test_tokenization_fast.py

import unittest
from collections import namedtuple
from itertools import takewhile

from tests.utils import require_torch
from transformers import (
    BertTokenizer,
    BertTokenizerFast,
    DistilBertTokenizer,
    GPT2Tokenizer,
    GPT2TokenizerFast,
    OpenAIGPTTokenizer,
    PreTrainedTokenizer,
    RobertaTokenizer,
    TransfoXLTokenizer,
    is_torch_available,
)
from transformers.tokenization_distilbert import DistilBertTokenizerFast
from transformers.tokenization_openai import OpenAIGPTTokenizerFast
from transformers.tokenization_roberta import RobertaTokenizerFast
from transformers.tokenization_transfo_xl import TransfoXLTokenizerFast


NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
Tokenizer = namedtuple("Tokenizer", ["name", "rust_cls", "python_cls", "vocab_key", "filter"])


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


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


class CommonFastTokenizerTest(unittest.TestCase):

    TOKENIZERS_CLASSES = frozenset([])

    def setUp(self) -> None:
        with open("tests/fixtures/sample_text.txt", encoding="utf-8") as f_data:
            self._data = f_data.read().replace("\n\n", "\n").strip()

    def test_all_tokenizers(self):
        for tok_case in self.TOKENIZERS_CLASSES:
            for pretrained_name in tok_case.python_cls.pretrained_vocab_files_map[tok_case.vocab_key].keys():

                # 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 tok_case.filter is None or (
                    tok_case.filter is not None and tok_case.filter(tok_case, pretrained_name)
                ):
                    with self.subTest("{} ({})".format(tok_case.name, pretrained_name)):
                        tokenizer_r = tok_case.rust_cls.from_pretrained(pretrained_name)
                        tokenizer_p = tok_case.python_cls.from_pretrained(pretrained_name)

                        self.fast_align_python(tokenizer_r, tokenizer_p)
                        self.fast_only(tokenizer_r)

    def fast_align_python(self, tokenizer_r, tokenizer_p):
        # Check is_fast is set correctly
        self.assertFalse(tokenizer_p.is_fast)
        self.assertTrue(tokenizer_r.is_fast)

        # Check that Rust and Python align
        self.assert_tokenization_python_rust_equals(tokenizer_r, tokenizer_p)
        self.assert_num_special_tokens_to_add_equal(tokenizer_r, tokenizer_p)
        self.assert_max_length_equal(tokenizer_r, tokenizer_p)
        self.assert_special_tokens_map_equal(tokenizer_r, tokenizer_p)
        self.assert_embeded_special_tokens(tokenizer_r, tokenizer_p)
        self.assert_padding(tokenizer_r, tokenizer_p)
        # TODO: enable for v3.0.0
        # self.assert_empty_output_no_special_tokens(tokenizer_r, tokenizer_p)

    def fast_only(self, tokenizer_r):
        # Ensure None raise an error
        self.assertRaises(ValueError, tokenizer_r.tokenize, None)
        self.assertRaises(ValueError, tokenizer_r.encode, None)
        self.assertRaises(ValueError, tokenizer_r.encode_plus, None)
        self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, None)

        self.assert_add_tokens(tokenizer_r)
        self.assert_offsets_mapping(tokenizer_r)
        self.assert_add_special_tokens(tokenizer_r)

    def assert_tokenization_python_rust_equals(self, tokenizer_p, tokenizer_r):
        # 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)
        input_r = tokenizer_r.encode_plus(self._data, max_length=512)

        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, stride=3, return_overflowing_tokens=True)
        input_r = tokenizer_r.encode_plus(self._data, max_length=512, 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])

    def assert_num_special_tokens_to_add_equal(self, tokenizer_r, tokenizer_p):
        # 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 assert_max_length_equal(self, tokenizer_r, tokenizer_p):
        # 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 assert_special_tokens_map_equal(self, tokenizer_r, tokenizer_p):
        # Assert the set of special tokens match.
        self.assertSequenceEqual(
            tokenizer_p.special_tokens_map.items(), tokenizer_r.special_tokens_map.items(),
        )

    def assert_add_tokens(self, tokenizer_r):
        vocab_size = tokenizer_r.vocab_size
        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.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
        )
        self.assertEqual(len(tokenizer_r), vocab_size + 6)

    def assert_offsets_mapping(self, tokenizer_r):
        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 assert_batch_encode_dynamic_overflowing(self, tokenizer: PreTrainedTokenizer):
        """
        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
        """
        returned_tensor = "pt" if is_torch_available() else "tf"

        tokens = tokenizer.encode_plus(
            "HuggingFace is solving NLP one commit at a time",
            max_length=6,
            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,
            pad_to_max_len=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)
            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,
            pad_to_max_len=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)
            self.assertEqual(tokens[key].shape[-1], 6)

    def assert_build_inputs_with_special_tokens(self, tokenizer_r, tokenizer_p):
        # Input string
        input_simple = tokenizer_p.tokenize("This is a sample input")
        input_pair = tokenizer_p.tokenize("This is a sample pair")

        # 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")
        input_pair = tokenizer_p.encode("This is a sample pair")

        # 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 assert_padding(self, tokenizer_r, tokenizer_p, max_length=15):
        def assert_padded_input_match(input_r: list, input_p: list, max_length: 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 == tokenizer_r.pad_token_id, reversed(input_r)))
            padded_tokens_p = list(takewhile(lambda i: i == tokenizer_p.pad_token_id, reversed(input_p)))
            self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)

        def assert_batch_padded_input_match(input_r: dict, input_p: dict):
            for i_r in input_r.values():
                self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), 15), self.assertEqual(len(i_r[1]), 15)
                self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), 15), self.assertEqual(len(i_r[1]), 15)

            for i_r, i_p in zip(input_r["input_ids"], input_p["input_ids"]):
                assert_padded_input_match(i_r, i_p, max_length)

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

        # 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)
        assert_padded_input_match(input_r, input_p, max_length)

        # 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
        )
        assert_padded_input_match(input_r, input_p, max_length)

        # 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)
        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
        self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

        # 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
        )
        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
        self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

        # Simple input
        # TODO: Re-enable this test when batch_encode_plus with padding correctly handles padding
        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
        )
        assert_batch_padded_input_match(input_r, input_p)

        # Pair input
        # TODO: Re-enable this test when batch_encode_plus with padding correctly handles padding
        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=15,
            pad_to_max_length=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"),
            ],
            max_length=15,
            pad_to_max_length=True,
        )
        assert_batch_padded_input_match(input_r, input_p)

    def assert_save_pretrained(self, tokenizer_r, tokenizer_p):
        # Checks it save with the same files
        self.assertSequenceEqual(tokenizer_r.save_vocabulary("."), tokenizer_p.save_vocabulary("."))

        # Checks everything loads correctly in the same way
        tokenizer_rp, tokenizer_pp = tokenizer_r.from_pretrained("."), tokenizer_p.from_pretrained(".")

        # 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"))

    def assert_embeded_special_tokens(self, tokenizer_r, tokenizer_p):
        sentence = "A, <mask> AllenNLP sentence."
        tokens_r = tokenizer_r.encode_plus(
            sentence, add_special_tokens=True, return_attention_mask=False, return_token_type_ids=True
        )
        tokens_p = tokenizer_p.encode_plus(
            sentence, add_special_tokens=True, return_attention_mask=False, return_token_type_ids=True
        )

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

        self.assertEqual(sum(tokens_r["token_type_ids"]), 0)
        self.assertEqual(sum(tokens_p["token_type_ids"]), 0)

        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 assert_add_special_tokens(self, tokenizer_r):
        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)


class WordPieceFastTokenizerTest(CommonFastTokenizerTest):
    """
    Override all the specific methods to test WordPiece behavior
    """

    TOKENIZERS_CLASSES = frozenset(
        [
            Tokenizer("Bert", BertTokenizerFast, BertTokenizer, "vocab_file", filter_non_english),
            Tokenizer("DistilBert", DistilBertTokenizerFast, DistilBertTokenizer, "vocab_file", filter_non_english),
        ]
    )

    def fast_only(self, tokenizer_r):
        super().fast_only(tokenizer_r)
        self.assert_offsets_with_special_characters(tokenizer_r)

    def assert_add_special_tokens(self, tokenizer_r):
        super().assert_add_special_tokens(tokenizer_r)

    def assert_offsets_with_special_characters(self, tokenizer_r):
        sentence = "A, naïve [MASK] AllenNLP sentence."
        tokens = tokenizer_r.encode_plus(
            sentence,
            return_attention_mask=False,
            return_token_type_ids=False,
            return_offsets_mapping=True,
            add_special_tokens=True,
        )

        expected_results = [
            ((0, 1), "A"),
            ((1, 2), ","),
            ((3, 8), "naive"),  # BERT normalizes this away
            # Append MASK here after lower-casing
            ((16, 21), "Allen"),
            ((22, 24), "##NL"),
            ((24, 25), "##P"),
            ((26, 34), "sentence"),
            ((35, 36), "."),
        ]

        # Check if the tokenizer is uncased
        if tokenizer_r.init_kwargs.get("do_lower_case"):
            expected_results = [(offset, token.lower()) for (offset, token) in expected_results]

        # Append the special tokens
        expected_results.insert(3, ((9, 15), "[MASK]"))
        expected_results.insert(0, (None, "[CLS]"))
        expected_results.append((None, "[SEP]"))

        self.assertEqual([e[1] for e in expected_results], tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]))
        # self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])


class RobertaFastTokenizerTest(CommonFastTokenizerTest):
    TOKENIZERS_CLASSES = frozenset(
        [Tokenizer("Roberta", RobertaTokenizerFast, RobertaTokenizer, "vocab_file", filter_roberta_detectors)]
    )

    def assert_embeded_special_tokens(self, tokenizer_r, tokenizer_p):
        sentence = "A, <mask> AllenNLP sentence."
        tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
        tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)

        # Rust correctly handles the space before the mask while python doesnt
        self.assertSequenceEqual(tokens_r["input_ids"], [0, 83, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
        self.assertSequenceEqual(tokens_p["input_ids"], [0, 83, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

        # token_type_ids should put 0 everywhere
        self.assertEquals(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))

        # attention_mask should put 1 everywhere, so sum over length should be 1
        self.assertEquals(
            sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
            sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
        )

        # Rust should have 'Ġ' before <mask> which should be left as an entire token
        tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
        self.assertSequenceEqual(tokens_r, ["<s>", "ĠA", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])


class NoPaddingTokenFastTokenizerMatchingTest(CommonFastTokenizerTest):
    TOKENIZERS_CLASSES = [
        Tokenizer("OpenAI GPT", OpenAIGPTTokenizerFast, OpenAIGPTTokenizer, "vocab_file", None),
        Tokenizer("GPT2", GPT2TokenizerFast, GPT2Tokenizer, "vocab_file", None),
    ]

    def assert_padding(self, tokenizer_r, tokenizer_p, max_length=15):
        # Simple input
        s = "This is a simple input"
        s2 = ["This is a simple input 1", "This is a simple input 2"]
        p = ("This is a simple input", "This is a pair")
        p2 = [
            ("This is a simple input 1", "This is a simple input 2"),
            ("This is a simple pair 1", "This is a simple pair 2"),
        ]

        # Simple input tests
        self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, pad_to_max_length=True)

        # Simple input
        self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, pad_to_max_length=True)

        # Simple input
        self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, pad_to_max_length=True)

        # Pair input
        self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, pad_to_max_length=True)

        # Pair input
        self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, pad_to_max_length=True)

        # Pair input
        self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, pad_to_max_length=True)


class TransfoXLFastTokenizerTest(NoPaddingTokenFastTokenizerMatchingTest):
    TOKENIZERS_CLASSES = frozenset(
        [Tokenizer("TransfoXL", TransfoXLTokenizerFast, TransfoXLTokenizer, "pretrained_vocab_file", None)]
    )

    @require_torch
    def test_all_tokenizers(self):
        super().test_all_tokenizers()