Package upstream model with reproducible local MMEB evaluation

bb2b5cc verified 16 days ago

14.3 kB

# Copyright 2021 VinAI Research and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License
"""Tokenization classes for BARTpho-syllable model."""

import os
from shutil import copyfile
from typing import Any

from ...tokenization_python import AddedToken
from ...tokenization_utils_sentencepiece import SentencePieceBackend
from ...utils import logging
from ...utils.import_utils import requires


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}


@requires(backends=("sentencepiece",))
class BartphoTokenizer(SentencePieceBackend):
    """
    Adapted from [`XLMRobertaTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece).
    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
    this superclass for more information regarding those methods.
    Args:
        vocab_file (`str`):
            Path to the vocabulary file. This vocabulary is the pre-trained SentencePiece model available from the
            multilingual XLM-RoBERTa, also used in mBART, consisting of 250K types.
        monolingual_vocab_file (`str`):
            Path to the monolingual vocabulary file. This monolingual vocabulary consists of Vietnamese-specialized
            types extracted from the multilingual vocabulary vocab_file of 250K types.
        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.
        mask_token (`str`, *optional*, defaults to `"<mask>"`):
            The token used for masking values. This is the token used when training this model with masked language
            modeling. This is the token which the model will try to predict.
        sp_model_kwargs (`dict`, *optional*):
            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
            to set:
            - `enable_sampling`: Enable subword regularization.
            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
              - `nbest_size = {0,1}`: No sampling is performed.
              - `nbest_size > 1`: samples from the nbest_size results.
              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                using forward-filtering-and-backward-sampling algorithm.
            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
              BPE-dropout.
    Attributes:
        sp_model (`SentencePieceProcessor`):
            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
    """

    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    is_fast = False

    def __init__(
        self,
        vocab_file,
        monolingual_vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        sp_model_kwargs: dict[str, Any] | None = None,
        **kwargs,
    ) -> None:
        # Mask token behave like a normal word, i.e. include the space before it
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

        self.monolingual_vocab_file = monolingual_vocab_file

        # Load the reduced vocab
        # Keep order of special tokens for backward compatibility
        self.fairseq_tokens_to_ids = {}
        cnt = 0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
            if str(token) not in self.fairseq_tokens_to_ids:
                self.fairseq_tokens_to_ids[str(token)] = cnt
                cnt += 1
        with open(monolingual_vocab_file, "r", encoding="utf-8") as f:
            for line in f:
                token = line.strip().split()[0]
                self.fairseq_tokens_to_ids[token] = len(self.fairseq_tokens_to_ids)
        if str(mask_token) not in self.fairseq_tokens_to_ids:
            self.fairseq_tokens_to_ids[str(mask_token)] = len(self.fairseq_tokens_to_ids)

        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}

        # Prepare sp_model_kwargs for parent class
        if sp_model_kwargs is not None:
            kwargs["sp_model_kwargs"] = sp_model_kwargs

        # Call parent init (which will load sp_model)
        super().__init__(
            vocab_file=vocab_file,
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            **kwargs,
        )
        self._align_added_tokens_with_fairseq_vocab()

    def build_inputs_with_special_tokens(
        self, token_ids_0: list[int], token_ids_1: list[int] | None = 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 BARTPho sequence has the following format:
        - single sequence: `<s> X </s>`
        - pair of sequences: `<s> A </s></s> B </s>`
        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.cls_token_id] + token_ids_0 + [self.sep_token_id]
        cls = [self.cls_token_id]
        sep = [self.sep_token_id]
        return cls + token_ids_0 + sep + sep + token_ids_1 + sep

    def get_special_tokens_mask(
        self, token_ids_0: list[int], token_ids_1: list[int] | None = 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
            )

        if token_ids_1 is None:
            return [1] + ([0] * len(token_ids_0)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]

    def create_token_type_ids_from_sequences(
        self, token_ids_0: list[int], token_ids_1: list[int] | None = None
    ) -> list[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. BARTPho 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]

    @property
    def vocab_size(self):
        """Override to return fairseq vocab size instead of sp_model vocab size"""
        return len(self.fairseq_ids_to_tokens)

    def get_vocab(self):
        """Override to use fairseq vocabulary"""
        vocab = dict(self.fairseq_tokens_to_ids)
        if hasattr(self, "_added_tokens_encoder"):
            for token, idx in self._added_tokens_encoder.items():
                if token not in vocab:
                    vocab[token] = idx
        return vocab

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the fairseq vocab."""
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        else:
            return self.unk_token_id

    def _convert_token_to_id_with_added_voc(self, token):
        """Override to use fairseq vocab instead of sp_model vocab."""
        if token is None:
            return None

        if token in self._added_tokens_encoder:
            return self._added_tokens_encoder[token]
        return self._convert_token_to_id(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the fairseq vocab."""
        return self.fairseq_ids_to_tokens[index]

    def _align_added_tokens_with_fairseq_vocab(self):
        """
        The slow tokenizer base class populates `_added_tokens_*` using SentencePiece ids. Remap those entries so that
        every token present in the reduced fairseq dictionary uses the same ids everywhere, otherwise conversions and
        special-token setters observe two different vocabularies.
        """
        if not hasattr(self, "_added_tokens_decoder") or not hasattr(self, "_added_tokens_encoder"):
            return

        remapped_decoder: dict[int, AddedToken] = {}
        for original_id, token_obj in self._added_tokens_decoder.items():
            token = token_obj.content
            new_id = self.fairseq_tokens_to_ids.get(token, original_id)
            remapped_decoder[new_id] = token_obj

        self._added_tokens_decoder = remapped_decoder
        self._added_tokens_encoder = {token.content: idx for idx, token in remapped_decoder.items()}

    def save_vocabulary(self, save_directory: str, filename_prefix: str | None = 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"]
        )
        out_monolingual_vocab_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_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)

        if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
            out_monolingual_vocab_file
        ) and os.path.isfile(self.monolingual_vocab_file):
            copyfile(self.monolingual_vocab_file, out_monolingual_vocab_file)
        elif not os.path.isfile(self.monolingual_vocab_file):
            with open(out_monolingual_vocab_file, "w", encoding="utf-8") as fp:
                for token in self.fairseq_tokens_to_ids:
                    if token not in self.all_special_tokens:
                        fp.write(f"{str(token)} \n")

        return out_vocab_file, out_monolingual_vocab_file


__all__ = ["BartphoTokenizer"]

	# Copyright 2021 VinAI Research and the HuggingFace Inc. team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License
	"""Tokenization classes for BARTpho-syllable model."""

	import os
	from shutil import copyfile
	from typing import Any

	from ...tokenization_python import AddedToken
	from ...tokenization_utils_sentencepiece import SentencePieceBackend
	from ...utils import logging
	from ...utils.import_utils import requires


	logger = logging.get_logger(__name__)

	VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}


	@requires(backends=("sentencepiece",))
	class BartphoTokenizer(SentencePieceBackend):
	"""
	Adapted from [`XLMRobertaTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece).

	This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
	this superclass for more information regarding those methods.

	Args:
	vocab_file (`str`):
	Path to the vocabulary file. This vocabulary is the pre-trained SentencePiece model available from the
	multilingual XLM-RoBERTa, also used in mBART, consisting of 250K types.
	monolingual_vocab_file (`str`):
	Path to the monolingual vocabulary file. This monolingual vocabulary consists of Vietnamese-specialized
	types extracted from the multilingual vocabulary vocab_file of 250K types.
	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.
	mask_token (`str`, optional, defaults to `"<mask>"`):
	The token used for masking values. This is the token used when training this model with masked language
	modeling. This is the token which the model will try to predict.
	sp_model_kwargs (`dict`, optional):
	Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
	SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
	to set:

	- `enable_sampling`: Enable subword regularization.
	- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

	- `nbest_size = {0,1}`: No sampling is performed.
	- `nbest_size > 1`: samples from the nbest_size results.
	- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
	using forward-filtering-and-backward-sampling algorithm.

	- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
	BPE-dropout.

	Attributes:
	sp_model (`SentencePieceProcessor`):
	The SentencePiece processor that is used for every conversion (string, tokens and IDs).
	"""

	vocab_files_names = VOCAB_FILES_NAMES
	model_input_names = ["input_ids", "attention_mask"]
	is_fast = False

	def __init__(
	self,
	vocab_file,
	monolingual_vocab_file,
	bos_token="<s>",
	eos_token="</s>",
	sep_token="</s>",
	cls_token="<s>",
	unk_token="<unk>",
	pad_token="<pad>",
	mask_token="<mask>",
	sp_model_kwargs: dict[str, Any] \| None = None,
	**kwargs,
	) -> None:
	# Mask token behave like a normal word, i.e. include the space before it
	mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

	self.monolingual_vocab_file = monolingual_vocab_file

	# Load the reduced vocab
	# Keep order of special tokens for backward compatibility
	self.fairseq_tokens_to_ids = {}
	cnt = 0
	for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
	if str(token) not in self.fairseq_tokens_to_ids:
	self.fairseq_tokens_to_ids[str(token)] = cnt
	cnt += 1
	with open(monolingual_vocab_file, "r", encoding="utf-8") as f:
	for line in f:
	token = line.strip().split()[0]
	self.fairseq_tokens_to_ids[token] = len(self.fairseq_tokens_to_ids)
	if str(mask_token) not in self.fairseq_tokens_to_ids:
	self.fairseq_tokens_to_ids[str(mask_token)] = len(self.fairseq_tokens_to_ids)

	self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}

	# Prepare sp_model_kwargs for parent class
	if sp_model_kwargs is not None:
	kwargs["sp_model_kwargs"] = sp_model_kwargs

	# Call parent init (which will load sp_model)
	super().__init__(
	vocab_file=vocab_file,
	bos_token=bos_token,
	eos_token=eos_token,
	unk_token=unk_token,
	sep_token=sep_token,
	cls_token=cls_token,
	pad_token=pad_token,
	mask_token=mask_token,
	**kwargs,
	)
	self._align_added_tokens_with_fairseq_vocab()

	def build_inputs_with_special_tokens(
	self, token_ids_0: list[int], token_ids_1: list[int] \| None = 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 BARTPho sequence has the following format:

	- single sequence: `<s> X </s>`
	- pair of sequences: `<s> A </s></s> B </s>`

	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.cls_token_id] + token_ids_0 + [self.sep_token_id]
	cls = [self.cls_token_id]
	sep = [self.sep_token_id]
	return cls + token_ids_0 + sep + sep + token_ids_1 + sep

	def get_special_tokens_mask(
	self, token_ids_0: list[int], token_ids_1: list[int] \| None = 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
	)

	if token_ids_1 is None:
	return [1] + ([0] * len(token_ids_0)) + [1]
	return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]

	def create_token_type_ids_from_sequences(
	self, token_ids_0: list[int], token_ids_1: list[int] \| None = None
	) -> list[int]:
	"""
	Create a mask from the two sequences passed to be used in a sequence-pair classification task. BARTPho 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]

	@property
	def vocab_size(self):
	"""Override to return fairseq vocab size instead of sp_model vocab size"""
	return len(self.fairseq_ids_to_tokens)

	def get_vocab(self):
	"""Override to use fairseq vocabulary"""
	vocab = dict(self.fairseq_tokens_to_ids)
	if hasattr(self, "_added_tokens_encoder"):
	for token, idx in self._added_tokens_encoder.items():
	if token not in vocab:
	vocab[token] = idx
	return vocab

	def _convert_token_to_id(self, token):
	"""Converts a token (str) in an id using the fairseq vocab."""
	if token in self.fairseq_tokens_to_ids:
	return self.fairseq_tokens_to_ids[token]
	else:
	return self.unk_token_id

	def _convert_token_to_id_with_added_voc(self, token):
	"""Override to use fairseq vocab instead of sp_model vocab."""
	if token is None:
	return None

	if token in self._added_tokens_encoder:
	return self._added_tokens_encoder[token]
	return self._convert_token_to_id(token)

	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the fairseq vocab."""
	return self.fairseq_ids_to_tokens[index]

	def _align_added_tokens_with_fairseq_vocab(self):
	"""
	The slow tokenizer base class populates `_added_tokens_*` using SentencePiece ids. Remap those entries so that
	every token present in the reduced fairseq dictionary uses the same ids everywhere, otherwise conversions and
	special-token setters observe two different vocabularies.
	"""
	if not hasattr(self, "_added_tokens_decoder") or not hasattr(self, "_added_tokens_encoder"):
	return

	remapped_decoder: dict[int, AddedToken] = {}
	for original_id, token_obj in self._added_tokens_decoder.items():
	token = token_obj.content
	new_id = self.fairseq_tokens_to_ids.get(token, original_id)
	remapped_decoder[new_id] = token_obj

	self._added_tokens_decoder = remapped_decoder
	self._added_tokens_encoder = {token.content: idx for idx, token in remapped_decoder.items()}

	def save_vocabulary(self, save_directory: str, filename_prefix: str \| None = 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"]
	)
	out_monolingual_vocab_file = os.path.join(
	save_directory,
	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_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)

	if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
	out_monolingual_vocab_file
	) and os.path.isfile(self.monolingual_vocab_file):
	copyfile(self.monolingual_vocab_file, out_monolingual_vocab_file)
	elif not os.path.isfile(self.monolingual_vocab_file):
	with open(out_monolingual_vocab_file, "w", encoding="utf-8") as fp:
	for token in self.fairseq_tokens_to_ids:
	if token not in self.all_special_tokens:
	fp.write(f"{str(token)} \n")

	return out_vocab_file, out_monolingual_vocab_file


	__all__ = ["BartphoTokenizer"]