# What does this PR do?

PyTorch 1.9 was related on 2021/06/15. It's sad, but 2 years is long enough :-)
Byebye pytorch 1.9. # What does this PR do?

PyTorch 1.9 was related on 2021/06/15. It's sad, but 2 years is long enough :-)
# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class XLMRobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = XLMRobertaTokenizer
    rust_tokenizer_class = XLMRobertaTokenizerFast
    test_rust_tokenizer = True
    test_sentencepiece = True

    def setUp(self):
        super().setUp()

        # We have a SentencePiece fixture for testing
        tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True)
        tokenizer.save_pretrained(self.tmpdirname)

    def test_convert_token_and_id(self):
        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
        token = "<pad>"
        token_id = 1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)

    def test_get_vocab(self):
        vocab_keys = list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0], "<s>")
        self.assertEqual(vocab_keys[1], "<pad>")
        self.assertEqual(vocab_keys[-1], "<mask>")
        self.assertEqual(len(vocab_keys), 1_002)

    def test_vocab_size(self):
        self.assertEqual(self.get_tokenizer().vocab_size, 1_002)

    def test_full_tokenizer(self):
        tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True)

        tokens = tokenizer.tokenize("This is a test")
        self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(tokens),
            [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
        )

        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
        self.assertListEqual(
            tokens,
            [
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ],
        )
        ids = tokenizer.convert_tokens_to_ids(tokens)
        self.assertListEqual(
            ids,
            [
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ],
        )

        back_tokens = tokenizer.convert_ids_to_tokens(ids)
        self.assertListEqual(
            back_tokens,
            [
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ],
        )

    # overwrite from test_tokenization_common to speed up test
    def test_save_pretrained(self):
        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

        self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                tmpdirname2 = tempfile.mkdtemp()

                tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
                tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
                tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
                self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)

                # Checks everything loads correctly in the same way
                tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
                tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                    self.assertTrue(hasattr(tokenizer_rp, key))
                    # 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)

                # 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)

    @cached_property
    def big_tokenizer(self):
        return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")

    def test_picklable_without_disk(self):
        with tempfile.NamedTemporaryFile() as f:
            shutil.copyfile(SAMPLE_VOCAB, f.name)
            tokenizer = XLMRobertaTokenizer(f.name, keep_accents=True)
            pickled_tokenizer = pickle.dumps(tokenizer)
        pickle.loads(pickled_tokenizer)

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

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

        sequence = "I was born in 92000, and this is falsé."

        tokens = tokenizer.tokenize(sequence)
        rust_tokens = rust_tokenizer.tokenize(sequence)
        self.assertListEqual(tokens, rust_tokens)

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

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

    @slow
    def test_tokenization_base_easy_symbols(self):
        symbols = "Hello World!"
        original_tokenizer_encodings = [0, 35378, 6661, 38, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))

    @slow
    def test_tokenization_base_hard_symbols(self):
        symbols = (
            'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
            " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
        )
        original_tokenizer_encodings = [
            0,
            3293,
            83,
            10,
            4552,
            4989,
            7986,
            678,
            10,
            5915,
            111,
            179459,
            124850,
            4,
            6044,
            237,
            12,
            6,
            5,
            6,
            4,
            6780,
            705,
            15,
            1388,
            44,
            378,
            10114,
            711,
            152,
            20,
            6,
            5,
            22376,
            642,
            1221,
            15190,
            34153,
            450,
            5608,
            959,
            1119,
            57702,
            136,
            186,
            47,
            1098,
            29367,
            47,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            6044,
            237,
            6284,
            50901,
            528,
            31,
            90,
            34,
            927,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))

    @slow
    def test_tokenizer_integration(self):
        # fmt: off
        expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=expected_encoding,
            model_name="xlm-roberta-base",
            revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3",
        )

# coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class XLMRobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = XLMRobertaTokenizer
    rust_tokenizer_class = XLMRobertaTokenizerFast
    test_rust_tokenizer = True
    test_sentencepiece = True

    def setUp(self):
        super().setUp()

        # We have a SentencePiece fixture for testing
        tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True)
        tokenizer.save_pretrained(self.tmpdirname)

    def test_convert_token_and_id(self):
        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
        token = "<pad>"
        token_id = 1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)

    def test_get_vocab(self):
        vocab_keys = list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0], "<s>")
        self.assertEqual(vocab_keys[1], "<pad>")
        self.assertEqual(vocab_keys[-1], "<mask>")
        self.assertEqual(len(vocab_keys), 1_002)

    def test_vocab_size(self):
        self.assertEqual(self.get_tokenizer().vocab_size, 1_002)

    def test_full_tokenizer(self):
        tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True)

        tokens = tokenizer.tokenize("This is a test")
        self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(tokens),
            [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
        )

        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
        self.assertListEqual(
            tokens,
            [
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ],
        )
        ids = tokenizer.convert_tokens_to_ids(tokens)
        self.assertListEqual(
            ids,
            [
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ],
        )

        back_tokens = tokenizer.convert_ids_to_tokens(ids)
        self.assertListEqual(
            back_tokens,
            [
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ],
        )

    # overwrite from test_tokenization_common to speed up test
    def test_save_pretrained(self):
        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

        self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

                tmpdirname2 = tempfile.mkdtemp()

                tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
                tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
                tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
                self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)

                # Checks everything loads correctly in the same way
                tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
                tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                    self.assertTrue(hasattr(tokenizer_rp, key))
                    # 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)

                # 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)

    @cached_property
    def big_tokenizer(self):
        return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")

    def test_picklable_without_disk(self):
        with tempfile.NamedTemporaryFile() as f:
            shutil.copyfile(SAMPLE_VOCAB, f.name)
            tokenizer = XLMRobertaTokenizer(f.name, keep_accents=True)
            pickled_tokenizer = pickle.dumps(tokenizer)
        pickle.loads(pickled_tokenizer)

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

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

        sequence = "I was born in 92000, and this is falsé."

        tokens = tokenizer.tokenize(sequence)
        rust_tokens = rust_tokenizer.tokenize(sequence)
        self.assertListEqual(tokens, rust_tokens)

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

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

    @slow
    def test_tokenization_base_easy_symbols(self):
        symbols = "Hello World!"
        original_tokenizer_encodings = [0, 35378, 6661, 38, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))

    @slow
    def test_tokenization_base_hard_symbols(self):
        symbols = (
            'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
            " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
        )
        original_tokenizer_encodings = [
            0,
            3293,
            83,
            10,
            4552,
            4989,
            7986,
            678,
            10,
            5915,
            111,
            179459,
            124850,
            4,
            6044,
            237,
            12,
            6,
            5,
            6,
            4,
            6780,
            705,
            15,
            1388,
            44,
            378,
            10114,
            711,
            152,
            20,
            6,
            5,
            22376,
            642,
            1221,
            15190,
            34153,
            450,
            5608,
            959,
            1119,
            57702,
            136,
            186,
            47,
            1098,
            29367,
            47,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            6044,
            237,
            6284,
            50901,
            528,
            31,
            90,
            34,
            927,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))

    @slow
    def test_tokenizer_integration(self):
        # fmt: off
        expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=expected_encoding,
            model_name="xlm-roberta-base",
            revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3",
        )
