# What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
Migrate Trainer from `Repository` to `upload_folder`. # What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import List

from transformers import (
    AddedToken,
    MarkupLMTokenizerFast,
    SpecialTokensMixin,
    is_tf_available,
    is_torch_available,
    logging,
)
from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES, MarkupLMTokenizer
from transformers.testing_utils import is_pt_tf_cross_test, require_tokenizers, require_torch, slow

from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings


logger = logging.get_logger(__name__)


@require_tokenizers
class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = MarkupLMTokenizer
    rust_tokenizer_class = MarkupLMTokenizerFast
    test_rust_tokenizer = True
    from_pretrained_kwargs = {"cls_token": "<s>"}
    test_seq2seq = False

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

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        # fmt: off
        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "<unk>",]  # noqa
        # fmt: on
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3}
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        self.tokenizer_config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")

        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))
        with open(self.tokenizer_config_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps({"tags_dict": self.tags_dict}))

    def get_nodes_and_xpaths(self):
        nodes = ["hello", "world"]
        xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]

        return nodes, xpaths

    def get_nodes_and_xpaths_batch(self):
        nodes = [["hello world", "running"], ["hello my name is bob"]]
        xpaths = [
            ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
            ["/html/body/div/li[2]/div/span"],
        ]

        return nodes, xpaths

    def get_question_nodes_and_xpaths(self):
        question = "what's his name?"
        nodes = ["hello world"]
        xpaths = ["/html/body/div/li[1]/div/span"]  # , "/html/body/div/li[1]/div/span"]

        return question, nodes, xpaths

    def get_question_nodes_and_xpaths_batch(self):
        questions = ["what's his name?", "how is he called?"]
        nodes = [["hello world", "running"], ["hello my name is bob"]]
        xpaths = [
            ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
            ["/html/body/div/li[2]/div/span"],
        ]

        return questions, nodes, xpaths

    def get_input_output_texts(self, tokenizer):
        input_text = "UNwant\u00E9d,running"
        output_text = "unwanted, running"
        return input_text, output_text

    def test_add_special_tokens(self):
        tokenizers: List[MarkupLMTokenizer] = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                special_token = "[SPECIAL_TOKEN]"
                special_token_xpath = "/html/body/div/li[1]/div/span"

                tokenizer.add_special_tokens({"cls_token": special_token})
                encoded_special_token = tokenizer.encode(
                    [special_token], xpaths=[special_token_xpath], add_special_tokens=False
                )
                self.assertEqual(len(encoded_special_token), 1)

                decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True)
                self.assertTrue(special_token not in decoded)

    def test_add_tokens_tokenizer(self):
        tokenizers: List[MarkupLMTokenizer] = 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)

                # 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)

                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))

                nodes = "aaaaa bbbbbb low cccccccccdddddddd l".split()
                xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]

                tokens = tokenizer.encode(nodes, xpaths=xpaths, 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))

                nodes = ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l".split()
                xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]

                tokens = tokenizer.encode(
                    nodes,
                    xpaths=xpaths,
                    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)

    @require_tokenizers
    def test_encode_decode_with_spaces(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

                new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)]
                tokenizer.add_tokens(new_toks)
                input = "[ABC][DEF][ABC][DEF]"
                if self.space_between_special_tokens:
                    output = "[ABC] [DEF] [ABC] [DEF]"
                else:
                    output = input
                encoded = tokenizer.encode(input.split(), xpaths=xpaths, add_special_tokens=False)
                decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
                self.assertIn(decoded, [output, output.lower()])

    @unittest.skip("Not implemented")
    def test_right_and_left_truncation(self):
        pass

    def test_encode_plus_with_padding(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

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

                padding_size = 10
                padding_idx = tokenizer.pad_token_id

                encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, 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"

                not_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    padding=False,
                    return_special_tokens_mask=True,
                )
                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)

                self.assertTrue(sequence_length == not_padded_sequence_length)
                self.assertTrue(input_ids == not_padded_input_ids)
                self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)

                not_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    padding=False,
                    return_special_tokens_mask=True,
                )
                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)

                self.assertTrue(sequence_length == not_padded_sequence_length)
                self.assertTrue(input_ids == not_padded_input_ids)
                self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)

                # Test right padding
                tokenizer.padding_side = "right"

                right_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    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)

                self.assertTrue(sequence_length + padding_size == right_padded_sequence_length)
                self.assertTrue(input_ids + [padding_idx] * padding_size == right_padded_input_ids)
                self.assertTrue(special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask)

                # Test left padding
                tokenizer.padding_side = "left"
                left_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    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)

                self.assertTrue(sequence_length + padding_size == left_padded_sequence_length)
                self.assertTrue([padding_idx] * padding_size + input_ids == left_padded_input_ids)
                self.assertTrue([1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask)

                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"]

                    assert token_type_ids + [0] * padding_size == right_padded_token_type_ids
                    assert [0] * padding_size + token_type_ids == left_padded_token_type_ids

                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"]

                    self.assertTrue(attention_mask + [0] * padding_size == right_padded_attention_mask)
                    self.assertTrue([0] * padding_size + attention_mask == left_padded_attention_mask)

    def test_internal_consistency(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

                tokens = []
                for word in nodes:
                    tokens.extend(tokenizer.tokenize(word))
                ids = tokenizer.convert_tokens_to_ids(tokens)
                ids_2 = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                self.assertListEqual(ids, ids_2)

                tokens_2 = tokenizer.convert_ids_to_tokens(ids)
                self.assertNotEqual(len(tokens_2), 0)
                text_2 = tokenizer.decode(ids)
                self.assertIsInstance(text_2, str)

    def test_mask_output(self):
        tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

                if (
                    tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
                    and "token_type_ids" in tokenizer.model_input_names
                ):
                    information = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
                    sequences, mask = information["input_ids"], information["token_type_ids"]
                    self.assertEqual(len(sequences), len(mask))

    def test_number_of_added_tokens(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # test 1: single sequence
                nodes, xpaths = self.get_nodes_and_xpaths()

                sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                attached_sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True)

                # Method is implemented (e.g. not GPT-2)
                if len(attached_sequences) != 2:
                    self.assertEqual(
                        tokenizer.num_special_tokens_to_add(pair=False), len(attached_sequences) - len(sequences)
                    )

                # test 2: two sequences
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()

                sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=False)
                attached_sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=True)

                # 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)
                    )

    def test_padding_to_max_length(self):
        """We keep this test for backward compatibility but it should be removed when `pad_to_max_length` will be deprecated"""
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                padding_size = 10

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

                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(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)
                # FIXME: the next line should be padding(max_length) to avoid warning
                padded_sequence = tokenizer.encode(
                    nodes, xpaths=xpaths, max_length=sequence_length + padding_size, pad_to_max_length=True
                )
                padded_sequence_length = len(padded_sequence)
                assert sequence_length + padding_size == padded_sequence_length
                assert encoded_sequence + [padding_idx] * padding_size == padded_sequence

                # Check that nothing is done when a maximum length is not specified
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, pad_to_max_length=True)
                padded_sequence_right_length = len(padded_sequence_right)
                assert sequence_length == padded_sequence_right_length
                assert encoded_sequence == padded_sequence_right

    def test_padding(self, max_length=50):
        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)

                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
                pad_token_id = tokenizer_p.pad_token_id

                # Encode - Simple input
                nodes, xpaths = self.get_nodes_and_xpaths()
                input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
                input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
                input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.encode(nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.encode(nodes, xpaths=xpaths, padding=True)
                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)

                # Encode - Pair input
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()
                input_r = tokenizer_r.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                input_r = tokenizer_r.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                input_r = tokenizer_r.encode(question, nodes, xpaths=xpaths, padding=True)
                input_p = tokenizer_p.encode(question, nodes, xpaths=xpaths, padding="longest")
                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)

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

                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, padding=True)
                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )

                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                # Encode_plus - Pair input
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()
                input_r = tokenizer_r.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus(question, nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.encode_plus(question, nodes, xpaths=xpaths, padding=True)
                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )
                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                # Batch_encode_plus - Simple input
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                input_r = tokenizer_r.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    pad_to_max_length=True,
                )
                input_p = tokenizer_p.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    pad_to_max_length=True,
                )
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding="max_length",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding="max_length",
                )
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding="longest",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding=True,
                )
                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.batch_encode_plus(nodes, xpaths=xpaths, padding=True)
                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                # Batch_encode_plus - Pair input
                questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch()

                input_r = tokenizer_r.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    max_length=max_length,
                    truncation=True,
                    padding="max_length",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    max_length=max_length,
                    truncation=True,
                    padding="max_length",
                )
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    padding=True,
                )
                input_p = tokenizer_p.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    padding="longest",
                )
                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                # Using pad on single examples after tokenization
                nodes, xpaths = self.get_nodes_and_xpaths()
                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r)

                input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.pad(input_p)

                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )

                # Using pad on single examples after tokenization
                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")

                input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")

                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)

                # Using pad after tokenization
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r)

                input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.pad(input_p)

                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                # Using pad after tokenization
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")

                input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.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)

    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__}"):
                # Test not batched
                nodes, xpaths = self.get_nodes_and_xpaths()
                encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths)
                encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test not batched pairs
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()
                encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths)
                encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test batched
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                encoded_sequences_1 = tokenizer.batch_encode_plus(nodes, is_pair=False, xpaths=xpaths)
                encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

    def test_batch_encode_plus_batch_sequence_length(self):
        # Tests that all encoded values have the correct size
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                encoded_sequences = [
                    tokenizer.encode_plus(nodes_example, xpaths=xpaths_example)
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, 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, nodes)

                encoded_sequences_padded = [
                    tokenizer.encode_plus(
                        nodes_example, xpaths=xpaths_example, max_length=maximum_length, padding="max_length"
                    )
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]

                encoded_sequences_batch_padded = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, 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(
                    nodes, is_pair=False, xpaths=xpaths, padding=True
                )
                encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding="longest"
                )
                for key in encoded_sequences_batch_padded_1.keys():
                    self.assertListEqual(
                        encoded_sequences_batch_padded_1[key],
                        encoded_sequences_batch_padded_2[key],
                    )

                # check 'no_padding' is unsensitive to a max length
                encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, padding=False
                )
                encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding=False
                )
                for key in encoded_sequences_batch_padded_1.keys():
                    self.assertListEqual(
                        encoded_sequences_batch_padded_1[key],
                        encoded_sequences_batch_padded_2[key],
                    )

    @unittest.skip("batch_encode_plus does not handle overflowing tokens.")
    def test_batch_encode_plus_overflowing_tokens(self):
        pass

    def test_batch_encode_plus_padding(self):
        # Test that padded sequences are equivalent between batch_encode_plus and encode_plus

        # Right padding tests
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                max_length = 100

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

                encoded_sequences = [
                    tokenizer.encode_plus(
                        nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length"
                    )
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                self.assertListEqual(
                    encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
                )

        # Left padding tests
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                tokenizer.padding_side = "left"
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                max_length = 100

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

                encoded_sequences = [
                    tokenizer.encode_plus(
                        nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length"
                    )
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, 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_padding_to_multiple_of(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.")
                else:
                    nodes, xpaths = self.get_nodes_and_xpaths()

                    # empty_tokens = tokenizer([""], [[]], padding=True, pad_to_multiple_of=8)
                    normal_tokens = tokenizer(nodes, xpaths=xpaths, padding=True, pad_to_multiple_of=8)
                    # for key, value in empty_tokens.items():
                    #     self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
                    for key, value in normal_tokens.items():
                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")

                    normal_tokens = tokenizer(nodes, xpaths=xpaths, pad_to_multiple_of=8)
                    for key, value in normal_tokens.items():
                        self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")

                    # Should also work with truncation
                    normal_tokens = tokenizer(
                        nodes, xpaths=xpaths, padding=True, truncation=True, pad_to_multiple_of=8
                    )
                    for key, value in normal_tokens.items():
                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")

                    # truncation to something which is not a multiple of pad_to_multiple_of raises an error
                    self.assertRaises(
                        ValueError,
                        tokenizer.__call__,
                        nodes,
                        xpaths=xpaths,
                        padding=True,
                        truncation=True,
                        max_length=12,
                        pad_to_multiple_of=8,
                    )

    def test_tokenizer_slow_store_full_signature(self):
        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_build_inputs_with_special_tokens(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

        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)

                # Input tokens id
                nodes, xpaths = self.get_nodes_and_xpaths()
                input_simple = tokenizer_p.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                input_pair = tokenizer_p.encode(nodes, xpaths=xpaths, 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_special_tokens_mask_input_pairs(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    add_special_tokens=True,
                    return_special_tokens_mask=True,
                    # add_prefix_space=False,
                )
                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)

    def test_special_tokens_mask(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                # Testing single inputs
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    nodes, xpaths=xpaths, add_special_tokens=True, return_special_tokens_mask=True
                )
                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)

    def test_save_and_load_tokenizer(self):
        # safety check on max_len default value so we are sure the test works
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                self.assertNotEqual(tokenizer.model_max_length, 42)

        # Now let's start the test
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Isolate this from the other tests because we save additional tokens/etc
                nodes, xpaths = self.get_nodes_and_xpaths()
                tmpdirname = tempfile.mkdtemp()

                before_tokens = tokenizer.encode(nodes, xpaths=xpaths, 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(nodes, xpaths=xpaths, 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)

    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__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                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(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
                assert sequence_length + padding_size == padded_sequence_length
                assert encoded_sequence + [padding_idx] * padding_size == padded_sequence

                # 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(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
                assert sequence_length + padding_size == padded_sequence_length
                assert [padding_idx] * padding_size + encoded_sequence == padded_sequence

                # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, padding=True)
                padded_sequence_right_length = len(padded_sequence_right)
                assert sequence_length == padded_sequence_right_length
                assert encoded_sequence == padded_sequence_right

                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding="longest")
                padded_sequence_left_length = len(padded_sequence_left)
                assert sequence_length == padded_sequence_left_length
                assert encoded_sequence == padded_sequence_left

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths)
                padded_sequence_right_length = len(padded_sequence_right)
                assert sequence_length == padded_sequence_right_length
                assert encoded_sequence == padded_sequence_right

                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding=False)
                padded_sequence_left_length = len(padded_sequence_left)
                assert sequence_length == padded_sequence_left_length
                assert encoded_sequence == padded_sequence_left

    def test_token_type_ids(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # test 1: single sequence
                nodes, xpaths = self.get_nodes_and_xpaths()

                output = tokenizer(nodes, xpaths=xpaths, return_token_type_ids=True)

                # Assert that the token type IDs have the same length as the input IDs
                self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))

                # Assert that the token type IDs have the same length as the attention mask
                self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"]))

                self.assertIn(0, output["token_type_ids"])
                self.assertNotIn(1, output["token_type_ids"])

                # test 2: two sequences (question + nodes)
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()

                output = tokenizer(question, nodes, xpaths, return_token_type_ids=True)

                # Assert that the token type IDs have the same length as the input IDs
                self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))

                # Assert that the token type IDs have the same length as the attention mask
                self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"]))

                self.assertIn(0, output["token_type_ids"])

    def test_offsets_mapping(self):
        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)

                text = ["a", "wonderful", "test"]
                xpaths = ["html/body" for _ in range(len(text))]

                # No pair
                tokens_with_offsets = tokenizer_r.encode_plus(
                    text,
                    xpaths=xpaths,
                    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
                text = "what's his name"
                pair = ["a", "wonderful", "test"]
                xpaths = ["html/body" for _ in range(len(pair))]
                tokens_with_offsets = tokenizer_r.encode_plus(
                    text,
                    pair,
                    xpaths=xpaths,
                    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)

    @require_torch
    @slow
    def test_torch_encode_plus_sent_to_model(self):
        import torch

        from transformers import MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)

        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

                config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
                config = config_class()

                if config.is_encoder_decoder or config.pad_token_id is None:
                    return

                model = model_class(config)

                # 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")
                assert (
                    (model.get_input_embeddings().weight.shape[0] >= len(tokenizer))
                    if is_using_common_embeddings
                    else True
                )

                # Build sequence
                nodes, xpaths = self.get_nodes_and_xpaths()
                encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, return_tensors="pt")
                batch_encoded_sequence = tokenizer.batch_encode_plus(
                    [nodes, nodes], [xpaths, xpaths], return_tensors="pt"
                )
                # This should not fail

                with torch.no_grad():  # saves some time
                    model(**encoded_sequence)
                    model(**batch_encoded_sequence)

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

        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

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

        nodes, xpaths = self.get_nodes_and_xpaths()

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

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

    def test_tokenization_python_rust_equals(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

        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)

                nodes, xpaths = self.get_nodes_and_xpaths()

                # Ensure basic input match
                input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)

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

                input_pairs_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths)
                input_pairs_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)

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

                nodes = ["hello" for _ in range(1000)]
                xpaths = ["html/body" for _ in range(1000)]

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

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

                # Ensure truncation with stride match
                input_p = tokenizer_p.encode_plus(
                    nodes, xpaths=xpaths, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
                )
                input_r = tokenizer_r.encode_plus(
                    nodes, xpaths=xpaths, 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", "xpath_tags_seq", "xpath_subs_seq"],
                    input_p.keys(),
                ):
                    self.assertSequenceEqual(input_p[key], input_r[key][0])

    def test_embeded_special_tokens(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

        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)
                nodes, xpaths = self.get_nodes_and_xpaths()
                tokens_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
                tokens_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, 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:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                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)

                nodes, xpaths = self.get_nodes_and_xpaths()
                # tokenize()
                no_special_tokens = tokenizer_r.tokenize(" ".join(nodes), add_special_tokens=False)
                with_special_tokens = tokenizer_r.tokenize(" ".join(nodes), 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(nodes, xpaths=xpaths, add_special_tokens=False)
                with_special_tokens = tokenizer_r.encode(nodes, xpaths=xpaths, 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(nodes, xpaths=xpaths, add_special_tokens=False)
                with_special_tokens = tokenizer_r.encode_plus(nodes, xpaths=xpaths, 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
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                no_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, add_special_tokens=False)
                with_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, 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)

    @slow
    def test_markuplm_truncation_integration_test(self):
        nodes, xpaths = self.get_nodes_and_xpaths()

        tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", model_max_length=512)

        for i in range(12, 512):
            new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, max_length=i, truncation=True)

            # Ensure that the input IDs are less than the max length defined.
            self.assertLessEqual(len(new_encoded_inputs), i)

        tokenizer.model_max_length = 20
        new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True)
        dropped_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True)

        # Ensure that the input IDs are still truncated when no max_length is specified
        self.assertListEqual(new_encoded_inputs, dropped_encoded_inputs)
        self.assertLessEqual(len(new_encoded_inputs), 20)

    @is_pt_tf_cross_test
    def test_batch_encode_plus_tensors(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

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

                if tokenizer.pad_token_id is None:
                    self.assertRaises(
                        ValueError,
                        tokenizer.batch_encode_plus,
                        nodes,
                        xpaths=xpaths,
                        padding=True,
                        return_tensors="pt",
                    )
                    self.assertRaises(
                        ValueError,
                        tokenizer.batch_encode_plus,
                        nodes,
                        xpaths=xpaths,
                        padding="longest",
                        return_tensors="tf",
                    )
                else:
                    pytorch_tensor = tokenizer.batch_encode_plus(
                        nodes, xpaths=xpaths, padding=True, return_tensors="pt"
                    )
                    tensorflow_tensor = tokenizer.batch_encode_plus(
                        nodes, xpaths=xpaths, padding="longest", return_tensors="tf"
                    )
                    encoded_sequences = tokenizer.batch_encode_plus(nodes, xpaths=xpaths, padding=True)

                    for key in encoded_sequences.keys():
                        pytorch_value = pytorch_tensor[key].tolist()
                        tensorflow_value = tensorflow_tensor[key].numpy().tolist()
                        encoded_value = encoded_sequences[key]

                        self.assertEqual(pytorch_value, tensorflow_value, encoded_value)

    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."]
                xpaths = ["html/body" for _ in range(len(seq_1))]

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

                output = tokenizer(seq_0, seq_1, xpaths=xpaths)
                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())

    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
                )
                nodes = "Hey this is a <special> token".split()
                xpaths = ["html/body" for _ in range(len(nodes))]
                r_output = tokenizer_r.encode(nodes, xpaths=xpaths)

                special_token_id = tokenizer_r.encode(["<special>"], xpaths=["html/body"], add_special_tokens=False)[0]

                self.assertTrue(special_token_id in r_output)

                if self.test_slow_tokenizer:
                    tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, additional_special_tokens=added_tokens, **kwargs
                    )
                    tokenizer_p = self.tokenizer_class.from_pretrained(
                        pretrained_name, additional_special_tokens=added_tokens, **kwargs
                    )

                    nodes = "Hey this is a <special> token".split()
                    xpaths = ["html/body" for _ in range(len(nodes))]

                    p_output = tokenizer_p.encode(nodes, xpaths=xpaths)
                    cr_output = tokenizer_cr.encode(nodes, xpaths=xpaths)

                    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)

    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
        text = [["this", "is", "the"], ["how", "are", "you"]]
        xpaths = [["html/body"] * 3, ["html/body"] * 3]
        inputs = new_tokenizer(text, xpaths=xpaths)
        self.assertEqual(len(inputs["input_ids"]), 2)
        decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
        expected_result = (  # original expected result "this is the" seems contradicts to roberta-based tokenizer
            "thisisthe"
        )

        if tokenizer.backend_tokenizer.normalizer is not None:
            expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
        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,
                    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}",
                )
            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
        nodes = [["this", "is"], ["hello", "🤗"]]
        xpaths = [["html/body"] * 2, ["html/body"] * 2]
        inputs = new_tokenizer(nodes, xpaths=xpaths)
        self.assertEqual(len(inputs["input_ids"]), 2)
        decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
        expected_result = "thisis"  # same as line 1399

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

    def test_prepare_for_model(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            # only test prepare_for_model for the slow tokenizer
            if tokenizer.__class__.__name__ == "MarkupLMTokenizerFast":
                continue
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                prepared_input_dict = tokenizer.prepare_for_model(nodes, xpaths=xpaths, add_special_tokens=True)

                input_dict = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)

                self.assertEqual(input_dict, prepared_input_dict)

    def test_padding_different_model_input_name(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

        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)
                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
                pad_token_id = tokenizer_p.pad_token_id

                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)

                # 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"
                )

    def test_batch_encode_dynamic_overflowing(self):
        """
        When calling batch_encode with multiple sequences, it can return 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)

            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
                if is_torch_available():
                    returned_tensor = "pt"
                elif is_tf_available():
                    returned_tensor = "tf"
                else:
                    returned_tensor = "jax"

                # Single example
                nodes, xpaths = self.get_nodes_and_xpaths()
                tokens = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=1,
                    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()):
                    if "xpath" not in key:
                        self.assertEqual(len(tokens[key].shape), 2)
                    else:
                        self.assertEqual(len(tokens[key].shape), 3)

                # Batch of examples
                # For these 2 examples, 3 training examples will be created
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                tokens = tokenizer.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    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()):
                    if "xpath" not in key:
                        self.assertEqual(len(tokens[key].shape), 2)
                        self.assertEqual(tokens[key].shape[-1], 6)
                    else:
                        self.assertEqual(len(tokens[key].shape), 3)
                        self.assertEqual(tokens[key].shape[-2], 6)

    @unittest.skip("TO DO: overwrite this very extensive test.")
    def test_alignement_methods(self):
        pass

    def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
        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].split(" "), xpaths=len(t[1]) * ["html/body"], add_special_tokens=False),
                toks,
            )
        )
        if max_length is not None and len(toks) > max_length:
            toks = toks[:max_length]
        if min_length is not None and len(toks) < min_length and len(toks) > 0:
            while len(toks) < min_length:
                toks = toks + toks
        # 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)
        # an extra blank will cause inconsistency: ["a","b",] & "a b"
        """
        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
        nodes = output_txt.split(" ")
        xpaths = ["html/body" for i in range(len(nodes))]
        output_ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
        return nodes, xpaths, output_ids

    def test_maximum_encoding_length_pair_input(self):
        # slow part fixed, fast part not
        tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Build a sequence from our model's vocabulary
                stride = 2
                seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
                question_0 = " ".join(map(str, seq_0))
                if len(ids) <= 2 + stride:
                    seq_0 = (seq_0 + " ") * (2 + stride)
                    ids = None

                seq0_tokens = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)
                self.assertGreater(len(seq0_tokens["input_ids"]), 2 + stride)
                question_1 = "This is another sentence to be encoded."
                seq_1 = ["hello", "world"]
                xpaths_1 = ["html/body" for i in range(len(seq_1))]
                seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
                if abs(len(seq0_tokens["input_ids"]) - len(seq1_tokens["input_ids"])) <= 2:
                    seq1_tokens_input_ids = seq1_tokens["input_ids"] + seq1_tokens["input_ids"]
                    seq_1 = tokenizer.decode(seq1_tokens_input_ids, clean_up_tokenization_spaces=False)
                    seq_1 = seq_1.split(" ")
                    xpaths_1 = ["html/body" for i in range(len(seq_1))]
                seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)

                self.assertGreater(len(seq1_tokens["input_ids"]), 2 + stride)

                smallest = (
                    seq1_tokens["input_ids"]
                    if len(seq0_tokens["input_ids"]) > len(seq1_tokens["input_ids"])
                    else seq0_tokens["input_ids"]
                )

                # We are not using the special tokens - a bit too hard to test all the tokenizers with this
                # TODO try this again later
                sequence = tokenizer(question_0, seq_1, xpaths=xpaths_1, add_special_tokens=False)

                # 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
                question_2 = " ".join(map(str, seq_2))
                xpaths_2 = xpaths_0 * model_max_length
                # assertgreater -> assertgreaterequal
                self.assertGreaterEqual(len(seq_2), model_max_length)

                sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                sequence2 = tokenizer(question_2, seq_1, xpaths=xpaths_1, add_special_tokens=False)
                total_length2 = len(sequence2["input_ids"])
                self.assertLess(total_length1, model_max_length, "Issue with the testing sequence, please update it.")
                self.assertGreater(
                    total_length2, model_max_length, "Issue with the testing sequence, please update it."
                )

                # 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"{tokenizer.__class__.__name__} Padding: {padding_state}"):
                        for truncation_state in [True, "longest_first", "only_first"]:
                            with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
                                output = tokenizer(
                                    question_2,
                                    seq_1,
                                    xpaths=xpaths_1,
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)

                                output = tokenizer(
                                    [question_2],
                                    [seq_1],
                                    xpaths=[xpaths_1],
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)

                        # Simple
                        output = tokenizer(
                            question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation="only_second"
                        )
                        self.assertEqual(len(output["input_ids"]), model_max_length)
                        self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
                        self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)

                        output = tokenizer(
                            [question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation="only_second"
                        )
                        self.assertEqual(len(output["input_ids"][0]), model_max_length)
                        self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                        self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)

                        # Simple with no truncation
                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(
                                question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation=False
                            )
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )

                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(
                                [question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation=False
                            )
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )
                # Check the order of Sequence of input ids, overflowing tokens and xpath_tags_seq sequence with truncation
                truncated_first_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][:-2]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"]
                )
                truncated_second_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][:-2]
                )
                truncated_longest_sequence = (
                    truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
                )

                overflow_first_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][-(2 + stride) :]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"]
                )
                overflow_second_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][-(2 + stride) :]
                )
                overflow_longest_sequence = (
                    overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence
                )

                xpath_tags_seq_first = [[5] * 50] * (
                    len(tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]) - 2
                )
                xpath_tags_seq_first_sequence = (
                    xpath_tags_seq_first
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"]
                )
                overflowing_token_xpath_tags_seq_first_sequence_slow = [[5] * 50] * (2 + stride)
                overflowing_token_xpath_tags_seq_first_sequence_fast = [[5] * 50] * (2 + stride) + tokenizer(
                    seq_1, xpaths=xpaths_1, add_special_tokens=False
                )["xpath_tags_seq"]

                xpath_tags_seq_second = [[5] * 50] * len(
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                )
                xpath_tags_seq_second_sequence = (
                    xpath_tags_seq_second
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][:-2]
                )
                overflowing_token_xpath_tags_seq_second_sequence_slow = tokenizer(
                    seq_1, xpaths=xpaths_1, add_special_tokens=False
                )["xpath_tags_seq"][-(2 + stride) :]
                overflowing_token_xpath_tags_seq_second_sequence_fast = [[5] * 50] * len(
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                ) + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][-(2 + stride) :]

                xpath_tags_seq_longest_sequence = (
                    xpath_tags_seq_first_sequence
                    if len(seq0_tokens) > len(seq1_tokens)
                    else xpath_tags_seq_second_sequence
                )
                overflowing_token_xpath_tags_seq_longest_sequence_fast = (
                    overflowing_token_xpath_tags_seq_first_sequence_fast
                    if len(seq0_tokens) > len(seq1_tokens)
                    else overflowing_token_xpath_tags_seq_second_sequence_fast
                )

                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    information = tokenizer(
                        question_0,
                        seq_1,
                        xpaths=xpaths_1,
                        max_length=len(sequence["input_ids"]) - 2,
                        add_special_tokens=False,
                        stride=stride,
                        truncation="longest_first",
                        return_overflowing_tokens=True,
                        # add_prefix_space=False,
                    )
                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    xpath_tags_seq = information["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_longest_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence)

                    self.assertEqual(len(overflowing_xpath_tags_seq), 2 + stride + len(smallest))
                    self.assertEqual(
                        overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast
                    )
                else:
                    # No overflowing tokens when using 'longest' in python tokenizers
                    with self.assertRaises(ValueError) as context:
                        information = tokenizer(
                            question_0,
                            seq_1,
                            xpaths=xpaths_1,
                            max_length=len(sequence["input_ids"]) - 2,
                            add_special_tokens=False,
                            stride=stride,
                            truncation="longest_first",
                            return_overflowing_tokens=True,
                            # add_prefix_space=False,
                        )

                    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`."
                        )
                    )

                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    information = tokenizer(
                        question_0,
                        seq_1,
                        xpaths=xpaths_1,
                        max_length=len(sequence["input_ids"]) - 2,
                        add_special_tokens=False,
                        stride=stride,
                        truncation=True,
                        return_overflowing_tokens=True,
                    )
                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    xpath_tags_seq = information["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_longest_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence)
                    self.assertEqual(
                        overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast
                    )
                else:
                    # No overflowing tokens when using 'longest' in python tokenizers
                    with self.assertRaises(ValueError) as context:
                        information = tokenizer(
                            question_0,
                            seq_1,
                            xpaths=xpaths_1,
                            max_length=len(sequence["input_ids"]) - 2,
                            add_special_tokens=False,
                            stride=stride,
                            truncation=True,
                            return_overflowing_tokens=True,
                        )

                    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`."
                        )
                    )

                information_first_truncated = tokenizer(
                    question_0,
                    seq_1,
                    xpaths=xpaths_1,
                    max_length=len(sequence["input_ids"]) - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="only_first",
                    return_overflowing_tokens=True,
                )
                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    truncated_sequence = information_first_truncated["input_ids"][0]
                    overflowing_tokens = information_first_truncated["input_ids"][1]
                    xpath_tags_seq = information_first_truncated["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information_first_truncated["xpath_tags_seq"][1]
                    self.assertEqual(len(information_first_truncated["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_first_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens["input_ids"]))
                    self.assertEqual(overflowing_tokens, overflow_first_sequence)
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence)
                    # ISSUE HAPPENS HERE ↓
                    self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_fast)
                else:
                    truncated_sequence = information_first_truncated["input_ids"]
                    overflowing_tokens = information_first_truncated["overflowing_tokens"]
                    overflowing_xpath_tags_seq = information_first_truncated["overflowing_xpath_tags_seq"]
                    xpath_tags_seq = information_first_truncated["xpath_tags_seq"]

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_first_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq0_tokens["input_ids"][-(2 + stride) :])
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence)
                    self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_slow)

                information_second_truncated = tokenizer(
                    question_0,
                    seq_1,
                    xpaths=xpaths_1,
                    max_length=len(sequence["input_ids"]) - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="only_second",
                    return_overflowing_tokens=True,
                    # add_prefix_space=False,
                )
                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    truncated_sequence = information_second_truncated["input_ids"][0]
                    overflowing_tokens = information_second_truncated["input_ids"][1]
                    xpath_tags_seq = information_second_truncated["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information_second_truncated["xpath_tags_seq"][1]

                    self.assertEqual(len(information_second_truncated["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_second_sequence)

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

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_second_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq1_tokens["input_ids"][-(2 + stride) :])
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_second_sequence)
                    self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_second_sequence_slow)

    def test_maximum_encoding_length_single_input(self):
        tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20)

                sequence = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)
                total_length = len(sequence["input_ids"])

                self.assertGreater(total_length, 4, "Issue with the testing sequence, please update it it's too short")

                # 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
                xpaths_1 = xpaths_0 * model_max_length
                sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                self.assertGreater(
                    total_length1, model_max_length, "Issue with the testing sequence, please update it it's too short"
                )

                # 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,
                                    xpaths=xpaths_1,
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)

                                output = tokenizer(
                                    [seq_1],
                                    xpaths=[xpaths_1],
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)

                        # Simple with no truncation
                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(seq_1, xpaths=xpaths_1, padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )

                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer([seq_1], xpaths=[xpaths_1], padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )
                # Check the order of Sequence of input ids, overflowing tokens, xpath_tags_seq and xpath_subs_seq sequence with truncation
                stride = 2
                information = tokenizer(
                    seq_0,
                    xpaths=xpaths_0,
                    max_length=total_length - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation=True,
                    return_overflowing_tokens=True,
                )

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

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

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :])

                    self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2])
                    self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :])
                else:
                    truncated_sequence = information["input_ids"]
                    overflowing_tokens = information["overflowing_tokens"]
                    xpath_tags_seq = information["xpath_tags_seq"]
                    overflowing_xpath_tags_seq = information["overflowing_xpath_tags_seq"]
                    self.assertEqual(len(truncated_sequence), total_length - 2)
                    self.assertEqual(truncated_sequence, sequence["input_ids"][:-2])

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :])
                    self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2])
                    self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :])

    @unittest.skip("MarkupLM tokenizer requires xpaths besides sequences.")
    def test_pretokenized_inputs(self):
        pass

    @unittest.skip("MarkupLM tokenizer always expects pretokenized inputs.")
    def test_compare_pretokenized_inputs(self):
        pass

    @unittest.skip("MarkupLM fast tokenizer does not support prepare_for_model")
    def test_compare_prepare_for_model(self):
        pass

    @slow
    def test_only_label_first_subword(self):
        nodes = ["hello", "niels"]
        xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]
        node_labels = [0, 1]

        # test slow tokenizer
        tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
        encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100])

        tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False)
        encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100])

        # test fast tokenizer
        tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")
        encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100])

        tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False)
        encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100])

    def test_markuplm_integration_test(self):
        tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
        tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")

        # There are 3 cases:
        # CASE 1: document image classification (training + inference), document image token classification (inference),
        # in which case only nodes and normalized bounding xpaths are provided to the tokenizer
        # CASE 2: document image token classification (training),
        # in which case one also provides word labels to the tokenizer
        # CASE 3: document image visual question answering (inference),
        # in which case one also provides a question to the tokenizer

        # We need to test all 3 cases both on batched and non-batched inputs.

        # CASE 1: not batched
        nodes, xpaths = self.get_nodes_and_xpaths()

        # fmt: off
        expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}  # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 1: batched
        nodes, xpaths = self.get_nodes_and_xpaths_batch()

        # fmt: off
        expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
        # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 2: not batched
        nodes, xpaths = self.get_nodes_and_xpaths()
        node_labels = [1, 2, 3]

        # fmt: off
        expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
        # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 2: batched
        nodes, xpaths = self.get_nodes_and_xpaths_batch()
        node_labels = [[1, 2, 3], [2, 46, 17, 22, 3]]

        # fmt: off
        expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, -100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 3: not batched
        question, nodes, xpaths = self.get_question_nodes_and_xpaths()

        # fmt: off
        expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
        # fmt: on

        encoding_p = tokenizer_p(question, nodes, xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(question, nodes, xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 3: batched
        questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch()

        # fmt: off
        expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]}
        # fmt: on

        encoding_p = tokenizer_p(questions, nodes, xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(questions, nodes, xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

    @unittest.skip("Doesn't support another framework than PyTorch")
    def test_np_encode_plus_sent_to_model(self):
        pass

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

        nodes, xpaths = self.get_nodes_and_xpaths()

        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, nodes)

        encoding_fast = tokenizer_fast(nodes, xpaths=xpaths)

        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, nodes)

        encoding_slow = tokenizer_slow(nodes, xpaths=xpaths)

        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,
        )

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import List

from transformers import (
    AddedToken,
    MarkupLMTokenizerFast,
    SpecialTokensMixin,
    is_tf_available,
    is_torch_available,
    logging,
)
from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES, MarkupLMTokenizer
from transformers.testing_utils import is_pt_tf_cross_test, require_tokenizers, require_torch, slow

from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings


logger = logging.get_logger(__name__)


@require_tokenizers
class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = MarkupLMTokenizer
    rust_tokenizer_class = MarkupLMTokenizerFast
    test_rust_tokenizer = True
    from_pretrained_kwargs = {"cls_token": "<s>"}
    test_seq2seq = False

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

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        # fmt: off
        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "<unk>",]  # noqa
        # fmt: on
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3}
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        self.tokenizer_config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")

        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))
        with open(self.tokenizer_config_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps({"tags_dict": self.tags_dict}))

    def get_nodes_and_xpaths(self):
        nodes = ["hello", "world"]
        xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]

        return nodes, xpaths

    def get_nodes_and_xpaths_batch(self):
        nodes = [["hello world", "running"], ["hello my name is bob"]]
        xpaths = [
            ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
            ["/html/body/div/li[2]/div/span"],
        ]

        return nodes, xpaths

    def get_question_nodes_and_xpaths(self):
        question = "what's his name?"
        nodes = ["hello world"]
        xpaths = ["/html/body/div/li[1]/div/span"]  # , "/html/body/div/li[1]/div/span"]

        return question, nodes, xpaths

    def get_question_nodes_and_xpaths_batch(self):
        questions = ["what's his name?", "how is he called?"]
        nodes = [["hello world", "running"], ["hello my name is bob"]]
        xpaths = [
            ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
            ["/html/body/div/li[2]/div/span"],
        ]

        return questions, nodes, xpaths

    def get_input_output_texts(self, tokenizer):
        input_text = "UNwant\u00E9d,running"
        output_text = "unwanted, running"
        return input_text, output_text

    def test_add_special_tokens(self):
        tokenizers: List[MarkupLMTokenizer] = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                special_token = "[SPECIAL_TOKEN]"
                special_token_xpath = "/html/body/div/li[1]/div/span"

                tokenizer.add_special_tokens({"cls_token": special_token})
                encoded_special_token = tokenizer.encode(
                    [special_token], xpaths=[special_token_xpath], add_special_tokens=False
                )
                self.assertEqual(len(encoded_special_token), 1)

                decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True)
                self.assertTrue(special_token not in decoded)

    def test_add_tokens_tokenizer(self):
        tokenizers: List[MarkupLMTokenizer] = 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)

                # 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)

                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))

                nodes = "aaaaa bbbbbb low cccccccccdddddddd l".split()
                xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]

                tokens = tokenizer.encode(nodes, xpaths=xpaths, 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))

                nodes = ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l".split()
                xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]

                tokens = tokenizer.encode(
                    nodes,
                    xpaths=xpaths,
                    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)

    @require_tokenizers
    def test_encode_decode_with_spaces(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

                new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)]
                tokenizer.add_tokens(new_toks)
                input = "[ABC][DEF][ABC][DEF]"
                if self.space_between_special_tokens:
                    output = "[ABC] [DEF] [ABC] [DEF]"
                else:
                    output = input
                encoded = tokenizer.encode(input.split(), xpaths=xpaths, add_special_tokens=False)
                decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
                self.assertIn(decoded, [output, output.lower()])

    @unittest.skip("Not implemented")
    def test_right_and_left_truncation(self):
        pass

    def test_encode_plus_with_padding(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

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

                padding_size = 10
                padding_idx = tokenizer.pad_token_id

                encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, 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"

                not_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    padding=False,
                    return_special_tokens_mask=True,
                )
                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)

                self.assertTrue(sequence_length == not_padded_sequence_length)
                self.assertTrue(input_ids == not_padded_input_ids)
                self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)

                not_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    padding=False,
                    return_special_tokens_mask=True,
                )
                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)

                self.assertTrue(sequence_length == not_padded_sequence_length)
                self.assertTrue(input_ids == not_padded_input_ids)
                self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)

                # Test right padding
                tokenizer.padding_side = "right"

                right_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    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)

                self.assertTrue(sequence_length + padding_size == right_padded_sequence_length)
                self.assertTrue(input_ids + [padding_idx] * padding_size == right_padded_input_ids)
                self.assertTrue(special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask)

                # Test left padding
                tokenizer.padding_side = "left"
                left_padded_sequence = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    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)

                self.assertTrue(sequence_length + padding_size == left_padded_sequence_length)
                self.assertTrue([padding_idx] * padding_size + input_ids == left_padded_input_ids)
                self.assertTrue([1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask)

                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"]

                    assert token_type_ids + [0] * padding_size == right_padded_token_type_ids
                    assert [0] * padding_size + token_type_ids == left_padded_token_type_ids

                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"]

                    self.assertTrue(attention_mask + [0] * padding_size == right_padded_attention_mask)
                    self.assertTrue([0] * padding_size + attention_mask == left_padded_attention_mask)

    def test_internal_consistency(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

                tokens = []
                for word in nodes:
                    tokens.extend(tokenizer.tokenize(word))
                ids = tokenizer.convert_tokens_to_ids(tokens)
                ids_2 = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                self.assertListEqual(ids, ids_2)

                tokens_2 = tokenizer.convert_ids_to_tokens(ids)
                self.assertNotEqual(len(tokens_2), 0)
                text_2 = tokenizer.decode(ids)
                self.assertIsInstance(text_2, str)

    def test_mask_output(self):
        tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()

                if (
                    tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
                    and "token_type_ids" in tokenizer.model_input_names
                ):
                    information = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
                    sequences, mask = information["input_ids"], information["token_type_ids"]
                    self.assertEqual(len(sequences), len(mask))

    def test_number_of_added_tokens(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # test 1: single sequence
                nodes, xpaths = self.get_nodes_and_xpaths()

                sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                attached_sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True)

                # Method is implemented (e.g. not GPT-2)
                if len(attached_sequences) != 2:
                    self.assertEqual(
                        tokenizer.num_special_tokens_to_add(pair=False), len(attached_sequences) - len(sequences)
                    )

                # test 2: two sequences
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()

                sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=False)
                attached_sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=True)

                # 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)
                    )

    def test_padding_to_max_length(self):
        """We keep this test for backward compatibility but it should be removed when `pad_to_max_length` will be deprecated"""
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                padding_size = 10

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

                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(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)
                # FIXME: the next line should be padding(max_length) to avoid warning
                padded_sequence = tokenizer.encode(
                    nodes, xpaths=xpaths, max_length=sequence_length + padding_size, pad_to_max_length=True
                )
                padded_sequence_length = len(padded_sequence)
                assert sequence_length + padding_size == padded_sequence_length
                assert encoded_sequence + [padding_idx] * padding_size == padded_sequence

                # Check that nothing is done when a maximum length is not specified
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, pad_to_max_length=True)
                padded_sequence_right_length = len(padded_sequence_right)
                assert sequence_length == padded_sequence_right_length
                assert encoded_sequence == padded_sequence_right

    def test_padding(self, max_length=50):
        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)

                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
                pad_token_id = tokenizer_p.pad_token_id

                # Encode - Simple input
                nodes, xpaths = self.get_nodes_and_xpaths()
                input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
                input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
                input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.encode(nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.encode(nodes, xpaths=xpaths, padding=True)
                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)

                # Encode - Pair input
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()
                input_r = tokenizer_r.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                input_r = tokenizer_r.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
                input_r = tokenizer_r.encode(question, nodes, xpaths=xpaths, padding=True)
                input_p = tokenizer_p.encode(question, nodes, xpaths=xpaths, padding="longest")
                self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)

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

                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, padding=True)
                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )

                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                # Encode_plus - Pair input
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()
                input_r = tokenizer_r.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                input_p = tokenizer_p.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
                )
                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                input_p = tokenizer_p.encode_plus(
                    question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
                input_r = tokenizer_r.encode_plus(question, nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.encode_plus(question, nodes, xpaths=xpaths, padding=True)
                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )
                self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])

                # Batch_encode_plus - Simple input
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                input_r = tokenizer_r.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    pad_to_max_length=True,
                )
                input_p = tokenizer_p.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    pad_to_max_length=True,
                )
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding="max_length",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding="max_length",
                )
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding="longest",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=max_length,
                    padding=True,
                )
                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, padding="longest")
                input_p = tokenizer_p.batch_encode_plus(nodes, xpaths=xpaths, padding=True)
                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                # Batch_encode_plus - Pair input
                questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch()

                input_r = tokenizer_r.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    max_length=max_length,
                    truncation=True,
                    padding="max_length",
                )
                input_p = tokenizer_p.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    max_length=max_length,
                    truncation=True,
                    padding="max_length",
                )
                self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)

                input_r = tokenizer_r.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    padding=True,
                )
                input_p = tokenizer_p.batch_encode_plus(
                    list(zip(questions, nodes)),
                    is_pair=True,
                    xpaths=xpaths,
                    padding="longest",
                )
                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                # Using pad on single examples after tokenization
                nodes, xpaths = self.get_nodes_and_xpaths()
                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r)

                input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.pad(input_p)

                self.assert_padded_input_match(
                    input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
                )

                # Using pad on single examples after tokenization
                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")

                input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")

                self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)

                # Using pad after tokenization
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r)

                input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.pad(input_p)

                self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)

                # Using pad after tokenization
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")

                input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.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)

    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__}"):
                # Test not batched
                nodes, xpaths = self.get_nodes_and_xpaths()
                encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths)
                encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test not batched pairs
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()
                encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths)
                encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

                # Test batched
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                encoded_sequences_1 = tokenizer.batch_encode_plus(nodes, is_pair=False, xpaths=xpaths)
                encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
                self.assertEqual(encoded_sequences_1, encoded_sequences_2)

    def test_batch_encode_plus_batch_sequence_length(self):
        # Tests that all encoded values have the correct size
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                encoded_sequences = [
                    tokenizer.encode_plus(nodes_example, xpaths=xpaths_example)
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, 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, nodes)

                encoded_sequences_padded = [
                    tokenizer.encode_plus(
                        nodes_example, xpaths=xpaths_example, max_length=maximum_length, padding="max_length"
                    )
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]

                encoded_sequences_batch_padded = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, 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(
                    nodes, is_pair=False, xpaths=xpaths, padding=True
                )
                encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding="longest"
                )
                for key in encoded_sequences_batch_padded_1.keys():
                    self.assertListEqual(
                        encoded_sequences_batch_padded_1[key],
                        encoded_sequences_batch_padded_2[key],
                    )

                # check 'no_padding' is unsensitive to a max length
                encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, padding=False
                )
                encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding=False
                )
                for key in encoded_sequences_batch_padded_1.keys():
                    self.assertListEqual(
                        encoded_sequences_batch_padded_1[key],
                        encoded_sequences_batch_padded_2[key],
                    )

    @unittest.skip("batch_encode_plus does not handle overflowing tokens.")
    def test_batch_encode_plus_overflowing_tokens(self):
        pass

    def test_batch_encode_plus_padding(self):
        # Test that padded sequences are equivalent between batch_encode_plus and encode_plus

        # Right padding tests
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                max_length = 100

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

                encoded_sequences = [
                    tokenizer.encode_plus(
                        nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length"
                    )
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, max_length=max_length, padding="max_length"
                )
                self.assertListEqual(
                    encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
                )

        # Left padding tests
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                tokenizer.padding_side = "left"
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                max_length = 100

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

                encoded_sequences = [
                    tokenizer.encode_plus(
                        nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length"
                    )
                    for nodes_example, xpaths_example in zip(nodes, xpaths)
                ]
                encoded_sequences_batch = tokenizer.batch_encode_plus(
                    nodes, is_pair=False, xpaths=xpaths, 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_padding_to_multiple_of(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.")
                else:
                    nodes, xpaths = self.get_nodes_and_xpaths()

                    # empty_tokens = tokenizer([""], [[]], padding=True, pad_to_multiple_of=8)
                    normal_tokens = tokenizer(nodes, xpaths=xpaths, padding=True, pad_to_multiple_of=8)
                    # for key, value in empty_tokens.items():
                    #     self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
                    for key, value in normal_tokens.items():
                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")

                    normal_tokens = tokenizer(nodes, xpaths=xpaths, pad_to_multiple_of=8)
                    for key, value in normal_tokens.items():
                        self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")

                    # Should also work with truncation
                    normal_tokens = tokenizer(
                        nodes, xpaths=xpaths, padding=True, truncation=True, pad_to_multiple_of=8
                    )
                    for key, value in normal_tokens.items():
                        self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")

                    # truncation to something which is not a multiple of pad_to_multiple_of raises an error
                    self.assertRaises(
                        ValueError,
                        tokenizer.__call__,
                        nodes,
                        xpaths=xpaths,
                        padding=True,
                        truncation=True,
                        max_length=12,
                        pad_to_multiple_of=8,
                    )

    def test_tokenizer_slow_store_full_signature(self):
        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_build_inputs_with_special_tokens(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

        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)

                # Input tokens id
                nodes, xpaths = self.get_nodes_and_xpaths()
                input_simple = tokenizer_p.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                input_pair = tokenizer_p.encode(nodes, xpaths=xpaths, 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_special_tokens_mask_input_pairs(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    add_special_tokens=True,
                    return_special_tokens_mask=True,
                    # add_prefix_space=False,
                )
                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)

    def test_special_tokens_mask(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                # Testing single inputs
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    nodes, xpaths=xpaths, add_special_tokens=True, return_special_tokens_mask=True
                )
                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)

    def test_save_and_load_tokenizer(self):
        # safety check on max_len default value so we are sure the test works
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                self.assertNotEqual(tokenizer.model_max_length, 42)

        # Now let's start the test
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Isolate this from the other tests because we save additional tokens/etc
                nodes, xpaths = self.get_nodes_and_xpaths()
                tmpdirname = tempfile.mkdtemp()

                before_tokens = tokenizer.encode(nodes, xpaths=xpaths, 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(nodes, xpaths=xpaths, 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)

    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__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                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(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
                assert sequence_length + padding_size == padded_sequence_length
                assert encoded_sequence + [padding_idx] * padding_size == padded_sequence

                # 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(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
                assert sequence_length + padding_size == padded_sequence_length
                assert [padding_idx] * padding_size + encoded_sequence == padded_sequence

                # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
                encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
                sequence_length = len(encoded_sequence)

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, padding=True)
                padded_sequence_right_length = len(padded_sequence_right)
                assert sequence_length == padded_sequence_right_length
                assert encoded_sequence == padded_sequence_right

                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding="longest")
                padded_sequence_left_length = len(padded_sequence_left)
                assert sequence_length == padded_sequence_left_length
                assert encoded_sequence == padded_sequence_left

                tokenizer.padding_side = "right"
                padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths)
                padded_sequence_right_length = len(padded_sequence_right)
                assert sequence_length == padded_sequence_right_length
                assert encoded_sequence == padded_sequence_right

                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding=False)
                padded_sequence_left_length = len(padded_sequence_left)
                assert sequence_length == padded_sequence_left_length
                assert encoded_sequence == padded_sequence_left

    def test_token_type_ids(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # test 1: single sequence
                nodes, xpaths = self.get_nodes_and_xpaths()

                output = tokenizer(nodes, xpaths=xpaths, return_token_type_ids=True)

                # Assert that the token type IDs have the same length as the input IDs
                self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))

                # Assert that the token type IDs have the same length as the attention mask
                self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"]))

                self.assertIn(0, output["token_type_ids"])
                self.assertNotIn(1, output["token_type_ids"])

                # test 2: two sequences (question + nodes)
                question, nodes, xpaths = self.get_question_nodes_and_xpaths()

                output = tokenizer(question, nodes, xpaths, return_token_type_ids=True)

                # Assert that the token type IDs have the same length as the input IDs
                self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))

                # Assert that the token type IDs have the same length as the attention mask
                self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"]))

                self.assertIn(0, output["token_type_ids"])

    def test_offsets_mapping(self):
        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)

                text = ["a", "wonderful", "test"]
                xpaths = ["html/body" for _ in range(len(text))]

                # No pair
                tokens_with_offsets = tokenizer_r.encode_plus(
                    text,
                    xpaths=xpaths,
                    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
                text = "what's his name"
                pair = ["a", "wonderful", "test"]
                xpaths = ["html/body" for _ in range(len(pair))]
                tokens_with_offsets = tokenizer_r.encode_plus(
                    text,
                    pair,
                    xpaths=xpaths,
                    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)

    @require_torch
    @slow
    def test_torch_encode_plus_sent_to_model(self):
        import torch

        from transformers import MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)

        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

                config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
                config = config_class()

                if config.is_encoder_decoder or config.pad_token_id is None:
                    return

                model = model_class(config)

                # 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")
                assert (
                    (model.get_input_embeddings().weight.shape[0] >= len(tokenizer))
                    if is_using_common_embeddings
                    else True
                )

                # Build sequence
                nodes, xpaths = self.get_nodes_and_xpaths()
                encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, return_tensors="pt")
                batch_encoded_sequence = tokenizer.batch_encode_plus(
                    [nodes, nodes], [xpaths, xpaths], return_tensors="pt"
                )
                # This should not fail

                with torch.no_grad():  # saves some time
                    model(**encoded_sequence)
                    model(**batch_encoded_sequence)

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

        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

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

        nodes, xpaths = self.get_nodes_and_xpaths()

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

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

    def test_tokenization_python_rust_equals(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

        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)

                nodes, xpaths = self.get_nodes_and_xpaths()

                # Ensure basic input match
                input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths)
                input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)

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

                input_pairs_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths)
                input_pairs_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)

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

                nodes = ["hello" for _ in range(1000)]
                xpaths = ["html/body" for _ in range(1000)]

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

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

                # Ensure truncation with stride match
                input_p = tokenizer_p.encode_plus(
                    nodes, xpaths=xpaths, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
                )
                input_r = tokenizer_r.encode_plus(
                    nodes, xpaths=xpaths, 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", "xpath_tags_seq", "xpath_subs_seq"],
                    input_p.keys(),
                ):
                    self.assertSequenceEqual(input_p[key], input_r[key][0])

    def test_embeded_special_tokens(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

        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)
                nodes, xpaths = self.get_nodes_and_xpaths()
                tokens_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
                tokens_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, 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:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                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)

                nodes, xpaths = self.get_nodes_and_xpaths()
                # tokenize()
                no_special_tokens = tokenizer_r.tokenize(" ".join(nodes), add_special_tokens=False)
                with_special_tokens = tokenizer_r.tokenize(" ".join(nodes), 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(nodes, xpaths=xpaths, add_special_tokens=False)
                with_special_tokens = tokenizer_r.encode(nodes, xpaths=xpaths, 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(nodes, xpaths=xpaths, add_special_tokens=False)
                with_special_tokens = tokenizer_r.encode_plus(nodes, xpaths=xpaths, 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
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                no_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, add_special_tokens=False)
                with_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, 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)

    @slow
    def test_markuplm_truncation_integration_test(self):
        nodes, xpaths = self.get_nodes_and_xpaths()

        tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", model_max_length=512)

        for i in range(12, 512):
            new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, max_length=i, truncation=True)

            # Ensure that the input IDs are less than the max length defined.
            self.assertLessEqual(len(new_encoded_inputs), i)

        tokenizer.model_max_length = 20
        new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True)
        dropped_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True)

        # Ensure that the input IDs are still truncated when no max_length is specified
        self.assertListEqual(new_encoded_inputs, dropped_encoded_inputs)
        self.assertLessEqual(len(new_encoded_inputs), 20)

    @is_pt_tf_cross_test
    def test_batch_encode_plus_tensors(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths_batch()

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

                if tokenizer.pad_token_id is None:
                    self.assertRaises(
                        ValueError,
                        tokenizer.batch_encode_plus,
                        nodes,
                        xpaths=xpaths,
                        padding=True,
                        return_tensors="pt",
                    )
                    self.assertRaises(
                        ValueError,
                        tokenizer.batch_encode_plus,
                        nodes,
                        xpaths=xpaths,
                        padding="longest",
                        return_tensors="tf",
                    )
                else:
                    pytorch_tensor = tokenizer.batch_encode_plus(
                        nodes, xpaths=xpaths, padding=True, return_tensors="pt"
                    )
                    tensorflow_tensor = tokenizer.batch_encode_plus(
                        nodes, xpaths=xpaths, padding="longest", return_tensors="tf"
                    )
                    encoded_sequences = tokenizer.batch_encode_plus(nodes, xpaths=xpaths, padding=True)

                    for key in encoded_sequences.keys():
                        pytorch_value = pytorch_tensor[key].tolist()
                        tensorflow_value = tensorflow_tensor[key].numpy().tolist()
                        encoded_value = encoded_sequences[key]

                        self.assertEqual(pytorch_value, tensorflow_value, encoded_value)

    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."]
                xpaths = ["html/body" for _ in range(len(seq_1))]

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

                output = tokenizer(seq_0, seq_1, xpaths=xpaths)
                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())

    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
                )
                nodes = "Hey this is a <special> token".split()
                xpaths = ["html/body" for _ in range(len(nodes))]
                r_output = tokenizer_r.encode(nodes, xpaths=xpaths)

                special_token_id = tokenizer_r.encode(["<special>"], xpaths=["html/body"], add_special_tokens=False)[0]

                self.assertTrue(special_token_id in r_output)

                if self.test_slow_tokenizer:
                    tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name, additional_special_tokens=added_tokens, **kwargs
                    )
                    tokenizer_p = self.tokenizer_class.from_pretrained(
                        pretrained_name, additional_special_tokens=added_tokens, **kwargs
                    )

                    nodes = "Hey this is a <special> token".split()
                    xpaths = ["html/body" for _ in range(len(nodes))]

                    p_output = tokenizer_p.encode(nodes, xpaths=xpaths)
                    cr_output = tokenizer_cr.encode(nodes, xpaths=xpaths)

                    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)

    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
        text = [["this", "is", "the"], ["how", "are", "you"]]
        xpaths = [["html/body"] * 3, ["html/body"] * 3]
        inputs = new_tokenizer(text, xpaths=xpaths)
        self.assertEqual(len(inputs["input_ids"]), 2)
        decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
        expected_result = (  # original expected result "this is the" seems contradicts to roberta-based tokenizer
            "thisisthe"
        )

        if tokenizer.backend_tokenizer.normalizer is not None:
            expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
        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,
                    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}",
                )
            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
        nodes = [["this", "is"], ["hello", "🤗"]]
        xpaths = [["html/body"] * 2, ["html/body"] * 2]
        inputs = new_tokenizer(nodes, xpaths=xpaths)
        self.assertEqual(len(inputs["input_ids"]), 2)
        decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
        expected_result = "thisis"  # same as line 1399

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

    def test_prepare_for_model(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            # only test prepare_for_model for the slow tokenizer
            if tokenizer.__class__.__name__ == "MarkupLMTokenizerFast":
                continue
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                nodes, xpaths = self.get_nodes_and_xpaths()
                prepared_input_dict = tokenizer.prepare_for_model(nodes, xpaths=xpaths, add_special_tokens=True)

                input_dict = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)

                self.assertEqual(input_dict, prepared_input_dict)

    def test_padding_different_model_input_name(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

        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)
                self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
                pad_token_id = tokenizer_p.pad_token_id

                nodes, xpaths = self.get_nodes_and_xpaths_batch()

                input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
                input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)

                # 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"
                )

    def test_batch_encode_dynamic_overflowing(self):
        """
        When calling batch_encode with multiple sequences, it can return 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)

            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
                if is_torch_available():
                    returned_tensor = "pt"
                elif is_tf_available():
                    returned_tensor = "tf"
                else:
                    returned_tensor = "jax"

                # Single example
                nodes, xpaths = self.get_nodes_and_xpaths()
                tokens = tokenizer.encode_plus(
                    nodes,
                    xpaths=xpaths,
                    max_length=1,
                    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()):
                    if "xpath" not in key:
                        self.assertEqual(len(tokens[key].shape), 2)
                    else:
                        self.assertEqual(len(tokens[key].shape), 3)

                # Batch of examples
                # For these 2 examples, 3 training examples will be created
                nodes, xpaths = self.get_nodes_and_xpaths_batch()
                tokens = tokenizer.batch_encode_plus(
                    nodes,
                    xpaths=xpaths,
                    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()):
                    if "xpath" not in key:
                        self.assertEqual(len(tokens[key].shape), 2)
                        self.assertEqual(tokens[key].shape[-1], 6)
                    else:
                        self.assertEqual(len(tokens[key].shape), 3)
                        self.assertEqual(tokens[key].shape[-2], 6)

    @unittest.skip("TO DO: overwrite this very extensive test.")
    def test_alignement_methods(self):
        pass

    def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
        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].split(" "), xpaths=len(t[1]) * ["html/body"], add_special_tokens=False),
                toks,
            )
        )
        if max_length is not None and len(toks) > max_length:
            toks = toks[:max_length]
        if min_length is not None and len(toks) < min_length and len(toks) > 0:
            while len(toks) < min_length:
                toks = toks + toks
        # 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)
        # an extra blank will cause inconsistency: ["a","b",] & "a b"
        """
        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
        nodes = output_txt.split(" ")
        xpaths = ["html/body" for i in range(len(nodes))]
        output_ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
        return nodes, xpaths, output_ids

    def test_maximum_encoding_length_pair_input(self):
        # slow part fixed, fast part not
        tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Build a sequence from our model's vocabulary
                stride = 2
                seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
                question_0 = " ".join(map(str, seq_0))
                if len(ids) <= 2 + stride:
                    seq_0 = (seq_0 + " ") * (2 + stride)
                    ids = None

                seq0_tokens = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)
                self.assertGreater(len(seq0_tokens["input_ids"]), 2 + stride)
                question_1 = "This is another sentence to be encoded."
                seq_1 = ["hello", "world"]
                xpaths_1 = ["html/body" for i in range(len(seq_1))]
                seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
                if abs(len(seq0_tokens["input_ids"]) - len(seq1_tokens["input_ids"])) <= 2:
                    seq1_tokens_input_ids = seq1_tokens["input_ids"] + seq1_tokens["input_ids"]
                    seq_1 = tokenizer.decode(seq1_tokens_input_ids, clean_up_tokenization_spaces=False)
                    seq_1 = seq_1.split(" ")
                    xpaths_1 = ["html/body" for i in range(len(seq_1))]
                seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)

                self.assertGreater(len(seq1_tokens["input_ids"]), 2 + stride)

                smallest = (
                    seq1_tokens["input_ids"]
                    if len(seq0_tokens["input_ids"]) > len(seq1_tokens["input_ids"])
                    else seq0_tokens["input_ids"]
                )

                # We are not using the special tokens - a bit too hard to test all the tokenizers with this
                # TODO try this again later
                sequence = tokenizer(question_0, seq_1, xpaths=xpaths_1, add_special_tokens=False)

                # 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
                question_2 = " ".join(map(str, seq_2))
                xpaths_2 = xpaths_0 * model_max_length
                # assertgreater -> assertgreaterequal
                self.assertGreaterEqual(len(seq_2), model_max_length)

                sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                sequence2 = tokenizer(question_2, seq_1, xpaths=xpaths_1, add_special_tokens=False)
                total_length2 = len(sequence2["input_ids"])
                self.assertLess(total_length1, model_max_length, "Issue with the testing sequence, please update it.")
                self.assertGreater(
                    total_length2, model_max_length, "Issue with the testing sequence, please update it."
                )

                # 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"{tokenizer.__class__.__name__} Padding: {padding_state}"):
                        for truncation_state in [True, "longest_first", "only_first"]:
                            with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
                                output = tokenizer(
                                    question_2,
                                    seq_1,
                                    xpaths=xpaths_1,
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)

                                output = tokenizer(
                                    [question_2],
                                    [seq_1],
                                    xpaths=[xpaths_1],
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)

                        # Simple
                        output = tokenizer(
                            question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation="only_second"
                        )
                        self.assertEqual(len(output["input_ids"]), model_max_length)
                        self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
                        self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)

                        output = tokenizer(
                            [question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation="only_second"
                        )
                        self.assertEqual(len(output["input_ids"][0]), model_max_length)
                        self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                        self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)

                        # Simple with no truncation
                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(
                                question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation=False
                            )
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )

                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(
                                [question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation=False
                            )
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )
                # Check the order of Sequence of input ids, overflowing tokens and xpath_tags_seq sequence with truncation
                truncated_first_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][:-2]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"]
                )
                truncated_second_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][:-2]
                )
                truncated_longest_sequence = (
                    truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
                )

                overflow_first_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][-(2 + stride) :]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"]
                )
                overflow_second_sequence = (
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][-(2 + stride) :]
                )
                overflow_longest_sequence = (
                    overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence
                )

                xpath_tags_seq_first = [[5] * 50] * (
                    len(tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]) - 2
                )
                xpath_tags_seq_first_sequence = (
                    xpath_tags_seq_first
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"]
                )
                overflowing_token_xpath_tags_seq_first_sequence_slow = [[5] * 50] * (2 + stride)
                overflowing_token_xpath_tags_seq_first_sequence_fast = [[5] * 50] * (2 + stride) + tokenizer(
                    seq_1, xpaths=xpaths_1, add_special_tokens=False
                )["xpath_tags_seq"]

                xpath_tags_seq_second = [[5] * 50] * len(
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                )
                xpath_tags_seq_second_sequence = (
                    xpath_tags_seq_second
                    + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][:-2]
                )
                overflowing_token_xpath_tags_seq_second_sequence_slow = tokenizer(
                    seq_1, xpaths=xpaths_1, add_special_tokens=False
                )["xpath_tags_seq"][-(2 + stride) :]
                overflowing_token_xpath_tags_seq_second_sequence_fast = [[5] * 50] * len(
                    tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
                ) + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][-(2 + stride) :]

                xpath_tags_seq_longest_sequence = (
                    xpath_tags_seq_first_sequence
                    if len(seq0_tokens) > len(seq1_tokens)
                    else xpath_tags_seq_second_sequence
                )
                overflowing_token_xpath_tags_seq_longest_sequence_fast = (
                    overflowing_token_xpath_tags_seq_first_sequence_fast
                    if len(seq0_tokens) > len(seq1_tokens)
                    else overflowing_token_xpath_tags_seq_second_sequence_fast
                )

                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    information = tokenizer(
                        question_0,
                        seq_1,
                        xpaths=xpaths_1,
                        max_length=len(sequence["input_ids"]) - 2,
                        add_special_tokens=False,
                        stride=stride,
                        truncation="longest_first",
                        return_overflowing_tokens=True,
                        # add_prefix_space=False,
                    )
                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    xpath_tags_seq = information["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_longest_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence)

                    self.assertEqual(len(overflowing_xpath_tags_seq), 2 + stride + len(smallest))
                    self.assertEqual(
                        overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast
                    )
                else:
                    # No overflowing tokens when using 'longest' in python tokenizers
                    with self.assertRaises(ValueError) as context:
                        information = tokenizer(
                            question_0,
                            seq_1,
                            xpaths=xpaths_1,
                            max_length=len(sequence["input_ids"]) - 2,
                            add_special_tokens=False,
                            stride=stride,
                            truncation="longest_first",
                            return_overflowing_tokens=True,
                            # add_prefix_space=False,
                        )

                    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`."
                        )
                    )

                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    information = tokenizer(
                        question_0,
                        seq_1,
                        xpaths=xpaths_1,
                        max_length=len(sequence["input_ids"]) - 2,
                        add_special_tokens=False,
                        stride=stride,
                        truncation=True,
                        return_overflowing_tokens=True,
                    )
                    truncated_sequence = information["input_ids"][0]
                    overflowing_tokens = information["input_ids"][1]
                    xpath_tags_seq = information["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
                    self.assertEqual(len(information["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_longest_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence)
                    self.assertEqual(
                        overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast
                    )
                else:
                    # No overflowing tokens when using 'longest' in python tokenizers
                    with self.assertRaises(ValueError) as context:
                        information = tokenizer(
                            question_0,
                            seq_1,
                            xpaths=xpaths_1,
                            max_length=len(sequence["input_ids"]) - 2,
                            add_special_tokens=False,
                            stride=stride,
                            truncation=True,
                            return_overflowing_tokens=True,
                        )

                    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`."
                        )
                    )

                information_first_truncated = tokenizer(
                    question_0,
                    seq_1,
                    xpaths=xpaths_1,
                    max_length=len(sequence["input_ids"]) - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="only_first",
                    return_overflowing_tokens=True,
                )
                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    truncated_sequence = information_first_truncated["input_ids"][0]
                    overflowing_tokens = information_first_truncated["input_ids"][1]
                    xpath_tags_seq = information_first_truncated["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information_first_truncated["xpath_tags_seq"][1]
                    self.assertEqual(len(information_first_truncated["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_first_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens["input_ids"]))
                    self.assertEqual(overflowing_tokens, overflow_first_sequence)
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence)
                    # ISSUE HAPPENS HERE ↓
                    self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_fast)
                else:
                    truncated_sequence = information_first_truncated["input_ids"]
                    overflowing_tokens = information_first_truncated["overflowing_tokens"]
                    overflowing_xpath_tags_seq = information_first_truncated["overflowing_xpath_tags_seq"]
                    xpath_tags_seq = information_first_truncated["xpath_tags_seq"]

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_first_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq0_tokens["input_ids"][-(2 + stride) :])
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence)
                    self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_slow)

                information_second_truncated = tokenizer(
                    question_0,
                    seq_1,
                    xpaths=xpaths_1,
                    max_length=len(sequence["input_ids"]) - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="only_second",
                    return_overflowing_tokens=True,
                    # add_prefix_space=False,
                )
                # Overflowing tokens are handled quite differently in slow and fast tokenizers
                if isinstance(tokenizer, MarkupLMTokenizerFast):
                    truncated_sequence = information_second_truncated["input_ids"][0]
                    overflowing_tokens = information_second_truncated["input_ids"][1]
                    xpath_tags_seq = information_second_truncated["xpath_tags_seq"][0]
                    overflowing_xpath_tags_seq = information_second_truncated["xpath_tags_seq"][1]

                    self.assertEqual(len(information_second_truncated["input_ids"]), 2)

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_second_sequence)

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

                    self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
                    self.assertEqual(truncated_sequence, truncated_second_sequence)

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq1_tokens["input_ids"][-(2 + stride) :])
                    self.assertEqual(xpath_tags_seq, xpath_tags_seq_second_sequence)
                    self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_second_sequence_slow)

    def test_maximum_encoding_length_single_input(self):
        tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20)

                sequence = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)
                total_length = len(sequence["input_ids"])

                self.assertGreater(total_length, 4, "Issue with the testing sequence, please update it it's too short")

                # 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
                xpaths_1 = xpaths_0 * model_max_length
                sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                self.assertGreater(
                    total_length1, model_max_length, "Issue with the testing sequence, please update it it's too short"
                )

                # 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,
                                    xpaths=xpaths_1,
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)

                                output = tokenizer(
                                    [seq_1],
                                    xpaths=[xpaths_1],
                                    padding=padding_state,
                                    truncation=truncation_state,
                                )
                                self.assertEqual(len(output["input_ids"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                                self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)

                        # Simple with no truncation
                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(seq_1, xpaths=xpaths_1, padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )

                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer([seq_1], xpaths=[xpaths_1], padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length)
                            self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )
                # Check the order of Sequence of input ids, overflowing tokens, xpath_tags_seq and xpath_subs_seq sequence with truncation
                stride = 2
                information = tokenizer(
                    seq_0,
                    xpaths=xpaths_0,
                    max_length=total_length - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation=True,
                    return_overflowing_tokens=True,
                )

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

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

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :])

                    self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2])
                    self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :])
                else:
                    truncated_sequence = information["input_ids"]
                    overflowing_tokens = information["overflowing_tokens"]
                    xpath_tags_seq = information["xpath_tags_seq"]
                    overflowing_xpath_tags_seq = information["overflowing_xpath_tags_seq"]
                    self.assertEqual(len(truncated_sequence), total_length - 2)
                    self.assertEqual(truncated_sequence, sequence["input_ids"][:-2])

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :])
                    self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2])
                    self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :])

    @unittest.skip("MarkupLM tokenizer requires xpaths besides sequences.")
    def test_pretokenized_inputs(self):
        pass

    @unittest.skip("MarkupLM tokenizer always expects pretokenized inputs.")
    def test_compare_pretokenized_inputs(self):
        pass

    @unittest.skip("MarkupLM fast tokenizer does not support prepare_for_model")
    def test_compare_prepare_for_model(self):
        pass

    @slow
    def test_only_label_first_subword(self):
        nodes = ["hello", "niels"]
        xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]
        node_labels = [0, 1]

        # test slow tokenizer
        tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
        encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100])

        tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False)
        encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100])

        # test fast tokenizer
        tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")
        encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100])

        tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False)
        encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels)
        self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100])

    def test_markuplm_integration_test(self):
        tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
        tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")

        # There are 3 cases:
        # CASE 1: document image classification (training + inference), document image token classification (inference),
        # in which case only nodes and normalized bounding xpaths are provided to the tokenizer
        # CASE 2: document image token classification (training),
        # in which case one also provides word labels to the tokenizer
        # CASE 3: document image visual question answering (inference),
        # in which case one also provides a question to the tokenizer

        # We need to test all 3 cases both on batched and non-batched inputs.

        # CASE 1: not batched
        nodes, xpaths = self.get_nodes_and_xpaths()

        # fmt: off
        expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}  # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 1: batched
        nodes, xpaths = self.get_nodes_and_xpaths_batch()

        # fmt: off
        expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
        # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 2: not batched
        nodes, xpaths = self.get_nodes_and_xpaths()
        node_labels = [1, 2, 3]

        # fmt: off
        expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
        # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 2: batched
        nodes, xpaths = self.get_nodes_and_xpaths_batch()
        node_labels = [[1, 2, 3], [2, 46, 17, 22, 3]]

        # fmt: off
        expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, -100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # fmt: on

        encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 3: not batched
        question, nodes, xpaths = self.get_question_nodes_and_xpaths()

        # fmt: off
        expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
        # fmt: on

        encoding_p = tokenizer_p(question, nodes, xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(question, nodes, xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

        # CASE 3: batched
        questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch()

        # fmt: off
        expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]}
        # fmt: on

        encoding_p = tokenizer_p(questions, nodes, xpaths, padding="max_length", max_length=20)
        encoding_r = tokenizer_r(questions, nodes, xpaths, padding="max_length", max_length=20)
        self.assertDictEqual(dict(encoding_p), expected_results)
        self.assertDictEqual(dict(encoding_r), expected_results)

    @unittest.skip("Doesn't support another framework than PyTorch")
    def test_np_encode_plus_sent_to_model(self):
        pass

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

        nodes, xpaths = self.get_nodes_and_xpaths()

        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, nodes)

        encoding_fast = tokenizer_fast(nodes, xpaths=xpaths)

        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, nodes)

        encoding_slow = tokenizer_slow(nodes, xpaths=xpaths)

        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,
        )
