first sentence.", "This is the second one."],
    ["This sentence (contains #) over symbols and numbers 12 3.", "But not this one."],
]

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def filter_non_english(_, pretrained_name: str):
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    """Filter all the model for non-english language"""
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    return not any([lang in pretrained_name for lang in NON_ENGLISH_TAGS])


def filter_roberta_detectors(_, pretrained_name: str):
    return "detector" not in pretrained_name


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def merge_model_tokenizer_mappings(
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    model_mapping: Dict["PretrainedConfig", Union["PreTrainedModel", "TFPreTrainedModel"]],
    tokenizer_mapping: Dict["PretrainedConfig", Tuple["PreTrainedTokenizer", "PreTrainedTokenizerFast"]],
) -> Dict[
    Union["PreTrainedTokenizer", "PreTrainedTokenizerFast"],
    Tuple["PretrainedConfig", Union["PreTrainedModel", "TFPreTrainedModel"]],
]:
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    configurations = list(model_mapping.keys())
    model_tokenizer_mapping = OrderedDict([])

    for configuration in configurations:
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        if configuration in model_mapping and configuration in tokenizer_mapping:
            model = model_mapping[configuration]
            tokenizer = tokenizer_mapping[configuration][0]
            tokenizer_fast = tokenizer_mapping[configuration][1]

            model_tokenizer_mapping.update({tokenizer: (configuration, model)})
            if tokenizer_fast is not None:
                model_tokenizer_mapping.update({tokenizer_fast: (configuration, model)})
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    return model_tokenizer_mapping


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class TokenizerTesterMixin:
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    tokenizer_class = None
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    rust_tokenizer_class = None
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    test_slow_tokenizer = True
    test_rust_tokenizer = True
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    space_between_special_tokens = False
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    from_pretrained_kwargs = None
    from_pretrained_filter = None
    from_pretrained_vocab_key = "vocab_file"
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    test_seq2seq = True
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    # set to True to test a sentencepiece tokenizer
    test_sentencepiece = False

    # set to True to ignore casing when testing a sentencepiece tokenizer
    # test_sentencepiece must also be set to True
    test_sentencepiece_ignore_case = False

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    def setUp(self) -> None:
        # Tokenizer.filter makes it possible to filter which Tokenizer to case based on all the
        # information available in Tokenizer (name, rust class, python class, vocab key name)
        if self.test_rust_tokenizer:
            tokenizers_list = [
                (
                    self.rust_tokenizer_class,
                    pretrained_name,
                    self.from_pretrained_kwargs if self.from_pretrained_kwargs is not None else {},
                )
                for pretrained_name in self.rust_tokenizer_class.pretrained_vocab_files_map[
                    self.from_pretrained_vocab_key
                ].keys()
                if self.from_pretrained_filter is None
                or (self.from_pretrained_filter is not None and self.from_pretrained_filter(pretrained_name))
            ]
            self.tokenizers_list = tokenizers_list[:1]  # Let's just test the first pretrained vocab for speed
        else:
            self.tokenizers_list = []
        with open(f"{get_tests_dir()}/fixtures/sample_text.txt", encoding="utf-8") as f_data:
            self._data = f_data.read().replace("\n\n", "\n").strip()
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        self.tmpdirname = tempfile.mkdtemp()
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    def tearDown(self):
        shutil.rmtree(self.tmpdirname)
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    def get_input_output_texts(self, tokenizer):
        input_txt = self.get_clean_sequence(tokenizer)[0]
        return input_txt, input_txt

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    def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> Tuple[str, list]:
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        toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))]
        toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
        toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], add_special_tokens=False), toks))
        if max_length is not None and len(toks) > max_length:
            toks = toks[:max_length]
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        if min_length is not None and len(toks) < min_length and len(toks) > 0:
            while len(toks) < min_length:
                toks = toks + toks
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        # toks_str = [t[1] for t in toks]
        toks_ids = [t[0] for t in toks]

        # Ensure consistency
        output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
        if " " not in output_txt and len(toks_ids) > 1:
            output_txt = (
                tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
                + " "
                + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
            )
        if with_prefix_space:
            output_txt = " " + output_txt
        output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
        return output_txt, output_ids

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    def get_tokenizers(self, fast=True, **kwargs) -> List[PreTrainedTokenizerBase]:
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        if fast and self.test_rust_tokenizer and self.test_slow_tokenizer:
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            return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
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        elif fast and self.test_rust_tokenizer:
            return [self.get_rust_tokenizer(**kwargs)]
        elif self.test_slow_tokenizer:
            return [self.get_tokenizer(**kwargs)]
        else:
            raise ValueError("This tokenizer class has no tokenizer to be tested.")
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    def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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    def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast:
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        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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    def tokenizer_integration_test_util(
        self,
        expected_encoding: Dict,
        model_name: str,
        revision: str = None,
        sequences: List[str] = None,
        decode_kwargs: Dict[str, Any] = None,
        padding: bool = True,
    ):
        """
        Util for integration test.

        Text is tokenized and then reverted back to text. Both results are then checked.

        Args:
            expected_encoding:
                The expected result of the tokenizer output.
            model_name:
                The model name of the tokenizer to load and use.
            revision:
                The full git revision number of the model. This is to pin the
                tokenizer config and to avoid that tests start to fail if the
                config gets changed upstream.
            sequences:
                Can overwrite the texts that are used to check the tokenizer.
                This is useful if the tokenizer supports non english languages
                like france.
            decode_kwargs:
                Additional args for the ``decode`` function which reverts the
                tokenized text back to a string.
            padding:
                Activates and controls padding of the tokenizer.
        """
        decode_kwargs = {} if decode_kwargs is None else decode_kwargs

        if sequences is None:
            sequences = [
                "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
                "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
                "Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained "
                "models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
                "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
                "conditioning on both left and right context in all layers.",
                "The quick brown fox jumps over the lazy dog.",
            ]

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        if self.test_sentencepiece_ignore_case:
            sequences = [sequence.lower() for sequence in sequences]

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        tokenizer_classes = [self.tokenizer_class]
        if self.test_rust_tokenizer:
            tokenizer_classes.append(self.rust_tokenizer_class)

        for tokenizer_class in tokenizer_classes:
            tokenizer = tokenizer_class.from_pretrained(
                model_name,
                revision=revision,  # to pin the tokenizer version
            )

            encoding = tokenizer(sequences, padding=padding)
            decoded_sequences = [
                tokenizer.decode(seq, skip_special_tokens=True, **decode_kwargs) for seq in encoding["input_ids"]
            ]

            encoding_data = encoding.data
            self.assertDictEqual(encoding_data, expected_encoding)

            for expected, decoded in zip(sequences, decoded_sequences):
                if self.test_sentencepiece_ignore_case:
                    expected = expected.lower()
                self.assertEqual(expected, decoded)
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    def assert_padded_input_match(self, input_r: list, input_p: list, max_length: int, pad_token_id: int):
        # Ensure we match max_length
        self.assertEqual(len(input_r), max_length)
        self.assertEqual(len(input_p), max_length)

        # Ensure the number of padded tokens is the same
        padded_tokens_r = list(takewhile(lambda i: i == pad_token_id, reversed(input_r)))
        padded_tokens_p = list(takewhile(lambda i: i == pad_token_id, reversed(input_p)))
        self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)

    def assert_batch_padded_input_match(
        self,
        input_r: dict,
        input_p: dict,
        max_length: int,
        pad_token_id: int,
        model_main_input_name: str = "input_ids",
    ):
        for i_r in input_r.values():
            self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
                len(i_r[1]), max_length
            )
            self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
                len(i_r[1]), max_length
            )

        for i_r, i_p in zip(input_r[model_main_input_name], input_p[model_main_input_name]):
            self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)

        for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
            self.assertSequenceEqual(i_r, i_p)

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    @staticmethod
    def convert_batch_encode_plus_format_to_encode_plus(batch_encode_plus_sequences):
        # Switch from batch_encode_plus format:   {'input_ids': [[...], [...]], ...}
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        # to the list of examples/ encode_plus format: [{'input_ids': [...], ...}, {'input_ids': [...], ...}]
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        return [
            {value: batch_encode_plus_sequences[value][i] for value in batch_encode_plus_sequences.keys()}
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            for i in range(len(batch_encode_plus_sequences["input_ids"]))
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        ]

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    # TODO: this test can be combined with `test_sentencepiece_tokenize_and_convert_tokens_to_string` after the latter is extended to all tokenizers.
    def test_tokenize_special_tokens(self):
        """Test `tokenize` with special tokens."""
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        tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                SPECIAL_TOKEN_1 = "[SPECIAL_TOKEN_1]"
                SPECIAL_TOKEN_2 = "[SPECIAL_TOKEN_2]"

                # TODO:
                # Can we combine `unique_no_split_tokens` and `all_special_tokens`(and properties related to it)
                # with one variable(property) for a better maintainability?

                # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=True)
                # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})

                token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1)
                token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2)

                self.assertEqual(len(token_1), 1)
                self.assertEqual(len(token_2), 1)
                self.assertEqual(token_1[0], SPECIAL_TOKEN_1)
                self.assertEqual(token_2[0], SPECIAL_TOKEN_2)

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    # TODO: this test could be extended to all tokenizers - not just the sentencepiece
    def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
        """Test ``_tokenize`` and ``convert_tokens_to_string``."""
        if not self.test_sentencepiece:
            return

        tokenizer = self.get_tokenizer()
        text = "This is text to test the tokenizer."

        if self.test_sentencepiece_ignore_case:
            text = text.lower()

        tokens = tokenizer.tokenize(text)

        self.assertTrue(len(tokens) > 0)

        # check if converting back to original text works
        reverse_text = tokenizer.convert_tokens_to_string(tokens)

        if self.test_sentencepiece_ignore_case:
            reverse_text = reverse_text.lower()

        self.assertEqual(reverse_text, text)

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    def test_subword_regularization_tokenizer(self) -> None:
        if not self.test_sentencepiece:
            return

        # Subword regularization is only available for the slow tokenizer.
        sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
        tokenizer = self.get_tokenizer(sp_model_kwargs=sp_model_kwargs)

        self.assertTrue(hasattr(tokenizer, "sp_model_kwargs"))
        self.assertIsNotNone(tokenizer.sp_model_kwargs)
        self.assertTrue(isinstance(tokenizer.sp_model_kwargs, dict))
        self.assertEqual(tokenizer.sp_model_kwargs, sp_model_kwargs)
        self.check_subword_sampling(tokenizer)

    def test_pickle_subword_regularization_tokenizer(self) -> None:
        if not self.test_sentencepiece:
            return

        """Google pickle __getstate__ __setstate__ if you are struggling with this."""
        # Subword regularization is only available for the slow tokenizer.
        sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
        tokenizer = self.get_tokenizer(sp_model_kwargs=sp_model_kwargs)
        tokenizer_bin = pickle.dumps(tokenizer)
        del tokenizer
        tokenizer_new = pickle.loads(tokenizer_bin)

        self.assertTrue(hasattr(tokenizer_new, "sp_model_kwargs"))
        self.assertIsNotNone(tokenizer_new.sp_model_kwargs)
        self.assertTrue(isinstance(tokenizer_new.sp_model_kwargs, dict))
        self.assertEqual(tokenizer_new.sp_model_kwargs, sp_model_kwargs)
        self.check_subword_sampling(tokenizer_new)

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    def test_save_sentencepiece_tokenizer(self) -> None:
        if not self.test_sentencepiece or not self.test_slow_tokenizer:
            return
        # We want to verify that we will be able to save the tokenizer even if the original files that were used to
        # build the tokenizer have been deleted in the meantime.
        text = "This is text to test the tokenizer."

        tokenizer_slow_1 = self.get_tokenizer()
        encoding_tokenizer_slow_1 = tokenizer_slow_1(text)

        tmpdirname_1 = tempfile.mkdtemp()
        tmpdirname_2 = tempfile.mkdtemp()

        tokenizer_slow_1.save_pretrained(tmpdirname_1)
        tokenizer_slow_2 = self.tokenizer_class.from_pretrained(tmpdirname_1)
        encoding_tokenizer_slow_2 = tokenizer_slow_2(text)

        shutil.rmtree(tmpdirname_1)
        tokenizer_slow_2.save_pretrained(tmpdirname_2)

        tokenizer_slow_3 = self.tokenizer_class.from_pretrained(tmpdirname_2)
        encoding_tokenizer_slow_3 = tokenizer_slow_3(text)
        shutil.rmtree(tmpdirname_2)

        self.assertEqual(encoding_tokenizer_slow_1, encoding_tokenizer_slow_2)
        self.assertEqual(encoding_tokenizer_slow_1, encoding_tokenizer_slow_3)

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    def test_model_input_names_signature(self):
        accepted_model_main_input_names = [
            "input_ids",  # nlp models
            "input_values",  # speech models
        ]

        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            # first name of model_input_names has to correspond to main model input name
            # to make sure `tokenizer.pad(...)` works correctly
            self.assertTrue(tokenizer.model_input_names[0] in accepted_model_main_input_names)

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    def test_rust_tokenizer_signature(self):
        if not self.test_rust_tokenizer:
            return

        signature = inspect.signature(self.rust_tokenizer_class.__init__)

        self.assertIn("tokenizer_file", signature.parameters)
        self.assertIsNone(signature.parameters["tokenizer_file"].default)

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    def test_tokenizer_slow_store_full_signature(self):
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        if not self.test_slow_tokenizer:
            return

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        signature = inspect.signature(self.tokenizer_class.__init__)
        tokenizer = self.get_tokenizer()

        for parameter_name, parameter in signature.parameters.items():
            if parameter.default != inspect.Parameter.empty:
                self.assertIn(parameter_name, tokenizer.init_kwargs)

    def test_tokenizer_fast_store_full_signature(self):
        if not self.test_rust_tokenizer:
            return

        signature = inspect.signature(self.rust_tokenizer_class.__init__)
        tokenizer = self.get_rust_tokenizer()

        for parameter_name, parameter in signature.parameters.items():
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            if parameter.default != inspect.Parameter.empty and parameter_name not in [
                "vocab_file",
                "merges_file",
                "tokenizer_file",
            ]:
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                self.assertIn(parameter_name, tokenizer.init_kwargs)

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    def test_rust_and_python_full_tokenizers(self):
        if not self.test_rust_tokenizer:
            return

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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

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        tokenizer = self.get_tokenizer()
        rust_tokenizer = self.get_rust_tokenizer()

        sequence, _ = self.get_input_output_texts(tokenizer)

        # We don't have an exact equivalence on `tokenize()` between Rust and Slow
        # Slow tokenizer only split tokens, Rust tokenizers will replace with <unk>
        # 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)

        ids = tokenizer.encode(sequence, add_special_tokens=True)
        rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=True)
        self.assertListEqual(ids, rust_ids)

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    def test_tokenizers_common_properties(self):
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        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                attributes_list = [
                    "bos_token",
                    "eos_token",
                    "unk_token",
                    "sep_token",
                    "pad_token",
                    "cls_token",
                    "mask_token",
                ]
                for attr in attributes_list:
                    self.assertTrue(hasattr(tokenizer, attr))
                    self.assertTrue(hasattr(tokenizer, attr + "_id"))

                self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
                self.assertTrue(hasattr(tokenizer, "additional_special_tokens_ids"))

                attributes_list = [
                    "model_max_length",
                    "init_inputs",
                    "init_kwargs",
                ]
                if not isinstance(tokenizer, PreTrainedTokenizerFast):
                    attributes_list += [
                        "added_tokens_encoder",
                        "added_tokens_decoder",
                    ]
                for attr in attributes_list:
                    self.assertTrue(hasattr(tokenizer, attr))
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    def test_tokenizers_common_ids_setters(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                attributes_list = [
                    "bos_token",
                    "eos_token",
                    "unk_token",
                    "sep_token",
                    "pad_token",
                    "cls_token",
                    "mask_token",
                ]

                vocab = tokenizer.get_vocab()
                token_id_to_test_setters = next(iter(vocab.values()))
                token_to_test_setters = tokenizer.convert_ids_to_tokens(
                    token_id_to_test_setters, skip_special_tokens=False
                )

                for attr in attributes_list:
                    setattr(tokenizer, attr + "_id", None)
                    self.assertEqual(getattr(tokenizer, attr), None)
                    self.assertEqual(getattr(tokenizer, attr + "_id"), None)

                    setattr(tokenizer, attr + "_id", token_id_to_test_setters)
                    self.assertEqual(getattr(tokenizer, attr), token_to_test_setters)
                    self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters)

                setattr(tokenizer, "additional_special_tokens_ids", [])
                self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [])
                self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [])

                setattr(tokenizer, "additional_special_tokens_ids", [token_id_to_test_setters])
                self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [token_to_test_setters])
                self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [token_id_to_test_setters])

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    @parameterized.expand([(True,), (False,)])
    def test_tokenizers_special_tokens_properties_unset(self, verbose):
        tokenizers = self.get_tokenizers(verbose=verbose)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                attributes_list = [
                    "bos_token",
                    "eos_token",
                    "unk_token",
                    "sep_token",
                    "pad_token",
                    "cls_token",
                    "mask_token",
                    "additional_special_tokens",
                ]
                for attr in attributes_list:
                    setattr(tokenizer, attr, None)
                    self.assertIsNone(getattr(tokenizer, attr))

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    def test_save_and_load_tokenizer(self):
        # safety check on max_len default value so we are sure the test works
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        tokenizers = self.get_tokenizers()
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                self.assertNotEqual(tokenizer.model_max_length, 42)
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        # Now let's start the test
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        tokenizers = self.get_tokenizers()
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                # Isolate this from the other tests because we save additional tokens/etc
                tmpdirname = tempfile.mkdtemp()

                sample_text = " He is very happy, UNwant\u00E9d,running"
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                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
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                before_vocab = tokenizer.get_vocab()
                tokenizer.save_pretrained(tmpdirname)

                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
                after_vocab = after_tokenizer.get_vocab()
                self.assertListEqual(before_tokens, after_tokens)
                self.assertDictEqual(before_vocab, after_vocab)

                shutil.rmtree(tmpdirname)
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        tokenizers = self.get_tokenizers(model_max_length=42)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Isolate this from the other tests because we save additional tokens/etc
                tmpdirname = tempfile.mkdtemp()

                sample_text = " He is very happy, UNwant\u00E9d,running"
                tokenizer.add_tokens(["bim", "bambam"])
                additional_special_tokens = tokenizer.additional_special_tokens
                additional_special_tokens.append("new_additional_special_token")
                tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
                before_vocab = tokenizer.get_vocab()
                tokenizer.save_pretrained(tmpdirname)
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                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
                after_vocab = after_tokenizer.get_vocab()
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                self.assertListEqual(before_tokens, after_tokens)
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                self.assertDictEqual(before_vocab, after_vocab)
                self.assertIn("bim", after_vocab)
                self.assertIn("bambam", after_vocab)
                self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
                self.assertEqual(after_tokenizer.model_max_length, 42)
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                tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
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                self.assertEqual(tokenizer.model_max_length, 43)
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                shutil.rmtree(tmpdirname)

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        # Test that we can also use the non-legacy saving format for fast tokenizers
        tokenizers = self.get_tokenizers(model_max_length=42)
        for tokenizer in tokenizers:
            if not tokenizer.is_fast:
                continue
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Isolate this from the other tests because we save additional tokens/etc
                tmpdirname = tempfile.mkdtemp()

                sample_text = " He is very happy, UNwant\u00E9d,running"
                tokenizer.add_tokens(["bim", "bambam"])
                additional_special_tokens = tokenizer.additional_special_tokens
                additional_special_tokens.append("new_additional_special_token")
                tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
                before_vocab = tokenizer.get_vocab()
                tokenizer.save_pretrained(tmpdirname)

                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
                after_vocab = after_tokenizer.get_vocab()
                self.assertListEqual(before_tokens, after_tokens)
                self.assertDictEqual(before_vocab, after_vocab)
                self.assertIn("bim", after_vocab)
                self.assertIn("bambam", after_vocab)
                self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
                self.assertEqual(after_tokenizer.model_max_length, 42)

                tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
                self.assertEqual(tokenizer.model_max_length, 43)

                shutil.rmtree(tmpdirname)

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    def test_pickle_tokenizer(self):
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        """Google pickle __getstate__ __setstate__ if you are struggling with this."""
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        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                self.assertIsNotNone(tokenizer)
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                text = "Munich and Berlin are nice cities"
                subwords = tokenizer.tokenize(text)
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                filename = os.path.join(self.tmpdirname, "tokenizer.bin")
                with open(filename, "wb") as handle:
                    pickle.dump(tokenizer, handle)
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                with open(filename, "rb") as handle:
                    tokenizer_new = pickle.load(handle)
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                subwords_loaded = tokenizer_new.tokenize(text)
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                self.assertListEqual(subwords, subwords_loaded)
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    @require_tokenizers
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    def test_pickle_added_tokens(self):
        tok1 = AddedToken("<s>", rstrip=True, lstrip=True, normalized=False, single_word=True)
        tok2 = pickle.loads(pickle.dumps(tok1))

        self.assertEqual(tok1.__getstate__(), tok2.__getstate__())

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    def test_added_tokens_do_lower_case(self):
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        tokenizers = self.get_tokenizers(do_lower_case=True)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                if not hasattr(tokenizer, "do_lower_case") or not tokenizer.do_lower_case:
                    continue

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                special_token = tokenizer.all_special_tokens[0]
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                text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
                text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token
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                toks_before_adding = tokenizer.tokenize(text)  # toks before adding new_toks
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                new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
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                added = tokenizer.add_tokens([AddedToken(tok, lstrip=True, rstrip=True) for tok in new_toks])
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                toks_after_adding = tokenizer.tokenize(text)
                toks_after_adding2 = tokenizer.tokenize(text2)
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                # Rust tokenizers dont't lowercase added tokens at the time calling `tokenizer.add_tokens`,
                # while python tokenizers do, so new_toks 0 and 2 would be treated as the same, so do new_toks 1 and 3.
                self.assertIn(added, [2, 4])

                self.assertListEqual(toks_after_adding, toks_after_adding2)
                self.assertTrue(
                    len(toks_before_adding) > len(toks_after_adding),  # toks_before_adding should be longer
                )
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                # Check that none of the special tokens are lowercased
                sequence_with_special_tokens = "A " + " yEs ".join(tokenizer.all_special_tokens) + " B"
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                # Convert the tokenized list to str as some special tokens are tokenized like normal tokens
                # which have a prefix spacee e.g. the mask token of Albert, and cannot match the original
                # special tokens exactly.
                tokenized_sequence = "".join(tokenizer.tokenize(sequence_with_special_tokens))
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                for special_token in tokenizer.all_special_tokens:
                    self.assertTrue(special_token in tokenized_sequence)
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        tokenizers = self.get_tokenizers(do_lower_case=True)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                if hasattr(tokenizer, "do_lower_case") and tokenizer.do_lower_case:
                    continue

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                special_token = tokenizer.all_special_tokens[0]
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                text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
                text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token
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                toks_before_adding = tokenizer.tokenize(text)  # toks before adding new_toks
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                new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
                added = tokenizer.add_tokens([AddedToken(tok, lstrip=True, rstrip=True) for tok in new_toks])
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                self.assertIn(added, [2, 4])
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                toks_after_adding = tokenizer.tokenize(text)
                toks_after_adding2 = tokenizer.tokenize(text2)
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                self.assertEqual(len(toks_after_adding), len(toks_after_adding2))  # Length should still be the same
                self.assertNotEqual(
                    toks_after_adding[1], toks_after_adding2[1]
                )  # But at least the first non-special tokens should differ
                self.assertTrue(
                    len(toks_before_adding) > len(toks_after_adding),  # toks_before_adding should be longer
                )
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    def test_add_tokens_tokenizer(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                vocab_size = tokenizer.vocab_size
                all_size = len(tokenizer)

                self.assertNotEqual(vocab_size, 0)
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                # We usually have added tokens from the start in tests because our vocab fixtures are
                # smaller than the original vocabs - let's not assert this
                # self.assertEqual(vocab_size, all_size)
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                new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
                added_toks = tokenizer.add_tokens(new_toks)
                vocab_size_2 = tokenizer.vocab_size
                all_size_2 = len(tokenizer)

                self.assertNotEqual(vocab_size_2, 0)
                self.assertEqual(vocab_size, vocab_size_2)
                self.assertEqual(added_toks, len(new_toks))
                self.assertEqual(all_size_2, all_size + len(new_toks))

                tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)

                self.assertGreaterEqual(len(tokens), 4)
                self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
                self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)

                new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
                added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
                vocab_size_3 = tokenizer.vocab_size
                all_size_3 = len(tokenizer)

                self.assertNotEqual(vocab_size_3, 0)
                self.assertEqual(vocab_size, vocab_size_3)
                self.assertEqual(added_toks_2, len(new_toks_2))
                self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))

                tokens = tokenizer.encode(
                    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
                )

                self.assertGreaterEqual(len(tokens), 6)
                self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
                self.assertGreater(tokens[0], tokens[1])
                self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
                self.assertGreater(tokens[-2], tokens[-3])
                self.assertEqual(tokens[0], tokenizer.eos_token_id)
                self.assertEqual(tokens[-2], tokenizer.pad_token_id)
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    def test_add_special_tokens(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                input_text, ids = self.get_clean_sequence(tokenizer)
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                special_token = "[SPECIAL_TOKEN]"
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                tokenizer.add_special_tokens({"cls_token": special_token})
                encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
                self.assertEqual(len(encoded_special_token), 1)
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                text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
                encoded = tokenizer.encode(text, add_special_tokens=False)
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                input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
                special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
                self.assertEqual(encoded, input_encoded + special_token_id)
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                decoded = tokenizer.decode(encoded, skip_special_tokens=True)
                self.assertTrue(special_token not in decoded)
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    def test_internal_consistency(self):
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        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                input_text, output_text = self.get_input_output_texts(tokenizer)
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                tokens = tokenizer.tokenize(input_text)
                ids = tokenizer.convert_tokens_to_ids(tokens)
                ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
                self.assertListEqual(ids, ids_2)
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                tokens_2 = tokenizer.convert_ids_to_tokens(ids)
                self.assertNotEqual(len(tokens_2), 0)
                text_2 = tokenizer.decode(ids)
                self.assertIsInstance(text_2, str)
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                self.assertEqual(text_2, output_text)
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    @require_tokenizers
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    def test_encode_decode_with_spaces(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                new_toks = [
                    AddedToken("[ABC]", normalized=False),
                    AddedToken("[DEF]", normalized=False),
                    AddedToken("GHI IHG", normalized=False),
                ]
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                tokenizer.add_tokens(new_toks)
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                input = "[ABC][DEF][ABC]GHI IHG[DEF]"
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                if self.space_between_special_tokens:
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                    output = "[ABC] [DEF] [ABC] GHI IHG [DEF]"
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                else:
                    output = input
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                encoded = tokenizer.encode(input, add_special_tokens=False)
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                decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
                self.assertIn(decoded, [output, output.lower()])
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    def test_pretrained_model_lists(self):
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        # 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)
        self.assertEqual(
            len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]),
            len(self.tokenizer_class.max_model_input_sizes),
        )

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        weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
        weights_lists_2 = []
        for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
            weights_lists_2.append(list(map_list.keys()))
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        for weights_list_2 in weights_lists_2:
            self.assertListEqual(weights_list, weights_list_2)
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    def test_mask_output(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):

                if (
                    tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
                    and "token_type_ids" in tokenizer.model_input_names
                ):
                    seq_0 = "Test this method."
                    seq_1 = "With these inputs."
                    information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
                    sequences, mask = information["input_ids"], information["token_type_ids"]
                    self.assertEqual(len(sequences), len(mask))
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    def test_token_type_ids(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                seq_0 = "Test this method."

                # We want to have sequence 0 and sequence 1 are tagged
                # respectively with 0 and 1 token_ids
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                # (regardless of whether the model use token type ids)
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                # We use this assumption in the QA pipeline among other place
                output = tokenizer(seq_0, return_token_type_ids=True)
                self.assertIn(0, output["token_type_ids"])

    def test_sequence_ids(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            if not tokenizer.is_fast:
                continue
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                seq_0 = "Test this method."
                seq_1 = "With these inputs."

                # We want to have sequence 0 and sequence 1 are tagged
                # respectively with 0 and 1 token_ids
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                # (regardless of whether the model use token type ids)
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                # We use this assumption in the QA pipeline among other place
                output = tokenizer(seq_0)
                self.assertIn(0, output.sequence_ids())

                output = tokenizer(seq_0, seq_1)
                self.assertIn(0, output.sequence_ids())
                self.assertIn(1, output.sequence_ids())

                if tokenizer.num_special_tokens_to_add(pair=True):
                    self.assertIn(None, output.sequence_ids())

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    def test_number_of_added_tokens(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                seq_0 = "Test this method."
                seq_1 = "With these inputs."
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                sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
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                attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
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                # Method is implemented (e.g. not GPT-2)
                if len(attached_sequences) != 2:
                    self.assertEqual(
                        tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences)
                    )
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    def test_maximum_encoding_length_single_input(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
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                sequence = tokenizer.encode(seq_0, add_special_tokens=False)
                total_length = len(sequence)
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                self.assertGreater(
                    total_length, 4, "Issue with the testing sequence, please update it, it's too short"
                )
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                # Test with max model input length
                model_max_length = tokenizer.model_max_length
                self.assertEqual(model_max_length, 100)
                seq_1 = seq_0 * model_max_length

                sequence1 = tokenizer(seq_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
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                self.assertGreater(
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                    total_length1,
                    model_max_length,
                    "Issue with the testing sequence, please update it, it's too short",
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                )
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                # Simple
                padding_strategies = (
                    [False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
                )
                for padding_state in padding_strategies:
                    with self.subTest(f"Padding: {padding_state}"):
                        for truncation_state in [True, "longest_first", "only_first"]:
                            with self.subTest(f"Truncation: {truncation_state}"):
                                output = tokenizer(seq_1, padding=padding_state, truncation=truncation_state)
                                self.assertEqual(len(output["input_ids"]), model_max_length)

                                output = tokenizer([seq_1], padding=padding_state, truncation=truncation_state)
                                self.assertEqual(len(output["input_ids"][0]), model_max_length)

                        # Simple with no truncation
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                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(seq_1, padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
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                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
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                            )
                        )

                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer([seq_1], padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
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                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
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                            )
                        )
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                # Overflowing tokens
                stride = 2
                information = tokenizer(
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                    seq_0,
                    max_length=total_length - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="longest_first",
                    return_overflowing_tokens=True,
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                    # add_prefix_space=False,
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                )

                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, PreTrainedTokenizerFast):
                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), total_length - 2)
                    self.assertEqual(truncated_sequence, sequence[:-2])

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
                else:
                    truncated_sequence = information["input_ids"]
                    overflowing_tokens = information["overflowing_tokens"]
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                    self.assertEqual(len(truncated_sequence), total_length - 2)
                    self.assertEqual(truncated_sequence, sequence[:-2])
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                    self.assertEqual(len(overflowing_tokens), 2 + stride)
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                    self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
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    def test_maximum_encoding_length_pair_input(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Build a sequence from our model's vocabulary
                stride = 2
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                seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
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                if len(ids) <= 2 + stride:
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                    seq_0 = (seq_0 + " ") * (2 + stride)
                    ids = None
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                seq0_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
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                self.assertGreater(len(seq0_tokens), 2 + stride)
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                seq_1 = "This is another sentence to be encoded."
                seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
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                if abs(len(seq0_tokens) - len(seq1_tokens)) <= 2:
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                    seq1_tokens = seq1_tokens + seq1_tokens
                    seq_1 = tokenizer.decode(seq1_tokens, clean_up_tokenization_spaces=False)
                seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)

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                self.assertGreater(len(seq1_tokens), 2 + stride)
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                smallest = seq1_tokens if len(seq0_tokens) > len(seq1_tokens) else seq0_tokens

                # We are not using the special tokens - a bit too hard to test all the tokenizers with this
                # TODO try this again later
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                sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)  # , add_prefix_space=False)
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                # Test with max model input length
                model_max_length = tokenizer.model_max_length
                self.assertEqual(model_max_length, 100)
                seq_2 = seq_0 * model_max_length
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                self.assertGreater(len(seq_2), model_max_length)
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                sequence1 = tokenizer(seq_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                sequence2 = tokenizer(seq_2, seq_1, add_special_tokens=False)
                total_length2 = len(sequence2["input_ids"])
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                self.assertLess(
                    total_length1, model_max_length - 10, "Issue with the testing sequence, please update it."
                )
                self.assertGreater(
                    total_length2, model_max_length, "Issue with the testing sequence, please update it."
                )
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                # Simple
                padding_strategies = (
                    [False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
                )
                for padding_state in padding_strategies:
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                    with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"):
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                        for truncation_state in [True, "longest_first", "only_first"]:
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                            with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
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                                output = tokenizer(seq_2, seq_1, padding=padding_state, truncation=truncation_state)
                                self.assertEqual(len(output["input_ids"]), model_max_length)

                                output = tokenizer(
                                    [seq_2], [seq_1], padding=padding_state, truncation=truncation_state
                                )
                                self.assertEqual(len(output["input_ids"][0]), model_max_length)

                        # Simple
                        output = tokenizer(seq_1, seq_2, padding=padding_state, truncation="only_second")
                        self.assertEqual(len(output["input_ids"]), model_max_length)

                        output = tokenizer([seq_1], [seq_2], padding=padding_state, truncation="only_second")
                        self.assertEqual(len(output["input_ids"][0]), model_max_length)

                        # Simple with no truncation
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                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(seq_1, seq_2, padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
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                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
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                            )
                        )

                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer([seq_1], [seq_2], padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
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                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
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                            )
                        )
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                truncated_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[:-2] + tokenizer.encode(
                    seq_1, add_special_tokens=False
                )
                truncated_second_sequence = (
                    tokenizer.encode(seq_0, add_special_tokens=False)
                    + tokenizer.encode(seq_1, add_special_tokens=False)[:-2]
                )
                truncated_longest_sequence = (
                    truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
                )

                overflow_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[
                    -(2 + stride) :
                ] + tokenizer.encode(seq_1, add_special_tokens=False)
                overflow_second_sequence = (
                    tokenizer.encode(seq_0, add_special_tokens=False)
                    + tokenizer.encode(seq_1, add_special_tokens=False)[-(2 + stride) :]
                )
                overflow_longest_sequence = (
                    overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence
                )

                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, PreTrainedTokenizerFast):
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                    information = tokenizer(
                        seq_0,
                        seq_1,
                        max_length=len(sequence) - 2,
                        add_special_tokens=False,
                        stride=stride,
                        truncation="longest_first",
                        return_overflowing_tokens=True,
                        # add_prefix_space=False,
                    )
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                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence) - 2)
                    self.assertEqual(truncated_sequence, truncated_longest_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                else:
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                    # No overflowing tokens when using 'longest' in python tokenizers
                    with self.assertRaises(ValueError) as context:
                        information = tokenizer(
                            seq_0,
                            seq_1,
                            max_length=len(sequence) - 2,
                            add_special_tokens=False,
                            stride=stride,
                            truncation="longest_first",
                            return_overflowing_tokens=True,
                            # add_prefix_space=False,
                        )
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                    self.assertTrue(
                        context.exception.args[0].startswith(
                            "Not possible to return overflowing tokens for pair of sequences with the "
                            "`longest_first`. Please select another truncation strategy than `longest_first`, "
                            "for instance `only_second` or `only_first`."
                        )
                    )
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                # Overflowing tokens are handled quite differently in slow and fast tokenizers
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                if isinstance(tokenizer, PreTrainedTokenizerFast):
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                    information = tokenizer(
                        seq_0,
                        seq_1,
                        max_length=len(sequence) - 2,
                        add_special_tokens=False,
                        stride=stride,
                        truncation=True,
                        return_overflowing_tokens=True,
                        # add_prefix_space=False,
                    )
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                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence) - 2)
                    self.assertEqual(truncated_sequence, truncated_longest_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                else:
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                    # No overflowing tokens when using 'longest' in python tokenizers
                    with self.assertRaises(ValueError) as context:
                        information = tokenizer(
                            seq_0,
                            seq_1,
                            max_length=len(sequence) - 2,
                            add_special_tokens=False,
                            stride=stride,
                            truncation=True,
                            return_overflowing_tokens=True,
                            # add_prefix_space=False,
                        )
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                    self.assertTrue(
                        context.exception.args[0].startswith(
                            "Not possible to return overflowing tokens for pair of sequences with the "
                            "`longest_first`. Please select another truncation strategy than `longest_first`, "
                            "for instance `only_second` or `only_first`."
                        )
                    )
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                information_first_truncated = tokenizer(
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                    seq_0,
                    seq_1,
                    max_length=len(sequence) - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="only_first",
                    return_overflowing_tokens=True,
                    # add_prefix_space=False,
                )
                # Overflowing tokens are handled quite differently in slow and fast tokenizers
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                if isinstance(tokenizer, PreTrainedTokenizerFast):
                    truncated_sequence = information_first_truncated["input_ids"][0]
                    overflowing_tokens = information_first_truncated["input_ids"][1]
                    self.assertEqual(len(information_first_truncated["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence) - 2)
                    self.assertEqual(truncated_sequence, truncated_first_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens))
                    self.assertEqual(overflowing_tokens, overflow_first_sequence)
                else:
                    truncated_sequence = information_first_truncated["input_ids"]
                    overflowing_tokens = information_first_truncated["overflowing_tokens"]

                    self.assertEqual(len(truncated_sequence), len(sequence) - 2)
                    self.assertEqual(truncated_sequence, truncated_first_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq0_tokens[-(2 + stride) :])

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                information_second_truncated = tokenizer(
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                    seq_0,
                    seq_1,
                    max_length=len(sequence) - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="only_second",
                    return_overflowing_tokens=True,
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                    # add_prefix_space=False,
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                )
                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, PreTrainedTokenizerFast):
                    truncated_sequence = information_second_truncated["input_ids"][0]
                    overflowing_tokens = information_second_truncated["input_ids"][1]
                    self.assertEqual(len(information_second_truncated["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence) - 2)
                    self.assertEqual(truncated_sequence, truncated_second_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq0_tokens))
                    self.assertEqual(overflowing_tokens, overflow_second_sequence)
                else:
                    truncated_sequence = information_second_truncated["input_ids"]
                    overflowing_tokens = information_second_truncated["overflowing_tokens"]

                    self.assertEqual(len(truncated_sequence), len(sequence) - 2)
                    self.assertEqual(truncated_sequence, truncated_second_sequence)
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                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq1_tokens[-(2 + stride) :])
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    # def test_encode_input_type(self):
    #     tokenizers = self.get_tokenizers(do_lower_case=False)
    #     for tokenizer in tokenizers:
    #         with self.subTest(f"{tokenizer.__class__.__name__}"):
    #             sequence = "Let's encode this sequence"
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    #             tokens = sequence.split()  # tokenizer.tokenize(sequence)
    #             # input_ids = tokenizer.convert_tokens_to_ids(tokens)
    #             formatted_input = tokenizer.encode(sequence, add_special_tokens=True, add_prefix_space=False)
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    #             self.assertEqual(
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    #             )
    #             # This is not supported with the Rust tokenizers
    #             # self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)
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    # def test_swap_special_token(self):
    #     tokenizers = self.get_tokenizers(do_lower_case=False)
    #     for tokenizer in tokenizers:
    #         with self.subTest(f"{tokenizer.__class__.__name__}"):
    #             # Our mask token
    #             mask = "<mask>"
    #             # We take a single word in the middle of the vocabulary
    #             all_tokens = sorted(tokenizer.get_vocab().keys())
    #             word = tokenizer.decode(tokenizer.encode(all_tokens[len(all_tokens)//2], add_special_tokens=False)[:1])

    #             sequence_0 = "Encode " + word + " sequence"
    #             sequence_masked_0 = "Encode " + mask + " sequence"

    #             sequence_1 = word + " this sequence"
    #             sequence_masked_1 = mask + " this sequence"

    #             # Add tokens so that masked token isn't split
    #             # tokens = [AddedToken(t, lstrip=True, normalized=False) for t in sequence.split()]
    #             # tokenizer.add_tokens(tokens)
    #             tokenizer.add_special_tokens(
    #                 {"mask_token": AddedToken(mask, normalized=False)}
    #             )  # Eat left space on Byte-level BPE tokenizers
    #             mask_ind = tokenizer.convert_tokens_to_ids(mask)

    #             # Test first masked sequence
    #             encoded_0 = tokenizer.encode(sequence_0, add_special_tokens=False)
    #             encoded_masked = tokenizer.encode(sequence_masked_0, add_special_tokens=False)
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    #             self.assertEqual(len(encoded_masked), len(encoded_0))
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    #             mask_loc = encoded_masked.index(mask_ind)
    #             encoded_masked[mask_loc] = encoded_0[mask_loc]

    #             self.assertEqual(encoded_masked, encoded_0)

    #             # Test second masked sequence
    #             encoded_1 = tokenizer.encode(sequence_1, add_special_tokens=False)
    #             encoded_masked = tokenizer.encode(sequence_masked_1, add_special_tokens=False)
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    #             self.assertEqual(len(encoded_masked), len(encoded_1))
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    #             mask_loc = encoded_masked.index(mask_ind)
    #             encoded_masked[mask_loc] = encoded_1[mask_loc]

    #             self.assertEqual(encoded_masked, encoded_1)
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    def test_special_tokens_mask(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence_0 = "Encode this."
                # Testing single inputs
                encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
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                )
                encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
                special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
                self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))

                filtered_sequence = [x for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]]
                self.assertEqual(encoded_sequence, filtered_sequence)
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    def test_special_tokens_mask_input_pairs(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence_0 = "Encode this."
                sequence_1 = "This one too please."
                encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
                encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    sequence_0,
                    sequence_1,
                    add_special_tokens=True,
                    return_special_tokens_mask=True,
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                    # add_prefix_space=False,
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                )
                encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
                special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
                self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))

                filtered_sequence = [
                    (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
                ]
                filtered_sequence = [x for x in filtered_sequence if x is not None]
                self.assertEqual(encoded_sequence, filtered_sequence)
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    def test_padding_side_in_kwargs(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                if self.test_rust_tokenizer:
                    tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, padding_side="left", **kwargs
                    )
                    self.assertEqual(tokenizer_r.padding_side, "left")

                    tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, padding_side="right", **kwargs
                    )
                    self.assertEqual(tokenizer_r.padding_side, "right")

                    self.assertRaises(
                        ValueError,
                        self.rust_tokenizer_class.from_pretrained,
                        pretrained_name,
                        padding_side="unauthorized",
                        **kwargs,
                    )

                if self.test_slow_tokenizer:
                    tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, padding_side="left", **kwargs)
                    self.assertEqual(tokenizer_p.padding_side, "left")

                    tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, padding_side="right", **kwargs)
                    self.assertEqual(tokenizer_p.padding_side, "right")

                    self.assertRaises(
                        ValueError,
                        self.tokenizer_class.from_pretrained,
                        pretrained_name,
                        padding_side="unauthorized",
                        **kwargs,
                    )

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    def test_truncation_side_in_kwargs(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                if self.test_rust_tokenizer:
                    tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, truncation_side="left", **kwargs
                    )
                    self.assertEqual(tokenizer_r.truncation_side, "left")

                    tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, truncation_side="right", **kwargs
                    )
                    self.assertEqual(tokenizer_r.truncation_side, "right")

                    self.assertRaises(
                        ValueError,
                        self.rust_tokenizer_class.from_pretrained,
                        pretrained_name,
                        truncation_side="unauthorized",
                        **kwargs,
                    )

                if self.test_slow_tokenizer:
                    tokenizer_p = self.tokenizer_class.from_pretrained(
                        pretrained_name, truncation_side="left", **kwargs
                    )
                    self.assertEqual(tokenizer_p.truncation_side, "left")

                    tokenizer_p = self.tokenizer_class.from_pretrained(
                        pretrained_name, truncation_side="right", **kwargs
                    )
                    self.assertEqual(tokenizer_p.truncation_side, "right")

                    self.assertRaises(
                        ValueError,
                        self.tokenizer_class.from_pretrained,
                        pretrained_name,
                        truncation_side="unauthorized",
                        **kwargs,
                    )

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    def test_right_and_left_padding(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence = "Sequence"
                padding_size = 10

                # check correct behaviour if no pad_token_id exists and add it eventually
                self._check_no_pad_token_padding(tokenizer, sequence)

                padding_idx = tokenizer.pad_token_id

                # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
                tokenizer.padding_side = "right"
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
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                self.assertEqual(sequence_length + padding_size, padded_sequence_length)
                self.assertEqual(encoded_sequence + [padding_idx] * padding_size, padded_sequence)
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                # LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
                tokenizer.padding_side = "left"
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
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                self.assertEqual(sequence_length + padding_size, padded_sequence_length)
                self.assertEqual([padding_idx] * padding_size + encoded_sequence, padded_sequence)
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                # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(sequence, padding=True)
                padded_sequence_right_length = len(padded_sequence_right)
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                self.assertEqual(sequence_length, padded_sequence_right_length)
                self.assertEqual(encoded_sequence, padded_sequence_right)
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                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(sequence, padding="longest")
                padded_sequence_left_length = len(padded_sequence_left)
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                self.assertEqual(sequence_length, padded_sequence_left_length)
                self.assertEqual(encoded_sequence, padded_sequence_left)
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                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(sequence)
                padded_sequence_right_length = len(padded_sequence_right)
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                self.assertEqual(sequence_length, padded_sequence_right_length)
                self.assertEqual(encoded_sequence, padded_sequence_right)
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                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(sequence, padding=False)
                padded_sequence_left_length = len(padded_sequence_left)
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                self.assertEqual(sequence_length, padded_sequence_left_length)
                self.assertEqual(encoded_sequence, padded_sequence_left)
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    def test_right_and_left_truncation(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence = "This is a test sequence"

                # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
                truncation_size = 3
                tokenizer.truncation_side = "right"
                encoded_sequence = tokenizer.encode(sequence, add_special_tokens=False)
                sequence_length = len(encoded_sequence)
                # Remove EOS/BOS tokens
                truncated_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length - truncation_size, truncation=True, add_special_tokens=False
                )
                truncated_sequence_length = len(truncated_sequence)
                self.assertEqual(sequence_length, truncated_sequence_length + truncation_size)
                self.assertEqual(encoded_sequence[:-truncation_size], truncated_sequence)

                # LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the truncation flag set to True
                tokenizer.truncation_side = "left"
                sequence_length = len(encoded_sequence)
                truncated_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length - truncation_size, truncation=True, add_special_tokens=False
                )
                truncated_sequence_length = len(truncated_sequence)
                self.assertEqual(sequence_length, truncated_sequence_length + truncation_size)
                self.assertEqual(encoded_sequence[truncation_size:], truncated_sequence)

                # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_truncation'
                sequence_length = len(encoded_sequence)

                tokenizer.truncation_side = "right"
                truncated_sequence_right = tokenizer.encode(sequence, truncation=True, add_special_tokens=False)
                truncated_sequence_right_length = len(truncated_sequence_right)
                self.assertEqual(sequence_length, truncated_sequence_right_length)
                self.assertEqual(encoded_sequence, truncated_sequence_right)

                tokenizer.truncation_side = "left"
                truncated_sequence_left = tokenizer.encode(
                    sequence, truncation="longest_first", add_special_tokens=False
                )
                truncated_sequence_left_length = len(truncated_sequence_left)
                self.assertEqual(sequence_length, truncated_sequence_left_length)
                self.assertEqual(encoded_sequence, truncated_sequence_left)

                tokenizer.truncation_side = "right"
                truncated_sequence_right = tokenizer.encode(sequence, add_special_tokens=False)
                truncated_sequence_right_length = len(truncated_sequence_right)
                self.assertEqual(sequence_length, truncated_sequence_right_length)
                self.assertEqual(encoded_sequence, truncated_sequence_right)

                tokenizer.truncation_side = "left"
                truncated_sequence_left = tokenizer.encode(sequence, truncation=False, add_special_tokens=False)
                truncated_sequence_left_length = len(truncated_sequence_left)
                self.assertEqual(sequence_length, truncated_sequence_left_length)
                self.assertEqual(encoded_sequence, truncated_sequence_left)

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    def test_padding_to_max_length(self):
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        """We keep this test for backward compatibility but it should be remove when `pad_to_max_length` is deprecated."""
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence = "Sequence"
                padding_size = 10

                # check correct behaviour if no pad_token_id exists and add it eventually
                self._check_no_pad_token_padding(tokenizer, sequence)

                padding_idx = tokenizer.pad_token_id

                # Check that it correctly pads when a maximum length is specified along with the padding flag set to True
                tokenizer.padding_side = "right"
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)
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                # FIXME: the next line should be padding(max_length) to avoid warning
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                padded_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length + padding_size, pad_to_max_length=True
                )
                padded_sequence_length = len(padded_sequence)
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                self.assertEqual(sequence_length + padding_size, padded_sequence_length)
                self.assertEqual(encoded_sequence + [padding_idx] * padding_size, padded_sequence)
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                # Check that nothing is done when a maximum length is not specified
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(sequence, pad_to_max_length=True)
                padded_sequence_right_length = len(padded_sequence_right)
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                self.assertEqual(sequence_length, padded_sequence_right_length)
                self.assertEqual(encoded_sequence, padded_sequence_right)
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    def test_padding_to_multiple_of(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
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            with self.subTest(f"{tokenizer.__class__.__name__}"):
                if tokenizer.pad_token is None:
                    self.skipTest("No padding token.")
                else:
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                    empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8)
                    normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8)
                    for key, value in empty_tokens.items():
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                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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                    for key, value in normal_tokens.items():
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                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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                    normal_tokens = tokenizer("This", pad_to_multiple_of=8)
                    for key, value in normal_tokens.items():
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                        self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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                    # Should also work with truncation
                    normal_tokens = tokenizer("This", padding=True, truncation=True, pad_to_multiple_of=8)
                    for key, value in normal_tokens.items():
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                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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                    # truncation to something which is not a multiple of pad_to_multiple_of raises an error
                    self.assertRaises(
                        ValueError,
                        tokenizer.__call__,
                        "This",
                        padding=True,
                        truncation=True,
                        max_length=12,
                        pad_to_multiple_of=8,
                    )

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    def test_padding_with_attention_mask(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                if tokenizer.pad_token is None:
                    self.skipTest("No padding token.")
                if "attention_mask" not in tokenizer.model_input_names:
                    self.skipTest("This model does not use attention mask.")

                features = [
                    {"input_ids": [1, 2, 3, 4, 5, 6], "attention_mask": [1, 1, 1, 1, 1, 0]},
                    {"input_ids": [1, 2, 3], "attention_mask": [1, 1, 0]},
                ]
                padded_features = tokenizer.pad(features)
                if tokenizer.padding_side == "right":
                    self.assertListEqual(padded_features["attention_mask"], [[1, 1, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0]])
                else:
                    self.assertListEqual(padded_features["attention_mask"], [[1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 0]])

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    def test_encode_plus_with_padding(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence = "Sequence"

                # check correct behaviour if no pad_token_id exists and add it eventually
                self._check_no_pad_token_padding(tokenizer, sequence)

                padding_size = 10
                padding_idx = tokenizer.pad_token_id
                token_type_padding_idx = tokenizer.pad_token_type_id

                encoded_sequence = tokenizer.encode_plus(sequence, return_special_tokens_mask=True)
                input_ids = encoded_sequence["input_ids"]
                special_tokens_mask = encoded_sequence["special_tokens_mask"]
                sequence_length = len(input_ids)

                # Test 'longest' and 'no_padding' don't do anything
                tokenizer.padding_side = "right"

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                not_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    padding=True,
                    return_special_tokens_mask=True,
                )
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                not_padded_input_ids = not_padded_sequence["input_ids"]

                not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
                not_padded_sequence_length = len(not_padded_input_ids)

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                self.assertEqual(sequence_length, not_padded_sequence_length)
                self.assertEqual(input_ids, not_padded_input_ids)
                self.assertEqual(special_tokens_mask, not_padded_special_tokens_mask)
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                not_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    padding=False,
                    return_special_tokens_mask=True,
                )
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                not_padded_input_ids = not_padded_sequence["input_ids"]

                not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
                not_padded_sequence_length = len(not_padded_input_ids)

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                self.assertEqual(sequence_length, not_padded_sequence_length)
                self.assertEqual(input_ids, not_padded_input_ids)
                self.assertEqual(special_tokens_mask, not_padded_special_tokens_mask)
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                # Test right padding
                tokenizer.padding_side = "right"

                right_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    max_length=sequence_length + padding_size,
                    padding="max_length",
                    return_special_tokens_mask=True,
                )
                right_padded_input_ids = right_padded_sequence["input_ids"]

                right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
                right_padded_sequence_length = len(right_padded_input_ids)

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                self.assertEqual(sequence_length + padding_size, right_padded_sequence_length)
                self.assertEqual(input_ids + [padding_idx] * padding_size, right_padded_input_ids)
                self.assertEqual(special_tokens_mask + [1] * padding_size, right_padded_special_tokens_mask)
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                # Test left padding
                tokenizer.padding_side = "left"
                left_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    max_length=sequence_length + padding_size,
                    padding="max_length",
                    return_special_tokens_mask=True,
                )
                left_padded_input_ids = left_padded_sequence["input_ids"]
                left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
                left_padded_sequence_length = len(left_padded_input_ids)

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                self.assertEqual(sequence_length + padding_size, left_padded_sequence_length)
                self.assertEqual([padding_idx] * padding_size + input_ids, left_padded_input_ids)
                self.assertEqual([1] * padding_size + special_tokens_mask, left_padded_special_tokens_mask)
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                if "token_type_ids" in tokenizer.model_input_names:
                    token_type_ids = encoded_sequence["token_type_ids"]
                    left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
                    right_padded_token_type_ids = right_padded_sequence["token_type_ids"]

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                    self.assertEqual(
                        token_type_ids + [token_type_padding_idx] * padding_size, right_padded_token_type_ids
                    )
                    self.assertEqual(
                        [token_type_padding_idx] * padding_size + token_type_ids, left_padded_token_type_ids
                    )
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                if "attention_mask" in tokenizer.model_input_names:
                    attention_mask = encoded_sequence["attention_mask"]
                    right_padded_attention_mask = right_padded_sequence["attention_mask"]
                    left_padded_attention_mask = left_padded_sequence["attention_mask"]

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                    self.assertEqual(attention_mask + [0] * padding_size, right_padded_attention_mask)
                    self.assertEqual([0] * padding_size + attention_mask, left_padded_attention_mask)
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    def test_padding_warning_message_fast_tokenizer(self):
        if not self.test_rust_tokenizer:
            return

        sequence = "This is a text"

        tokenizer_fast = self.get_rust_tokenizer()
        # check correct behaviour if no pad_token_id exists and add it eventually
        self._check_no_pad_token_padding(tokenizer_fast, sequence)

        encoding_fast = tokenizer_fast(sequence)

        with self.assertLogs("transformers", level="WARNING") as cm:
            tokenizer_fast.pad(encoding_fast)
        self.assertEqual(len(cm.records), 1)
        self.assertIn(
            "Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
            " encode the text followed by a call to the `pad` method to get a padded encoding.",
            cm.records[0].message,
        )

        if not self.test_slow_tokenizer:
            return

        tokenizer_slow = self.get_tokenizer()
        # check correct behaviour if no pad_token_id exists and add it eventually
        self._check_no_pad_token_padding(tokenizer_slow, sequence)

        encoding_slow = tokenizer_slow(sequence)

        with self.assertLogs(level="WARNING") as cm:
            # We want to assert there are no warnings, but the 'assertLogs' method does not support that.
            # Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
            logger.warning("Dummy warning")
            tokenizer_slow.pad(encoding_slow)
        self.assertEqual(len(cm.records), 1)
        self.assertIn(
            "Dummy warning",
            cm.records[0].message,
        )

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    def test_separate_tokenizers(self):
        # This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
        # we're loading an S3 configuration from a pre-trained identifier, and we have no way of testing those today.

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        tokenizers = self.get_tokenizers(random_argument=True)
        new_tokenizers = self.get_tokenizers(random_argument=False)

        for tokenizer, new_tokenizer in zip(tokenizers, new_tokenizers):
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                self.assertTrue(tokenizer.init_kwargs["random_argument"])
                self.assertTrue(tokenizer.init_kwargs["random_argument"])
                self.assertFalse(new_tokenizer.init_kwargs["random_argument"])
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    def test_get_vocab(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                vocab_dict = tokenizer.get_vocab()
                self.assertIsInstance(vocab_dict, dict)
                self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
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                vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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                self.assertEqual(len(vocab), len(tokenizer))
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                tokenizer.add_tokens(["asdfasdfasdfasdf"])
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                vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
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                self.assertEqual(len(vocab), len(tokenizer))
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    def test_conversion_reversible(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                vocab = tokenizer.get_vocab()
                for word, ind in vocab.items():
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                    if word == tokenizer.unk_token:
                        continue
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                    self.assertEqual(tokenizer.convert_tokens_to_ids(word), ind)
                    self.assertEqual(tokenizer.convert_ids_to_tokens(ind), word)

    def test_call(self):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                # Test not batched
                encoded_sequences_1 = tokenizer.encode_plus(sequences[0])
                encoded_sequences_2 = tokenizer(sequences[0])
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test not batched pairs
                encoded_sequences_1 = tokenizer.encode_plus(sequences[0], sequences[1])
                encoded_sequences_2 = tokenizer(sequences[0], sequences[1])
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test batched
                encoded_sequences_1 = tokenizer.batch_encode_plus(sequences)
                encoded_sequences_2 = tokenizer(sequences)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test batched pairs
                encoded_sequences_1 = tokenizer.batch_encode_plus(list(zip(sequences, sequences)))
                encoded_sequences_2 = tokenizer(sequences, sequences)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)
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    def test_batch_encode_plus_batch_sequence_length(self):
        # Tests that all encoded values have the correct size
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                encoded_sequences = [tokenizer.encode_plus(sequence) for sequence in sequences]
                encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, padding=False)
                self.assertListEqual(
                    encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
                )

                maximum_length = len(
                    max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
                )

                # check correct behaviour if no pad_token_id exists and add it eventually
                self._check_no_pad_token_padding(tokenizer, sequences)

                encoded_sequences_padded = [
                    tokenizer.encode_plus(sequence, max_length=maximum_length, padding="max_length")
                    for sequence in sequences
                ]

                encoded_sequences_batch_padded = tokenizer.batch_encode_plus(sequences, padding=True)
                self.assertListEqual(
                    encoded_sequences_padded,
                    self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
                )

                # check 'longest' is unsensitive to a max length
                encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(sequences, padding=True)
                encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
                    sequences, max_length=maximum_length + 10, padding="longest"
                )
                for key in encoded_sequences_batch_padded_1.keys():
                    self.assertListEqual(
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                        encoded_sequences_batch_padded_1[key],
                        encoded_sequences_batch_padded_2[key],
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                    )

                # check 'no_padding' is unsensitive to a max length
                encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(sequences, padding=False)
                encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
                    sequences, max_length=maximum_length + 10, padding=False
                )
                for key in encoded_sequences_batch_padded_1.keys():
                    self.assertListEqual(
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                        encoded_sequences_batch_padded_1[key],
                        encoded_sequences_batch_padded_2[key],
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                    )
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    @require_tokenizers
    def test_added_token_are_matched_longest_first(self):
        if not self.test_slow_tokenizer:
            self.skipTest("This test is only for slow tokenizers")
            return
        tokenizers = self.get_tokenizers(fast=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                try:
                    tokenizer.add_tokens([AddedToken("extra_id_1")])
                    tokenizer.add_tokens([AddedToken("extra_id_100")])
                except Exception:
                    # Canine cannot add tokens which are not codepoints
                    self.skipTest("Cannot add those Added tokens")

                # XXX: This used to split on `extra_id_1` first we're matching
                # longest first now.
                tokens = tokenizer.tokenize("This is some extra_id_100")
                self.assertIn("extra_id_100", tokens)

        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                tokenizer.add_tokens([AddedToken("extra_id_100")])
                tokenizer.add_tokens([AddedToken("extra_id_1")])

                tokens = tokenizer.tokenize("This is some extra_id_100")
                self.assertIn("extra_id_100", tokens)

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    def test_added_token_serializable(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
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            with self.subTest(f"{tokenizer.__class__.__name__}"):
                new_token = AddedToken("new_token", lstrip=True)
                tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})
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                with tempfile.TemporaryDirectory() as tmp_dir_name:
                    tokenizer.save_pretrained(tmp_dir_name)
                    tokenizer.from_pretrained(tmp_dir_name)
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    def test_batch_encode_plus_padding(self):
        # Test that padded sequences are equivalent between batch_encode_plus and encode_plus

        # Right padding tests
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                max_length = 100

                # check correct behaviour if no pad_token_id exists and add it eventually
                self._check_no_pad_token_padding(tokenizer, sequences)

                encoded_sequences = [
                    tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
                    for sequence in sequences
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    sequences, max_length=max_length, padding="max_length"
                )
                self.assertListEqual(
                    encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
                )
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        # Left padding tests
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                tokenizer.padding_side = "left"
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                max_length = 100

                # check correct behaviour if no pad_token_id exists and add it eventually
                self._check_no_pad_token_padding(tokenizer, sequences)

                encoded_sequences = [
                    tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
                    for sequence in sequences
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    sequences, max_length=max_length, padding="max_length"
                )
                self.assertListEqual(
                    encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
                )

    def test_pretokenized_inputs(self):
        # Test when inputs are pretokenized

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        tokenizers = self.get_tokenizers(do_lower_case=False)  # , add_prefix_space=True)
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        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):

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                if hasattr(tokenizer, "add_prefix_space") and not tokenizer.add_prefix_space:
                    continue

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                # Prepare a sequence from our tokenizer vocabulary
                sequence, ids = self.get_clean_sequence(tokenizer, with_prefix_space=True, max_length=20)
                # sequence = " " + sequence  # To be sure the byte-level tokenizers are feeling good
                token_sequence = sequence.split()
                # sequence_no_prefix_space = sequence.strip()

                # Test encode for pretokenized inputs
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                output = tokenizer.encode(token_sequence, is_split_into_words=True, add_special_tokens=False)
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                output_sequence = tokenizer.encode(sequence, add_special_tokens=False)
                self.assertEqual(output, output_sequence)

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                output = tokenizer.encode(token_sequence, is_split_into_words=True, add_special_tokens=True)
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                output_sequence = tokenizer.encode(sequence, add_special_tokens=True)
                self.assertEqual(output, output_sequence)

                # Test encode_plus for pretokenized inputs
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                output = tokenizer.encode_plus(token_sequence, is_split_into_words=True, add_special_tokens=False)
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                output_sequence = tokenizer.encode_plus(sequence, add_special_tokens=False)
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])
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                output = tokenizer.encode_plus(token_sequence, is_split_into_words=True, add_special_tokens=True)
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                output_sequence = tokenizer.encode_plus(sequence, add_special_tokens=True)
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])

                # Test batch_encode_plus for pretokenized inputs
                sequence_batch = [sequence.strip()] * 2 + [sequence.strip() + " " + sequence.strip()]
                token_sequence_batch = [s.split() for s in sequence_batch]
                sequence_batch_cleaned_up_spaces = [" " + " ".join(s) for s in token_sequence_batch]

                output = tokenizer.batch_encode_plus(
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                )
                output_sequence = tokenizer.batch_encode_plus(
                    sequence_batch_cleaned_up_spaces, add_special_tokens=False
                )
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])
                output = tokenizer.batch_encode_plus(
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                )
                output_sequence = tokenizer.batch_encode_plus(
                    sequence_batch_cleaned_up_spaces, add_special_tokens=True
                )
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])

                # Test encode for pretokenized inputs pairs
                output = tokenizer.encode(
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                )
                output_sequence = tokenizer.encode(sequence, sequence, add_special_tokens=False)
                self.assertEqual(output, output_sequence)
                output = tokenizer.encode(
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                )
                output_sequence = tokenizer.encode(sequence, sequence, add_special_tokens=True)
                self.assertEqual(output, output_sequence)

                # Test encode_plus for pretokenized inputs pairs
                output = tokenizer.encode_plus(
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                )
                output_sequence = tokenizer.encode_plus(sequence, sequence, add_special_tokens=False)
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])
                output = tokenizer.encode_plus(
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                )
                output_sequence = tokenizer.encode_plus(sequence, sequence, add_special_tokens=True)
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])

                # Test batch_encode_plus for pretokenized inputs pairs
                sequence_pair_batch = [(sequence.strip(), sequence.strip())] * 2 + [
                    (sequence.strip() + " " + sequence.strip(), sequence.strip())
                ]
                token_sequence_pair_batch = [tuple(s.split() for s in pair) for pair in sequence_pair_batch]
                sequence_pair_batch_cleaned_up_spaces = [
                    tuple(" " + " ".join(s) for s in pair) for pair in token_sequence_pair_batch
                ]

                output = tokenizer.batch_encode_plus(
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                )
                output_sequence = tokenizer.batch_encode_plus(
                    sequence_pair_batch_cleaned_up_spaces, add_special_tokens=False
                )
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])
                output = tokenizer.batch_encode_plus(
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                )
                output_sequence = tokenizer.batch_encode_plus(
                    sequence_pair_batch_cleaned_up_spaces, add_special_tokens=True
                )
                for key in output.keys():
                    self.assertEqual(output[key], output_sequence[key])
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    def test_prepare_for_model(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
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            with self.subTest(f"{tokenizer.__class__.__name__}"):
                string_sequence = "Testing the prepare_for_model method."
                ids = tokenizer.encode(string_sequence, add_special_tokens=False)
                prepared_input_dict = tokenizer.prepare_for_model(ids, add_special_tokens=True)

                input_dict = tokenizer.encode_plus(string_sequence, add_special_tokens=True)
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                self.assertEqual(input_dict, prepared_input_dict)
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    def test_batch_encode_plus_overflowing_tokens(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            string_sequences = ["Testing the prepare_for_model method.", "Test"]

            if tokenizer.pad_token is None:
                tokenizer.add_special_tokens({"pad_token": "[PAD]"})

            tokenizer.batch_encode_plus(
                string_sequences, return_overflowing_tokens=True, truncation=True, padding=True, max_length=3
            )

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    @is_pt_tf_cross_test
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    def test_batch_encode_plus_tensors(self):
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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                # A Tensor cannot be build by sequences which are not the same size
                self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="pt")
                self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="tf")

                if tokenizer.pad_token_id is None:
                    self.assertRaises(
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                        ValueError,
                        tokenizer.batch_encode_plus,
                        sequences,
                        padding=True,
                        return_tensors="pt",
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                    )
                    self.assertRaises(
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                        ValueError,
                        tokenizer.batch_encode_plus,
                        sequences,
                        padding="longest",
                        return_tensors="tf",
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                    )
                else:
                    pytorch_tensor = tokenizer.batch_encode_plus(sequences, padding=True, return_tensors="pt")
                    tensorflow_tensor = tokenizer.batch_encode_plus(sequences, padding="longest", return_tensors="tf")
                    encoded_sequences = tokenizer.batch_encode_plus(sequences, padding=True)
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                    for key in encoded_sequences.keys():
                        pytorch_value = pytorch_tensor[key].tolist()
                        tensorflow_value = tensorflow_tensor[key].numpy().tolist()
                        encoded_value = encoded_sequences[key]
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                        self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
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    def _check_no_pad_token_padding(self, tokenizer, sequences):
        # if tokenizer does not have pad_token_id, an error should be thrown
        if tokenizer.pad_token_id is None:
            with self.assertRaises(ValueError):
                if isinstance(sequences, list):
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                    tokenizer.batch_encode_plus(sequences, padding="longest")
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                else:
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                    tokenizer.encode_plus(sequences, padding=True)
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            # add pad_token_id to pass subsequent tests
            tokenizer.add_special_tokens({"pad_token": "<PAD>"})
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    def check_subword_sampling(
        self,
        tokenizer: PreTrainedTokenizer,
        text: str = None,
    ) -> None:
        """
        Check if the tokenizer generates different results when subword regularization is enabled.

        Subword regularization augments training data with subword sampling.
        This has a random component.

        Args:
            tokenizer: The tokenizer to check.
            text: The text to use for the checks.
        """
        text = "This is a test for subword regularization." if text is None else text
        if self.test_sentencepiece_ignore_case:
            text = text.lower()

        tokens_list = []
        for _ in range(5):
            tokens_list.append(tokenizer.tokenize(text))

        # the list of different pairs of tokens_list
        combinations = itertools.combinations(tokens_list, 2)

        # check of sampling is done
        subword_sampling_found = False
        for combination in combinations:
            if combination[0] != combination[1]:
                subword_sampling_found = True
        self.assertTrue(subword_sampling_found)

        # check if converting back to original text works
        for tokens in tokens_list:
            if self.test_sentencepiece_ignore_case:
                self.assertEqual(text, tokenizer.convert_tokens_to_string(tokens).lower())
            else:
                self.assertEqual(text, tokenizer.convert_tokens_to_string(tokens))

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    @require_torch
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    @slow
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    def test_torch_encode_plus_sent_to_model(self):
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        import torch
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        from transformers import MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)

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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
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                if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
                    return
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                config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
                config = config_class()
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                if config.is_encoder_decoder or config.pad_token_id is None:
                    return
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                model = model_class(config)
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                # Make sure the model contains at least the full vocabulary size in its embedding matrix
                is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight")
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                if is_using_common_embeddings:
                    self.assertGreaterEqual(model.get_input_embeddings().weight.shape[0], len(tokenizer))
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                # Build sequence
                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
                sequence = " ".join(first_ten_tokens)
                encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="pt")
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                # Ensure that the BatchEncoding.to() method works.
                encoded_sequence.to(model.device)

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                batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
                # This should not fail
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                with torch.no_grad():  # saves some time
                    model(**encoded_sequence)
                    model(**batch_encoded_sequence)
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        # if self.test_rust_tokenizer:
        #     fast_tokenizer = self.get_rust_tokenizer()
        #     encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="pt")
        #     batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
        #     # This should not fail
        #     model(**encoded_sequence_fast)
        #     model(**batch_encoded_sequence_fast)
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    @require_tf
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    @slow
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    def test_tf_encode_plus_sent_to_model(self):
        from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)

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        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
                    return
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                config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
                config = config_class()
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                if config.is_encoder_decoder or config.pad_token_id is None:
                    return
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                model = model_class(config)
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                # Make sure the model contains at least the full vocabulary size in its embedding matrix
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                self.assertGreaterEqual(model.config.vocab_size, len(tokenizer))
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                # Build sequence
                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
                sequence = " ".join(first_ten_tokens)
                encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="tf")
                batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="tf")
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                # This should not fail
                model(encoded_sequence)
                model(batch_encoded_sequence)
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    # TODO: Check if require_torch is the best to test for numpy here ... Maybe move to require_flax when available
    @require_torch
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    @slow
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    def test_np_encode_plus_sent_to_model(self):
        from transformers import MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)

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        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
                    return
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                config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
                config = config_class()
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                if config.is_encoder_decoder or config.pad_token_id is None:
                    return
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                # Build sequence
                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
                sequence = " ".join(first_ten_tokens)
                encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="np")
                batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="np")

                # TODO: add forward through JAX/Flax when PR is merged
                # This is currently here to make flake8 happy !
                if encoded_sequence is None:
                    raise ValueError("Cannot convert list to numpy tensor on  encode_plus()")

                if batch_encoded_sequence is None:
                    raise ValueError("Cannot convert list to numpy tensor on  batch_encode_plus()")

                if self.test_rust_tokenizer:
                    fast_tokenizer = self.get_rust_tokenizer()
                    encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="np")
                    batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus(
                        [sequence, sequence], return_tensors="np"
                    )
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                    # TODO: add forward through JAX/Flax when PR is merged
                    # This is currently here to make flake8 happy !
                    if encoded_sequence_fast is None:
                        raise ValueError("Cannot convert list to numpy tensor on  encode_plus() (fast)")
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                    if batch_encoded_sequence_fast is None:
                        raise ValueError("Cannot convert list to numpy tensor on  batch_encode_plus() (fast)")
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    @require_torch
    def test_prepare_seq2seq_batch(self):
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        if not self.test_seq2seq:
            return

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        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Longer text that will definitely require truncation.
                src_text = [
                    " UN Chief Says There Is No Military Solution in Syria",
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                    " 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.",
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                ]
                tgt_text = [
                    "Şeful ONU declară că nu există o soluţie militară în Siria",
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                    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
                    ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
                    " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
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                ]
                try:
                    batch = tokenizer.prepare_seq2seq_batch(
                        src_texts=src_text,
                        tgt_texts=tgt_text,
                        max_length=3,
                        max_target_length=10,
                        return_tensors="pt",
                        src_lang="en_XX",  # this should be ignored (for all but mbart) but not cause an error
                    )
                except NotImplementedError:
                    return
                self.assertEqual(batch.input_ids.shape[1], 3)
                self.assertEqual(batch.labels.shape[1], 10)
                # max_target_length will default to max_length if not specified
                batch = tokenizer.prepare_seq2seq_batch(
                    src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt"
                )
                self.assertEqual(batch.input_ids.shape[1], 3)
                self.assertEqual(batch.labels.shape[1], 3)
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                batch_encoder_only = tokenizer.prepare_seq2seq_batch(
                    src_texts=src_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)
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    def test_is_fast(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                # Check is_fast is set correctly
                self.assertTrue(tokenizer_r.is_fast)

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                if self.test_slow_tokenizer:
                    tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                    self.assertFalse(tokenizer_p.is_fast)

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    def test_fast_only_inputs(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                # Ensure None raise an error
                self.assertRaises(TypeError, tokenizer_r.tokenize, None)
                self.assertRaises(TypeError, tokenizer_r.encode, None)
                self.assertRaises(TypeError, tokenizer_r.encode_plus, None)
                self.assertRaises(TypeError, tokenizer_r.batch_encode_plus, None)

    def test_alignement_methods(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
                text = " ".join(words)
                batch_size = 3

                encoding = tokenizer_r.encode_plus(text, add_special_tokens=False)

                batch_encoding = tokenizer_r.batch_encode_plus([text] * batch_size, add_special_tokens=False)
                num_tokens = len(encoding["input_ids"])

                last_word_index = len(words) - 1
                last_token_index = num_tokens - 1
                last_batch_index = batch_size - 1
                last_char_index = len(text) - 1

                # words, tokens
                self.assertEqual(len(encoding.words(0)), num_tokens)
                self.assertEqual(max(encoding.words(0)), last_word_index)
                self.assertEqual(min(encoding.words(0)), 0)
                self.assertEqual(len(batch_encoding.words(last_batch_index)), num_tokens)
                self.assertEqual(max(batch_encoding.words(last_batch_index)), last_word_index)
                self.assertEqual(min(batch_encoding.words(last_batch_index)), 0)
                self.assertEqual(len(encoding.tokens(0)), num_tokens)

                # Assert token_to_word
                self.assertEqual(encoding.token_to_word(0), 0)
                self.assertEqual(encoding.token_to_word(0, 0), 0)
                self.assertEqual(encoding.token_to_word(last_token_index), last_word_index)
                self.assertEqual(encoding.token_to_word(0, last_token_index), last_word_index)
                self.assertEqual(batch_encoding.token_to_word(1, 0), 0)
                self.assertEqual(batch_encoding.token_to_word(0, last_token_index), last_word_index)
                self.assertEqual(batch_encoding.token_to_word(last_batch_index, last_token_index), last_word_index)

                # Assert word_to_tokens
                self.assertEqual(encoding.word_to_tokens(0).start, 0)
                self.assertEqual(encoding.word_to_tokens(0, 0).start, 0)
                self.assertEqual(encoding.word_to_tokens(last_word_index).end, last_token_index + 1)
                self.assertEqual(encoding.word_to_tokens(0, last_word_index).end, last_token_index + 1)
                self.assertEqual(batch_encoding.word_to_tokens(1, 0).start, 0)
                self.assertEqual(batch_encoding.word_to_tokens(0, last_word_index).end, last_token_index + 1)
                self.assertEqual(
                    batch_encoding.word_to_tokens(last_batch_index, last_word_index).end, last_token_index + 1
                )

                # Assert token_to_chars
                self.assertEqual(encoding.token_to_chars(0).start, 0)
                self.assertEqual(encoding.token_to_chars(0, 0).start, 0)
                self.assertEqual(encoding.token_to_chars(last_token_index).end, last_char_index + 1)
                self.assertEqual(encoding.token_to_chars(0, last_token_index).end, last_char_index + 1)
                self.assertEqual(batch_encoding.token_to_chars(1, 0).start, 0)
                self.assertEqual(batch_encoding.token_to_chars(0, last_token_index).end, last_char_index + 1)
                self.assertEqual(
                    batch_encoding.token_to_chars(last_batch_index, last_token_index).end, last_char_index + 1
                )

                # Assert char_to_token
                self.assertEqual(encoding.char_to_token(0), 0)
                self.assertEqual(encoding.char_to_token(0, 0), 0)
                self.assertEqual(encoding.char_to_token(last_char_index), last_token_index)
                self.assertEqual(encoding.char_to_token(0, last_char_index), last_token_index)
                self.assertEqual(batch_encoding.char_to_token(1, 0), 0)
                self.assertEqual(batch_encoding.char_to_token(0, last_char_index), last_token_index)
                self.assertEqual(batch_encoding.char_to_token(last_batch_index, last_char_index), last_token_index)

                # Assert char_to_word
                self.assertEqual(encoding.char_to_word(0), 0)
                self.assertEqual(encoding.char_to_word(0, 0), 0)
                self.assertEqual(encoding.char_to_word(last_char_index), last_word_index)
                self.assertEqual(encoding.char_to_word(0, last_char_index), last_word_index)
                self.assertEqual(batch_encoding.char_to_word(1, 0), 0)
                self.assertEqual(batch_encoding.char_to_word(0, last_char_index), last_word_index)
                self.assertEqual(batch_encoding.char_to_word(last_batch_index, last_char_index), last_word_index)

                # Assert word_to_chars
                self.assertEqual(encoding.word_to_chars(0).start, 0)
                self.assertEqual(encoding.word_to_chars(0, 0).start, 0)
                self.assertEqual(encoding.word_to_chars(last_word_index).end, last_char_index + 1)
                self.assertEqual(encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
                self.assertEqual(batch_encoding.word_to_chars(1, 0).start, 0)
                self.assertEqual(batch_encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
                self.assertEqual(
                    batch_encoding.word_to_chars(last_batch_index, last_word_index).end, last_char_index + 1
                )

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                # Assert token_to_sequence
                self.assertEqual(encoding.token_to_sequence(num_tokens // 2), 0)
                self.assertEqual(encoding.token_to_sequence(0, num_tokens // 2), 0)
                self.assertEqual(batch_encoding.token_to_sequence(1, num_tokens // 2), 0)
                self.assertEqual(batch_encoding.token_to_sequence(0, num_tokens // 2), 0)
                self.assertEqual(batch_encoding.token_to_sequence(last_batch_index, num_tokens // 2), 0)

                # Pair of input sequences

                words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
                text = " ".join(words)
                pair_words = ["Amazing", "example", "full", "of", "inspiration"]
                pair_text = " ".join(pair_words)
                batch_size = 3
                index_word_in_first_seq = words.index("inspiration")
                index_word_in_pair_seq = pair_words.index("inspiration")
                index_char_in_first_seq = text.find("inspiration")
                index_char_in_pair_seq = pair_text.find("inspiration")

                pair_encoding = tokenizer_r.encode_plus(text, pair_text, add_special_tokens=False)

                pair_batch_encoding = tokenizer_r.batch_encode_plus(
                    [(text, pair_text)] * batch_size, add_special_tokens=False
                )
                num_tokens = len(encoding["input_ids"])

                last_word_index = len(words) - 1
                last_token_index = num_tokens - 1
                last_batch_index = batch_size - 1
                last_char_index = len(text) - 1

                # Assert word_to_tokens
                self.assertNotEqual(
                    pair_encoding.word_to_tokens(index_word_in_first_seq, sequence_index=0).start,
                    pair_encoding.word_to_tokens(index_word_in_pair_seq, sequence_index=1).start,
                )
                self.assertEqual(
                    pair_encoding["input_ids"][
                        pair_encoding.word_to_tokens(index_word_in_first_seq, sequence_index=0).start
                    ],
                    pair_encoding["input_ids"][
                        pair_encoding.word_to_tokens(index_word_in_pair_seq, sequence_index=1).start
                    ],
                )
                self.assertNotEqual(
                    pair_batch_encoding.word_to_tokens(1, index_word_in_first_seq, sequence_index=0).start,
                    pair_batch_encoding.word_to_tokens(1, index_word_in_pair_seq, sequence_index=1).start,
                )
                self.assertEqual(
                    pair_batch_encoding["input_ids"][1][
                        pair_batch_encoding.word_to_tokens(1, index_word_in_first_seq, sequence_index=0).start
                    ],
                    pair_batch_encoding["input_ids"][1][
                        pair_batch_encoding.word_to_tokens(1, index_word_in_pair_seq, sequence_index=1).start
                    ],
                )

                # Assert char_to_token
                self.assertNotEqual(
                    pair_encoding.char_to_token(index_char_in_first_seq, sequence_index=0),
                    pair_encoding.char_to_token(index_char_in_pair_seq, sequence_index=1),
                )
                self.assertEqual(
                    pair_encoding["input_ids"][pair_encoding.char_to_token(index_char_in_first_seq, sequence_index=0)],
                    pair_encoding["input_ids"][pair_encoding.char_to_token(index_char_in_pair_seq, sequence_index=1)],
                )
                self.assertNotEqual(
                    pair_batch_encoding.char_to_token(1, index_char_in_first_seq, sequence_index=0),
                    pair_batch_encoding.char_to_token(1, index_char_in_pair_seq, sequence_index=1),
                )
                self.assertEqual(
                    pair_batch_encoding["input_ids"][1][
                        pair_batch_encoding.char_to_token(1, index_char_in_first_seq, sequence_index=0)
                    ],
                    pair_batch_encoding["input_ids"][1][
                        pair_batch_encoding.char_to_token(1, index_char_in_pair_seq, sequence_index=1)
                    ],
                )

                # Assert char_to_word
                self.assertNotEqual(
                    pair_encoding.char_to_word(index_char_in_first_seq, sequence_index=0),
                    pair_encoding.char_to_word(index_char_in_pair_seq, sequence_index=1),
                )
                self.assertEqual(
                    words[pair_encoding.char_to_word(index_char_in_first_seq, sequence_index=0)],
                    pair_words[pair_encoding.char_to_word(index_char_in_pair_seq, sequence_index=1)],
                )
                self.assertNotEqual(
                    pair_batch_encoding.char_to_word(1, index_char_in_first_seq, sequence_index=0),
                    pair_batch_encoding.char_to_word(1, index_char_in_pair_seq, sequence_index=1),
                )
                self.assertEqual(
                    words[pair_batch_encoding.char_to_word(1, index_char_in_first_seq, sequence_index=0)],
                    pair_words[pair_batch_encoding.char_to_word(1, index_char_in_pair_seq, sequence_index=1)],
                )

                # Assert word_to_chars
                self.assertNotEqual(
                    pair_encoding.word_to_chars(index_word_in_first_seq, sequence_index=0).start,
                    pair_encoding.word_to_chars(index_word_in_pair_seq, sequence_index=1).start,
                )
                self.assertEqual(
                    text[pair_encoding.word_to_chars(index_word_in_first_seq, sequence_index=0).start],
                    pair_text[pair_encoding.word_to_chars(index_word_in_pair_seq, sequence_index=1).start],
                )
                self.assertNotEqual(
                    pair_batch_encoding.word_to_chars(1, index_word_in_first_seq, sequence_index=0).start,
                    pair_batch_encoding.word_to_chars(1, index_word_in_pair_seq, sequence_index=1).start,
                )
                self.assertEqual(
                    text[pair_batch_encoding.word_to_chars(1, index_word_in_first_seq, sequence_index=0).start],
                    pair_text[pair_batch_encoding.word_to_chars(1, index_word_in_pair_seq, sequence_index=1).start],
                )

                # Assert token_to_sequence
                pair_encoding = tokenizer_r.encode_plus(text, pair_text, add_special_tokens=True)

                pair_sequence_ids = [
                    pair_encoding.token_to_sequence(i) for i in range(len(pair_encoding["input_ids"]))
                ]
                self.assertIn(0, pair_sequence_ids)
                self.assertIn(1, pair_sequence_ids)
                if tokenizer_r.num_special_tokens_to_add(pair=True):
                    self.assertIn(None, pair_sequence_ids)

                pair_batch_encoding = tokenizer_r.batch_encode_plus(
                    [(text, pair_text)] * batch_size, add_special_tokens=True
                )
                pair_batch_sequence_ids = [
                    pair_batch_encoding.token_to_sequence(1, i)
                    for i in range(len(pair_batch_encoding["input_ids"][0]))
                ]
                self.assertIn(0, pair_batch_sequence_ids)
                self.assertIn(1, pair_batch_sequence_ids)
                if tokenizer_r.num_special_tokens_to_add(pair=True):
                    self.assertIn(None, pair_batch_sequence_ids)

2762
    def test_tokenization_python_rust_equals(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

2767
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2768
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                # Ensure basic input match
                input_p = tokenizer_p.encode_plus(self._data)
                input_r = tokenizer_r.encode_plus(self._data)

                for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
                    self.assertSequenceEqual(input_p[key], input_r[key])

                input_pairs_p = tokenizer_p.encode_plus(self._data, self._data)
                input_pairs_r = tokenizer_r.encode_plus(self._data, self._data)

                for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
                    self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])

                # Ensure truncation match
                input_p = tokenizer_p.encode_plus(self._data, max_length=512, truncation=True)
                input_r = tokenizer_r.encode_plus(self._data, max_length=512, truncation=True)

                for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
                    self.assertSequenceEqual(input_p[key], input_r[key])

                # Ensure truncation with stride match
                input_p = tokenizer_p.encode_plus(
                    self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
                )
                input_r = tokenizer_r.encode_plus(
                    self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
                )

                for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
                    self.assertSequenceEqual(input_p[key], input_r[key][0])

    def test_num_special_tokens_to_add_equal(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

2808
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2809
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                # Check we have the same number of added_tokens for both pair and non-pair inputs.
                self.assertEqual(
                    tokenizer_r.num_special_tokens_to_add(False), tokenizer_p.num_special_tokens_to_add(False)
                )
                self.assertEqual(
                    tokenizer_r.num_special_tokens_to_add(True), tokenizer_p.num_special_tokens_to_add(True)
                )

    def test_max_length_equal(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

2826
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2827
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                # Check we have the correct max_length for both pair and non-pair inputs.
                self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
                self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

    def test_special_tokens_map_equal(self):
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2838
2839
        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

2840
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2841
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
2842
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                # Assert the set of special tokens match.
                self.assertSequenceEqual(
                    tokenizer_p.special_tokens_map.items(),
                    tokenizer_r.special_tokens_map.items(),
                )

    def test_add_tokens(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2853
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                vocab_size = len(tokenizer_r)
                self.assertEqual(tokenizer_r.add_tokens(""), 0)
                self.assertEqual(tokenizer_r.add_tokens("testoken"), 1)
                self.assertEqual(tokenizer_r.add_tokens(["testoken1", "testtoken2"]), 2)
                self.assertEqual(len(tokenizer_r), vocab_size + 3)

                self.assertEqual(tokenizer_r.add_special_tokens({}), 0)
                self.assertEqual(tokenizer_r.add_special_tokens({"bos_token": "[BOS]", "eos_token": "[EOS]"}), 2)
                self.assertRaises(
                    AssertionError, tokenizer_r.add_special_tokens, {"additional_special_tokens": "<testtoken1>"}
                )
                self.assertEqual(tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
                self.assertEqual(
                    tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
                )
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                self.assertIn("<testtoken3>", tokenizer_r.special_tokens_map["additional_special_tokens"])
                self.assertIsInstance(tokenizer_r.special_tokens_map["additional_special_tokens"], list)
                self.assertGreaterEqual(len(tokenizer_r.special_tokens_map["additional_special_tokens"]), 2)

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2878
                self.assertEqual(len(tokenizer_r), vocab_size + 8)

    def test_offsets_mapping(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2879
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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2921
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                text = "Wonderful no inspiration example with subtoken"
                pair = "Along with an awesome pair"

                # No pair
                tokens_with_offsets = tokenizer_r.encode_plus(
                    text, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
                )
                added_tokens = tokenizer_r.num_special_tokens_to_add(False)
                offsets = tokens_with_offsets["offset_mapping"]

                # Assert there is the same number of tokens and offsets
                self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))

                # Assert there is online added_tokens special_tokens
                self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)

                # Pairs
                tokens_with_offsets = tokenizer_r.encode_plus(
                    text, pair, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
                )
                added_tokens = tokenizer_r.num_special_tokens_to_add(True)
                offsets = tokens_with_offsets["offset_mapping"]

                # Assert there is the same number of tokens and offsets
                self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))

                # Assert there is online added_tokens special_tokens
                self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)

    def test_batch_encode_dynamic_overflowing(self):
        """
        When calling batch_encode with multiple sequence it can returns different number of
        overflowing encoding for each sequence:
        [
          Sequence 1: [Encoding 1, Encoding 2],
          Sequence 2: [Encoding 1],
          Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
        ]
        This needs to be padded so that it can represented as a tensor
        """
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

2925
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
2926

2927
2928
2929
2930
2931
2932
                if is_torch_available():
                    returned_tensor = "pt"
                elif is_tf_available():
                    returned_tensor = "tf"
                else:
                    returned_tensor = "jax"
2933
2934
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2936
2937
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2939
2940
2941
2942
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2970
2971
2972
2973
2974
2975
2976
2977

                if not tokenizer.pad_token or tokenizer.pad_token_id < 0:
                    return

                tokens = tokenizer.encode_plus(
                    "HuggingFace is solving NLP one commit at a time",
                    max_length=6,
                    padding=True,
                    truncation=True,
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)

                # Mono sample
                tokens = tokenizer.batch_encode_plus(
                    ["HuggingFace is solving NLP one commit at a time"],
                    max_length=6,
                    padding=True,
                    truncation="only_first",
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)
                    self.assertEqual(tokens[key].shape[-1], 6)

                # Multi sample
                tokens = tokenizer.batch_encode_plus(
                    ["HuggingFace is solving NLP one commit at a time", "Very tiny input"],
                    max_length=6,
                    padding=True,
                    truncation="only_first",
                    return_tensors=returned_tensor,
                    return_overflowing_tokens=True,
                )

                for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
                    self.assertEqual(len(tokens[key].shape), 2)
                    self.assertEqual(tokens[key].shape[-1], 6)

    def test_compare_pretokenized_inputs(self):
2978
2979
2980
2981
        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

2982
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
2983
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
2984
2985
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2987
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2989
2990
2991
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2993
2994
2995
2996
2997
2998
2999
3000
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3003
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3007
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3031
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3046
3047
3048
3049
3050
3051
3052
3053
3054
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3059
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                if hasattr(tokenizer_p, "add_prefix_space") and not tokenizer_p.add_prefix_space:
                    continue  # Too hard to test for now

                # Input string
                pretokenized_input_simple = "This is a sample input".split()
                pretokenized_input_pair = "This is a sample pair".split()

                # Test encode for pretokenized inputs
                output_r = tokenizer_r.encode(
                    pretokenized_input_simple, is_split_into_words=True, add_special_tokens=False
                )
                output_p = tokenizer_p.encode(
                    pretokenized_input_simple, is_split_into_words=True, add_special_tokens=False
                )
                self.assertEqual(output_p, output_r)

                kwargs = {
                    "is_split_into_words": True,
                    # "return_token_type_ids": True,  # Use the defaults for each tokenizers
                    # "return_attention_mask": True,  # Use the defaults for each tokenizers
                    "return_overflowing_tokens": False,
                    "return_special_tokens_mask": True,
                    "return_offsets_mapping": False,  # Not implemented in python tokenizers
                    # "add_special_tokens": False,
                }
                batch_kwargs = {
                    "is_split_into_words": True,
                    # "return_token_type_ids": True,  # Use the defaults for each tokenizers
                    # "return_attention_mask": True,  # Use the defaults for each tokenizers
                    "return_overflowing_tokens": False,
                    "return_special_tokens_mask": True,
                    "return_offsets_mapping": False,  # Not implemented in python tokenizers
                    # "add_special_tokens": False,
                }
                # Test encode_plus for pretokenized inputs
                output_r = tokenizer_r.encode_plus(pretokenized_input_simple, **kwargs)
                output_p = tokenizer_p.encode_plus(pretokenized_input_simple, **kwargs)
                for key in output_p.keys():
                    self.assertEqual(output_p[key], output_r[key])

                # Test batch_encode_plus for pretokenized inputs
                input_batch = ([pretokenized_input_simple] * 2) + [pretokenized_input_simple + pretokenized_input_pair]
                output_r = tokenizer_r.batch_encode_plus(input_batch, **batch_kwargs)
                output_p = tokenizer_p.batch_encode_plus(input_batch, **batch_kwargs)
                for key in output_p.keys():
                    self.assertEqual(output_p[key], output_r[key])

                # Test encode for pretokenized inputs pairs
                output_r = tokenizer_r.encode(
                    pretokenized_input_simple, pretokenized_input_pair, is_split_into_words=True
                )
                output_p = tokenizer_p.encode(
                    pretokenized_input_simple, pretokenized_input_pair, is_split_into_words=True
                )
                self.assertEqual(output_p, output_r)

                # Test encode_plus for pretokenized inputs
                output_r = tokenizer_r.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
                output_p = tokenizer_p.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
                for key in output_p.keys():
                    self.assertEqual(output_p[key], output_r[key])

                # Test batch_encode_plus for pretokenized inputs
                input_batch_pair = ([pretokenized_input_simple, pretokenized_input_pair] * 2) + [
                    pretokenized_input_simple + pretokenized_input_pair,
                    pretokenized_input_pair,
                ]
                output_r = tokenizer_r.batch_encode_plus(input_batch_pair, **batch_kwargs)
                output_p = tokenizer_p.batch_encode_plus(input_batch_pair, **batch_kwargs)
                for key in output_p.keys():
                    self.assertEqual(output_p[key], output_r[key])

    def test_create_token_type_ids(self):
3060
3061
3062
3063
        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

3064
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
3065
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                input_simple = [1, 2, 3]
                input_pair = [1, 2, 3]

                # Generate output
                output_r = tokenizer_r.create_token_type_ids_from_sequences(input_simple)
                output_p = tokenizer_p.create_token_type_ids_from_sequences(input_simple)
                self.assertEqual(output_p, output_r)

                # Generate pair output
                output_r = tokenizer_r.create_token_type_ids_from_sequences(input_simple, input_pair)
                output_p = tokenizer_p.create_token_type_ids_from_sequences(input_simple, input_pair)
                self.assertEqual(output_p, output_r)

    def test_build_inputs_with_special_tokens(self):
3082
3083
3084
3085
        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

3086
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
3087
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
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3100
3101
3102
3103
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3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                # # Input string
                # input_simple = tokenizer_p.tokenize("This is a sample input", add_special_tokens=False)
                # input_pair = tokenizer_p.tokenize("This is a sample pair", add_special_tokens=False)

                # # Generate output
                # output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
                # output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
                # self.assertEqual(output_p, output_r)

                # # Generate pair output
                # output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
                # output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
                # self.assertEqual(output_p, output_r)

                # Input tokens id
                input_simple = tokenizer_p.encode("This is a sample input", add_special_tokens=False)
                input_pair = tokenizer_p.encode("This is a sample pair", add_special_tokens=False)

                # Generate output
                output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
                output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
                self.assertEqual(output_p, output_r)

                # Generate pair output
                output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
                output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
                self.assertEqual(output_p, output_r)

    def test_padding(self, max_length=50):
3119
3120
3121
3122
        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

3123
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
3124
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
3125
3126
3127
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

3128
3129
                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
                pad_token_id = tokenizer_p.pad_token_id
3130
3131
3132
3133

                # Encode - Simple input
                input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
                input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
3134
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
3135
3136
                input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
                input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
3137
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
3138
3139
3140

                input_r = tokenizer_r.encode("This is a simple input", padding="longest")
                input_p = tokenizer_p.encode("This is a simple input", padding=True)
3141
                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
3142
3143
3144
3145
3146
3147
3148
3149

                # Encode - Pair input
                input_r = tokenizer_r.encode(
                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode(
                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
                )
3150
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
3151
3152
3153
3154
3155
3156
                input_r = tokenizer_r.encode(
                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode(
                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                )
3157
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
3158
3159
                input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
                input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
3160
                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
3161
3162
3163
3164
3165
3166
3167
3168

                # Encode_plus - Simple input
                input_r = tokenizer_r.encode_plus(
                    "This is a simple input", max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode_plus(
                    "This is a simple input", max_length=max_length, pad_to_max_length=True
                )
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                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus(
                    "This is a simple input", max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode_plus(
                    "This is a simple input", max_length=max_length, padding="max_length"
                )
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                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
                input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
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                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )
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                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                # Encode_plus - Pair input
                input_r = tokenizer_r.encode_plus(
                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode_plus(
                    "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
                )
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                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus(
                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode_plus(
                    "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
                )
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                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
                input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
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                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )
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                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                # Batch_encode_plus - Simple input
                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"],
                    max_length=max_length,
                    pad_to_max_length=True,
                )
                input_p = tokenizer_p.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"],
                    max_length=max_length,
                    pad_to_max_length=True,
                )
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                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"],
                    max_length=max_length,
                    padding="max_length",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"],
                    max_length=max_length,
                    padding="max_length",
                )
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                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"],
                    max_length=max_length,
                    padding="longest",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"],
                    max_length=max_length,
                    padding=True,
                )
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                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"], padding="longest"
                )
                input_p = tokenizer_p.batch_encode_plus(
                    ["This is a simple input 1", "This is a simple input 2"], padding=True
                )
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                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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                # Batch_encode_plus - Pair input
                input_r = tokenizer_r.batch_encode_plus(
                    [
                        ("This is a simple input 1", "This is a simple input 2"),
                        ("This is a simple pair 1", "This is a simple pair 2"),
                    ],
                    max_length=max_length,
                    truncation=True,
                    padding="max_length",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    [
                        ("This is a simple input 1", "This is a simple input 2"),
                        ("This is a simple pair 1", "This is a simple pair 2"),
                    ],
                    max_length=max_length,
                    truncation=True,
                    padding="max_length",
                )
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                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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                input_r = tokenizer_r.batch_encode_plus(
                    [
                        ("This is a simple input 1", "This is a simple input 2"),
                        ("This is a simple pair 1", "This is a simple pair 2"),
                    ],
                    padding=True,
                )
                input_p = tokenizer_p.batch_encode_plus(
                    [
                        ("This is a simple input 1", "This is a simple input 2"),
                        ("This is a simple pair 1", "This is a simple pair 2"),
                    ],
                    padding="longest",
                )
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                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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                # Using pad on single examples after tokenization
                input_r = tokenizer_r.encode_plus("This is a input 1")
                input_r = tokenizer_r.pad(input_r)

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                input_p = tokenizer_p.encode_plus("This is a input 1")
                input_p = tokenizer_p.pad(input_p)
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                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )
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                # Using pad on single examples after tokenization
                input_r = tokenizer_r.encode_plus("This is a input 1")
                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")

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                input_p = tokenizer_p.encode_plus("This is a input 1")
                input_p = tokenizer_p.pad(input_p, max_length=max_length, padding="max_length")
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                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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                # Using pad after tokenization
                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a input 1", "This is a much longer input whilch should be padded"]
                )
                input_r = tokenizer_r.pad(input_r)

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                input_p = tokenizer_p.batch_encode_plus(
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                    ["This is a input 1", "This is a much longer input whilch should be padded"]
                )
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                input_p = tokenizer_p.pad(input_p)
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                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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                # Using pad after tokenization
                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a input 1", "This is a much longer input whilch should be padded"]
                )
                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")

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                input_p = tokenizer_p.batch_encode_plus(
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                    ["This is a input 1", "This is a much longer input whilch should be padded"]
                )
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                input_p = tokenizer_p.pad(input_p, max_length=max_length, padding="max_length")
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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                # Test padding nested empty lists (in some use-cases, there is no any token id in the `input_ids` list).
                input_r = tokenizer_r.pad({"input_ids": [[], []]}, max_length=max_length, padding="max_length")
                input_p = tokenizer_p.pad({"input_ids": [[], []]}, max_length=max_length, padding="max_length")
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                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

    def test_padding_different_model_input_name(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

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        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
                pad_token_id = tokenizer_p.pad_token_id

                input_r = tokenizer_r.batch_encode_plus(
                    ["This is a input 1", "This is a much longer input whilch should be padded"]
                )
                input_p = tokenizer_r.batch_encode_plus(
                    ["This is a input 1", "This is a much longer input whilch should be padded"]
                )

                # rename encoded batch to "inputs"
                input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]]
                del input_r[tokenizer_r.model_input_names[0]]

                input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]]
                del input_p[tokenizer_p.model_input_names[0]]

                # Renaming `input_ids` to `inputs`
                tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:]
                tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:]

                input_r = tokenizer_r.pad(input_r, padding="longest")
                input_p = tokenizer_r.pad(input_p, padding="longest")

                max_length = len(input_p["inputs"][0])
                self.assert_batch_padded_input_match(
                    input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs"
                )
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    def test_save_pretrained(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

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        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                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)
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                # make sure that all ".json" files are saved in the correct format
                for file_path in tokenizer_r_files + tokenizer_p_files:
                    if os.path.exists(file_path) and file_path.endswith(".json"):
                        check_json_file_has_correct_format(file_path)

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                # 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)
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                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))
                    # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                    # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                shutil.rmtree(tmpdirname2)

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                # 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)

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    def test_embeded_special_tokens(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

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        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                sentence = "A, <mask> AllenNLP sentence."
                tokens_r = tokenizer_r.encode_plus(
                    sentence,
                    add_special_tokens=True,
                )
                tokens_p = tokenizer_p.encode_plus(
                    sentence,
                    add_special_tokens=True,
                )

                for key in tokens_p.keys():
                    self.assertEqual(tokens_r[key], tokens_p[key])

                if "token_type_ids" in tokens_r:
                    self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))

                tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                tokens_p = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
                self.assertSequenceEqual(tokens_r, tokens_p)

    def test_compare_add_special_tokens(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                simple_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=False)
                # pair_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=True)

                for text in ["", " "]:
                    # tokenize()
                    no_special_tokens = tokenizer_r.tokenize(text, add_special_tokens=False)
                    with_special_tokens = tokenizer_r.tokenize(text, add_special_tokens=True)
                    self.assertEqual(
                        len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add
                    )

                    # encode()
                    no_special_tokens = tokenizer_r.encode(text, add_special_tokens=False)
                    with_special_tokens = tokenizer_r.encode(text, add_special_tokens=True)
                    self.assertEqual(
                        len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add
                    )

                    # encode_plus()
                    no_special_tokens = tokenizer_r.encode_plus(text, add_special_tokens=False)
                    with_special_tokens = tokenizer_r.encode_plus(text, add_special_tokens=True)
                    for key in no_special_tokens.keys():
                        self.assertEqual(
                            len(no_special_tokens[key]),
                            len(with_special_tokens[key]) - simple_num_special_tokens_to_add,
                        )

                    # # batch_encode_plus
                    no_special_tokens = tokenizer_r.batch_encode_plus([text, text], add_special_tokens=False)
                    with_special_tokens = tokenizer_r.batch_encode_plus([text, text], add_special_tokens=True)
                    for key in no_special_tokens.keys():
                        for i_no, i_with in zip(no_special_tokens[key], with_special_tokens[key]):
                            self.assertEqual(len(i_no), len(i_with) - simple_num_special_tokens_to_add)

    def test_compare_prepare_for_model(self):
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        if not self.test_slow_tokenizer:
            # as we don't have a slow version, we can't compare the outputs between slow and fast versions
            return

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        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                string_sequence = "Asserting that both tokenizers are equal"
                python_output = tokenizer_p.prepare_for_model(
                    tokenizer_p.encode(string_sequence, add_special_tokens=False)
                )
                rust_output = tokenizer_r.prepare_for_model(
                    tokenizer_r.encode(string_sequence, add_special_tokens=False)
                )
                for key in python_output:
                    self.assertEqual(python_output[key], rust_output[key])
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    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)
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                if self.test_slow_tokenizer:
                    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")

                    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)
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    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)

                special_tokens_map["additional_special_tokens"] = ["an_additional_special_token"]
                tokenizer_config["additional_special_tokens"] = ["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())
                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 = [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"])
                    ),
                )

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    def test_training_new_tokenizer(self):
        # This feature only exists for fast tokenizers
        if not self.test_rust_tokenizer:
            return

        tokenizer = self.get_rust_tokenizer()
        new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100)

        # Test we can use the new tokenizer with something not seen during training
        inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
        self.assertEqual(len(inputs["input_ids"]), 2)
        decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
        expected_result = "This is the first sentence"

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        if tokenizer.backend_tokenizer.normalizer is not None:
            expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
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        self.assertEqual(expected_result, decoded_input)

        # We check that the parameters of the tokenizer remained the same
        # Check we have the same number of added_tokens for both pair and non-pair inputs.
        self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False))
        self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True))

        # Check we have the correct max_length for both pair and non-pair inputs.
        self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence)
        self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair)

        # Assert the set of special tokens match as we didn't ask to change them
        self.assertSequenceEqual(
            tokenizer.all_special_tokens_extended,
            new_tokenizer.all_special_tokens_extended,
        )

        self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map)

    def test_training_new_tokenizer_with_special_tokens_change(self):
        # This feature only exists for fast tokenizers
        if not self.test_rust_tokenizer:
            return

        tokenizer = self.get_rust_tokenizer()
        # Test with a special tokens map
        class_signature = inspect.signature(tokenizer.__class__)
        if "cls_token" in class_signature.parameters:
            new_tokenizer = tokenizer.train_new_from_iterator(
                SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: "<cls>"}
            )
            cls_id = new_tokenizer.get_vocab()["<cls>"]
            self.assertEqual(new_tokenizer.cls_token, "<cls>")
            self.assertEqual(new_tokenizer.cls_token_id, cls_id)

        # Create a new mapping from the special tokens defined in the original tokenizer
        special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
        special_tokens_list.remove("additional_special_tokens")
        special_tokens_map = {}
        for token in special_tokens_list:
            # Get the private one to avoid unnecessary warnings.
            if getattr(tokenizer, f"_{token}") is not None:
                special_token = getattr(tokenizer, token)
                special_tokens_map[special_token] = f"{special_token}a"

        # Train new tokenizer
        new_tokenizer = tokenizer.train_new_from_iterator(
            SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map
        )

        # Check the changes
        for token in special_tokens_list:
            # Get the private one to avoid unnecessary warnings.
            if getattr(tokenizer, f"_{token}") is None:
                continue
            special_token = getattr(tokenizer, token)
            if special_token in special_tokens_map:
                new_special_token = getattr(new_tokenizer, token)
                self.assertEqual(special_tokens_map[special_token], new_special_token)

                new_id = new_tokenizer.get_vocab()[new_special_token]
                self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id)

        # Check if the AddedToken / string format has been kept
        for special_token in tokenizer.all_special_tokens_extended:
            if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map:
                # The special token must appear identically in the list of the new tokenizer.
                self.assertTrue(
                    special_token in new_tokenizer.all_special_tokens_extended,
                    f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
                )
            elif isinstance(special_token, AddedToken):
                # The special token must appear in the list of the new tokenizer as an object of type AddedToken with
                # the same parameters as the old AddedToken except the content that the user has requested to change.
                special_token_str = special_token.content
                new_special_token_str = special_tokens_map[special_token_str]

                find = False
                for candidate in new_tokenizer.all_special_tokens_extended:
                    if (
                        isinstance(candidate, AddedToken)
                        and candidate.content == new_special_token_str
                        and candidate.lstrip == special_token.lstrip
                        and candidate.rstrip == special_token.rstrip
                        and candidate.normalized == special_token.normalized
                        and candidate.single_word == special_token.single_word
                    ):
                        find = True
                        break
                self.assertTrue(
                    find,
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                    f"'{new_special_token_str}' doesn't appear in the list "
                    f"'{new_tokenizer.all_special_tokens_extended}' as an AddedToken with the same parameters as "
                    f"'{special_token}' in the list {tokenizer.all_special_tokens_extended}",
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                )
            elif special_token not in special_tokens_map:
                # The special token must appear identically in the list of the new tokenizer.
                self.assertTrue(
                    special_token in new_tokenizer.all_special_tokens_extended,
                    f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
                )

            else:
                # The special token must appear in the list of the new tokenizer as an object of type string.
                self.assertTrue(special_tokens_map[special_token] in new_tokenizer.all_special_tokens_extended)

        # Test we can use the new tokenizer with something not seen during training
        inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
        self.assertEqual(len(inputs["input_ids"]), 2)
        decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
        expected_result = "This is the first sentence"

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        if tokenizer.backend_tokenizer.normalizer is not None:
            expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
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        self.assertEqual(expected_result, decoded_input)

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    def test_tokenizer_mismatch_warning(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                with self.assertLogs("transformers", level="WARNING") as cm:
                    try:
                        if self.tokenizer_class == BertTokenizer:
                            AlbertTokenizer.from_pretrained(pretrained_name)
                        else:
                            BertTokenizer.from_pretrained(pretrained_name)
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                    except EnvironmentError as e:
                        # Some tokenizer will raised an error before reaching the logged warning because there are no
                        # corresponding files to load
                        error_message = str(e)
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                    except (TypeError, AttributeError):
                        # Some tokenizers cannot be loaded into the target tokenizer at all and errors are returned,
                        # here we just check that the warning has been logged before the error is raised
                        pass
                    finally:
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                        logged_msg_target = (
                            "The tokenizer class you load from this checkpoint is not the same type as the class "
                            "this function is called from."
                        )
                        raised_error_msg_target = "Can't load tokenizer for"
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                        self.assertTrue(
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                            cm.records[0].message.startswith(logged_msg_target)
                            if len(cm.records) > 0
                            else False or raised_error_msg_target in error_message
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                        )
                    try:
                        if self.rust_tokenizer_class == BertTokenizerFast:
                            AlbertTokenizerFast.from_pretrained(pretrained_name)
                        else:
                            BertTokenizerFast.from_pretrained(pretrained_name)
                    except (TypeError, AttributeError):
                        # Some tokenizers cannot be loaded into the target tokenizer at all and errors are returned,
                        # here we just check that the warning has been logged before the error is raised
                        pass
                    finally:
                        self.assertTrue(
                            cm.records[0].message.startswith(
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                                "The tokenizer class you load from this checkpoint is not the same type as the class"
                                " this function is called from."
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                            )
                        )

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    @require_torch
    def test_saving_tokenizer_trainer(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                with tempfile.TemporaryDirectory() as tmp_dir:
                    # Save the fast tokenizer files in a temporary directory
                    tokenizer_old = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs, use_fast=True)
                    tokenizer_old.save_pretrained(tmp_dir, legacy_format=False)  # save only fast version

                    # Initialize toy model for the trainer
                    model = nn.Module()

                    # Load tokenizer from a folder without legacy files
                    tokenizer = self.rust_tokenizer_class.from_pretrained(tmp_dir)
                    training_args = TrainingArguments(output_dir=tmp_dir, do_train=True, no_cuda=True)
                    trainer = Trainer(model=model, args=training_args, tokenizer=tokenizer)

                    # Should not raise an error
                    trainer.save_model(os.path.join(tmp_dir, "checkpoint"))
                    self.assertIn("tokenizer.json", os.listdir(os.path.join(tmp_dir, "checkpoint")))

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    def test_convert_tokens_to_string_format(self):
        tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                tokens = ["this", "is", "a", "test"]
                string = tokenizer.convert_tokens_to_string(tokens)

                self.assertIsInstance(string, str)

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    def test_save_slow_from_fast_and_reload_fast(self):
        if not self.test_slow_tokenizer or not self.test_rust_tokenizer:
            # we need both slow and fast versions
            return

        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                with tempfile.TemporaryDirectory() as tmp_dir_1:
                    # Here we check that even if we have initialized a fast tokenizer with a tokenizer_file we can
                    # still save only the slow version and use these saved files to rebuild a tokenizer
                    tokenizer_fast_old_1 = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, **kwargs, use_fast=True
                    )
                    tokenizer_file = os.path.join(tmp_dir_1, "tokenizer.json")
                    tokenizer_fast_old_1.backend_tokenizer.save(tokenizer_file)

                    tokenizer_fast_old_2 = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, **kwargs, use_fast=True, tokenizer_file=tokenizer_file
                    )

                    tokenizer_fast_old_2.save_pretrained(tmp_dir_1, legacy_format=True)  # save only slow version

                    tokenizer_slow = self.tokenizer_class.from_pretrained(tmp_dir_1)
                with tempfile.TemporaryDirectory() as tmp_dir_2:
                    tokenizer_slow.save_pretrained(tmp_dir_2)

                    # Should not raise an error
                    self.rust_tokenizer_class.from_pretrained(tmp_dir_2)

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class TokenizerUtilTester(unittest.TestCase):
    def test_cached_files_are_used_when_internet_is_down(self):
        # A mock response for an HTTP head request to emulate server down
        response_mock = mock.Mock()
        response_mock.status_code = 500
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        response_mock.headers = {}
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        response_mock.raise_for_status.side_effect = HTTPError

        # Download this model to make sure it's in the cache.
        _ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")

        # Under the mock environment we get a 500 error when trying to reach the model.
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        with mock.patch("requests.request", return_value=response_mock) as mock_head:
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            _ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
            # This check we did call the fake head request
            mock_head.assert_called()


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@is_staging_test
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class TokenizerPushToHubTester(unittest.TestCase):
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    vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

    @classmethod
    def setUpClass(cls):
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        cls._token = TOKEN
        set_access_token(TOKEN)
        HfFolder.save_token(TOKEN)
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    @classmethod
    def tearDownClass(cls):
        try:
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            delete_repo(token=cls._token, repo_id="test-tokenizer")
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        except HTTPError:
            pass

        try:
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            delete_repo(token=cls._token, repo_id="valid_org/test-tokenizer-org")
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        except HTTPError:
            pass

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        try:
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            delete_repo(token=cls._token, repo_id="test-dynamic-tokenizer")
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        except HTTPError:
            pass

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    def test_push_to_hub(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            vocab_file = os.path.join(tmp_dir, "vocab.txt")
            with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
            tokenizer = BertTokenizer(vocab_file)

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        tokenizer.push_to_hub("test-tokenizer", use_auth_token=self._token)
        new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
        self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)

        # Reset repo
        delete_repo(token=self._token, repo_id="test-tokenizer")

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(tmp_dir, repo_id="test-tokenizer", push_to_hub=True, use_auth_token=self._token)

        new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
        self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
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    def test_push_to_hub_in_organization(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            vocab_file = os.path.join(tmp_dir, "vocab.txt")
            with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
            tokenizer = BertTokenizer(vocab_file)
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        tokenizer.push_to_hub("valid_org/test-tokenizer-org", use_auth_token=self._token)
        new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
        self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)

        # Reset repo
        delete_repo(token=self._token, repo_id="valid_org/test-tokenizer-org")

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
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            tokenizer.save_pretrained(
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                tmp_dir, repo_id="valid_org/test-tokenizer-org", push_to_hub=True, use_auth_token=self._token
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            )

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        new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
        self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
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    @require_tokenizers
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    def test_push_to_hub_dynamic_tokenizer(self):
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        CustomTokenizer.register_for_auto_class()
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        with tempfile.TemporaryDirectory() as tmp_dir:
            vocab_file = os.path.join(tmp_dir, "vocab.txt")
            with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
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            tokenizer = CustomTokenizer(vocab_file)
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        # No fast custom tokenizer
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        tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
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        tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
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        # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
        self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
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        # Fast and slow custom tokenizer
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        CustomTokenizerFast.register_for_auto_class()
        with tempfile.TemporaryDirectory() as tmp_dir:
            vocab_file = os.path.join(tmp_dir, "vocab.txt")
            with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))

            bert_tokenizer = BertTokenizerFast.from_pretrained(tmp_dir)
            bert_tokenizer.save_pretrained(tmp_dir)
            tokenizer = CustomTokenizerFast.from_pretrained(tmp_dir)

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        tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token)
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        tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True)
        # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
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        tokenizer = AutoTokenizer.from_pretrained(
            f"{USER}/test-dynamic-tokenizer", use_fast=False, trust_remote_code=True
        )
        # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
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        self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer")
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class TrieTest(unittest.TestCase):
    def test_trie(self):
        trie = Trie()
        trie.add("Hello 友達")
        self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}})
        trie.add("Hello")
        trie.data
        self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}})

    def test_trie_split(self):
        trie = Trie()
        self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS] This is a extra_id_100"])
        trie.add("[CLS]")
        trie.add("extra_id_1")
        trie.add("extra_id_100")
        self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS]", " This is a ", "extra_id_100"])
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    def test_trie_single(self):
        trie = Trie()
        trie.add("A")
        self.assertEqual(trie.split("ABC"), ["A", "BC"])
        self.assertEqual(trie.split("BCA"), ["BC", "A"])

    def test_trie_final(self):
        trie = Trie()
        trie.add("TOKEN]")
        trie.add("[SPECIAL_TOKEN]")
        self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])
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    def test_trie_subtokens(self):
        trie = Trie()
        trie.add("A")
        trie.add("P")
        trie.add("[SPECIAL_TOKEN]")
        self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"])

    def test_trie_suffix_tokens(self):
        trie = Trie()
        trie.add("AB")
        trie.add("B")
        trie.add("C")
        self.assertEqual(trie.split("ABC"), ["AB", "C"])

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    def test_trie_skip(self):
        trie = Trie()
        trie.add("ABC")
        trie.add("B")
        trie.add("CD")
        self.assertEqual(trie.split("ABCD"), ["ABC", "D"])

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    def test_cut_text_hardening(self):
        # Even if the offsets are wrong, we necessarily output correct string
        # parts.
        trie = Trie()
        parts = trie.cut_text("ABC", [0, 0, 2, 1, 2, 3])
        self.assertEqual(parts, ["AB", "C"])