# coding=utf-8
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Tokenization classes for SeamlessM4T."""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...convert_slow_tokenizer import import_protobuf
from ...tokenization_utils import (
    BatchEncoding,
    PreTokenizedInput,
    PreTrainedTokenizer,
    TextInput,
)
from ...tokenization_utils_base import AddedToken
from ...utils import PaddingStrategy, logging


logger = logging.get_logger(__name__)

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/hf-seamless-m4t-medium": (
            "https://huggingface.co/facebook/hf-seamless-m4t-medium/blob/main/sentencepiece.bpe.model"
        ),
    }
}

SPIECE_UNDERLINE = "▁"


VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}


PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/hf-seamless-m4t-medium": 2048,
}


class SeamlessM4TTokenizer(PreTrainedTokenizer):
    """
    Construct a SeamlessM4T tokenizer.

    Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
    [SentencePiece](https://github.com/google/sentencepiece).

    The tokenization method is `<language code> <tokens> <eos>` for source language documents, and `<eos> <language
    code> <tokens> <eos>` for target language documents.

    Examples:

    ```python
    >>> from transformers import SeamlessM4TTokenizer

    >>> tokenizer = SeamlessM4TTokenizer.from_pretrained(
    ...     "facebook/hf-seamless-m4t-medium", src_lang="eng", tgt_lang="fra"
    ... )
    >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
    >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
    >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")
    ```

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        bos_token (`str`, *optional*, defaults to `"<s>"`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the beginning of
            sequence. The token used is the `cls_token`.

            </Tip>

        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
            The token used is the `sep_token`.

            </Tip>

        sep_token (`str`, *optional*, defaults to `"</s>"`):
            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
            sequence classification or for a text and a question for question answering. It is also used as the last
            token of a sequence built with special tokens.
        cls_token (`str`, *optional*, defaults to `"<s>"`):
            The classifier token which is used when doing sequence classification (classification of the whole sequence
            instead of per-token classification). It is the first token of the sequence when built with special tokens.
        unk_token (`str`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        pad_token (`str`, *optional*, defaults to `"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        tokenizer_file (`str`, *optional*):
            The path to a tokenizer file to use instead of the vocab file.
        src_lang (`str`, *optional*, defaults to `"eng"`):
            The language to use as source language for translation.
        tgt_lang (`str`, *optional*, defaults to `"fra"`):
            The language to use as target language for translation.
        sp_model_kwargs (`Dict[str, Any]`, *optional*):
            Additional keyword arguments to pass to the model initialization.
        additional_special_tokens (tuple or list of `str` or `tokenizers.AddedToken`, *optional*):
            A tuple or a list of additional special tokens. Can be used to specify the list of languages that will be
            supported by the tokenizer.
        add_prefix_space (`bool`, *optional*, defaults to `True`):
            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
            other word.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    model_input_names = ["input_ids", "attention_mask"]

    prefix_tokens: List[int] = []
    suffix_tokens: List[int] = []

    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        tokenizer_file=None,
        src_lang="eng",
        tgt_lang="fra",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        additional_special_tokens=None,
        add_prefix_space=True,
        **kwargs,
    ):
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        # Add this unused argument to keep some important Copied from statements
        self.legacy = False
        self.vocab_file = vocab_file

        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))

        # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
        # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
        # spm  | '<unk>'   | '<s>' | '</s>' | 'an' | 'en' | '_d' | 'er' | 'in' | '_s' | '_a'
        # fairseq  | '<pad>'   | '<unk>' | '<s>' | '</s>' | 'an' | 'en' | '▁d' | 'er' | 'in' | '▁s'

        # Mimic fairseq token-to-id alignment for the first 4 token
        self._added_tokens_decoder = {
            0: AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token,
            1: AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token,
            2: AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token,
            3: AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token,
        }

        # The first "real" token "an" has position 4 in the original fairseq vocab and position 3 in the spm vocab
        self.fairseq_offset = 1

        self.sp_model_size = len(self.sp_model)

        self._src_lang = f"__{src_lang}__" if "__" not in src_lang else src_lang
        self._tgt_lang = f"__{tgt_lang}__" if "__" not in tgt_lang else tgt_lang
        self.add_prefix_space = add_prefix_space

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            tokenizer_file=tokenizer_file,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            additional_special_tokens=additional_special_tokens,
            sp_model_kwargs=self.sp_model_kwargs,
            add_prefix_space=add_prefix_space,
            **kwargs,
        )

        self.set_src_lang_special_tokens(self._src_lang)
        self.set_tgt_lang_special_tokens(self._tgt_lang)

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.__getstate__
    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
        return state

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.__setstate__
    def __setstate__(self, d):
        self.__dict__ = d

        # for backward compatibility
        if not hasattr(self, "sp_model_kwargs"):
            self.sp_model_kwargs = {}

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

    @property
    def vocab_size(self):
        return len(self.sp_model)

    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
        text_target: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        text_pair_target: Optional[
            Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]
        ] = None,
        padding: Union[bool, str, PaddingStrategy] = True,
        pad_to_multiple_of: Optional[int] = 2,
        src_lang: Optional[str] = None,
        tgt_lang: Optional[str] = None,
        **kwargs,
    ):
        """
        Args:
            text (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_target (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
                list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
                you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair_target (`str`, `List[str]`, `List[List[str]]`, *optional*):
                The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a
                list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized),
                you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                `>= 7.5` (Volta).
            src_lang (`str`, *optional*):
                A string representing the source language. If not specified, the last `src_lang` specified (either
                during initialization or when calling this tokenizer) will be used.
            tgt_lang (`str`, *optional*):
                A string representing the target language. If not specified, the last `tgt_lang` specified (either
                during initialization or when calling this tokenizer) will be used.
            kwargs (*optional*):
                Remaining dictionary of keyword arguments that will be passed to [`PreTrainedTokenizer.__call__`].
        """
        if src_lang is not None:
            self.src_lang = src_lang
        if tgt_lang is not None:
            self.tgt_lang = tgt_lang

        output = super().__call__(
            text=text,
            text_pair=text_pair,
            text_target=text_target,
            text_pair_target=text_pair_target,
            padding=padding,
            pad_to_multiple_of=pad_to_multiple_of,
            **kwargs,
        )

        return BatchEncoding(output, tensor_type=kwargs.get("return_tensors"))

    @property
    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.src_lang
    def src_lang(self) -> str:
        return self._src_lang

    @src_lang.setter
    def src_lang(self, new_src_lang: str) -> None:
        if "__" not in new_src_lang:
            self._src_lang = f"__{new_src_lang}__"
        else:
            self._src_lang = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)

    @property
    def tgt_lang(self) -> str:
        return self._tgt_lang

    @tgt_lang.setter
    def tgt_lang(self, new_tgt_lang: str) -> None:
        if "__" not in new_tgt_lang:
            self._tgt_lang = f"__{new_tgt_lang}__"
        else:
            self._tgt_lang = new_tgt_lang
        self.set_tgt_lang_special_tokens(self._tgt_lang)

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.get_special_tokens_mask
    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` method.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        prefix_ones = [1] * len(self.prefix_tokens)
        suffix_ones = [1] * len(self.suffix_tokens)
        if token_ids_1 is None:
            return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
        return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.build_inputs_with_special_tokens
    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens. An NLLB sequence has the following format, where `X` represents the sequence:

        - `input_ids` (for encoder) `X [eos, src_lang_code]`
        - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`

        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
        separator.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
        """
        if token_ids_1 is None:
            return self.prefix_tokens + token_ids_0 + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.create_token_type_ids_from_sequences
    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not
        make use of token type ids, therefore a list of zeros is returned.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of zeros.

        """

        sep = [self.sep_token_id]
        cls = [self.cls_token_id]

        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

    def _build_translation_inputs(
        self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
    ):
        """Used by translation pipeline, to prepare inputs for the generate function"""
        if src_lang is None or tgt_lang is None:
            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model.")
        self.src_lang = src_lang
        inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
        if "__" not in tgt_lang:
            tgt_lang = f"__{tgt_lang}__"
        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
        inputs["forced_bos_token_id"] = tgt_lang_id
        return inputs

    def get_vocab(self):
        vocab = {
            self.convert_ids_to_tokens(i): i for i in range(self.fairseq_offset, self.vocab_size + self.fairseq_offset)
        }
        vocab.update(self.added_tokens_encoder)
        return vocab

    @property
    def unk_token_length(self):
        return len(self.sp_model.encode(str(self.unk_token)))

    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
    def get_spm_processor(self, from_slow=False):
        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        if self.legacy or from_slow:  # no dependency on protobuf
            tokenizer.Load(self.vocab_file)
            return tokenizer

        with open(self.vocab_file, "rb") as f:
            sp_model = f.read()
            model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
            model = model_pb2.ModelProto.FromString(sp_model)
            normalizer_spec = model_pb2.NormalizerSpec()
            normalizer_spec.add_dummy_prefix = False
            model.normalizer_spec.MergeFrom(normalizer_spec)
            sp_model = model.SerializeToString()
            tokenizer.LoadFromSerializedProto(sp_model)
        return tokenizer

    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
    def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
        """
        Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
        first token is special.
        """
        if self.legacy or len(text) == 0:
            return super().tokenize(text, **kwargs)

        text = text.replace(SPIECE_UNDERLINE, " ")
        if self.add_prefix_space:
            text = SPIECE_UNDERLINE + text

        tokens = super().tokenize(text, add_special_tokens=add_special_tokens, **kwargs)

        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
            tokens = tokens[1:]
        return tokens

    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
    def _tokenize(self, text, **kwargs):
        """
        Returns a tokenized string.

        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
        """
        tokens = self.sp_model.encode(text, out_type=str)
        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
            return tokens

        # 1. Encode string + prefix ex: "<unk> Hey"
        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        spm_id = self.sp_model.PieceToId(token)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.sp_model.IdToPiece(index - self.fairseq_offset)

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (strings for sub-words) in a single string."""
        # since we manually add the prefix space, we have to remove it when decoding
        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
            tokens[0] = tokens[0][1:]

        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
        return out_string

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.save_vocabulary
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer.prepare_seq2seq_batch with eng_Latn->eng, fra_Latn->fra
    def prepare_seq2seq_batch(
        self,
        src_texts: List[str],
        src_lang: str = "eng",
        tgt_texts: Optional[List[str]] = None,
        tgt_lang: str = "fra",
        **kwargs,
    ) -> BatchEncoding:
        self.src_lang = src_lang
        self.tgt_lang = tgt_lang
        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer._switch_to_input_mode
    def _switch_to_input_mode(self):
        return self.set_src_lang_special_tokens(self.src_lang)

    # Copied from transformers.models.nllb.tokenization_nllb.NllbTokenizer._switch_to_target_mode
    def _switch_to_target_mode(self):
        return self.set_tgt_lang_special_tokens(self.tgt_lang)

    def set_src_lang_special_tokens(self, src_lang) -> None:
        """Reset the special tokens to the source lang setting.
        Prefix=[src_lang_code], suffix = [eos]
        """
        self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
        self.init_kwargs["src_lang"] = src_lang

        if self.cur_lang_code == self.unk_token_id:
            logger.warning_once(
                f"`src_lang={src_lang}` has not be found in the vocabulary. Behaviour will probably be unexpected because the language token id will be replaced by the unknown token id."
            )

        self.prefix_tokens = [self.cur_lang_code]
        self.suffix_tokens = [self.eos_token_id]

    # https://github.com/facebookresearch/fairseq2/blob/c53f18e6be6b8b46b722f2249b8397b7eccd7ad3/src/fairseq2/models/nllb/tokenizer.py#L112-L116
    def set_tgt_lang_special_tokens(self, lang: str) -> None:
        """Reset the special tokens to the target lang setting.
        Prefix=[eos, tgt_lang_code] and suffix=[eos].
        """
        self.cur_lang_code = self.convert_tokens_to_ids(lang)
        self.init_kwargs["tgt_lang"] = lang

        if self.cur_lang_code == self.unk_token_id:
            logger.warning_once(
                f"`tgt_lang={lang}` has not be found in the vocabulary. Behaviour will probably be unexpected because the language token id will be replaced by the unknown token id."
            )

        self.prefix_tokens = [self.eos_token_id, self.cur_lang_code]
        self.suffix_tokens = [self.eos_token_id]
