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

import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    PreTrainedTokenizerFast,
    SeamlessM4TTokenizer,
    SeamlessM4TTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
    from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right

EN_CODE = 256047
RO_CODE = 256145

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


@require_sentencepiece
@require_tokenizers
class SeamlessM4TTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = SeamlessM4TTokenizer
    rust_tokenizer_class = SeamlessM4TTokenizerFast
    test_rust_tokenizer = True
    test_sentencepiece = True
    from_pretrained_kwargs = {}

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

        # We have a SentencePiece fixture for testing
        tokenizer = SeamlessM4TTokenizer(SAMPLE_VOCAB, keep_accents=True)
        tokenizer.save_pretrained(self.tmpdirname)

    def test_full_tokenizer(self):
        tokenizer = SeamlessM4TTokenizer(SAMPLE_VOCAB, keep_accents=True)

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

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

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

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

    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)

                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

                sequence1 = tokenizer(seq_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, 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
                        # Reset warnings
                        tokenizer.deprecation_warnings = {}
                        with self.assertLogs("transformers", level="WARNING") as cm:
                            output = tokenizer(seq_1, padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "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], padding=padding_state, truncation=False)
                            self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
                        self.assertEqual(len(cm.records), 1)
                        self.assertTrue(
                            cm.records[0].message.startswith(
                                "Token indices sequence length is longer than the specified maximum sequence length"
                                " for this model"
                            )
                        )

                # Overflowing tokens
                stride = 2

                # modify padding because it's activated by default in seamlessM4T
                information = tokenizer(
                    seq_0,
                    max_length=total_length - 2,
                    add_special_tokens=False,
                    stride=stride,
                    truncation="longest_first",
                    return_overflowing_tokens=True,
                    padding=False,
                    # 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)
                    self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])

    @unittest.skip("By defaults, uses pad_to_multiple_of which breaks the test")
    def test_maximum_encoding_length_pair_input(self):
        pass

    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:
                    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, 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")

                    # default to padding=True so need to precise which padding is called
                    normal_tokens = tokenizer("This", pad_to_multiple_of=8, padding=False)
                    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("This", 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__,
                        "This",
                        padding=True,
                        truncation=True,
                        max_length=12,
                        pad_to_multiple_of=8,
                    )

    @require_torch
    def test_prepare_seq2seq_batch(self):
        if not self.test_seq2seq:
            return

        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Longer text that will definitely require truncation.
                src_text = [
                    " UN Chief Says There Is No Military Solution in Syria",
                    " 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="eng",
                        tgt_lang="ron",
                        pad_to_multiple_of=None,
                    )
                except NotImplementedError:
                    return
                self.assertEqual(batch.input_ids.shape[1], 3)
                self.assertEqual(batch.labels.shape[1], 10)

                # TODO: not working for tgt_text
                # max_target_length will default to max_length if not specified
                batch = tokenizer.prepare_seq2seq_batch(
                    src_texts=src_text,
                    tgt_texts=tgt_text,
                    max_length=4,
                    return_tensors="pt",
                    pad_to_multiple_of=None,
                )
                self.assertEqual(batch.input_ids.shape[1], 4)
                self.assertEqual(batch.labels.shape[1], 4)

                batch_encoder_only = tokenizer.prepare_seq2seq_batch(
                    src_texts=src_text,
                    max_length=4,
                    max_target_length=10,
                    return_tensors="pt",
                    pad_to_multiple_of=None,
                )
                self.assertEqual(batch_encoder_only.input_ids.shape[1], 4)
                self.assertEqual(batch_encoder_only.attention_mask.shape[1], 4)
                self.assertNotIn("decoder_input_ids", batch_encoder_only)

    @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
    def test_save_slow_from_fast_and_reload_fast(self):
        pass

    # Copied from tests.models.nllb.test_tokenization_nllb.NllbTokenizationTest.test_special_tokens_initialization
    def test_special_tokens_initialization(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                added_tokens = [AddedToken("<special>", lstrip=True)]

                tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    pretrained_name, additional_special_tokens=added_tokens, **kwargs
                )
                r_output = tokenizer_r.encode("Hey this is a <special> token")

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

                self.assertTrue(special_token_id in r_output)

                if self.test_slow_tokenizer:
                    tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
                        pretrained_name,
                        additional_special_tokens=added_tokens,
                        **kwargs,  # , from_slow=True <- unfortunately too slow to convert
                    )
                    tokenizer_p = self.tokenizer_class.from_pretrained(
                        pretrained_name, additional_special_tokens=added_tokens, **kwargs
                    )

                    p_output = tokenizer_p.encode("Hey this is a <special> token")

                    cr_output = tokenizer_cr.encode("Hey this is a <special> token")

                    self.assertEqual(p_output, r_output)
                    self.assertEqual(cr_output, r_output)
                    self.assertTrue(special_token_id in p_output)
                    self.assertTrue(special_token_id in cr_output)

    @unittest.skip(
        "encode_plus and batch_encode_plus are deprecated and __call__ do some processing, so we expect different results."
    )
    def test_call(self):
        pass

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

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

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

        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.
        # make sure it has the same prefix tokens first
        new_tokenizer.tgt_lang = tokenizer.tgt_lang
        tokenizer.tgt_lang = tokenizer.tgt_lang
        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)

    @unittest.skip("Fails because of the hack of adding <unk> in _tokenize")
    def test_pickle_subword_regularization_tokenizer(self):
        pass

    @unittest.skip("Fails because of the hack of adding <unk> in _tokenize")
    def test_subword_regularization_tokenizer(self):
        pass


@require_torch
@require_sentencepiece
@require_tokenizers
class SeamlessM4TDistilledIntegrationTest(unittest.TestCase):
    checkpoint_name = "facebook/hf-seamless-m4t-medium"
    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.",
    ]

    expected_src_tokens = [256047, 16297, 134408, 8165, 248066, 14734, 950, 1135, 105721, 3573, 83, 27352, 108, 49486, 3]  # fmt: skip

    @classmethod
    def setUpClass(cls):
        cls.tokenizer: SeamlessM4TTokenizer = SeamlessM4TTokenizer.from_pretrained(
            cls.checkpoint_name, src_lang="eng", tgt_lang="ron"
        )
        # cls.pad_token_id = 1
        return cls

    def test_language_codes(self):
        self.assertEqual(self.tokenizer.convert_tokens_to_ids("__ace_Latn__"), 256002)
        self.assertEqual(self.tokenizer.convert_tokens_to_ids("__shn__"), 256152)
        self.assertEqual(self.tokenizer.convert_tokens_to_ids("__eng__"), 256047)
        self.assertEqual(self.tokenizer.convert_tokens_to_ids("__fra__"), 256057)
        self.assertEqual(self.tokenizer.convert_tokens_to_ids("__quy__"), 256144)

    def test_tokenizer_tgt_lang(self):
        ids = self.tokenizer(self.src_text, src_lang="fra").input_ids[0]
        self.assertListEqual(self.expected_src_tokens[1:], ids[1 : len(self.expected_src_tokens)])
        self.assertEqual(256057, ids[0])

        rest_ids = ids[len(self.expected_src_tokens) :]
        self.assertListEqual([0] * len(rest_ids), rest_ids)

        ids = self.tokenizer(self.src_text, src_lang="__shn__").input_ids[0]
        self.assertListEqual(self.expected_src_tokens[1:], ids[1 : len(self.expected_src_tokens)])
        self.assertEqual(256152, ids[0])

    # Copied from tests.models.nllb.test_tokenization_nllb.NllbDistilledIntegrationTest.test_enro_tokenizer_decode_ignores_language_codes
    def test_enro_tokenizer_decode_ignores_language_codes(self):
        self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
        generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]  # fmt: skip

        result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
        expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
        self.assertEqual(result, expected_romanian)
        self.assertNotIn(self.tokenizer.eos_token, result)

    def test_enro_tokenizer_truncation(self):
        src_text = ["this is gunna be a long sentence " * 20]
        assert isinstance(src_text[0], str)
        desired_max_length = 10
        ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
        self.assertEqual(ids[-1], 3)
        self.assertEqual(ids[0], EN_CODE)
        self.assertEqual(len(ids), desired_max_length)

    # Copied from tests.models.nllb.test_tokenization_nllb.NllbDistilledIntegrationTest.test_special_tokens_unaffacted_by_save_load with fairseq_tokens_to_ids->additional_special_tokens, Nllb->SeamlessM4T, Dict->List
    def test_special_tokens_unaffacted_by_save_load(self):
        tmpdirname = tempfile.mkdtemp()
        original_special_tokens = self.tokenizer.additional_special_tokens
        self.tokenizer.save_pretrained(tmpdirname)
        new_tok = SeamlessM4TTokenizer.from_pretrained(tmpdirname)
        self.assertListEqual(new_tok.additional_special_tokens, original_special_tokens)

    @require_torch
    def test_enro_tokenizer_prepare_batch(self):
        batch = self.tokenizer(
            self.src_text,
            text_target=self.tgt_text,
            padding=True,
            truncation=True,
            max_length=len(self.expected_src_tokens),
            pad_to_multiple_of=None,
            return_tensors="pt",
        )
        batch["decoder_input_ids"] = shift_tokens_right(
            batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.convert_tokens_to_ids("__ron__")
        )

        self.assertIsInstance(batch, BatchEncoding)

        self.assertEqual((2, 15), batch.input_ids.shape)
        self.assertEqual((2, 15), batch.attention_mask.shape)
        result = batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens, result)
        self.assertEqual(RO_CODE, batch.decoder_input_ids[0, 0])  # EOS
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE])
        self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])

    def test_seq2seq_max_length(self):
        batch = self.tokenizer(
            self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt", pad_to_multiple_of=None
        )
        targets = self.tokenizer(
            text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
        )
        labels = targets["input_ids"]
        batch["decoder_input_ids"] = shift_tokens_right(
            labels,
            self.tokenizer.pad_token_id,
            decoder_start_token_id=self.tokenizer.convert_tokens_to_ids(self.tokenizer.tgt_lang),
        )

        self.assertEqual(batch.input_ids.shape[1], 3)
        self.assertEqual(batch.decoder_input_ids.shape[1], 10)

    @require_torch
    def test_tokenizer_translation(self):
        inputs = self.tokenizer._build_translation_inputs(
            "A test", return_tensors="pt", src_lang="eng", tgt_lang="fra"
        )

        self.assertEqual(
            nested_simplify(inputs),
            {
                # A, test, EOS, en_XX
                "input_ids": [[256047, 70, 7356, 3]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 256057,
            },
        )


@require_sentencepiece
@require_tokenizers
class CommonSpmIntegrationTests(unittest.TestCase):
    """
    A class that regroups important test to make sure that we properly handle the special tokens.
    """

    @classmethod
    def setUpClass(cls):
        tokenizer = SeamlessM4TTokenizer(SAMPLE_VOCAB, extra_ids=0, add_bos_token=False, legacy=False)
        tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("<s>", rstrip=False, lstrip=False)]})
        cls.tokenizer = tokenizer
        return cls

    def test_add_dummy_prefix(self):
        # make sure `'▁'` is prepended, and outputs match sp_model's
        # `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
        input_ids = self.tokenizer.encode(". Hello")
        self.assertEqual(input_ids, [3, 1, 8, 5, 157, 87, 21, 3])
        sp_encode = self.tokenizer.sp_model.encode(". Hello")

        # [bos, lang_id, _] + offset_sp_encode
        self.assertEqual(input_ids[:-1], [3, 1, 8] + [i + self.tokenizer.fairseq_offset for i in sp_encode])
        tokens = self.tokenizer.tokenize(". Hello")
        self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])

        tokens = self.tokenizer.tokenize("")
        self.assertEqual(tokens, [])
        self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str))

        tokens = self.tokenizer.tokenize(" ")
        self.assertEqual(tokens, [])
        self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str))

        tokens = self.tokenizer.tokenize("▁")
        self.assertEqual(tokens, [])
        self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str))

    def test_remove_extra_whitespaces(self):
        # make sure the extra spaces are eaten. Since the sample vocab does not have
        # `______`. sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute is set to False

        input_ids = self.tokenizer.encode("       . Hello")
        self.assertEqual(input_ids, [3, 1, 8, 5, 157, 87, 21, 3])
        sp_encode = self.tokenizer.sp_model.encode("       . Hello")
        self.assertEqual([i - self.tokenizer.fairseq_offset for i in input_ids[2:-1]], [7] + sp_encode)
        tokens = self.tokenizer.tokenize(" . Hello")
        self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])

        # `'▁'` is also a whitespace
        input_ids = self.tokenizer.encode("▁He is not")
        self.assertEqual(input_ids, [3, 1, 157, 47, 45, 3])
        tokens = self.tokenizer.tokenize("▁He is not")
        sp_encode = [
            self.tokenizer.sp_model.piece_to_id("▁He"),
            self.tokenizer.sp_model.piece_to_id("▁is"),
            self.tokenizer.sp_model.piece_to_id("▁not"),
        ]
        self.assertEqual([i - self.tokenizer.fairseq_offset for i in input_ids[2:-1]], sp_encode)
        self.assertEqual(tokens, ["▁He", "▁is", "▁not"])  # no extra space added

        input_ids = self.tokenizer.encode("▁He is not<s>             ▁He")
        self.assertEqual(input_ids, [3, 1, 157, 47, 45, 2, 157, 3])
        tokens = self.tokenizer.tokenize("▁He is not<s>              ▁He")
        self.assertEqual(tokens, ["▁He", "▁is", "▁not", "<s>", "▁He"])  # spaces are eaten by spm + our strip
        # make sure that the output after the extra id is the same as if
        # extra_id was not there
        input_ids = self.tokenizer.encode("▁He is not             ▁He")
        self.assertEqual(input_ids, [3, 1, 157, 47, 45, 157, 3])
        tokens = self.tokenizer.tokenize("▁He is not              ▁He")
        self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"])  # spaces are eaten by spm even if not start

    def test_character_after_special_token(self):
        # Make sure that `tokenizer.tokenize` is similar to
        # adding the equivalent special token to the vocab
        input_ids = self.tokenizer.encode("Hey <s>I")
        self.assertEqual(input_ids, [3, 1, 157, 31, 2, 101, 3])
        sp_encode = self.tokenizer.sp_model.encode("Hey .I")

        # the last token besides eos should be 100 offset
        self.assertEqual(input_ids[-2] - self.tokenizer.fairseq_offset, sp_encode[-1])
        tokens = self.tokenizer.tokenize("<s>I")
        self.assertEqual(tokens, ["<s>", "I"])

        input_ids = self.tokenizer.encode("Hello, <s>,")
        self.assertEqual(input_ids, [3, 1, 157, 87, 21, 4, 2, 4, 3])
        tokens = self.tokenizer.tokenize("Hello, <s>,")
        self.assertEqual(tokens, ["▁He", "ll", "o", ",", "<s>", ","])

    def test_special_tokens_strip(self):
        input_ids = self.tokenizer.encode(" <s> ,")
        self.assertEqual(input_ids, [3, 1, 2, 8, 4, 3])
        tokens = self.tokenizer.tokenize(" <s> ,")
        # spaces are eaten by rstrip / lstrip + spm sp_model.encode("  ") = []
        self.assertEqual(tokens, ["<s>", "▁", ","])

        input_ids = self.tokenizer.encode("No <s> ▁He")
        self.assertEqual(input_ids, [3, 1, 285, 2, 157, 3])
        tokens = self.tokenizer.tokenize("No <s> ▁He")
        self.assertEqual(tokens, ["▁No", "<s>", "▁He"])  # spaces are eaten by rstrip / lstrip
