# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import os
import pickle
import re
import shutil
import tempfile
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, List, Tuple, Union

from transformers import PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast
from transformers.testing_utils import require_tf, require_torch, slow
from transformers.tokenization_utils import AddedToken


if TYPE_CHECKING:
    from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel


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

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

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

    return model_tokenizer_mapping


class TokenizerTesterMixin:

    tokenizer_class = None
    test_rust_tokenizer = False

    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def get_input_output_texts(self, tokenizer):
        input_txt = self.get_clean_sequence(tokenizer)[0]
        return input_txt, input_txt

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

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

    def get_tokenizers(self, fast=True, **kwargs) -> List[PreTrainedTokenizerBase]:
        if fast and self.test_rust_tokenizer:
            return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
        return [self.get_tokenizer(**kwargs)]

    def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast:
        raise NotImplementedError

    # def get_input_output_texts(self) -> Tuple[str, str]:
    #     """Feel free to overwrite"""
    #     # TODO: @property
    #     return (
    #         "This is a test",
    #         "This is a test",
    #     )

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

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

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

                attributes_list = [
                    "model_max_length",
                    "init_inputs",
                    "init_kwargs",
                ]
                if not isinstance(tokenizer, PreTrainedTokenizerFast):
                    attributes_list += [
                        "added_tokens_encoder",
                        "added_tokens_decoder",
                    ]
                for attr in attributes_list:
                    self.assertTrue(hasattr(tokenizer, attr))

    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
                tmpdirname = tempfile.mkdtemp()

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

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

                shutil.rmtree(tmpdirname)

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

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

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

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

                shutil.rmtree(tmpdirname)

    def test_pickle_tokenizer(self):
        """Google pickle __getstate__ __setstate__ if you are struggling with this."""
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                self.assertIsNotNone(tokenizer)

                text = "Munich and Berlin are nice cities"
                subwords = tokenizer.tokenize(text)

                filename = os.path.join(self.tmpdirname, "tokenizer.bin")
                with open(filename, "wb") as handle:
                    pickle.dump(tokenizer, handle)

                with open(filename, "rb") as handle:
                    tokenizer_new = pickle.load(handle)

                subwords_loaded = tokenizer_new.tokenize(text)

                self.assertListEqual(subwords, subwords_loaded)

    def test_pickle_added_tokens(self):
        tok1 = AddedToken("<s>", rstrip=True, lstrip=True, normalized=False, single_word=True)
        tok2 = pickle.loads(pickle.dumps(tok1))

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

    def test_added_tokens_do_lower_case(self):
        # TODO(thom) activate fast tokenizer tests once Rust tokenizers accepts white spaces in added tokens
        tokenizers = self.get_tokenizers(fast=False, do_lower_case=True)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                special_token = tokenizer.all_special_tokens[0]

                text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
                text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token

                toks0 = tokenizer.tokenize(text)  # toks before adding new_toks

                new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
                added = tokenizer.add_tokens(new_toks)
                self.assertEqual(added, 2)

                toks = tokenizer.tokenize(text)
                toks2 = tokenizer.tokenize(text2)

                self.assertEqual(len(toks), len(toks2))
                self.assertListEqual(toks, toks2)
                if not isinstance(tokenizer, PreTrainedTokenizerFast):
                    # Python tokenizers can have added tokens with spaces inside them
                    # cf https://github.com/huggingface/tokenizers/issues/302
                    self.assertNotEqual(len(toks), len(toks0))  # toks0 should be longer

                # Check that none of the special tokens are lowercased
                sequence_with_special_tokens = "A " + " yEs ".join(tokenizer.all_special_tokens) + " B"
                tokenized_sequence = tokenizer.tokenize(sequence_with_special_tokens)

                for special_token in tokenizer.all_special_tokens:
                    self.assertTrue(special_token in tokenized_sequence)

        tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                special_token = tokenizer.all_special_tokens[0]

                text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
                text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token

                new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]

                toks0 = tokenizer.tokenize(text)  # toks before adding new_toks

                added = tokenizer.add_tokens(new_toks)
                self.assertEqual(added, 4)

                toks = tokenizer.tokenize(text)
                toks2 = tokenizer.tokenize(text2)

                self.assertEqual(len(toks), len(toks2))  # Length should still be the same
                self.assertNotEqual(toks[1], toks2[1])  # But at least the first non-special tokens should differ
                if not isinstance(tokenizer, PreTrainedTokenizerFast):
                    # Python tokenizers can have added tokens with spaces inside them
                    # cf https://github.com/huggingface/tokenizers/issues/302
                    self.assertNotEqual(len(toks), len(toks0))  # toks0 should be longer

    def test_add_tokens_tokenizer(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                vocab_size = tokenizer.vocab_size
                all_size = len(tokenizer)

                self.assertNotEqual(vocab_size, 0)

                # 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))

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

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

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

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

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

                self.assertGreaterEqual(len(tokens), 6)
                self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
                self.assertGreater(tokens[0], tokens[1])
                self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
                self.assertGreater(tokens[-2], tokens[-3])
                self.assertEqual(tokens[0], tokenizer.eos_token_id)
                self.assertEqual(tokens[-2], tokenizer.pad_token_id)

    def test_add_special_tokens(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                input_text, ids = self.get_clean_sequence(tokenizer)

                special_token = "[SPECIAL_TOKEN]"

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

                text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
                encoded = tokenizer.encode(text, add_special_tokens=False)

                input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
                special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
                self.assertEqual(encoded, input_encoded + special_token_id)

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

    def test_internal_consistency(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                input_text, output_text = self.get_input_output_texts(tokenizer)

                tokens = tokenizer.tokenize(input_text)
                ids = tokenizer.convert_tokens_to_ids(tokens)
                ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
                self.assertListEqual(ids, ids_2)

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

                self.assertEqual(text_2, output_text)

    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__}"):

                new_toks = ["[ABC]", "[DEF]"]  # TODO(thom) add this one back when Rust toks are ready: , "GHI IHG"]
                tokenizer.add_tokens(new_toks)
                input = "[ABC] [DEF] [ABC] [DEF]"  # TODO(thom) add back cf above: "[ABC] [DEF] [ABC] GHI IHG [DEF]"
                encoded = tokenizer.encode(input, add_special_tokens=False)
                decoded = tokenizer.decode(encoded)
                self.assertEqual(decoded, input)

    def test_pretrained_model_lists(self):
        weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
        weights_lists_2 = []
        for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
            weights_lists_2.append(list(map_list.keys()))

        for weights_list_2 in weights_lists_2:
            self.assertListEqual(weights_list, weights_list_2)

    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__}"):

                if (
                    tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
                    and "token_type_ids" in tokenizer.model_input_names
                ):
                    seq_0 = "Test this method."
                    seq_1 = "With these inputs."
                    information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
                    sequences, mask = information["input_ids"], information["token_type_ids"]
                    self.assertEqual(len(sequences), len(mask))

    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__}"):

                seq_0 = "Test this method."
                seq_1 = "With these inputs."

                sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
                attached_sequences = tokenizer.encode(seq_0, seq_1, 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_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, ids = self.get_clean_sequence(tokenizer, max_length=20)

                sequence = tokenizer.encode(seq_0, add_special_tokens=False)
                total_length = len(sequence)

                assert total_length > 1, "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

                sequence1 = tokenizer(seq_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                assert (
                    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, padding=padding_state, truncation=truncation_state)
                                self.assertEqual(len(output["input_ids"]), model_max_length)

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

                        # Simple with no truncation
                        output = tokenizer(seq_1, padding=padding_state, truncation=False)
                        self.assertNotEqual(len(output["input_ids"]), model_max_length)

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

                # Overflowing tokens
                stride = 2
                information = tokenizer(
                    seq_0,
                    max_length=total_length - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="longest_first",
                    return_overflowing_tokens=True,
                    # add_prefix_space=False,
                )

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

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

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

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

                    self.assertEqual(len(overflowing_tokens), 2 + stride)

    def test_maximum_encoding_length_pair_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__}"):
                # Build a sequence from our model's vocabulary
                stride = 2
                seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
                if len(ids) <= 2 + stride:
                    seq_0 = (seq_0 + " ") * (2 + stride)
                    ids = None

                seq0_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
                assert len(seq0_tokens) > 2 + stride

                seq_1 = "This is another sentence to be encoded."
                seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
                if abs(len(seq0_tokens) - len(seq1_tokens)) <= 2:
                    seq1_tokens = seq1_tokens + seq1_tokens
                    seq_1 = tokenizer.decode(seq1_tokens, clean_up_tokenization_spaces=False)
                seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)

                assert len(seq1_tokens) > 2 + stride

                smallest = seq1_tokens if len(seq0_tokens) > len(seq1_tokens) else seq0_tokens

                # We are not using the special tokens - a bit too hard to test all the tokenizers with this
                # TODO try this again later
                sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)  # , add_prefix_space=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

                sequence1 = tokenizer(seq_1, add_special_tokens=False)
                total_length1 = len(sequence1["input_ids"])
                sequence2 = tokenizer(seq_2, seq_1, add_special_tokens=False)
                total_length2 = len(sequence2["input_ids"])
                assert total_length1 < model_max_length - 10, "Issue with the testing sequence, please update it."
                assert 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"Padding: {padding_state}"):
                        for truncation_state in [True, "longest_first", "only_first"]:
                            with self.subTest(f"Truncation: {truncation_state}"):
                                output = tokenizer(seq_2, seq_1, padding=padding_state, truncation=truncation_state)
                                self.assertEqual(len(output["input_ids"]), model_max_length)

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

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

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

                        # Simple with no truncation
                        output = tokenizer(seq_1, seq_2, padding=padding_state, truncation=False)
                        self.assertNotEqual(len(output["input_ids"]), model_max_length)

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

                truncated_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[:-2] + tokenizer.encode(
                    seq_1, add_special_tokens=False
                )
                truncated_second_sequence = (
                    tokenizer.encode(seq_0, add_special_tokens=False)
                    + tokenizer.encode(seq_1, add_special_tokens=False)[:-2]
                )
                truncated_longest_sequence = (
                    truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
                )

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

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

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

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                else:
                    truncated_sequence = information["input_ids"]
                    overflowing_tokens = information["overflowing_tokens"]

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

                    self.assertEqual(
                        len(overflowing_tokens), 2 + stride
                    )  # No overflowing tokens when using 'longest' in python tokenizers

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

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

                    self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
                    self.assertEqual(overflowing_tokens, overflow_longest_sequence)
                else:
                    truncated_sequence = information["input_ids"]
                    overflowing_tokens = information["overflowing_tokens"]

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

                    self.assertEqual(
                        len(overflowing_tokens), 2 + stride
                    )  # No overflowing tokens when using 'longest' in python tokenizers

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

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

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

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

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

                information_second_truncated = tokenizer.encode_plus(
                    seq_0,
                    seq_1,
                    max_length=len(sequence) - 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, PreTrainedTokenizerFast):
                    truncated_sequence = information_second_truncated["input_ids"][0]
                    overflowing_tokens = information_second_truncated["input_ids"][1]
                    self.assertEqual(len(information_second_truncated["input_ids"]), 2)

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

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

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

                    self.assertEqual(len(overflowing_tokens), 2 + stride)
                    self.assertEqual(overflowing_tokens, seq1_tokens[-(2 + stride) :])

    # def test_encode_input_type(self):
    #     tokenizers = self.get_tokenizers(do_lower_case=False)
    #     for tokenizer in tokenizers:
    #         with self.subTest(f"{tokenizer.__class__.__name__}"):
    #             sequence = "Let's encode this sequence"

    #             tokens = sequence.split()  # tokenizer.tokenize(sequence)
    #             # input_ids = tokenizer.convert_tokens_to_ids(tokens)
    #             formatted_input = tokenizer.encode(sequence, add_special_tokens=True, add_prefix_space=False)

    #             self.assertEqual(
    #                 tokenizer.encode(tokens, is_split_into_words=True, add_special_tokens=True), formatted_input
    #             )
    #             # This is not supported with the Rust tokenizers
    #             # self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)

    def test_swap_special_token(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                mask = "<mask>"
                sequence = "Encode this sequence"
                sequence_masked_0 = "Encode <mask> sequence"
                sequence_masked_1 = "<mask> this sequence"

                # Add tokens so that masked token isn't split
                tokenizer.add_tokens(sequence.split())
                tokenizer.add_special_tokens({"mask_token": mask})
                mask_ind = tokenizer.convert_tokens_to_ids(mask)
                encoded = tokenizer.encode(sequence, add_special_tokens=False)

                # Test first masked sequence
                encoded_masked = tokenizer.encode(sequence_masked_0, add_special_tokens=False)
                mask_loc = encoded_masked.index(mask_ind)
                encoded_masked[mask_loc] = encoded[mask_loc]

                self.assertEqual(encoded_masked, encoded)

                # Test second masked sequence
                encoded_masked = tokenizer.encode(sequence_masked_1, add_special_tokens=False)
                mask_loc = encoded_masked.index(mask_ind)
                encoded_masked[mask_loc] = encoded[mask_loc]

                self.assertEqual(encoded_masked, encoded)

    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__}"):
                sequence_0 = "Encode this."
                # Testing single inputs
                encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    sequence_0, 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 for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]]
                self.assertEqual(encoded_sequence, filtered_sequence)

    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__}"):
                sequence_0 = "Encode this."
                sequence_1 = "This one too please."
                encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
                encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
                encoded_sequence_dict = tokenizer.encode_plus(
                    sequence_0,
                    sequence_1,
                    add_special_tokens=True,
                    return_special_tokens_mask=True,
                    # 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_right_and_left_padding(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence = "Sequence"
                padding_size = 10

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

                padding_idx = tokenizer.pad_token_id

                # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
                tokenizer.padding_side = "right"
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
                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(sequence)
                sequence_length = len(encoded_sequence)
                padded_sequence = tokenizer.encode(
                    sequence, max_length=sequence_length + padding_size, padding="max_length"
                )
                padded_sequence_length = len(padded_sequence)
                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(sequence)
                sequence_length = len(encoded_sequence)

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

                tokenizer.padding_side = "left"
                padded_sequence_left = tokenizer.encode(sequence, 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(sequence)
                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(sequence, 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_padding_to_max_length(self):
        """We keep this test for backward compatibility but it should be remove when `pad_to_max_length` will e deprecated"""
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                sequence = "Sequence"
                padding_size = 10

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

                padding_idx = tokenizer.pad_token_id

                # Check that it correctly pads when a maximum length is specified along with the padding flag set to True
                tokenizer.padding_side = "right"
                encoded_sequence = tokenizer.encode(sequence)
                sequence_length = len(encoded_sequence)
                # FIXME: the next line should be padding(max_length) to avoid warning
                padded_sequence = tokenizer.encode(
                    sequence, 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(sequence)
                sequence_length = len(encoded_sequence)

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

    def test_padding_to_multiple_of(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            if tokenizer.pad_token is None:
                self.skipTest("No padding token.")
            else:
                with self.subTest(f"{tokenizer.__class__.__name__}"):
                    empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8)
                    normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8)
                    for key, value in empty_tokens.items():
                        self.assertEqual(len(value) % 8, 0, "BatchEncoding.{} is not multiple of 8".format(key))
                    for key, value in normal_tokens.items():
                        self.assertEqual(len(value) % 8, 0, "BatchEncoding.{} is not multiple of 8".format(key))

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

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

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

    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__}"):
                sequence = "Sequence"

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

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

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

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

                not_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    padding=True,
                    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)

                assert sequence_length == not_padded_sequence_length
                assert input_ids == not_padded_input_ids
                assert special_tokens_mask == not_padded_special_tokens_mask

                not_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    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)

                assert sequence_length == not_padded_sequence_length
                assert input_ids == not_padded_input_ids
                assert special_tokens_mask == not_padded_special_tokens_mask

                # Test right padding
                tokenizer.padding_side = "right"

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

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

                assert sequence_length + padding_size == right_padded_sequence_length
                assert input_ids + [padding_idx] * padding_size == right_padded_input_ids
                assert special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask

                # Test left padding
                tokenizer.padding_side = "left"
                left_padded_sequence = tokenizer.encode_plus(
                    sequence,
                    max_length=sequence_length + padding_size,
                    padding="max_length",
                    return_special_tokens_mask=True,
                )
                left_padded_input_ids = left_padded_sequence["input_ids"]
                left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
                left_padded_sequence_length = len(left_padded_input_ids)

                assert sequence_length + padding_size == left_padded_sequence_length
                assert [padding_idx] * padding_size + input_ids == left_padded_input_ids
                assert [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 + [token_type_padding_idx] * padding_size == right_padded_token_type_ids
                    assert [token_type_padding_idx] * 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"]

                    assert attention_mask + [0] * padding_size == right_padded_attention_mask
                    assert [0] * padding_size + attention_mask == left_padded_attention_mask

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

        tokenizer = self.get_tokenizer(random_argument=True)
        assert tokenizer.init_kwargs["random_argument"] is True
        new_tokenizer = self.get_tokenizer(random_argument=False)
        assert tokenizer.init_kwargs["random_argument"] is True
        assert new_tokenizer.init_kwargs["random_argument"] is False

    def test_get_vocab(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                vocab = tokenizer.get_vocab()

                self.assertIsInstance(vocab, dict)
                self.assertEqual(len(vocab), len(tokenizer))

                tokenizer.add_tokens(["asdfasdfasdfasdf"])
                vocab = tokenizer.get_vocab()
                self.assertIsInstance(vocab, dict)
                self.assertEqual(len(vocab), len(tokenizer))

    def test_conversion_reversible(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                vocab = tokenizer.get_vocab()
                for word, ind in vocab.items():
                    self.assertEqual(tokenizer.convert_tokens_to_ids(word), ind)
                    self.assertEqual(tokenizer.convert_ids_to_tokens(ind), word)

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

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

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

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

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

    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__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

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

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

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

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

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

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

    def test_added_token_serializable(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            new_token = AddedToken("new_token", lstrip=True)
            tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                tokenizer.save_pretrained(tmp_dir_name)
                tokenizer.from_pretrained(tmp_dir_name)

    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__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                max_length = 100

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

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

        # 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"
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

                max_length = 100

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    def test_prepare_for_model(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            string_sequence = "Testing the prepare_for_model method."
            ids = tokenizer.encode(string_sequence, add_special_tokens=False)
            input_dict = tokenizer.encode_plus(string_sequence)
            prepared_input_dict = tokenizer.prepare_for_model(ids)

            self.assertEqual(input_dict, prepared_input_dict)

    def test_batch_encode_plus_overflowing_tokens(self):
        tokenizers = self.get_tokenizers(do_lower_case=False)
        for tokenizer in tokenizers:
            string_sequences = ["Testing the prepare_for_model method.", "Test"]

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

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

    @require_torch
    @require_tf
    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__}"):
                sequences = [
                    "Testing batch encode plus",
                    "Testing batch encode plus with different sequence lengths",
                    "Testing batch encode plus with different sequence lengths correctly pads",
                ]

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

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

                    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 _check_no_pad_token_padding(self, tokenizer, sequences):
        # if tokenizer does not have pad_token_id, an error should be thrown
        if tokenizer.pad_token_id is None:
            with self.assertRaises(ValueError):
                if isinstance(sequences, list):
                    tokenizer.batch_encode_plus(sequences, padding="longest")
                else:
                    tokenizer.encode_plus(sequences, padding=True)

            # add pad_token_id to pass subsequent tests
            tokenizer.add_special_tokens({"pad_token": "<PAD>"})

    @slow
    @require_torch
    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
                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
                sequence = " ".join(first_ten_tokens)
                encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="pt")
                batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
                # This should not fail

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

        # if self.test_rust_tokenizer:
        #     fast_tokenizer = self.get_rust_tokenizer()
        #     encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="pt")
        #     batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
        #     # This should not fail
        #     model(**encoded_sequence_fast)
        #     model(**batch_encoded_sequence_fast)

    @slow
    @require_tf
    def test_tf_encode_plus_sent_to_model(self):
        from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)

        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
                assert model.config.vocab_size >= len(tokenizer)

                # Build sequence
                first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
                sequence = " ".join(first_ten_tokens)
                encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="tf")
                batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="tf")

                # This should not fail
                model(encoded_sequence)
                model(batch_encoded_sequence)

    # TODO: Check if require_torch is the best to test for numpy here ... Maybe move to require_flax when available
    @slow
    @require_torch
    def test_np_encode_plus_sent_to_model(self):
        from transformers import MODEL_MAPPING, TOKENIZER_MAPPING

        MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)

        tokenizer = self.get_tokenizer()
        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

        # Build sequence
        first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
        sequence = " ".join(first_ten_tokens)
        encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="np")
        batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="np")

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

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

        if self.test_rust_tokenizer:
            fast_tokenizer = self.get_rust_tokenizer()
            encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="np")
            batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="np")

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

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

    @require_torch
    def test_prepare_seq2seq_batch(self):
        tokenizer = self.get_tokenizer()

        if not hasattr(tokenizer, "prepare_seq2seq_batch"):
            return
        # Longer text that will definitely require truncation.
        src_text = [
            " UN Chief Says There Is No Military Solution in Syria",
            " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
        ]
        tgt_text = [
            "Şeful ONU declară că nu există o soluţie militară în Siria",
            "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei "
            'pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu '
            "vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
        ]
        try:
            batch = tokenizer.prepare_seq2seq_batch(
                src_texts=src_text,
                tgt_texts=tgt_text,
                max_length=3,
                max_target_length=10,
                return_tensors="pt",
                src_lang="en_XX",  # this should be ignored (for all but mbart) but not cause an error
            )
        except NotImplementedError:
            return
        self.assertEqual(batch.input_ids.shape[1], 3)
        self.assertEqual(batch.labels.shape[1], 10)
        # max_target_length will default to max_length if not specified
        batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, max_length=3)
        self.assertEqual(batch.input_ids.shape[1], 3)
        self.assertEqual(batch.labels.shape[1], 3)

        batch_encoder_only = tokenizer.prepare_seq2seq_batch(
            src_texts=src_text, max_length=3, max_target_length=10, return_tensors="pt"
        )
        self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
        self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
        self.assertNotIn("decoder_input_ids", batch_encoder_only)
